Transition Matrix Research Articles

Tracing the educational journey of Indian women across generations: A study of multigenerational education mobility in India

This study investigates intergenerational mobility and socioeconomic disparities in women's education in India, utilizing data from the Longitudinal Ageing Study in India (LASI) Wave 1. It uniquely examines three generations of women: the second generation (G2), aged 45–75, who reported on their own education, their mother's education (G1), and their daughter's education (G3). Despite India's economic growth over the past three decades, women's educational status remains poor, influenced by enduring gender discrimination. The study constructs educational transition matrices and measures two mobility indices: M1, which represents the expected proportion of daughters transitioning from their mother's educational level, and M2, which measures the extent of transitions between educational categories. M1 is further broken down into upward and downward mobility. The findings highlight significant progress in educational mobility across generations, with notable increases in upward mobility from the grandmother-mother to the mother-daughter generations, indicating a positive trend. Urban women show significantly higher levels of educational mobility compared to their rural counterparts. However, the study also identifies a decline in multigenerational educational mobility among Muslim women. Persistent caste-based disparities in education are observed, although there is a promising trend of narrowing gaps in recent years. Additionally, the study reveals substantial progress in educational attainment among the most economically disadvantaged groups compared to previous generations.

Quantitative analysis of high-frequency radiometric databases. Application to the case of Seville

AbstractThe short-term variability of solar resource is one of the main challenges faced by the large-scale implementation of photovoltaic (PV) systems today; this will increase as the installed power of solar PV increases. To evaluate its influence on electrical grid operation, it is essential to analyse the variability of solar resource based on high-frequency data, 30 s and shorter. In this study, the high-frequency measurements of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI) recorded every 5 s at the radiometric station of the University of Seville for 20 years are analysed and characterised. For this purpose, a corrected database for GHI and DNI was obtained from the application of a correction methodology. To understand the reliability of this database it is required to know its uncertainty. This work proposes a methodology to quantify the uncertainty of high-frequency radiometric databases whose wrong data have been corrected. Applying it, the daily uncertainty is 2.69% for GHI and 4.07% for DNI. To characterize the distribution of the database, the distribution of high frequency kt and kb indices are modelled providing the fitting parameters. To characterize the variability of high-frequency measurements, transition matrices are proposed, which allow identifying both the magnitude and frequency of jumps. The results obtained show that jumps of up to 800 W/m2 occur in both GHI and DNI. In the case of GHI, the percentage of jumps equal to or greater than 500 W/m2 is 1.36%, about 43,000 jumps in a year in measurements every 5 s.

Research on Complete Coverage Path Planning of Agricultural Robots Based on Markov Chain Improved Genetic Algorithm

Due to the limitations of low coverage, high repetition rate, and slow convergence speed of the basic genetic algorithm (GA) in robot complete coverage path planning, the state transition matrix of the Markov chain is introduced to guide individual mutation based on the genetic mutation path planning algorithm, which can improve the quality of population individuals, enhancing the search ability and convergence speed of the genetic algorithm. The proposed improved genetic algorithm is used for complete coverage path planning simulation analysis in different work areas. The analysis results show that compared to traditional genetic algorithms, the improved genetic algorithm proposed in this paper reduces the average path length by 21.8%, the average number of turns by 6 times, the repetition rate by 83.8%, and the coverage rate by 7.76% in 6 different work areas. The results prove that the proposed improved genetic algorithm is applicable in complete coverage path planning. To verify whether the Markov chain genetic algorithm (MCGA) proposed is suitable for agricultural robot path tracking and operation, it was used to plan the path of an actual land parcel. An automatic navigation robot can track the planned path, which can verify the feasibility of the MCGA proposed.

Mixed quantum/classical theory for rotationally inelastic scattering of identical collision partners revised.

Mixed quantum/classical theory (MQCT) for the treatment of rotationally inelastic transitions during collisions of two identical molecules, described either as indistinguishable or distinguishable partners, is reviewed. The treatment of two molecules as indistinguishable includes symmetrization of rotational wavefunctions, introduces exchange parity, and leads to state-to-state transition matrix elements different from those in the straightforward treatment of molecules as distinguishable. Moreover, the treatment of collision partners as indistinguishable is eight times faster. Numerical results presented herein for H2 + H2, CO + CO and H2O + H2O systems indicate good agreement of MQCT calculations with full-quantum calculations from the literature and show that an a posteriori correction, applied after treatment of the collision partners as distinguishable, generally produces good results that agree well with the rigorous treatment of collision partners as indistinguishable. This correction for the cross section includes either multiplication by 2 or a summation over physically indistinguishable processes, depending on the transition type. After this correction, the results of the two treatments agree within 5% for most but may reach 10-20% for some transitions. At low collision energies dominated by scattering resonances, these differences can be larger, but they tend to decrease as collision energy is increased. It is also shown that if the system is artificially forced to follow the same collision path in the indistinguishable and distinguishable treatments, then all differences between the results of the two treatments disappear. This interesting finding gives new insight into the collision process and indicates that the indistinguishability of identical collision partners comes into play through the collision path itself, rather than through matrix elements of inelastic transitions.

Expression of Markers Associated with Epithelial-Mesenchymal Transition and Extracellular Matrix Degradation in Human Uveal Melanoma.

The expression of markers associated with epithelial-mesenchymal transition (EMT) and extracellular matrix degradation in human uveal melanoma tissue samples and postequatorial zone of the choroid was assessed by immunohistochemical staining. Increased expression of EMT markers E-cadherin and vimentin was observed in the tumor. The ratio of MMP-9 to TIMP-1 proteins related to the extracellular matrix degradation was higher in the tumor. These results may indicate activation of EMT-like process in the uveal melanoma cells and degradation of the extracellular matrix, which can contribute to the development of collective invasion in uveal melanoma.

An Algebraic Approach to Light–Matter Interactions

AbstractA theoretical and computational framework for the study and engineering of light–matter interactions is reviewed in here. The framework rests on the invariance properties of electromagnetism, and is formalized in a Hilbert space whose conformally invariant scalar product provides connections to physical quantities, such as the energy or momentum of a given field, or the outcome of measurements. The light–matter interaction is modeled by the polychromatic scattering operator, which establishes a natural connection to a popular computational formalism, the transition matrix, or T‐matrix. This review contains a succinct yet comprehensive description of the main theoretical ideas, and illustrates some of the practical benefits of the approach.

Forecasting Using the Markov Chain Transition Matrix for Exchange Rate Fluctuations

The current research is preparing an economic statistical study on exchange rate fluctuations for the period from ( 1996 to mid-2005 ) according to the well-known phrase: (The present is the past and the future is the present ). It is a modest attempt to crystallize this type of price in a scientific-statistic method by adopting the matrix of transitional probabilities in Markov chains, as these chains are among the models that are relied upon in the process of forecasting when the data are in the present time and in three cases: - High - Low - Stability. Between the past, the present and the future, the researcher was able to reach in his research to the analysis by applying the method of the greatest places, and to achieve the main purpose of this research, the researcher dealt with four chapters as follows: The first chapter dealt with the problem of research, its importance, need, purpose and determination of its terms. The second chapter reviewed the previous studies published on the subject, as well as the theoretical aspect, in which the matrix of transitional and stable possibilities, the state of stability and independence of the Markov chains, and the estimation of transitional possibilities in the greatest possible way were presented. As for the third chapter, it included the practical aspect and the field application. The data related to the research procedures were collected from the date of (17/10/2003 to 30/6/2005), in which the Iraqi currency was exchanged from the old to the currency currently in circulation, and in which the situation of the Iraqi market suggested great relative stability at the time. Finally, the fourth chapter of this research included the conclusions reached by the researcher from which it came: The market was characterized by a state of stability after the rise in the exchange rate against the dollar, which confirmed the accuracy of the results of the matrix of transitional possibilities, which was reflected on the local stock exchange, as well as the development of recommendations that can be used by the relevant parties.

TiRNA-Gly-GCC-002 promotes epithelial-mesenchymal transition and fibrosis in lupus nephritis via FKBP5-mediated activation of Smad.

Renal interstitial fibrosis is a frequent pathological manifestation of lupus nephritis (LN). tRNA halves (tiRNAs) are acquired from tRNA-derived small non-coding RNAs (sncRNAs) and are associated with fibrosis. Our previous study indicated enhanced tiRNA-Gly-GCC-002 (tiRNA002) levels in kidneys were positively related to LN-related fibrosis. However, the precise molecular mechanism remains unclear. The mimic and agomiR of tiRNA002 were introduced into tubular epithelial cells (TECs) and MRL/lpr mice by transfection. The levels of gene and protein expressions were quantified using real-time quantitative polymerase chain reaction (RT-qPCR), Western blot and immunofluorescence assays. In TECs treated with LN serum, as well as in the kidneys of MRL/lpr mice, high levels of tiRNA002 directly influenced the epithelial-mesenchymal transition (EMT) and extracellular matrix (ECM) deposition. Furthermore, tiRNA002 overexpression promoted EMT in TECs and accelerated renal interstitial fibrosis in MRL/lpr mice via Smad signalling. The target gene of tiRNA002, FKBP prolyl isomerase 5 (FKBP5), improved Smad signalling by interacting with phosphorylated Smad2/3. Silencing FKBP5 alleviated LN serum- or tiRNA002-mimic-induced EMT in TECs. In addition, FKBP5 overexpression reversed the tiRNA002 knockdown-mediated reduction of EMT and ECM accumulation. These findings indicated that tiRNA002 is markedly increased in LN, which facilitates renal fibrosis by promoting EMT via FKBP5-mediated Smad signalling. Therefore, targeting tiRNA002 may be an innovative approach to treat renal interstitial fibrosis in LN.

Investigation, Evaluation, and Dynamic Monitoring of Traditional Chinese Village Buildings Based on Unmanned Aerial Vehicle Images and Deep Learning Methods

The investigation, evaluation, and dynamic monitoring of traditional village buildings are crucial for the protection and inheritance of their architectural styles. This study takes traditional villages in Shandong Province, China, as an example, employing UAV images and deep learning technology. Utilizing the YOLOv8 instance segmentation model, it introduces three key features reflecting the condition of traditional village buildings: roof status, roof form, and courtyard vegetation coverage. By extracting feature data on the condition of traditional village buildings and constructing a transition matrix for building condition changes, combined with corresponding manual judgment assistance, the study classifies, counts, and visually outputs the conditions and changes of buildings. This approach enables the investigation, evaluation, and dynamic monitoring of traditional village buildings. The results show that deep learning technology significantly enhances the efficiency and accuracy of traditional village architectural investigation and evaluations, and it performs well in dynamic monitoring of building condition changes. The “UAV image + deep learning” technical system, with its simplicity, accuracy, efficiency, and low cost, can provide further data and technical support for the planning, protection supervision, and development strategy formulation of traditional Chinese villages.

Immune and non-immune mediators in the fibrosis pathogenesis of salivary gland in Sjögren's syndrome.

Sjögren's syndrome (SS) or Sjögren's disease (SjD) is a systemic autoimmune disease clinically manifested as sicca symptoms. This disease primarily impacts the functionality of exocrine glands, specifically the lacrimal and salivary glands (SG). SG fibrosis, an irreversible morphological change, is a severe consequence that occurs in the later stages of the disease due to sustained inflammation. However, the mechanism underlying SG fibrosis in SS remains under-investigated. Glandular fibrosis may arise from chronic sialadenitis, in which the interactions between infiltrating lymphocytes and epithelial cells potentially contributes to fibrotic pathogenesis. Thus, both immune and non-immune cells are closely involved in this process, while their interplays are not fully understood. The molecular mechanism of tissue fibrosis is partly associated with an imbalance of immune responses, in which the transforming growth factor-beta (TGF-β)-dependent epithelial-mesenchymal transition (EMT) and extracellular matrix remodeling are recently investigated. In addition, viral infection has been implicated in the pathogenesis of SS. Viral-specific innate immune response could exacerbate the autoimmune progression, resulting in overt inflammation in SG. Notably, post-COVID patients exhibit typical SS symptoms and severe inflammatory sialadenitis, which are positively correlated with SG damage. In this review, we discuss the immune and non-immune risk factors in SG fibrosis and summarize the evidence to understand the mechanisms upon autoimmune progression in SS.

Performance Analysis and Parallel Scalability of Numerical Methods for Fractional-in-Space Diffusion Problems with Adaptive Time Stepping

We study numerical methods and algorithms for time-dependent fractional-in-space diffusion problems. The considered anomalous diffusion is modelled by the fractional Laplacian (−Δ)α, 0<α<1, following the integral definition. Fractional diffusion is non-local, and the finite element method (FEM) discretization in space leads to a dense stiffness matrix. It is well known that numerically solving such non-local boundary value problems is expensive. Difficulties increase significantly when the problem is time-dependent. The aim of the article is to develop computationally efficient methods and algorithms. There are two main features of our approach. Hierarchical semi-separable (HSS) compression is applied for an approximate solution of the arising linear systems. For time discretization, we use an adaptive forward–backward Euler scheme. The properties of the composite algorithm thus obtained are investigated. In particular, the block representation of HSS compression allowed us to upgrade the HSS solver to efficiently handle varying diagonally perturbed transition matrices corresponding to changing time steps. The contribution of the paper is threefold. The methods are completely constructive, which allows for a clearly structured description of the algorithms. A theoretical estimate of the computational complexity is presented. It shows the advantages of the adaptive time stepping in combination with the HSS solver. Theoretical results are supported by representative numerical experiments. Both sequential and parallel scalability and efficiency are analyzed. The presented results provide convincing proof of the concept of the proposed methods and algorithms.

Geometry of Kirkwood–Dirac classical states: a case study based on discrete Fourier transform

Abstract The characterization of Kirkwood-Dirac (KD) classicality or non-classicality is very important in quantum information processing. In general, the set of KD classical states with respect to two bases is not a convex polytope[J. Math. Phys. \textbf{65} 072201 (2024)], which makes us interested in finding out in which circumnstances they do form a polytope. In this paper, we focus on the characterization of KD classicality of mixed states for the case where the transition matrix between two bases is a discrete Fourier transform (DFT) matrix in Hilbert space with dimensions $p^2$ and $pq$, respectively, where $p, q$ are prime. For the two particular cases we investigate, the sets of extremal points are finite, implying that the set of KD classical states we characterize forms a convex polytope. We show that for $p^2$ dimensional system, the set $\rm{KD}_{\mathcal{A},\mathcal{B}}^+$ is a convex hull of the set $\rm {pure}({\rm {KD}_{\mathcal{A},\mathcal{B}}^+})$ based on DFT, where $\rm{KD}_{\mathcal{A},\mathcal{B}}^+$ is the set of KD classical states with respect to two bases and $\rm {pure}({\rm {KD}_{\mathcal{A},\mathcal{B}}^+})$ is the set of all the rank-one projectors of KD classical pure states with respect to two bases. In $pq$ dimensional system, we believe that this result also holds. Unfortunately, we do not completely prove it, but some meaningful conclusions are obtained about the characterization of KD classicality.

Diffusion process with structural changes for subspace clustering

Spectral clustering-based methods have gained significant popularity in subspace clustering due to their ability to capture the underlying data structure effectively. Standard spectral clustering focuses on only pairwise relationships between data points, neglecting interactions among high-order neighboring points. Integrating the diffusion process can address this limitation by leveraging a Markov random walk. However, ensuring that diffusion methods capture sufficient information while maintaining stability against noise remains challenging. In this paper, we propose the Diffusion Process with Structural Changes (DPSC) method, a novel affinity learning framework that enhances the robustness of the diffusion process. Our approach broadens the scope of nearest neighbors and leverages the dropout idea to generate random transition matrices. Furthermore, inspired by the structural changes model, we use two transition matrices to optimize the iteration rule. The resulting affinity matrix undergoes self-supervised learning and is subsequently integrated back into the diffusion process for refinement. Notably, the convergence of the proposed DPSC is theoretically proven. Extensive experiments on benchmark datasets demonstrate that the proposed method outperforms existing subspace clustering methods. The code of our proposed DPSC is available at https://github.com/zhudafa/DPSC.

How the characteristics of land cover changes affect vegetation greenness in Guangdong, a rapid urbanization region of China during 2001-2022.

To evaluate the quantitative impacts of land cover change on vegetation greenness in the significantly human-impacted subtropical region, the characteristics of land cover change were explored by land use dynamic degree, transition matrix and normalized entropy. Various methods including Standardized coefficient, LMG (Lindeman-Merenda-Gold), GEN (Genizi measure) and CAR (Correlation-Adjusted Marginal Correlation) were employed to estimate the contributions of land cover changes on vegetation greenness using MODIS data during 2001-2022 in Guangdong. The conclusions revealed that land cover changes exhibited obvious temporal characteristics in Guangdong with a significantly increasing trend of normalized entropy indicating a more balanced distribution of land cover types under human intervention. NDVI (Normalized Difference Vegetation Index) tended to increase likely due to the large-scale increase in evergreen forest. With regard to the contributions of impact factors on vegetation greenness, the contributions evaluated by LMG, GEN and CAR showed that the natural variation of NDVI accounted for the major contribution (> 33%), while the changes of evergreen forest and grassland had the highest contribution (> 37%) according to Standardized coefficient. These differences were mainly due to the characteristics of land cover changes in Guangdong, the correlations among impact factors and the inherent attributions of the methods. Moreover, the expansions of evergreen forest and urban at the expense of the reductions of grassland and cropland also had significant impacts on NDVI (> 10%) according to LMG, GEN and CAR indicating that human-induced land cover changes had remarkable influences on NDVI.

Experimental studies of sensors based on fiber Bragg gratings embedded in the internal structure of composite plates

The main purpose of this article is to study the structural characteristics of carbon fiber samples printed on a 3D printer, including embedded sensors, during 3D printing, through numerical and experimental studies. There is a widespread tendency to replace metals with modern composite materials. Various methods can be used to measure the destruction of the composite structure. Fiber Bragg array sensors are known as intelligent localized and global structural condition prediction devices for all kinds of structural applications, especially those using composite materials. In this work, we created control plates by impregnating a selected number of layers of carbon fabric with epoxy resin. Fiber-optic Bragg sensors were additionally located between the layers of carbon fabric. In the course of experimental studies, a fiber Bragg grating sensor was embedded in the middle of the sample, and a second sensor was attached to the finished surface of the sample. We conducted environmental tests to examine the durability of the additively manufactured standard’s components and the impact of integrated sensors on the composite sample. We applied the concept of equations, known as conjugate modes, and used the transition matrix method to solve them numerically. The scientific novelty of the work is the development of a new method and technology for measuring spatial deformation in composite materials using fiber-optic Bragg gratings.

Monitoring urban growth and landscape fragmentation in Kaduna, Nigeria, using remote sensing approach

This study assessed urban growth in Kaduna using remote sensing indices, landscape metrics, and Landsat images (2003, 2013, and 2023). Land use/land cover was carried out using the decision tree algorithm, while the transition matrix was computed using ArcGIS 10.8. Landscape fragmentation was assessed using Fragstat 4.3. The results revealed that between 2003 and 2023, bare land, built-up areas, and water bodies increased by 596 km2, 121.04 km2, and 0.22 km2, respectively, while cultivated land and tree cover decreased by 525.54 km2 and 191.91 km2, respectively. For the rate of change, bare lands, built-up areas, and water bodies increased by 29.81 km2, 6.05 km2, and 0.01 km2 annually, respectively, while cultivated lands and water bodies decreased by 26.28 km2 and 9.60 km2, respectively. For the land-use transition, 112.71 km2 of the built-up areas remained persistent. Cultivated lands, tree cover, and water bodies transitioned to built-up areas, losing 85.43 km2, 13.91 km2, and 0.91 km2, respectively. Landscape fragmentation analysis revealed that the number of patches increased from 405 in 2003 to 614 in 2023. Patch density, largest patch index, total edge, edge density, area-weighted mean shape index, and fractal dimension index decreased between 2003 and 2013 but increased between 2013 and 2023, indicating urban sprawl. However, the area-weighted mean contiguity index consistently increased between 2003 and 2023. The study concluded that urbanization is the leading driver of landscape fragmentation. This study recommends that development should be controlled to reduce encroachment on agricultural lands and habitats, thereby reducing landscape fragmentation and land degradation.

Pinning control design for the output regulation of Boolean networks

In previous studies, the output regulation problem (ORP) of Boolean networks (BNs) is usually studied by the augmented system method, however, the state transition matrix dimension of augmented system is growing exponentially as the number of network nodes increases. To reduce the computational complexity, a new method to solve the ORP is adopted in this paper. Firstly, the relationship among the attractors of two BNs and their augmented system is studied, based on which, the ORP is converted into the set stabilisation problem of the original BN, and some necessary and sufficient conditions are given to detect the solvability of the ORP. Secondly, a pinning control for the ORP is considered according to the attractors of the original BNs, and an algorithm to design this kind of controls is provided. Finally, a practical example of signal transduction network is given to illustrate the effectiveness of the method in this paper.

Differential-algebraic equation-based model predictive control for trajectory tracking of spacecraft-mounted continuum manipulators

Spacecraft-mounted continuum manipulators (SMCMs) exhibit great potential for performing dexterous operations in unstructured environments due to their inherent compliance and dexterity. However, the dynamic model of the rigid-flexible coupling SMCM is highly nonlinear and typically formulated as a set of implicit differential-algebraic equations (DAEs), posing significant challenges for precise trajectory tracking control. This paper proposes a novel model predictive control (MPC) framework specifically designed for generic DAEs to achieve precise trajectory tracking of the SMCM under uncertain disturbances and input limitations. The DAE model of the SMCM is discretized into a set of nonlinear algebraic equations. By performing implicit differentiation of these equations with respect to the system state, the state transition matrix (STM) for the DAE model is derived. The optimal control action for the SMCM can be further determined based on the derived STM. Additionally, nonlinear complementary functions are introduced to address the issue of input limitations, allowing the problem of determining the optimal control sequence to be equivalently transformed into a set of nonlinear algebraic equations for solving. Numerical simulations demonstrate that the proposed approach can achieve precise trajectory tracking of the SMCM while strictly adhering to input limitations.

Estimating the dynamics and driving factors of gross primary productivity over the Chinese Loess Plateau by the modified vegetation photosynthesis model

Gross primary productivity (GPP) is a key parameter in research on the global carbon cycle and changes. Understanding the spatiotemporal dynamics and influencing factors of GPP on the Loess Plateau (LP) helps identify the health status of ecosystems, thereby enabling the implementation of effective conservation and restoration measures. In this study, we used a modified vegetation photosynthesis model (VPM) to simulate a long-term series of GPP in the LP from 2001 to 2022, and the impacts of different land-use patterns and meteorological factors on GPP were investigated using a transition matrix, linear regression, and partial correlation analyses. The findings suggested that the modified simulation yielded a more reliable performance (coefficient of determination (R2) = 0.89, root mean square error (RMSE) = 143.47 gC·m−2·yr−1) and was suitable for further research endeavors. (1) The GPP on the LP significantly increased by 232.65 TgC from 2001 to 2022. The southeastern region contributed more than the northwestern region, and the GPP exhibited higher multi-year averages and growth rates below 1000 m elevation. (2) Forests in the southeastern region of the LP, characterized by a heightened growth rate, will influence future spatial variations in GPP increases across the LP. Despite the decline in grassland and cultivated land areas, substantial land coverage has significantly contributed to the overall GPP growth. Urbanization encroaching on cultivated land has emerged as a key contributor to the decline in GPP in low-altitude regions. (3) Air temperature was the main physical driving force for GPP change in the LP. Additionally, the GPP in forested regions exhibited a negative correlation with rainfall, whereas the GPP in areas undergoing the return of cropland to forest–grassland and cropland reclamation correlated negatively with solar radiation. (4) The attribution analysis indicated that the surge in vegetation GPP on the LP was collectively driven by human activities and meteorological changes, with human activities dominating these changes by 61.41 %. This study deepens the understanding of terrestrial ecology in semi-humid regions and provides scientific insights for implementing ecological governance strategies in the LP.

Three-dimensional bioprinted cell-adaptive hydrogel with anisotropic micropores for enhancing skin wound healing

Engineered matrices with aligned microarchitectures are pivotal in regulating the fibroblast-to-myofibroblast transition, a critical process for wound healing and scar reduction. However, developing a three-dimensional (3D) aligned matrix capable of effectively controlling this transition remains challenging. Herein, we developed a cell-adaptive hydrogel with highly oriented microporous structures, fabricated through bioprinting of thermo/ion/photo-crosslinked gelatin methacrylate/sodium alginate (GelMA/SA) incorporating shear-oriented polyethylene oxide (PEO) filler. The synergistic interactions among GelMA, PEO, and SA yield a homogeneous mixture conducive to the printing of biomimetic 3D constructs with anisotropic micropores. These anisotropic micropores, along with the biochemical cues provided by the GelMA/PEO/SA scaffolds, enhance the oriented spreading and organization of fibroblasts. The resultant spread and aligned cellular morphologies promote the transition of fibroblasts into myofibroblasts. By co-culturing human keratinocytes on the engineered dermal layer, we successfully create a bilayer skin construct, wherein the keratinocytes establish tight junctions accompanied by elevated expression of cytokeratin-14, while the fibroblasts display a highly spread morphology with increased fibronectin expression. The printed hydrogels accelerate full-thickness wound closure by establishing a bioactive microenvironment that mitigate inflammation and stimulate angiogenesis, myofibroblast transition, and extracellular matrix remodeling. This anisotropic hydrogel demonstrates substantial promise for applications in skin tissue engineering.