Deformation failure characteristics of coal body and mining induced stress evolution law.

The results of the interaction between coal failure and mining pressure field evolution during mining are presented. Not only the mechanical model of stope and its relative structure division, but also the failure and behavior characteristic of coal body under different mining stages are built and demonstrated. Namely, the breaking arch and stress arch which influence the mining area are quantified calculated. A systematic method of stress field distribution is worked out. All this indicates that the pore distribution of coal body with different compressed volume has fractal character; it appears to be the linear relationship between propagation range of internal stress field and compressed volume of coal body and nonlinear relationship between the range of outburst coal mass and the number of pores which is influenced by mining pressure. The results provide theory reference for the research on the range of mining-induced stress and broken coal wall.

Super-resolution of diffusion-weighted images using space-customized learning model.

BACKGROUND: Diffusion-weighted imaging (DWI) is a noninvasive method used for investigating the microstructural properties of the brain. However, a tradeoff exists between resolution and scanning time in clinical practice. Super-resolution has been employed to enhance spatial resolution in natural images, but its application on high-dimensional and non-Euclidean DWI remains challenging. OBJECTIVE: This study aimed to develop an end-to-end deep learning network for enhancing the spatial resolution of DWI through post-processing. METHODS: We proposed a space-customized deep learning approach that leveraged convolutional neural networks (CNNs) for the grid structural domain (x-space) and graph CNNs (GCNNs) for the diffusion gradient domain (q-space). Moreover, we represented the output of CNN as a graph using correlations defined by a Gaussian kernel in q-space to bridge the gap between CNN and GCNN feature formats. RESULTS: Our model was evaluated on the Human Connectome Project, demonstrating the effective improvement of DWI quality using our proposed method. Extended experiments also highlighted its advantages in downstream tasks. CONCLUSION: The hybrid convolutional neural network exhibited distinct advantages in enhancing the spatial resolution of DWI scans for the feature learning of heterogeneous spatial data.

Histopathology Image Classification with Noisy Labels via The Ranking Margins.

Clinically, histopathology images always offer a golden standard for disease diagnosis. With the development of artificial intelligence, digital histopathology significantly improves the efficiency of diagnosis. Nevertheless, noisy labels are inevitable in histopathology images, which lead to poor algorithm efficiency. Curriculum learning is one of the typical methods to solve such problems. However, existing curriculum learning methods either fail to measure the training priority between difficult samples and noisy ones or need an extra clean dataset to establish a valid curriculum scheme. Therefore, a new curriculum learning paradigm is designed based on a proposed ranking function, which is named The Ranking Margins (TRM). The ranking function measures the 'distances' between samples and decision boundaries, which helps distinguish difficult samples and noisy ones. The proposed method includes three stages: the warm-up stage, the main training stage and the fine-tuning stage. In the warm-up stage, the margin of each sample is obtained through the ranking function. In the main training stage, samples are progressively fed into the networks for training, starting from those with larger margins to those with smaller ones. Label correction is also performed in this stage. In the fine-tuning stage, the networks are retrained on the samples with corrected labels. In addition, we provide theoretical analysis to guarantee the feasibility of TRM. The experiments on two representative histopathologies image datasets show that the proposed method achieves substantial improvements over the latest Label Noise Learning (LNL) methods.

Spatial and Modal Optimal Transport for Fast Cross-Modal MRI Reconstruction.

Multi-modal magnetic resonance imaging (MRI) plays a crucial role in comprehensive disease diagnosis in clinical medicine. However, acquiring certain modalities, such as T2-weighted images (T2WIs), is time-consuming and prone to be with motion artifacts. It negatively impacts subsequent multi-modal image analysis. To address this issue, we propose an end-to-end deep learning framework that utilizes T1-weighted images (T1WIs) as auxiliary modalities to expedite T2WIs' acquisitions. While image pre-processing is capable of mitigating misalignment, improper parameter selection leads to adverse pre-processing effects, requiring iterative experimentation and adjustment. To overcome this shortage, we employ Optimal Transport (OT) to synthesize T2WIs by aligning T1WIs and performing cross-modal synthesis, effectively mitigating spatial misalignment effects. Furthermore, we adopt an alternating iteration framework between the reconstruction task and the cross-modal synthesis task to optimize the final results. Then, we prove that the reconstructed T2WIs and the synthetic T2WIs become closer on the T2 image manifold with iterations increasing, and further illustrate that the improved reconstruction result enhances the synthesis process, whereas the enhanced synthesis result improves the reconstruction process. Finally, experimental results from FastMRI and internal datasets confirm the effectiveness of our method, demonstrating significant improvements in image reconstruction quality even at low sampling rates.

Decoupled learning for brain image registration.

Image registration is one of the important parts in medical image processing and intelligent analysis. The accuracy of image registration will greatly affect the subsequent image processing and analysis. This paper focuses on the problem of brain image registration based on deep learning, and proposes the unsupervised deep learning methods based on model decoupling and regularization learning. Specifically, we first decompose the highly ill-conditioned inverse problem of brain image registration into two simpler sub-problems, to reduce the model complexity. Further, two light neural networks are constructed to approximate the solution of the two sub-problems and the training strategy of alternating iteration is used to solve the problem. The performance of algorithms utilizing model decoupling is evaluated through experiments conducted on brain MRI images from the LPBA40 dataset. The obtained experimental results demonstrate the superiority of the proposed algorithm over conventional learning methods in the context of brain image registration tasks.

Oral delivery of porous starch-loaded bilayer microgels for controlled drug delivery and treatment of ulcerative colitis.

We prepared one type of bilayer microgels for oral administration with three effects: pH responsiveness, time lag, and colon enzyme degradation. Combined with the dual biological effects of curcumin (Cur) for reducing inflammation and promoting repair of colonic mucosal injury, targeted colonic localization and release of Cur according to the colonic microenvironment were enhanced. The inner core, derived from guar gum and low-methoxyl pectin, afforded colonic adhesion and degradation behavior; the outer layer, modified by alginate and chitosan via polyelectrolyte interaction, achieved colonic localization. The porous starch (PS)-mediated strong adsorption allowed Cur loading in inner core to achieve a multifunctional delivery system. In vitro, the formulations exhibited good bioresponses at different pH conditions, potentially delaying Cur release in the upper gastrointestinal tract. In vivo, dextran sulfate sodium-induced ulcerative colitis (UC) symptoms were significantly alleviated after oral administration, accompanied by reduced levels of inflammatory factors. The formulations facilitated colonic delivery, allowing Cur accumulation in colonic tissue. Moreover, the formulations could alter gut microbiota composition in mice. During Cur delivery, each formulation increased species richness, decreased pathogenic bacterial content, and afforded synergistic effects against UC. These PS-loaded bilayer microgels, exhibiting excellent biocompatibility, multi-bioresponsiveness, and colon targeting, could be beneficial in UC therapy, allowing development into a novel oral formulation.

Construction of redox-sensitive liposomes modified by glycyrrhetinic acid and evaluation of anti-hepatocellular carcinoma activity.

The aim of this study was to construct a bifunctional liposome with hepatic-targeting capacity by modifying with a targeting ligand and an intracellular tumor reduction response functional group to deliver drugs precisely to focal liver tissues and release them in large quantities in hepatocellular carcinoma cells. This could improve drug efficacy and reduce toxic side effects at the same time. First, the bifunctional ligand for liposome was successfully obtained by chemically synthesizing it from the hepatic-targeting glycyrrhetinic acid (GA) molecule, cystamine, and the membrane component cholesterol. Then the ligand was used to modify the liposomes. The particle size, PDI and zeta potential of the liposomes were determined with a nanoparticle sizer, and the morphology was observed by transmission electron microscopy. The encapsulation efficiency and drug release behavior were also determined. Further, the stability in vitro of the liposomes and the changes in the simulated reducing environment were determined. Finally, the antitumor activity in vitro and cellular uptake efficiency of the drug-loaded liposomes were investigated by performing cellular assays. The results showed that the prepared liposomes had a uniform particle size of 143.6 ± 2.86 nm with good stability and an encapsulation rate of 84.3 ± 2.1 %. Moreover, the particle size of the liposomes significantly increased and the structure was destroyed in a DTT reducing environment. Cellular experiments showed that the modified liposoes had better cytotoxic effects on hepatocarcinoma cells than both normal liposomes and free drugs. This study has great potential for tumor therapy and provides novel ideas for the clinical use of oncology drugs in dosage forms.

Analysis of coal face stability of lower coal seam under repeated mining in close coal seams group.

Aiming at the problem of coal face failure of lower coal seam under the influence of repeated mining in close coal seams, with the working face 17,101 as a background, the coal samples mechanics test clarified the strength characteristics of the coal face under repeated mining, through similar simulation experiments, the development of stable roof structure and surrounding rock cracks under repeated mining of close coal seams are further explored. And based on this, establish a coal face failure mechanics model to comprehensively analyze the influence of multiple roof structural instabilities on the stability of the coal face. Finally, numerical simulation is used to further supplement and verify the completeness and rationality of similar simulation experiment and theoretical analysis results. The results show that: affected by repeated mining disturbances, the cracks in the coal face are relatively developed, the strength of the coal body is reduced, and the coal face is more prone to failure under the same roof pressure; During the mining of coal seam 17#, the roofs of different layers above the stope form two kinds of "arch" structures and one kind of "voussoir beam" structure, and there are three different degrees of frequent roof pressure phenomenon, which is easy to cause coal face failure; Under repeated mining of close coal seams, the roof pressure acting on the coal face is not large. The main controlling factor of coal face failure is the strength of the coal body, and the form of coal face failure is mostly the shear failure of soft coal. The research results can provide a theoretical basis for coal face failure under similar conditions.

Evaluation of white sandstone mechanical behaviour and the energy evolution of prepeak unloading damage.

Deep high-stress roadway excavation under unloading disturbance inevitably leads to damage deterioration of the surrounding rock, which poses a serious threat to its stability. To explore the energy characteristics of white sandstone damaged by peak front unloading, uniaxial compression tests were conducted on damaged rock samples. The results show that the peak strength and modulus of elasticity of the rock sample gradually decrease with increasing damage degrees. The external work input energy, releasable elastic strain energy and dissipation energy all decreased with increasing damage. Damage evolution curves and equations of the rock samples were obtained based on the damage instantiation model established by the principle of energy dissipation and release. The effects of unloading damage on the fracture characteristics of the rock samples were analysed from both macro and microscopic viewpoints, and the results showed that a micro fracture in the rock is transformed from brittle-ductile damage, while macroscopic damage occurs in the form of a "shear"-"splitting"-"mixed shear-splitting" damage process. This paper has certain research and reference value for understanding the damage evolution characteristics of rocks with peak front unloading damage.

Promoting the mechanism of OMS-2 for gas adsorption in different K+ concentrations.

Catalytic combustion technology is an efficient and green method to deal with low concentration methane. Gas adsorption over the catalyst surface is a key step in the catalytic combustion process, which has attracted much interest. In this work, the first-principles density functional theory calculation method has been applied to explore the adsorption processes of CH4 and O2 molecules on the surface of cryptomelane type manganese oxide octahedral molecular sieves (OMS-2). In addition, the effect of K+ concentration in the OMS-2 tunnel on the adsorption of the two gaseous molecules has also been investigated. The results of adsorption energy and structural characteristics show that the adsorption energies of CH4 and O2 molecules over the catalyst surface are favorable. Adsorption sites of CH4 are the K+ and O sites, among which the K+ site is the most stable adsorption site. In addition, Mn sites are favorable for adsorbing O2 molecules. The interactions between the catalyst and the adsorbed CH4 and O2 are enhanced with the increasing tunnel potassium ions. It should be noted that with the increasing strength of the adsorption energies, equilibrium distances from the two gaseous molecules to the active sites become shorter and the bond lengths of C-H and O-O bonds become longer. Moreover, the adsorption sites of CH4 on the catalyst surface increase with the increasing K+ concentration. Bader charge and cohesive energy calculations reveal that the tunnel K+ can balance charges and help strengthen the structural stability of OMS-2. Interestingly, the electronegativity of the catalyst has been altered after introducing K+, which leads to better adsorption of gaseous CH4 and O2. The microscopic mechanism of the effect of K+ concentration on the adsorption of CH4 and O2 over the catalyst surface paves the way for further deciphering the mechanism underlying the catalytic oxidation process and helps design more efficient catalysts for methane utilization.

Mechanical and Acoustic Emission Characteristics of Coal-like Rock Specimens under Static Direct Shear and Dynamic Normal Load.

In underground engineering, shear failure is a common failure type in coal-rock mass under medium and low strain-rate disturbance loads. Analyzing the shear failure mechanical properties of coal-rock mass under dynamic normal load is significant. In order to reveal the influence of disturbance load on the shear mechanical properties of coal rock, a dynamic and static load coupling electro-hydraulic servo testing machine was used to conduct the shear tests of coal-like rock materials under dynamic and constant normal load. The amplitude of dynamic load is 10 kN and the frequency is 5 Hz. The damage process of the specimens was detected by the acoustic emission (AE) detection system. The results imply that the shear failure process of coal-like rock materials under constant normal load can be divided into four stages. The normal disturbance decreased the shear strength of the specimens and increased the shear modulus of the specimens. With the increase in normal load, the influence of disturbance on the shear strength of the specimen decreased. By analyzing the AE parameters, it was found that the dynamic load made the internal damage of the specimen more severe during the shear failure process. The damage variable was calculated by AE cumulative energy, and the damage evolution was divided into three stages. The shear failure mechanism of the specimen was judged by RA (rise time/amplitude) and AF (average frequency). It was found that from the elastic deformation stage to the unstable development fracture stage, the proportion of shear fracture increased. When the dynamic normal load was 10 kN and 30 kN, the fracture was mainly shear fracture; When the dynamic normal load was 50 kN, the fracture was mainly tensile or mixed fracture. The dynamic normal load affects the shear strength and failure mechanism. Therefore, the influence of disturbance load on coal-rock mass strength cannot be ignored in underground engineering.

Oral delivery of curcumin via multi-bioresponsive polyvinyl alcohol and guar gum based double-membrane microgels for ulcerative colitis therapy.

Anti-inflammatory drugs for ulcerative colitis (UC) treatment should specifically penetrate and accumulate in the colon tissue. Herein, a multi-bioresponsive anti-inflammatory drug (curcumin, CUR)-loaded heterogeneous double-membrane microgels (CUR@microgels) for oral administration was fabricated in this study, in which the inner core was derived from polyvinyl alcohol (PVA) and guar gum (GG) and the outer gel was decoration with alginate and chitosan by polyelectrolyte interactions. The structure and morphology of microgels were characterized. In vitro, the formulation exhibited good bio-responses at different pH conditions and sustained-release properties in simulated colon fluid with a drug-release rate of 84.6 % over 34 h. With the assistance of the outlayer gels, the microgels effectively delayed the premature drug release of CUR in the upper gastrointestinal tract. In vivo studies revealed that CUR@microgels specifically accumulated in the colon tissue for 24 h, which suggest that the interlayer gels were apt to reach colon lesion. As expected, the oral administration of microgels remarkably alleviated the symptoms of UC and protected the colon tissue in DSS-induced UC mice. The above results indicated that these facilely fabricated microgels which exhibited excellent biocompatibility and multi-bioresponsive drug release, had an apparent effect on the treatment of UC, which represents a promising drug delivery strategy for CUR in a clinical application.

GCSBA-Net: Gabor-Based and Cascade Squeeze Bi-Attention Network for Gland Segmentation.

Colorectal cancer is the second and the third most common cancer in women and men, respectively. Pathological diagnosis is the "gold standard" for tumor diagnosis. Accurate segmentation of glands from tissue images is a crucial step in assisting pathologists in their diagnosis. The typical methods for gland segmentation form a dense image representation, ignoring its texture and multi-scale attention information. Therefore, we utilize a Gabor-based module to extract texture information at different scales and directions in histopathology images. This paper also designs a Cascade Squeeze Bi-Attention (CSBA) module. Specifically, we add Atrous Cascade Spatial Pyramid (ACSP), Squeeze Position Attention (SPA) module and Squeeze Channel Attention module (SCA) to model semantic correlation and maintain the multi-level aggregation on the spatial pyramid with different dilations. Besides, to solve the imbalance of data distribution and boundary blur, we propose a hybrid loss function to response the object boudary better. The experimental results show that the proposed method achieves state-of-the-art performance on the GlaS challenge dataset and CRAG colorectal adenocarcinoma dataset, respectively.

Experimental Study on Flammability and Flame Spread Characteristics of Polyvinyl Chloride (PVC) Cable.

Polyvinyl chloride (PVC) is widely applied in cables as insulation materials, which are vital for operation and control of industrial processes. However, PVC cables fires frequently occur, arousing public concern. Therefore, experimental methods are used to study flammability and flame-spread characteristics of PVC cable in this paper. Influences of cable structure and number are investigated, which is scanty in previous works. As cable core number of single cable or cable number of multiple cables rises, average flame height and width increase while the increment decreases. Formulas concerning dimensionless flame height and single cable diameter (or total width of multiple cables) are obtained. The former is negatively correlated with the latter. For single cable, convective heat transfer is dominant, and flame-spread rate decreases as cable core number increases. Cable maximum temperature, which drops first and then rises as cable core number increases, is observed in the cable core area. For multiple cable, the flame-spread rate increases as cable number increases. As the cable number rises, the length of pyrolysis and combustion zone increases while the maximum temperature of cable surface decreases. This work is beneficial to fire hazard evaluation and safety design of PVC cables.

Applicability of Yielding-Resisting Sand Column and Three-Dimensional Coordination Support in Stopes.

In view of the existing problems of stope roadways, which are difficult to maintain under the influence of high ground and mining-induced stresses, the structural characteristics and movement regularities of stopes surrounding rocks were analysed. Through the construction of a three-dimensional mechanical model of the coordination support of a stope, the adaptability index of the support in stope is presented, and its mechanism of operation is expounded. Yielding-resisting sand column (YRSC) sidewall-support technology with satisfactory compressibility and supporting strength was developed. The structure and actual mechanical properties of the YRSC were investigated through laboratory experiments, and the optimum ratio of filling materials was obtained. The good applicability of the load and deformation adaptability index of the three-dimensional coordination support in the stope and YRSC sidewall-support technology were demonstrated in practice at the No. 12306 working face of the Dongda coal mine. It was shown that the designed carrying capacity and compression of the sand columns satisfied the site requirements. The actual stress and deformation of the YRSC exhibited three stages: Slow growth at the initial stage, a large increase in the medium term, and a stable trend at the end. The adaptability index of the three-dimensional coordination support in the stope considers all bearing structure units of the stope as an interconnected whole, and the stability conditions of the stope roadway can be quantitatively described. The supporting effect of the YRSC is remarkable and can be applied to the construction of tunnels, bridge systems and other engineering fields.

Analysis of novel geometry-independent method for dialysis access pressure-flow monitoring.

BACKGROUND: End-stage renal disease (ESRD) confers a large health-care burden for the United States, and the morbidity associated with vascular access failure has stimulated research into detection of vascular access stenosis and low flow prior to thrombosis. We present data investigating the possibility of using differential pressure (DeltaP) monitoring to estimate access flow (Q) for dialysis access monitoring, with the goal of utilizing micro-electro-mechanical systems (MEMS) pressure sensors integrated within the shaft of dialysis needles. METHODS: A model of the arteriovenous graft fluid circuit was used to study the relationship between Q and the DeltaP between two dialysis needles placed 2.5-20.0 cm apart. Tubing was varied to simulate grafts with inner diameters of 4.76-7.95 mm. Data were compared with values from two steady-flow models. These results, and those from computational fluid dynamics (CFD) modeling of DeltaP as a function of needle position, were used to devise and test a method of estimating Q using DeltaP and variable dialysis pump speeds (variable flow) that diminishes dependence on geometric factors and fluid characteristics. RESULTS: In the fluid circuit model, DeltaP increased with increasing volume flow rate and with increasing needle-separation distance. A nonlinear model closely predicts this DeltaP-Q relationship (R2 > 0.98) for all graft diameters and needle-separation distances tested. CFD modeling suggested turbulent needle effects are greatest within 1 cm of the needle tip. Utilizing linear, quadratic and combined variable flow algorithms, dialysis access flow was estimated using geometry-independent models and an experimental dialysis system with the pressure sensors separated from the dialysis needle tip by distances ranging from 1 to 5 cm. Real-time DeltaP waveform data were also observed during the mock dialysis treatment, which may be useful in detecting low or reversed flow within the access. CONCLUSION: With further experimentation and needle design, this geometry-independent approach may prove to be a useful access flow monitoring method.

Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning.

In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. Second, we convert the model to a minimization problem whose variable is symmetric positive-definite matrix. Third, in implementation, we deduce an intrinsic steepest descent method, which assures that the metric matrix is strictly symmetric positive-definite at each iteration, with the manifold structure of the symmetric positive-definite matrix manifold. Finally, we test the proposed algorithm on conventional data sets, and compare it with other four representative methods. The numerical results validate that the proposed method significantly improves the classification with the same computational efficiency.

Inhibition of EP300 and DDR1 synergistically alleviates pulmonary fibrosis in vitro and in vivo.

OBJECTIVES: Pulmonary fibrosis is strongly correlated with inflammation factors, cytokine, and collagen secretion, whereby discoidin domain receptor 1 (DDR1) signaling plays an important role. EP300 is defined as an acetyltransferase that can acetylate histone and has been broadly studied in several chronic diseases, including cancer, inflammation and fibrosis. This study aimed to investigate the relationship between p300 and DDR1 in the pathological processes of pulmonary fibrosis. MATERIALS AND METHODS: Transcriptome analysis of single cell RNA-sequencing for idiopathic pulmonary fibrosis (IPF) bronchial epithelial cells demonstrated that both DDR1 and EP300 were up-regulated and involved in the regulation of autophagy, cellular response to organonitrogen compounds, and collagen metabolic pathways, respectively. The anti-fibrotic and anti-inflammation effects of Pim1 and DDR1 inhibitors in bleomycin-induced IPF murine models were estimated. RESULTS: We discovered that overexpression of EP300 signaling induced MRC5 human fibroblast cells that up-regulated the expression of DDR1 and FN1; however, no effects on COL1 A1 and DDR1 phosphorylation were observed. Mechanistically, TGF-ß1 activated FN1, collagen, and DDR1 signaling could be reversed by the combination of p300 siRNA and DDR1 inhibitors. Moreover, the EP300 inhibitor SGC-CBP30 displayed synergistic effects with DDR1 inhibitors in pathogenic scores, airway goblet cell counts in bronchoalveolar lavage fluid (BALF), IL-4, IFN-γ, FN1COL1 A1 secretion and α-SMA, a marker of myofibroblast. CONCLUSIONS: The EP300 siRNA and inhibitors sensitized DDR1 inhibitors in our pulmonary fibrosis models in vitro and in vivo, implicating a combined inhibition of DDR1 with EP300 as potential therapies for IPF.

A novel flexure-based vertical nanopositioning stage with large travel range.

This paper presents the design of a novel flexure-based vertical (or Z-axis) nanopositioning stage driven by a piezoelectric actuator (PZT), which is capable of executing large travel range. The proposed stage consists mainly of a hybrid displacement amplification mechanism (DAM), a motion guiding mechanism, and a decoupling mechanism. The hybrid DAM with amplification ratio of 12.1 is developed to transfer the transverse motion of the PZT actuator into the vertical motion. The motion guiding mechanism is introduced to avoid cross coupling at the output end. The decoupling mechanism can significantly reduce the cross coupling at the driving end to protect the PZT. The stiffness and dynamics of the proposed stage are improved by these mechanisms. Analytical modeling and finite element analysis (FEA) are then adopted to optimize dimensions of the stage. Finally, a prototype of the stage is fabricated and tested for verification. The results of static and dynamic tests show that the proposed stage is capable of vertical travel range of 214 µm with resolution of 8 nm, and the first two resonance frequencies are 205 Hz and 1206 Hz, respectively. Cross coupling tests under various lateral loads (0 g-1000 g) show that the maximum variances of the lateral and angular cross couplings are less than 0.78 µm and 95 µrad, respectively, indicating good decoupling capability. In addition, the low-profile structure of the stage is well suited to be used in limited vertical space.

Design of a fine alignment system featuring active orientation adjustment for nano imprint lithography.

The fine alignment between a template with nano patterns and a wafer substrate, especially the parallelism between the two surfaces, is critical to Nano Imprint Lithography. A fine alignment system featuring active orientation adjustment which is composed of an imprint unit and a 6-DOF micromanipulator is proposed in this work. Deformations of a compact flexure layer caused by imprint loads are measured by four identical force sensors embedded in the imprint unit. The tilt of the flexure layer can thus be eliminated by adjusting the orientation of the 6-DOF micromanipulator. Kinematics and stiffness analysis are then developed, followed by dynamic performance evaluations. Based on the proposed system, an imprint tool is further developed and corresponding experiments are conducted. A saw shape grating pattern with 1.6 µm linewidth and a lattice pattern with 0.9 µm period are both imprinted with a minimum feature of 30 nm well reserved on the substrate. A maximum parallelism error of 14 nm across the template surface is also demonstrated by further section analyses on the imprinted patterns, hence the feasibility and superiority of the proposed method is verified.