Impact of modifications to antidementia drug reimbursement in France: Analysis of the FRA-DEM cohort.

AIMS: Alzheimer's disease and related diseases (ADRD) is a progressive and inexorable disease. In France, acetylcholinesterase inhibitors and memantine were reimbursed for subjects with ADRD, until 2 modifications of their reimbursement rate (2012, 2018). We aimed to study the consequences of these measures on ADRD subjects' healthcare use. METHODS: We analysed data from the FRA-DEM cohort, including subjects with presumed incident ADRD identified since 2011 in the French health insurance system. We studied the healthcare use of subjects identified with incident ADRD in 2011, 2013, 2015, 2017 and 2019, notably the annual number of defined daily doses of various psychotropic groups. We performed 2 multivariate multinomial logistic regressions with the subcohort year as the dependent variable. RESULTS: In total, 165 120 subjects were included. A progressive decrease in exposure to antidementia drugs was observed between 2011 and 2019. Consultations with private neurologists or psychiatrists, and exposure to risperidone, antidepressants and benzodiazepines increased in the 2019 subcohort, following the 2018 reimbursement withdrawal. Meanwhile, the use of nursing/allied healthcare and emergency care increased over the subcohort years, whereas we observed a decrease in general practitioner consultations. CONCLUSION: These results suggest increases in private neurologist or psychiatrist consultations and exposure to recommended drugs after the reimbursement withdrawal, contrary to the fears expressed. However, antidementia drug exposure decreased long before the reimbursement modifications, probably due to the growing evidence of the modest effect of these drugs, and exposure to benzodiazepines increased after the reimbursement withdrawal.

Calibration and Validation of Simulation Parameters for Maize Straw Based on Discrete Element Method and Genetic Algorithm-Backpropagation.

There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett-Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32-0.36, 1.24 × 108-1.72 × 108 Pa, and 5.9 × 106-6.7 × 106 N/m3, respectively. Using the experimental data of the central composite design as the dataset, a GA-BP neural network prediction model for the peak compression force of maize straw was established, analyzed, and evaluated. The GA-BP prediction model's accuracy was verified via experiments. It was found that the ideal combination of parameters was a Poisson ratio of 0.357, a shear modulus of 1.511 × 108 Pa, and a normal stiffness of 6.285 × 106 N/m3 for the maize straw. The results provide a basis for analyzing the damage mechanism of maize straw during the grinding process.

An On-Site InSAR Terrain Imaging Method with Unmanned Aerial Vehicles.

An on-site InSAR imaging method carried out with unmanned aerial vehicles (UAVs) is proposed to monitor terrain changes with high spatial resolution, short revisit time, and high flexibility. To survey and explore a specific area of interest in real time, a combination of a least-square phase unwrapping technique and a mean filter for removing speckles is effective in reconstructing the terrain profile. The proposed method is validated by simulations on three scenarios scaled down from the high-resolution digital elevation models of the US geological survey (USGS) 3D elevation program (3DEP) datasets. The efficacy of the proposed method and the efficiency in CPU time are validated by comparing with several state-of-the-art techniques.

Validation of a 3D Local-Scale Adaptive Solar Radiation Model by Using Pyranometer Measurements and a High-Resolution Digital Elevation Model.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows us to estimate direct, diffuse, and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterization techniques and allows the generation of high-quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data, and its resolution is two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

Two-dimensional perfusion angiography permits direct visualization of redistribution of flow in hepatocellular carcinoma during b-TACE.

OBJECTIVES: To demonstrate in vivo redistribution of the blood flow towards HCC's lesions by utilizing two-dimensional perfusion angiography in b-TACE procedures. MATERIAL AND METHODS: In total, 30 patients with 35 HCC nodules treated in the period between January 2019 and November 2021. For each patient, a post-processing software leading to a two-dimensional perfusion angiography was applied on each angiography performed via balloon microcatheter, before and after inflation. On the colour map obtained, reflecting the evolution of contrast intensity change over time, five regions of interests (ROIs) were assessed: one on the tumour (ROI-t), two in the immediate peritumoural healthy liver parenchyma (ROI-ihl) and two in the peripheral healthy liver parenchyma (ROI-phl). The results have been interpreted with a novel in silico model that simulates the hemodynamics of the hepatic arterial system. RESULTS: Among the ROIs drawn inside the same segment of target lesion, the time-to-peak of the ROI-t and of the ROI-ihl have a significantly higher mean value when the balloon was inflated compared with the ROIs obtained with deflated balloon (10.33 ± 3.66 s vs 8.87 ± 2.60 s (p = 0.015) for ROI-t; 10.50 ± 3.65 s vs 9.23 ± 2.70 s (p = 0.047) for ROI-ihl). The in silico model prediction time-to-peak delays when balloon was inflated, match with those observed in vivo. The numerical flow analysis shows how time-to-peak delays are caused by the obstruction of the balloon-occluded artery and the opening of intra-hepatic collateral. CONCLUSION: The measurements identify predictively the flow redistribution in the hepatic arteries during b-TACE, supporting a proper positioning of the balloon microcatheter.

A comparative study of satellite altimetry-based and DEM-based methods for estimating lake water volume changes.

This study compared two different methods, the satellite altimetry-based and DEM (digital elevation model)-based, for estimating lake water volume changes. We focused on 34 lakes in China as the testing sites to compare the two methods for lake water volume changes from 2005 to 2020. The satellite altimetry-based method used water levels provided by the DAHITI (Database for Hydrological Time Series of Inland Waters) data and surface areas derived from Landsat imagery. The DEM-based method used the SRTM DEM data in combination with Landsat-derived lake extents. Our results showed a high degree of consistency in lake water volume changes estimated between the two methods (R2 > 0.90), but each method has its limitations. In terms of temporal coverage, the satellite altimetry-based method with the DAHITI data is limited by missing water level data in certain periods. The performance of the DEM-based method in extracting lake shore boundaries in regions with flat terrains (slope <1.5°) is not satisfactory. The DEM-based method has complete regional applicability (100%) in the Tibetan Plateau (TP) Lake Region, yet its effectiveness drops significantly in the Xinjiang and Eastern China Plain Lake Regions, with applicability rates of 50 and 40%, respectively.

Application of an ultrasonic bone knife combined with a dental electric motor in the extraction of mandibular middle and low impacted teeth.

OBJECTIVE: To investigate the clinical application of an ultrasonic bone knife (UBK) combined with a dental electric motor (DEM) in the extraction of mandibular middle and low impacted teeth. METHODS: From January 2022 to May 2023,200 patients with wisdom teeth were randomly divided into three groups: experimental group A (UBK combined with DEM), experimental group B (UBK combined with high-speed turbine mobile phone (HSTMP)), and the control group (bone chisel split crown (BCSC)). The operation time, psychological state during operation, pain, swelling, limitation of mouth opening and other complications on the first, second and third days after operation were recorded. RESULTS: The operation time of experimental group A (EAG) (12.95 ± 2.12) minutes was shorter than that of experimental group B (EBG) (17.06 ± 2.25) minutes and the control group (CG) (23.43 ± 2.18) minutes, and the difference was statistically significant (P < 0.05). The psychological state of the EAG was significantly lower than that of the EBG and CG (P < 0.05). The postoperative pain, swelling, limitation of mouth opening and complications in the EAG were significantly lower than those in the EBG and CG (P < 0.05). CONCLUSION: UBK combined with DEM in the extraction of mandibular middle and low obstructed teeth has good results, good prognosis, high safety, short operation time, better psychological status of patients, low postoperative pain, swelling, mouth opening restriction and complication rate, and is currently the preferred extraction method.

Aus Klinik und Praxis / From Clinic and Practice. Die Erweiterung der psychotherapeutischen Perspektive durch bifokale Young Child Observation. Methodisches und Kasuistisches zur Konzeption der Vienna Parent-Toddler Group Study.

Expanding the Psychotherapeutic PerspectiveThrough Bifocal Young Child Observation. Methodological and Case-Related Considerations on the Design of the Vienna Parent-Toddler Group Study The parent-toddler group concept was developed at the Anna Freud Centre in London. Since 2021 it has also been used at the Child Guidance Clinic in Vienna.The Vienna Parent-Toddler Group (PTG) study investigates the significance of participation in such a therapeutic group for changes in children's relational experiences and developmental problems in their families. Among other research tools, the bifocal use of Young Child Observation according to the Tavistock concept is used:The children are observed as continuously as possible in their interactions and relations both in the group and at home.The article first describes the background and the design of the research project. Subsequently, with reference to accounts from the observation of a child with eating problems, it is shown in which respect this observation design provides a deeper understanding of the eating problem. Finally, the arguments for and against an early exchange of considerations between the observers and the psychotherapists are discussed.

A New Lunar Dome Detection Method Based on Improved YOLOv7.

Volcanism is an important geological evolutionary process on the Moon. The study of lunar volcanic features is of great significance and value to understanding the geological evolution of the Moon better. Lunar domes are one of the essential volcanic features of the Moon. However, the existing lunar dome detection methods are still traditional manual or semiautomatic identification approaches that require extensive prior knowledge and have a complex identification process. Therefore, this paper proposes an automatic detection method based on improved YOLOv7 for lunar dome detection. First, a new lunar dome dataset was created by digital elevation model (DEM) data, and the effective squeeze and excitation (ESE) attention mechanism module was added to the backbone and neck sections to reduce information loss in the feature map and enhance network expressiveness. Then, a new SPPCSPC-RFE module was proposed by adding the receptive field enhancement (RFE) module into the neck section, which can adapt to dome feature maps of different shapes and sizes. Finally, the bounding box regression loss function complete IOU (CIOU) was replaced by wise IOU (WIOU). The WIOU loss function improved the model's performance for the dome detection effect. Furthermore, this study combined several data enhancement strategies to improve the robustness of the network. To evaluate the performance of the proposed model, we conducted several experiments using the dome dataset developed in this study. The experimental results indicate that the improved method outperforms related methods with a mean average precision (mAP@0.5) value of 88.7%, precision (P) value of 85.6%, and recall (R) value of 86.4%. This study provides an effective solution for lunar dome detection.

Relief Modeling in the Restoration of Extractive Activities Using Drone Imagery.

In the field of mine engineering, a cross-section topographic survey is usually carried out to perform volumetric calculations of earth movement in order to restore areas affected by extractive activities. Nowadays, Remote Sensing and Geographical Information System (GIS) technologies make it possible to perform the same work by using indirect methods such as images obtained by photogrammetric flights. In this context, Unmanned Aerial Systems (UAS) are considered a very convenient option to develop mapping projects in short periods of time and to provide quality geospatial information such as Digital Elevation Models (DEM) and orthophotos of centimetric spatial resolution. In the present study, this approach has been applied in a gravel extraction area to obtain data for estimating the filling volume of material required for the restoration of the relief (DEM(r)). The estimation of the DEM(r) is later used to calculate a difference of height values (DEM(r)-DEM) that will serve as a variable in the basic operation of volume calculation. The novelty of the presented method is the simulation of a relief adapted to the surrounding morphology, including the derived channel network and the visibility impact, improving what would be a simple clogging. Likewise, the generation of 3D models allows visualizing a new morphological structure of the relief. The proposed approach, based on GIS tools, allows analyzing water flow connectivity integration of the DEM(r) with the environment and estimating potential landscape impacts from the main focuses of a visual basin, both of which are key aspects of restoration modeling that are not always properly addressed.

Evaluation of DEM Accuracy Improvement Methods Based on Multi-Source Data Fusion in Typical Gully Areas of Loess Plateau.

Improving the accuracy of DEMs is a critical goal in digital terrain analysis. The combination of multi-source data can be used to increase DEM accuracy. Five typical geomorphic study areas in the Loess Plateau in Shaanxi were selected for a case study and a 5 m DEM unit was used as the basic data input. Data from three open-source databases of DEM images, the ALOS, SRTM and ASTER, were obtained and processed uniformly through a previously geographical registration process. Three methods, Gram-Schmidt pan sharpening (GS), weighted fusion and feature-point-embedding fusion, were used for mutual enhancement of the three kinds of data. We combined the effect of these three fusion methods in the five sample areas and compared the eigenvalues taken before and after the fusion. The main conclusions are as follows: (1) The GS fusion method is convenient and simple, and the three combined fusion methods can be improved. Generally speaking, the fusion of ALOS and SRTM data led to the best performance, but was greatly affected by the original data. (2) By embedding feature points into three publicly available types of DEM data, the errors and extreme error value of the data obtained through fusion were significantly improved. Overall, ALOS fusion resulted in the best performance because it had the best raw data quality. The original eigenvalues of the ASTER were all inferior and the improvement in the error and the error extreme value after fusion was evident. (3) By dividing the sample area into different areas and fusing them separately according to the weights of each area, the accuracy of the data obtained was significantly improved. In comparing the improvement in accuracy in each region, it was observed that the fusion of ALOS and SRTM data relies on a gentle area. A high accuracy of these two data will lead to a better fusion. Merging ALOS and ASTER data led to the greatest increase in accuracy, especially in the areas with a steep slope. Additionally, when SRTM and ASTER data were merged, the observed improvement was relatively stable with little difference.

Enhancing the accuracy of digital soil mapping using the surface and subsurface soil characteristics as continuous diagnostic layers.

Digital soil mapping relies on relating soils to a particular set of covariates, which capture inherent soil spatial variation. In digital mapping of soil classes, the most commonly used covariates are topographic attributes, RS attributes, and maps, including geology, geomorphology, and land use; in contrast, the subsurface soil characteristics are usually ignored. Therefore, we investigate the possibility of using soil diagnostic characteristics as covariates in a mountainous landscape as the main aim of this study. Conventional covariates (CC) and a combination of soil subsurface covariates with conventional covariates (SCC) were used as covariates, and random forest (RF), Multinomial Logistic Regression (LR), and C5.0 Decision Trees (C5) were used as different machine learning algorithms in digital mapping of soil family classes. Based on the results, the RF model with the SCC dataset had the best performance (KC = 0.85, OA = 90). In all three models, adding soil covariates to the sets of covariates increased the model performance. Soil covariates, slope, and aspect were selected as the principal auxiliary variables in describing the distribution of soil family classes.

Soil erosion estimation and erosion risk area prioritization using GIS-based RUSLE model and identification of conservation strategies in Jejebe watershed, Southwestern Ethiopia.

Erosion of soil refers to the process of detaching and transporting topsoil from the land surface by natural forces such as water, wind, and other factors. As a result of this process, soil fertility is lost, water bodies' depth is reduced, water turbidity rises, and flood hazard problems, etc. Using a numerical model of erosion rates and erosion risks in the Jejebe watershed of the Baro Akobo basin in western Ethiopia, this study mapped erosion risks to prioritize conservation measures. In this study, the Revised Universal Soil Loss Equation (RUSLE) model was used, which was adapted to Ethiopian conditions. To estimate soil loss with RUSLE, the rainfall erosivity (R) factor was generated by interpolating rainfall data, the soil erodibility (K) factor was derived from the soil map, the topography (LS) factor was determined from the digital elevation model (DEM), cover and management (C) factor derived from the land use/cover data, and conservation practices (P) factor generated from digital elevation model (DEM) and land use/cover data were integrated with remote sensing data and the GIS 10.5 environment. The findings indicated that the watershed annual soil loss varies from nearly 0 on a gentle slope of forest lands to 265.8 t ha-1 year-1 in the very steep slope upper part of the watershed, with a mean annual soil loss of 36.2 t ha-1 year-1. The total annual soil loss in the watershed is estimated to be around 919,886.5 tons per year. To minimize the amount of soil erosion in the watershed that had been most severely affected, we identified eight conservation strategies that could be implemented. These strategies were based on the participatory watershed development (PWD) principles established by the Ethiopian government and the severity of the erosion in the watershed. The study's findings showed that a GIS-based RUSLE soil erosion assessment model can provide a realistic prediction of the amount of soil loss that will occur in the watershed. This tool can also help identify the priority areas for implementing effective erosion control measures.

Death Anxiety in the Context of Religion, Personality and Life Meanings.

Previous studies on the context between death anxiety and religion do not provide any clear evidence regarding "anxiety buffer" function. In this explorative study, death anxiety and attitude to death were determined in the context of mood, personality and meaning of life among groups of Muslims (n = 60) and Christian Protestants (n = 60). Death anxiety and attitude to death were assessed using the Bochum questionnaire for recording death anxiety and attitudes to death. Death anxiety was mild to moderate in our healthy Participants of Muslim and Christian faith. Attitude towards death was therefore much more pronounced among Muslim members than Christians. The influence of religious beliefs on the fear of death does not appear to be direct and linear. Sources that provide meaning in life and emotional stability can contribute to a reduction in death anxiety and a less problematic attitude towards death.

Measuring habitat complexity and spatial heterogeneity in ecology.

Habitat complexity has been considered a key driver of biodiversity and other ecological phenomena for nearly a century. However, there is still no consensus over the definition of complexity or how to measure it. Up-to-date and clear guidance on measuring complexity is urgently needed, particularly given the rise of remote sensing and advent of technologies that allow environments to be scanned at unprecedented spatial extents and resolutions. Here we review how complexity is measured in ecology. We provide a framework for metrics of habitat complexity, and for the related concept of spatial heterogeneity. We focus on the two most commonly used complexity metrics in ecology: fractal dimension and rugosity. We discuss the pros and cons of these metrics using practical examples from our own empirical data and from simulations. Fractal dimension is particularly widely used, and we provide a critical examination of it drawing on research from other scientific fields. We also discuss informational metrics of complexity and their potential benefits. We chart a path forward for research on measuring habitat complexity by presenting, as a guide, sets of essential and desirable criteria that a metric of complexity should possess. Lastly, we discuss the applied significance of our review.

On the Applicability of the Coarse Grained Coupled CFD-DEM Model to Predict the Heat Transfer During the Fluidized Bed Drying of Pharmaceutical Granules.

Fluidized bed dryer often used in the pharmaceutical industry for drying of wet granules. Coupled computational fluid dynamics (CFD) - discrete element method (DEM) is frequently used to model the drying process because of its ability to obtain the relevant information at the particle level. However, it becomes almost impossible to model the industrial scale fluidized bed dryer using the coupled CFD-DEM method because of the presence of large number of particles [Formula: see text]. To reduce the number of particles to be tracked in the simulation, coarse grained coupled CFD-DEM method was developed by researchers where a certain number of particles of the original system was represented by a relatively bigger particle in the coarse-grained system. The appropriate scaling of the particle-particle and particle-fluid interaction forces is necessary to make sure that the dynamics of the coarse-grained particles/parcels accurately represent the dynamics of the original particles. The coarse-graining of the drying process of pharmaceutical granules during fluidization needs systematic coarse-graining of the momentum, heat, and solvent vapor transfer process. A coarse grained coupled CFD-DEM method was used to model the momentum and heat transfer during the fluidization of pharmaceutical granules. It was shown that the heat transfer during the fluidization of large number of particles could be predicted by simulating a smaller number of bigger particles with appropriate scaling of particle-particle heat and momentum transfer, and particle-fluid heat and momentum transfer at significantly smaller computational time. This model can be further extended by including a coarse-grained moisture transport model in future.

An Improved Spatiotemporal Data Fusion Method for Snow-Covered Mountain Areas Using Snow Index and Elevation Information.

Remote sensing images with high spatial and temporal resolution in snow-covered areas are important for forecasting avalanches and studying the local weather. However, it is difficult to obtain images with high spatial and temporal resolution by a single sensor due to the limitations of technology and atmospheric conditions. The enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) can fill in the time-series gap of remote sensing images, and it is widely used in spatiotemporal fusion. However, this method cannot accurately predict the change when there is a change in surface types. For example, a snow-covered surface will be revealed as the snow melts, or the surface will be covered with snow as snow falls. These sudden changes in surface type may not be predicted by this method. Thus, this study develops an improved spatiotemporal method ESTARFM (iESTARFM) for the snow-covered mountain areas in Nepal by introducing NDSI and DEM information to simulate the snow-covered change to improve the accuracy of selecting similar pixels. Firstly, the change in snow cover is simulated according to NDSI and DEM. Then, similar pixels are selected according to the change in snow cover. Finally, NDSI is added to calculate the weights to predict the pixels at the target time. Experimental results show that iESTARFM can reduce the bright abnormal patches in the land area compared to ESTARFM. For spectral accuracy, iESTARFM performs better than ESTARFM with the root mean square error (RMSE) being reduced by 0.017, the correlation coefficient (r) being increased by 0.013, and the Structural Similarity Index Measure (SSIM) being increased by 0.013. For spatial accuracy, iESTARFM can generate clearer textures, with Robert's edge (Edge) being reduced by 0.026. These results indicate that iESTARFM can obtain higher prediction results and maintain more spatial details, which can be used to generate dense time series images for snow-covered mountain areas.

Comparison of DEM Super-Resolution Methods Based on Interpolation and Neural Networks.

High-resolution digital elevation models (DEMs) play a critical role in geospatial databases, which can be applied to many terrain-related studies such as facility siting, hydrological analysis, and urban design. However, due to the limitation of precision of equipment, there are big gaps to collect high-resolution DEM data. A practical idea is to recover high-resolution DEMs from easily obtained low-resolution DEMs, and this process is termed DEM super-resolution (SR). However, traditional DEM SR methods (e.g., bicubic interpolation) tend to over-smooth high-frequency regions on account of the operation of averaging local variations. With the recent development of machine learning, image SR methods have made great progress. Nevertheless, due to the complexity of terrain characters (e.g., peak and valley) and the huge difference between elevation field and image RGB (Red, Green, and Blue) value field, there are few works that apply image SR methods to the task of DEM SR. Therefore, this paper investigates the question of whether the state-of-the-art image SR methods are appropriate for DEM SR. More specifically, the traditional interpolation method and three excellent SR methods based on neural networks are chosen for comparison. Experimental results suggest that SRGAN (Super-Resolution with Generative Adversarial Network) presents the best performance on accuracy evaluation over a series of DEM SR experiments.

Safety Risk Analysis of a New Design of Basalt Fiber Gabion Slope Based on Improved 3D Discrete Element Method and Monitoring Data.

Gabion has been extensively used in retaining walls and slope protection. This study carries out a safety risk analysis of a new structure combining basalt fiber reinforcement (BFR) and the traditional gabion structure. The micro-parameters of BFR and soil were calibrated by using the 3D discrete element method after the tensile test of BFR was completed. The mechanical property of the gabion unit was investigated by using a refined model and a numerical test of uniaxial compression. This work developed a simplified method to simulate the seepage effect. The stress condition and sliding displacement between gabions were also investigated. Deformation, stress, and porosity were all used to evaluate the stability of the new type of gabion slope. According to this study, BFR has a tensile strength of 68.22 MPa, and the safety factor increased by 25.68% after using these BFR gabions. The damage is mainly manifested by bending the BFRs and the dislocation of the gabion units, as the slope does not slip. It is indicated this novel gabion structure has a lower safety risk compared to traditional ones, and thus can be popularized and used in retaining walls and slope protection.

A Novel DEM Block Adjustment Method for Spaceborne InSAR Using Constraint Slices.

The lack and uneven distribution of Ground Control Points (GCPs) will lead to the deterioration of Digital Elevation Model (DEM) block adjustment results in the bistatic Interferometric Synthetic Aperture Radar (InSAR) system. Given this issue, we first explain the relationship between the stability of adjustment parameters and the GCP distribution pattern theoretically using matrix perturbation theory. Second, we put forward the Constraint Slices (CSs) concept and first introduce CSs into the adjustment optimization model as constraint conditions rather than actual values as GCPs. Finally, we propose a novel DEM block adjustment method for spaceborne InSAR using CSs based on an optimization model with nonlinear constraints. The simulated experiment shows the instability of the conventional method and validates the proposed method under different parallel baseline errors. Four groups of real experiments were carried out according to the size of the uncontrolled area using twelve Co-registered Single-look Slant-range Complex (CoSSC) datasets for Henan Province, China. The adjustment results verified by the ICESat-2 ATL08 data demonstrate that the performance of the proposed method is better than the conventional method in the uncontrolled area; the corresponding improvements in adjustment accuracies compared with the conventional method are 0.13 m, 1.02 m, 2.12 m, and 8.18 m, respectively. At the same time, the proposed method can enhance the height consistency in overlapping areas, which is vital for seamless DEM production.