Inverse Method Research Articles

Adaptive Compensation for Robotic Joint Failures Using Partially Observable Reinforcement Learning

Robotic manipulators are widely used in various industries for complex and repetitive tasks. However, they remain vulnerable to unexpected hardware failures. In this study, we address the challenge of enabling a robotic manipulator to complete tasks despite joint malfunctions. Specifically, we develop a reinforcement learning (RL) framework to adaptively compensate for a nonfunctional joint during task execution. Our experimental platform is the Franka robot with seven degrees of freedom (DOFs). We formulate the problem as a partially observable Markov decision process (POMDP), where the robot is trained under various joint failure conditions and tested in both seen and unseen scenarios. We consider scenarios where a joint is permanently broken and where it functions intermittently. Additionally, we demonstrate the effectiveness of our approach by comparing it with traditional inverse kinematics-based control methods. The results show that the RL algorithm enables the robot to successfully complete tasks even with joint failures, achieving a high success rate with an average rate of 93.6%. This showcases its robustness and adaptability. Our findings highlight the potential of RL to enhance the resilience and reliability of robotic systems, making them better suited for unpredictable environments.

Research on Digital Twin Technology for Enhancing Power Communication System Performance

Abstract Digital twin technology simulates physical systems through real-time data and virtual models, providing an innovative solution to challenges in power communication systems, such as multiple access interference (MAI) and the near-far effect. By constructing a digital twin model, we can accurately measure the non-orthogonality of each user’s spread spectrum code, enabling the elimination of mutual interference through matrix inversion or iterative methods. Our simulations show that the application of digital twin technology improves system performance by reducing MAI and stabilizing communication quality under complex conditions. When the number of stages increases, the system performance improves, but when it is greater than level 5, the system performance does not improve significantly, so the level 5 is selected in the practical application. In this case, the interference factor of the grid communication system is equal to 0.55, and the resulting system gain is 2.

Bridging Three Years of Insights: Examining the Association Between Depression and Gallstone Disease.

Despite sharing common pathophysiological risk factors, the relationship between gallstones and depression requires further evidence for a clearer understanding. This study combines the National Health and Nutrition Examination Survey 2017 - 2020 observational data and Mendelian randomization (MR) analysis to shed light on the potential correlation between these conditions. By analyzing the National Health and Nutrition Examination Survey 2017 - 2020 data through weighted multivariable-adjusted logistic regression, we examined the association between depression and gallstone risk. MR was subsequently applied, utilizing genetic instruments from a large genome-wide association study on depression (excluding 23andMe, 500,199 participants) and gallstone data (28,627 cases, 348,373 controls), employing the main inverse variance-weighted method alongside other MR methods to explore the causal relationship. Sensitivity analyses validated the study's conclusions. Among the 5,303 National Health and Nutrition Examination Survey participants, a significant association was found between depressive symptoms and increased gallstone risk (initial odds ratio (OR) = 2.001; 95% confidence interval (CI) = 1.523 - 2.598; P < 0.001), with the association persisting after comprehensive adjustments (final OR = 1.687; 95% CI = 1.261 - 2.234; P < 0.001). MR findings also indicated a causal link between genetically predicted depression and higher gallstone risk (OR = 1.164; 95% CI = 1.053 - 1.286; P = 0.003). Depression is significantly associated with a higher risk of gallstones, supported by genetic evidence suggesting a causal link. These findings highlight the importance of considering depression in gallstone risk assessments and management strategies.

Determination of the Gurson-Tvergaard-Needleman damage model parameters for simulating small punch tests of heat-resistant alloys

The small punch (SP) test is utilized to assess the mechanical characteristics and damage progression of heat-resistant alloys. The inverse finite element analysis method incorporating SP tests is a parameter identification method based on adjusting the accuracy of the simulated load-displacement curves. In this paper, the elastoplastic parameters of the Hollomon model and the damage parameters of the Gurson-Tvergaard-Needleman (GTN) model are determined based on the undamaged and damaged stages of the load-displacement curves, respectively. The whole stress-strain curves of the tested materials are then built using the results of finite element simulations of the tensile specimens of ZG15Cr2Mo1, P91, 316H, and Hastelloy X at room and elevated temperatures. Comparison with uniaxial tensile tests indicates that the simulated stress-strain curves closely resemble the experimental data from the tensile testing. In addition, the simulated damage evolution characteristics of the SP specimens are consistent with the mechanical model based on the actual deformation behavior. It is possible to comprehend the damage evolution process by analyzing the SP specimens’ stress and strain change characteristics.

Functional and radiological sinonasal outcomes of CFTR modulators for sinus disease in cystic fibrosis: A meta-analysis.

Cystic fibrosis transmembrane conductance regulator (CFTR) modulators improve pulmonary outcomes in cystic fibrosis (CF) by stabilizing the CFTR protein on respiratory epithelial surfaces. To determine the efficacy of CFTR modulators on sinonasal outcomes in patients with CF, we performed a meta-analysis of clinical trials to date that include functional and radiographic evidence of sinus disease. English full-text articles were searched in PubMed, Embase, and Scopus databases. Two reviewers screened articles and a third reviewer resolved disagreements. Articles were included if they reported functional or radiological sinonasal outcomes in patients with CF before and after CFTR modulator therapies. Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed, and the risk of bias in non-randomized studies of interventions tool was used for quality assessment. The generic inverse variance method with random effects model was used for meta-analysis. Standardized mean difference (SMD) and mean difference (MD) were used as effect measurements. Seven prospective and two retrospective studies representing 248 patients were included in this analysis. There was a significant improvement in sinonasal outcome test-22 scores on elexacaftor‒tezacaftor‒ivacaftor (MD=12.80, [95% confidence interval, CI: 10.46‒15.13], p<0.001, n=222), with no heterogeneity detected (I2=0%, p=0.820). There was also a significant improvement in Lund‒Mackay scores (SMD=1.25, [95% CI: 0.58‒1.91], p<0.001, n=88), with heterogeneity detected (I2=67%, p=0.030). CFTR modulators improve functional and radiologic sinonasal outcomes. Given the utility of CFTR modulators, the treatment paradigm for CF-related chronic rhinosinusitis promises to evolve.

Genicular Nerve Block for Knee Osteoarthritis: A Systematic Review and Meta-analysis of Randomized Clinical Trials.

Genicular nerve block (GNB) has emerged as a novel nonsurgical therapy for symptomatic knee osteoarthritis (KOA). The objective was to evaluate GNB versus placebo and other intra-articular (IA) therapies. The Medline, Embase, and Scopus databases were searched from their inception to January 2021. Only randomized controlled trials (RCTs) were included. A random-effects model and the generic inverse variance method were used for quantitative data synthesis. Heterogeneity was tested with the I2 index. Regarding pain statistically significant scores were demonstrated at 1 and 3 months overall total effect of 1.43 (95% CI, 0.86, 1.99; P= 0.00001; I2 =85%). Similarly, for knee function a total effect of 0.71 (95% CI, 0.35, 1.06; P= 0.00001; I2 =69%) at 1 and 3 months, statistically significant. The minimal clinically important difference regarding pain was achieved at 1 and 3 months. Although, GNB achieved the MCID for KOA pain and statistically significant results for knee functions. We cannot recommend its routine use besides being a safe procedure so far, evidence is fair on GNB for primary KOA due to study heterogeneity and limited follow-up.

Ferritin and Iron Levels Inversely Associated With Lymphoma Risk: A Mendelian Randomization Study.

Current knowledge on iron's role in lymphoma development is very limited, with studies yielding inconsistent findings. To address this gap, we conducted a rigorous two-sample mendelian randomization study, aiming to elucidate the potential associations between iron storage and the risk of developing lymphoma. This study leveraged extensive genetic data derived from a comprehensive genome-wide association study (GWAS) comprising 257,953 individuals. The primary objective was to pinpoint single-nucleotide polymorphisms (SNPs) that are significantly associated with iron storage. Subsequently, this genetic information was analyzed in conjunction with summary-level data pertaining to lymphoma cases and controls, sourced from the IEU open GWAS project, which included a sample size of 3,546 lymphoma cases and 487,257 controls. To evaluate the relationship between iron storage and lymphoma risk, an inverse variance-weighted method with random effects was employed, complemented by rigorous sensitivity analyses. Genetic predisposition to high ferritin and serum iron status was causally associated with lower odds of lymphoma. Ferritin exhibited an odds ratio (OR) of 0.777 (95% confidence interval (CI): 0.628 - 0.961, P = 0.020), indicating 22.3% reduced odds of lymphoma associated with a one standard deviation increase in ferritin levels. Similarly, serum iron demonstrated an OR of 0.776 (95% CI: 0.609 - 0.989, P = 0.040), corresponding to 22.4% decreased odds of lymphoma for a one standard deviation increase in serum iron. This study suggests that individuals with genes linked to higher iron storage levels have a lower risk of developing lymphoma, but further research is necessary before making any clinical recommendations.

A four-node inverse curved shell element coupling MITC method for deformation reconstruction of plate and shell structures

In the field of structural deformation monitoring, the inverse finite element method (iFEM) has significant engineering value as a structural health monitoring technique that provides timely and reliable warnings for shell structures. However, existing inverse finite elements are mainly based on first-order shear deformation theory and kirchhoff-love theory, which are not suitable for deformation reconstruction in plate and shell structures of arbitrary thickness. This study integrates iFEM with the Mixed Interpolation of Tensorial Components (MITC) method to develop a novel four-node quadrilateral inverse curved shell element, named iMICS(inverse Mixed Interpolation Curved Shell)4, aimed at enhancing the accuracy and efficiency of deformation reconstruction in complex plate and shell structures. The method uses the MITC4 shell element as the kinematic framework and applies the least squares variational principle to achieve deformation reconstruction, effectively alleviating shear and membrane locking issues across structures of varying thickness. Numerical examples validate the superior performance of the iMICS4 element, demonstrating its promising application prospects.

Associations between amyloid-β load and cognition in cerebrovascular disease beyond cerebral amyloid angiopathy: a systematic review and meta-analysis of positron emission tomography studies.

There is growing interest in the comorbidity of vascular and neurodegenerative pathologies in patients with cerebrovascular disease (CVD) beyond cerebral amyloid angiopathy (CAA). However, the relationship between amyloid-β and vascular cognitive impairment (VCI) remains debated. To investigate the association between VCI and amyloid-β deposition in non-CAA CVD patients. PubMed, Embase, Web of Science, PsycINFO and CENTRAL databases were systematically searched. Observational studies, including case-control and cohort studies, associating cognitive scores with amyloid load measured by positron emission tomography were selected. Meta-analyses were performed to assess the strength of amyloid-cognition associations across CVD subtypes and cognitive domains. A random-effects model using the inverse variance method was used, with heterogeneity evaluated by Q-statistics and I2 statistics. Meta-regression analyses were conducted to examine the influence of moderators, and publication bias was assessed using funnel plots and Egger's test. All statistical analyses were performed using StataMP 18. Twenty-seven eligible studies encompassing 2894 participants were included. Among non-CAA CVD patients, global cognitive performance was significantly lower in those with higher amyloid-β deposition (standardized mean difference = -0.43, P < 0.001). The correlation strength varied across cognitive domains (executive function: r = -0.41; language: r = -0.36; memory: r = -0.29; all P < 0.001). The correlation was significant in patients with subcortical vascular disease (r = -0.43, P < 0.001) but not post-stroke patients (r = -0.19, P > 0.05). Amyloid-β load is associated with cognitive decline in non-CAA CVD patients. This is more pronounced in patients with subcortical vascular disease than in post-stroke patients. Executive function is the most susceptible domain in VCI when the level of amyloid-β increases.

Rotational Risley prisms: Fast and high-accuracy inverse solution and application based on back propagation neural networks

In the application of rotational Risley prism systems, there is a conflict between inverse solution time and accuracy. This paper proposes an inverse solution method based on back propagation neural networks to solve this problem. The training set data for prism rotation, azimuth, and elevation angles are produced based on the beam deflection model of the actual application. After that, two independent networks are trained individually. One network is used to calculate the angular difference, while the other network calculates the synchronous rotation angle to satisfy the solutions of elevation and azimuth angles of target. The azimuth and elevation root mean square errors for the inverse solution of the spiral trajectory using the trained network were 8.04e-04 arcsec and 2.60e-3 arcsec, respectively. At a solution time of 80 µs, the pointing accuracy in the experiment was less than 1 arcsec. The experimental results demonstrate that the proposed inverse solution method can obtain high-accuracy analytical solutions with low time consumption.

Spin states of X-complex asteroids in the inner main belt

Context. Based on the V-shape search method, two families, Athor and Zita, have been identified within the X-complex population of asteroids located in the inner main belt. The Athor family is ~3 Gyr old while the Zita family could be as old as the Solar System. Both families were found to be capable of delivering near-Earth asteroids (NEAs). Moreover, the Athor family was linked to the low-iron enstatite (EL) meteorites. Aims. The aim of our study is to characterise the spin states of the members of the Athor and Zita collisional families and test whether these members have a spin distribution consistent with a common origin from the break up of their respective family parent asteroids. Methods. To perform this test, our method is based on the well-established asteroid family evolution, which indicates that there should be a statistical predominance of retrograde-rotating asteroids on the inward side of family’s V-shape, and prograde-rotating asteroids on the outward side of family’s V-shape. To implement the method, we used photometric data from our campaign and the literature in order to reveal the spin states, and hence their rotation sense (prograde or retrograde), of the asteroids belonging to these families. We combined dense and sparse-in-time photometric data in order to construct asteroid rotational light curves; we performed the light curve inversion method to estimate the sidereal period and 3D convex shape along with the spin axis orientation in space of several family member asteroids. Results. We obtained 34 new asteroid models for Athor family members and 17 for Zita family members. Along with the literature and revised models, the Athor family contains 60% (72% considering only the family’s core) of retrograde asteroids on the inward side and, conversely, 76% (77% considering only the family’s core) of prograde asteroids on the outward side. We also found that the Zita family exhibits 80% of retrograde asteroids on the inward side. In addition, the Zita family presents an equal amount of prograde and retrograde rotators (50% each) on the outward side. However, when we applied Kernel density estimation (KDE), we also found a clear peak for prograde asteroids on the outward side, as expected from the theory. Conclusions. The spin states of these asteroids validate the existence of both families, with the Athor family exhibiting a stronger signature for the presence of retrograde-rotating and prograde-rotating asteroids on the inner and outer side of the family, respectively. Our work provides an independent confirmation and characterisation of these very old families, whose presence and characteristics offer constraints for theories and models of the Solar System’s evolution.

A global approach to detecting and characterizing water leakage in a concrete bridge deck: Parametric study to validate an adapted full-waveform inversion method

The objective of this study is to address issues related to defects in waterproofing membranes through the use of Ground Penetration Radar to detect water leakage into the concrete layer of bridge decks. Given that processing radar signals based solely on temporal data introduces significant estimation errors, an advanced method optimized from Full-Waveform Inversion (FWI) is employed. This method accounts for several unknown factors, including boundary conditions, antenna positioning inaccuracies, and approximations inherent in 2D modeling during the inversion process. The method is adaptable and capable of reconstructing both the dielectric and geometric parameters of a multilayered structure with any number of layers and unknown parameters using just a few A-scans (up to 10, depending on the number of parameters and layers). In contrast, other methods typically require several hundred A-scans. This efficiency is achieved due to the two-dimensional nature of the layer system. Additionally, the simplicity of the structure facilitates a much faster and more straightforward inversion algorithm, hence making these features especially advantageous for practical applications. The optimized Full-Waveform Inversion approach allows for an accurate determination of unknown parameters within a multilayered medium. The high accuracy of this method is validated through a direct comparison with experiments, wherein the exact parameters are known. Such an approach enables the attainment of a low relative error using the currently available measurement device.

Effect of Mercerization on the Properties of Cellulose Fiber and Cellulose Hydrogel Extracted from Pineapple Leaf Fiber

AbstractThis study investigated the effects of alkaline treatment on the pineapple leaf fiber (PALF) cellulose extraction and the properties of cellulose hydrogel. PALF was treated with different concentrations of sodium hydroxide (NaOH) (2 to 10 wt %) and at room temperature (RT) and 80 °C. Then, extracted cellulose was then dissolved in N,N’‐dimethyl acetamide (DMAc)/lithium chloride (LiCl) solvent system and formed into hydrogel by phase inversion method. The Fourier transform infrared spectra shows the peak disappearance at 1606 cm−1 proves that lignin was removed starting from 6 and 4 wt % NaOH at RT and 80 °C, respectively. Higher NaOH concentrations treatment increased thermal stability and the crystallinity (71 to 79 %) of the fibers. Higher NaOH concentration and treatment temperature enhanced cellulose extraction from PALF, leading to more physical crosslinking during hydrogel formation. High amount of cellulose extracted decreased the hydrogel's swelling equilibrium and increased the gel fraction. The highest transmittance (87.8 %) and the lowest intensity of absorbance (at 280 nm corresponding to lignin) were obtained by the hydrogel prepared with PALF treated with 8 wt % NaOH at 80 °C. Clearly, this research findings provide new insights into optimizing cellulose extraction using alkaline treatment specifically for cellulose hydrogel formation.

Representative CO2 emissions pathways for China's provinces toward carbon neutrality under the Paris Agreement's 2 °C target

China has committed to achieving carbon neutrality by 2060. It is essential to develop representative CO2 emissions pathways at the provincial level that align with the national target to facilitate effective policy implementation and scientific research. To address inconsistencies between provincial aggregate emissions and national estimates, this study compares the 2021 CO2 emissions estimates of China's provinces from the bottom‒up emissions factor method and the top‒down atmospheric CO2 concentration inversion method. We find that these methods yield comparable results for the emissions from energy combustion and industrial processes at the provincial level. Based on a review of existing research on CO2 pathways for China's provinces, we propose a set of representative pathways for China's provinces. These pathways align with past practices of allocating national emissions intensity reduction targets to provinces and are consistent with the national Tsinghua‒CMA pathway. The proposed pathways require provinces to sequentially peak their emissions by 2030, followed by rapid emissions reduction. Compared to a reference scenario without the carbon neutrality target, these pathways would incur an estimated cumulative GDP loss of about 1% between 2020 and 2060. However, there are notable regional variations, with some regions in the northwest potentially experiencing higher economic growth due to the availability of high-quality low-carbon resources. We recommend that China enhance provincial-level CO2 emissions accounting by cross-validating bottom‒up and top‒down methods. Additionally, careful consideration should be given to aligning provincial targets with national and global commitments when updating pathways toward carbon neutrality.

High-resolution observations of shallow-water acoustic propagation with distributed acoustic sensing.

Distributed acoustic sensing (DAS), converting fiber-optic cables into dense acoustic sensors, is a promising technology that offers a cost-effective and scalable solution for long-term, high-resolution studies in ocean acoustics. In this paper, the telecommunication cable of Martha's Vineyard Coastal Observatory (MVCO) is used to explore the feasibility of cable localization and shallow-water sound propagation with a mobile acoustic source. The MVCO DAS array records coherent, high-quality acoustic signals in the frequency band of 105-160 Hz, and a two-step inversion method is used to improve the location accuracy of DAS channels, reducing the location uncertainty to ∼2 m. The DAS array with refined channel positions enables the high-resolution observation of acoustic modal interference. Numerical simulations that reproduce the observed interference pattern suggest a compressional speed of 1750 m/s in the sediment, which is consistent with previous in situ geoacoustic measurements. These findings demonstrate the long-term potential of DAS for high-resolution ocean acoustic studies.

Electron Density Profile Derived From Ionogram Using Ray Tracing Inversion Method

AbstractThe ionosonde is widely used for detecting electron density profiles below the F2 peak altitude. Extracting precise profiles from ionograms is crucial, as it serves as a significant data source for ionospheric studies and applications. In our study, we utilized the ray tracing profile inversion method (RTPI) to derive more realistic electron density profiles from the ionosonde observations. By comparing the electron density profiles inverted by RTPI method with and without geomagnetic field against the profiles observed by Incoherent Scatter Radar (ISR) plasma lines, we validated the high precision of the RTPI with magnetic field effect method. The results showed that the average height difference and average peak height difference between profiles inverted by RTPI and plasma line observations are less than 10 and 5 km, respectively. Additionally, we quantified the errors associated with the geomagnetic field effect. It would cause an ∼8–10 km overestimation in true height and a ∼10%–15% underestimation in electron density if the geomagnetic field effect is not considered. These errors induced by the magnetic field accumulate with the frequency of the radio waves. Moreover, we conducted a comparative analysis of simulated echo traces using profiles with different E‐layer shapes. It was demonstrated that the key parameters of the bottom structure have a significant impact on ionogram retrieval, while the E‐layer shape has negligible influence on inversion. Furthermore, we analyzed echo traces simulated using ray tracing with and without collision. The collision effect has weak effect on the delay of the radio waves.

Attitude motion classification of resident space objects using light curve spectral analysis

The characterization of Earth-orbiting objects in terms of attitude motion, material composition, and shape is crucial for Space Domain Awareness. In this respect, light curves, i.e., graphs showing the brightness variation of space objects over a specific time interval, can be used to infer both their dynamical and physical properties. However, such light curve inversion problem is challenging due to measurement noise, data gaps, and the intrinsic difficulty in separating the multiple effects impacting on the object’s brightness. Existing light curve inversion methods rely on estimation filters exploiting data fusion, Machine Learning approaches, or are based on the analysis of the light-curve frequency spectra. Many of these approaches make assumptions about a-priori knowledge of target object’s characteristics, such as shape, size, surface reflective properties, and/or observation parameters, e.g., target’s orbit and observation geometry. In this framework, this paper presents an innovative architecture for the classification of the attitude motion of space objects using only photometric light curve data, not requiring any a-priori assumption or knowledge. The proposed architecture exploits the Lomb-Scargle Periodogram algorithm to retrieve a frequency spectrum from light curve measurements, and then performs the classification by analyzing the spectrum characteristics testing different candidate rotation periods through the Phase Dispersion Minimization method. The proposed classification algorithm is first tested using a light curve simulator, in which any complex geometric model and different surface properties can be generated. Finally, the method is applied to real light curves retrieved from a publicly available database to assess the classification performance.

Solar light-induced photocatalytic degradation of 2,4-dichlorophenoxyacetic acid using chitosan/g-C3N4 beads

Remediation of persistent organic pollutants such as pesticides and personal care products in water has attained immense research interest due to the commercialization feasibility of Advanced Oxidation Processes. Thus, the present study focuses on the solar-light assisted photocatalytic degradation of commonly used pesticide, i.e., 2,4-dichlorophenoxyacetic acid. The chitosan-modified g-C3N4 beads (CS/g-C3N4) were synthesized using the phase inversion method. Synergistic activity of chitosan with g-C3N4 in the form of recoverable beads with solar light-based photocatalytic activity has been brought as the novelty of this work. The chitosan-modified g-C3N4 beads have been studied for their morphological and structural characterization. The diffuse reflectance spectroscopy shows the variation in the bandgap from 2.79 (g-C3N4) to 2.6 eV (CS/g-C3N4 beads) due to the incorporation of chitosan. The photocatalytic removal of 2,4-D under solar light using CS/g-C3N4 is found to be 83 %. The prepared beads showed no significant loss in catalytic activity even after four cycles. Therefore, CS/g-C3N4 is a low-cost, affordable, and environmentally friendly material for the redemption of pesticides like 2,4-D.

A composite laser ablation diagnosis method based on multiple spectroscopic and imaging analyses

This study proposes a novel diagnostic approach for target ablation to comprehensively elucidate the physical mechanisms of laser ablation in aluminium alloys and stainless steel, precisely measure sample temperatures, and predict the ablation state. The method utilizes a spatially weighted emissivity model in conjunction with multispectral thermometry techniques to analyze spatial variations in temperature and emissivity distributions, facilitating the evaluation of target ablation status. Through a series of experiments, temperature data obtained using an enhanced weighted radiative spectral inversion technique were compared with temperatures recorded by thermal imaging cameras, confirming the effectiveness and accuracy of the weighted radiative spectral inversion method in multispectral thermometry. Additionally, a detailed examination of laser ablation in aluminium alloys and stainless steel was conducted to elucidate the underlying damage mechanisms. This refined approach establishes a solid groundwork for further investigation into the characteristics and dynamic Evolution of laser-damaged regions.

A High-Precision Size Inversion Method for Pipeline Defects With the Influence of Velocity Effects

A High-Precision Size Inversion Method for Pipeline Defects With the Influence of Velocity Effects