Inverse Method Research Articles

Effect of porous irregular ZrO2 nanoparticles on the performance of alkaline water electrolysis composite separator membranes under complex conditions

Developing composite separator membranes with low area resistance, high bubble point pressure, and long-term safety and stability is crucial for alkaline water electrolysis for hydrogen production as a key component of electrolyzer systems. In this study, PPS mesh fabric reinforced PSF@ZrO2 composite separator membranes were successfully prepared using the immersion-drawing phase inversion method, with PSF as the alkali-resistant polymer matrix and porous irregular ZrO2 nanoparticles as the hydrophilic additive. The experimental results showed that replacing commercial ZrO2 with porous irregular ZrO2 nanoparticles at an 85 wt% ZrO2 nanoparticle loading improved both bubble point pressure and current transmission efficiency, attributed to the change in the morphological structure of the ZrO2 nanoparticles. The P-Z85 composite separator membrane exhibited highly promising characteristics, with a high bubble point pressure of 3.76 bar and a low area resistance of 0.20 Ω cm2. Stability tests conducted in 30 wt% KOH electrolyte at 80 °C and a current density of 0.65 A cm−2 demonstrated excellent continuous electrolysis stability for the P-Z85 composite separator membrane. These results indicate that the PSF@ZrO2/PPS composite separator membrane prepared in this study exhibits excellent performance in 30 wt% KOH electrolyte, significantly extending its service life.

Fabrication and evaluation of PVDF membranes modified with cellulose and cellulose esters from peanut (Arachis hypogea L.) shell for application in methylene blue filtration

Polyvinylidene fluoride (PVDF) membrane is frequently employed for filtration due to its excellent properties. The hydrophobicity of PVDF membrane causes easy fouling; therefore, hydrophilic polymer materials are required to increase hydrophilicity. This study applies cellulose and cellulose esters as fillers for PVDF membranes to solve the fouling problem. Cellulose esters, such as cellulose acetate (PSCA), cellulose benzoate (PSCB), and cellulose citrate (PSCC), were successfully synthesized from peanut shell cellulose (PSC) using Fischer and non-Fischer reactions. The phase inversion method was successfully used to fabricate PVDF membranes with cellulose or cellulose esters as fillers. The fabricated membranes have been applied for methylene blue (MB) filtration. Adding PSC fillers improved the hydrophilicity and performance of the PVDF membranes up to 23.49 ± 2.40 L m−2 h−1 for water flux and 95.75 ± 0.78 % for rejection of MB. Regarding cellulose esters, cellulose acetate gave the highest value of 77.63 L m−2 h−1 for water flux, and cellulose citrate gave the highest value of 86.88 ± 3.54 % for MB rejection. Hence, cellulose or cellulose esters from peanut shells are suitable fillers for MB filtration in PVDF membranes.

An optimized single-side inverse finite element method for displacement field reconstruction of wind turbine tower

Wind turbines are subjected to complex dynamic loads during actual operation, which are difficult to predict accurately in advance, which may lead to inaccurate structural strength assessment in the structural design stage, and thus bring safety risks to wind power towers. Inverse Finite Element Method (iFEM) is a reconstruction method of structural displacement distribution, which can estimate the full-field displacement distribution of tower, and used for structural on-line health monitoring. However, conventional iFEM require sensors to be placed on both sides of the structure, which greatly limits its engineering practicality. For this reason, a single-side inverse finite element (iFEM_S) was developed in this study to enhance the engineering applicability of iFEM. Both iFEM and iFEM_S are based on the principle of least squares variation, and the solution equations can be established when the measured strain is known. The core idea of iFEM_S is to find the optimal node displacement s to minimize the sum of mean square errors between the theoretical and measured strains on one side of the structure, thereby generating a set of algebraic equations. Solving these linear equations can achieve the structural displacement field. Additionally, A cyclic optimization algorithm (COA) was developed in this study to improve the robustness of reconstruction results through optimizing strain gauge locations, in which the condition number of pseudo stiffness matrix was used as the objective function, and the condition number is minimized, named iFEM_S_O. Finally, the reconstruction accuracy and robustness and of iFEM, iFEM_S and iFEM_S_O were verified through several numerical examples. The simulated reconstruction results indicated that iFEM_S_O is more accurate and stable than iFEM_S, and iFEM_S is more accurate and stable than iFEM.

Research on failure mechanism of landslide with retaining-wall-like locked segment and instability prediction by inverse velocity method

The locked segment is critical for determining the stability of locked segment-type landslides. Research indicates that the volume expansion point marks the transition from the secondary creep stage to the tertiary creep stage in a landslide’s evolution, and also separates the stable crack growth stage from the unstable crack growth stage in the locked segment. Identifying the volume expansion point is essential for early warning and predicting locked segment-type landslides. A series of instruments (resistance strain gauges, acoustic emission system, piezoelectric acceleration sensors, etc.) were used to conduct physical model tests of the landslide with retaining-wall-like locked segment under external load on the landslide’s trailing edge. The evolution process of this landslide was analyzed through changes in slope shape and stress response characteristics. The experimental results reveal the failure mechanism of the landslide with retaining-wall-like locked segment: the upper part of the landslide thrusts and slides, the middle part squeezes and uplifts, the retaining-wall-like locked segment produces a locking effect, and compression-shear fracture of the retaining-wall-like locked segment leads to landslide failure. Based on the deformation and acoustic emission characteristics of the locked segment, a method for identifying the volume expansion point was established. This point was used as the onset of acceleration point in the inverse velocity method to predict the failure time of the locked segment-type landslides, incorporating the three-stage creep model and Fukumoto’s theory.

Adaptive neural inverse optimal control for uncertain switched nonlinear systems: an improved predefined-time method

For the first time, this paper introduces an improved predefined-time inverse optimal method to address the adaptive tracking control problem of a class of uncertain switched nonlinear systems. The proposed approach enables the system to achieve optimal performance without resorting to the HJB equation. Firstly, we design two-time constants to improve the traditional predefined-time lemma. The improved predefined-time lemma exhibits faster convergence. Subsequently, to realise the design of a predefined-time inverse optimal controller, a specific form of the auxiliary controller is designed using Sontag functions and nonsingular functions. Furthermore, the stability of the system and the optimality of the inverse are proven through the use of a common Lyapunov function method, ensuring that tracking errors converge to the neighbourhood of zero within the predefined time. Finally, the rationality of the proposed control structure is demonstrated through the example results.

Dynamic analysis and shape sensing of pipes subjected to water hammer by the inverse finite element method

Pipe structures are commonly employed in modern industries. However, the transient pressure induced by water hammer leads to a high risk of structural damage, highlighting the importance of Structural Health Monitoring (SHM). The inverse Finite Element Method (iFEM) offers a way to reverse structural deformations based on a set of discrete strain data. In this paper, the water hammer phenomenon in a Reservoir-Pipe-Valve (RPV) system is investigated. The element iQS4 is adopted to reconstruct dynamic deformations for the pipe using iFEM. The water hammer resulting from an instantaneous closure of the valve can generate significant fluid pressure, leading to severe pipe vibrations. The vibration deformations can be monitored by iFEM. The maximum error of the pipe nodal displacement is below 3 % and the average error is below 2 % in the proposed several sensor location cases, even the mesh of iFEM is coarser than the FE model. Among the sparse sensor location cases, the X path which is suitable for Fiber Bragg Grating (FBG) sensors has the highest accuracy of the displacement reconstruction. The results reveal a promising prospect in the real-time monitoring based on iFEM for pipes.

Novel method for inversion of microphysical properties of clouds using Raman lidar data

Aerosol–cloud–precipitation interactions are important in the balance of Earth’s radiation budget. To further explore the relationship between clouds and precipitation, and to improve operational weather modification, it is necessary to study the microphysical parameters of liquid water clouds. Here, an inversion method that uses a back propagation (BP) neural network based on a genetic algorithm (GA), namely a GABP, is proposed to invert cloud microphysical parameters using ground-based dual-field-of-view (FOV) Raman lidar data. To verify the feasibility of the method, long-term continuous observations were conducted in the Liupan Mountains (China). Results revealed that the proposed inversion method using the GABP is feasible for retrieving the liquid water content (LWC) and the cloud droplet effective radius after training a large number of data measured simultaneously by the Raman lidar and a microwave radiometer. When inverting LWC, the root mean square error (RMSE) of the GABP algorithm was found in the range 0–0.005, whereas the RMSE of the BP algorithm fluctuated in the range 0–0.01. It was evident that the GABP algorithm yields better inversion results and finer detail. When maintaining other variables and comparing the inversion results of signals in the inner and outer FOVs, the RMSE of the inner FOV signal was within 0.005 at near-ground heights (i.e., <2 km), whereas the outer FOV signal exceeded 0.005 at certain heights. This study developed a feasible solution for detecting characteristic cloud microphysical parameters using a Raman lidar, which could be used to study aerosol–cloud–precipitation interactions, and thereby have considerable practical importance for improving artificial rainfall operations.

Numerical Investigation of the Quantum Inverse Algorithm on Small Molecules.

We evaluate the accuracy of the quantum inverse (Q-Inv) algorithm, in which the multiplication of Ĥ-k to the reference wave function is replaced by the Fourier transformed multiplication of e-iλĤ, as a function of the integration parameters and the iteration power k for various systems, including H2, LiH, BeH2 and the notorious H4 molecule at square geometry. We further consider the possibility of employing the Gaussian-quadrature rule as an alternate integration method and compared it to the results employing trapezoidal integration. The Q-Inv algorithm is compared to the inverse iteration method using the Ĥ-1 inverse (I-Iter) and the exact inverse by lower-upper decomposition. Energy values are evaluated as the expectation values of the Hamiltonian. Results suggest that the Q-Inv method provides lower energy results than the I-Iter method up to a certain k, after which the energy increases due to errors in the numerical integration that are dependent on the integration interval. A combined Gaussian-quadrature and trapezoidal integration method proved to be more effective at reaching convergence while decreasing the number of operations. For systems like H4, in which the Q-Inv cannot reach the expected error threshold, we propose a combination of Q-Inv and I-Iter methods to further decrease the error with k at lower computational cost. Finally, we summarize the recommended procedure when treating unknown systems.

Multidepth quantitative analysis of liver cell viscoelastic properties: Fusion of nanoindentation and finite element modeling techniques.

Liver cells are the basic functional unit of the liver. However, repeated or sustained injury leads to structural disorders of liver lobules, proliferation of fibrous tissue and changes in structure, thus increasing scar tissue. Cellular fibrosis affects tissue stiffness, shear force, and other cellular mechanical forces. Mechanical force characteristics can serve as important indicators of cell damage and cirrhosis. Atomic force microscopy (AFM) has been widely used to study cell surface mechanics. However, characterization of the deep mechanical properties inside liver cells remains an underdeveloped field. In this work, cell nanoindentation was combined with finite element analysis to simulate and analyze the mechanical responses of liver cells at different depths in vitro and their internal responses and stress diffusion distributions after being subjected to normal stress. The sensitivities of the visco-hyperelastic parameters of the finite element model to the effects of the peak force and equilibrium force were compared. The force curves of alcohol-damaged liver cells at different depths were measured and compared with those of undamaged liver cells. The inverse analysis method was used to simulate the finite element model in vitro. Changes in the parameters of the cell model after injury were explored and analyzed, and their potential for characterizing hepatocellular injury and related treatments was evaluated. RESEARCH HIGHLIGHTS: This study aims to establish an in vitro hyperelastic model of liver cells and analyze the mechanical changes of cells in vitro. An analysis method combining finite element analysis model and nanoindentation was used to obtain the key parameters of the model. The multi-depth mechanical differences and internal structural changes of injured liver cells were analyzed.

Large‐signal stability analysis for hybridized vehicular power supply systems of rolling stocks

AbstractHybridized vehicular power supply (HVPS) are acquiring widespread adoption in various applications such as providing power for passenger or heavy‐duty electric vehicles, more‐electric ships, electrified rolling stocks, and more. Stability analysis is a critical issue for HVPS, but it presents significant challenges due to their prominent high‐dimensional and nonlinear characteristics. This paper presents a large‐signal stability analysis scheme for HVPS of rolling stocks, enabling the assessment of system stability under sudden load changes. Based on the state‐space averaging method, a mathematical model for the large‐signal behavior of the system is established. Subsequently, the small‐signal stability parameter range of the system is determined by analyzing the eigenvalues of the Jacobian matrix. To further analyze the stability of the system during large‐signal disturbances, a nonlinear decoupling transformation approach is employed to decompose the original high‐order state equations into multiple lower‐order equations. For the decoupled lower‐order subsystems, their stability is analyzed using phase portrait, and the region of attraction (ROA) is determined using the inverse trajectory method. Based on the stability analysis results, the controller is enhanced to bring the points outside the ROA back within it, thereby mitigating the transient instability phenomenon of the system. The effectiveness of the proposed method was demonstrated using two case studies.

Field measurement and numerical simulation on vertical temperature gradient of steel box girders

The vertical temperature gradient (VTG) is a critical factor contributing to thermal stresses or deformations in bridge structures. However, the current provisions concerning the VTG of steel box girders are inadequate. Accordingly, this paper systematically investigated the VTG of a steel box girder in Wuhan using field measurement and numerical simulations. The temperature distributions of the steel box girder were first characterised based on measured temperature data. A thermal model of the steel box girder was then established using measured time-varying meteorological data, with the convective heat transfer coefficient obtained through an inversion method to update the model. The numerical findings align well with the observed temperature, and the fitted unfavourable VTG values exceed the current normative threshold. Furthermore, parametric investigations were carried out to reveal the impacts of meteorological and structural parameters on the VTG. The results indicate that solar radiation and pavement thickness have a considerable impact on the VTG. Finally, this research recommends that the present VTG guidelines should be modified by introducing appropriate adjustment factors related to the level of solar radiation intensity in different regions to strengthen their capacity for safeguarding.

Seismic Facies-Controlled Porosity Prediction in a Tight Sandstone Reservoir Based on the XGBoost Algorithm

Porosity is one of the most important properties for evaluating hydrocarbon reservoirs. There is a strong nonlinearity between seismic data and rock physical properties. Machine learning methods possess the powerful ability to capture complex nonlinear relationships between these two parameters, holding significant potential for porosity prediction in tight sandstone reservoirs. In this study, we propose a seismic facies-controlled porosity prediction method based on the extreme gradient boosting algorithm. Through seismic meme inversion method,we obtain high-resolution P-impedance data. In the inversion process, lateral variations of seismic waveforms were utilized instead of the variogram function. We create labels for model training using porosity curves and borehole side P-impedance data, applying them to the extreme gradient boosting regressor. To demonstrate its feasibility, we apply the model to a real 3D case from an oil field in the Songliao Basin. Our application results demonstrate that this method can provide porosity predictions for tight sandstone reservoirs, maintaining consistency with seismic waveforms and adhering to seismic facies control patterns. The method uses only acoustic travel time, density, gamma rays, and spontaneous potential well-log data and post-stack 3D seismic data as inputs, allowing for quick porosity predictions in the field. Compared to the seismic facies-controlled inversion combined with support vector machine and multi-Layer perceptron methods, the method proposed in this study provides more reliable porosity predictions, offering insights and references for the exploration and development of tight sandstone reservoirs.

Association between obesity paradox in the all-cause mortality among patients with cardiac resynchronization therapy device.

Recent studies have demonstrated an obesity paradox, where obese patients with cardiovascular disease have a better outcome compared to those with normal weight. However, the effect of obesity and body mass index (BMI) on the outcome of patients with cardiac resynchronization therapy (CRT) devices remains unclear. The current study aims to investigate this relationship using all available published data. We systematically reviewed studies from Medline and EMBASE databases from inception to January 2024. Eligible studies must investigate the association between BMI status and all-cause mortality in individuals with CRT devices. Relative risk (RR) or hazard ratio (HR) and 95% CIs were retrieved from each study and combined using the generic inverse variance method. A total of 12 cohort studies were included in the meta-analysis. Pooled analysis showed that overweight and obesity patients had lower all-cause mortality compared to those with normal body weight with the pooled risk ratios (RR) for overweight of 0.77 (95% CI 0.69-0.87, I2 47%) and for obesity of 0.81 (95% CI 0.67-0.97, I2 59%). Conversely, the underweight exhibited higher all-cause mortality than the group with normal weight, with a pooled RR of 1.37 (95% CI 1.14-1.64, I2 0%). Additionally, higher BMI as continuous data was associated with decreased all-cause mortality, with a pooled HR of 0.94 (95% CI 0.89-0.98, I2 72%). The pooled analyses observed an obesity paradox in patients with CRT, where overweight and obesity were associated with reduced all-cause mortality, while underweight individuals exhibited higher all-cause mortality. Further research is necessary to investigate the underlying mechanisms and their implications for clinical practice.

A Closed-Form Inverse Kinematic Analytical Method for Seven-DOF Space Manipulator with Aspheric Wrist Structure

The seven-degree-of-freedom space manipulator, characterized by its redundant and aspheric wrist structure, is extensively used in space missions due to its exceptional dexterity and multi-joint capabilities. However, the non-spherical wrist structure presents challenges in solving inverse kinematics, as it cannot decouple joints using the Pieper criterion, unlike spherical wrist structures. To address this issue, this paper presents a closed-form analytical method for solving the inverse kinematics of seven-degree-of-freedom aspheric wrist space manipulators. The method begins by identifying the redundant joint through comparing the volumes of the workspace with different joints fixed. The redundant joint angle is then treated as a parametric joint angle, enabling the derivation of closed-form expressions for the non-parametric joint angles using screw theory. The optimal solution branch is identified through a comparative analysis of various self-motion manifold branches. Additionally, a hybrid approach, combining analytical and numerical methods, is proposed to optimize the parametric joint angle for a trajectory tracking task. Simulation results confirm the effectiveness of the proposed method.

Physical analysis and inverse methods applied to archaeological FIRE replications on ATACAMA desert soils, northern Chile

Physical analysis and inverse methods applied to archaeological FIRE replications on ATACAMA desert soils, northern Chile

The relationship between mental health problems and risk of infectious diseases: A Mendelian randomization analysis.

The causal effects of mental health problems on the risk of infectious diseases remain vague. Investigating them via observational study is challenging as it presents possible confounding factors. Therefore, the objective of this study was to utilize Mendelian randomization (MR) techniques to evaluate the causal relationship between mental health problems and the risk of infectious diseases. Multivariable MR analyses were performed using genome-wide association data for sleep disorders (N = 216,700), depression (N = 500,199), anxiety (N = 290,361), nervous feelings (N = 450,700), unspecified mental disorder (N = 218,792), pneumonia (N = 486,484), skin and subcutaneous tissue infection (SSTI; N = 218,792), intestinal infectious diseases (IIDs; N = 218,792), urinary tract infection (N = 463,010), and central nervous system (CNS) infections (N = 218,792) among individuals of European ancestry. Independent genetic variants significantly (P < 10-8) associated with each exposure were considered instruments. The primary analysis used an inverse variance-weighted method, followed by a series of sensitivity analyses. Genetically predicted sleep disorders were associated with an increased risk of SSTI (odds ratio [OR], 1.29 [95% confidence interval (CI), 1.05-1.59]; P = .017). Genetically predicted depression was linked with an increased risk of CNS infections (OR, 1.59 [95% CI, 1.00-2.53]; P = .049) and SSTI (1.24 [95% CI, 1.03-1.49]; P = .024). Genetically predicted anxiety was associated with IIDs (OR, 1.19 [95% CI, 1.03-1.37]; P = .017) and SSTI (OR, 1.21 [95% CI, 1.02-1.43]; P = .029). There was no significant causal evidence for genetic prediction of nervous feelings and unspecified mental disorders in IIDs, CNS infections, SSTI, pneumonia, or urinary tract infection. Sensitivity analyses showed that the above causal association estimates were robust. In this MR study, we demonstrated a causal relationship between sleep disorders, depression, anxiety, and the risk of infectious diseases. However, no evidence was found to support causality between nervous feelings, unspecified mental disorders, and the risk of infectious diseases.

Chemical profile and biotechnological potential larvicidal of a nanoemulsion (o/w) of the essential oil of Salvia officinalis L

This study aims to evaluate the chemical profile and biotechnological larvicidal potential of the nanoemulsion of the essential oil of Salvia officinalis L. The leaves of the plant were collected in São Luís, MA, from January to May 2021. The essential oil was extracted by hydrodistillation at 100°C for 3h. Chemical characterization was obtained by GC-MS. The oil-in-water nanoemulsion was formulated by the low-energy phase inversion method and subjected to thermodynamic stability tests. Antioxidant activity is performed by the spectrophotometric method of scavenging hydroxyl radicals from salicylic acid. For larvicidal activity, Aedes aegypti larvae were subjected to EO solutions and nanoemulsions in concentrations (10-100 mg L-1), larval mortality was evaluated, and the LC50 was determined by the Probit method. The majority compounds of the EO were: eucalyptol with 65.14%, camphor (30.63%), and α-Terpineol (1.53%). The formulations were characterized as nanoemulsions with a droplet size &lt;200 nm. The PDI was &lt;0.200, indicating a narrow size distribution. The antioxidant activity exhibited EC50 of 136.29 mg L-1 and 51.59 mg L-1. The nanoemulsion with larvicidal potential showed an LC50 of 71.17 mg L-1. The nanoemulsion showed bioactive potential for larvicidal action, which may be related to the presence of its chemical compounds, and its use is encouraged in the fight against Aedes aegypti.

Colchicine in Patients With Coronary Disease Who Underwent Coronary Artery Bypass Surgery: A Meta-Analysis of Randomized Controlled Trials

Recent randomized evidence has shown that low-dose colchicine lowers the risk of cardiovascular events in patients with chronic coronary artery disease. Colchicine has also been used in coronary artery bypass grafting (CABG), with individual studies suggesting protective effects for postoperative atrial fibrillation (POAF). We performed a meta-analysis of studies assessing the effect of colchicine on outcomes in CABG surgery. We systematically searched 3 libraries (MEDLINE, Web of Science, and the Cochrane Library), selecting all randomized control trials including patients who underwent CABG and were randomized for perioperative administration of colchicine versus standard of care. The primary outcome was incidence of POAF. The inverse variance method (DerSimonian&Laird) and random-effects model were performed. The leave-one-out analysis was carried out as a sensitivity analysis to address possible outliers. From 205 screened studies, 5 met the inclusion criteria and were selected. The data from 839 patients were included in the final analysis. The included studies were published between 2014 and 2022. The perioperative administration of colchicine was associated with the reduction of POAF rates after CABG compared with standard of care (relative risk 0.54, 95% confidence interval 0.40 to 0.73, p <0.01). The leave-one-out analysis confirmed the robustness of the analysis, with minimal variations of the confidence interval. This meta-analysis of randomized studies suggests that the perioperative administration of colchicine is associated with significant reduction of POAF after CABG.

LoopNet for fine-grained fashion attributes editing

Generative Adversarial Networks (GANs) have revolutionized the field of image synthesis by transforming randomly sampled latent codes into high-fidelity synthesized images. However, current methods fall short in manipulating a wide range of fashion attributes due to semantic ambiguity and lack of disentanglement. This work focuses on fashion attribute editing by proposing an encoder-based GAN inversion method, namely LoopNet. To enable high-fidelity image inversion and fine-grained attribute editing, it refines edited images through two encoder–decoder stages, utilizing predefined directions from principal component analysis on latent codes and canny loss for detail enhancement. Experiments show LoopNet’s effectiveness in attribute disentanglement and manipulation, outperforming seven state-of-the-art image inversion methods.

Impact of thick N-polar AlN growth on crystalline quality and electrical properties of N-polar GaN/AlGaN/AlN FET

In this study, we attempted to fabricate N-polar GaN/AlGaN/AlN heterostructure FET by changing the thickness of the AlN layer. An Al-polar tiny-pit AlN layer and a polarity inversion method were used to grow N-polar AlN on vicinal sapphire via the metal-organic vapor phase epitaxy. The samples with an AlN thickness of up to 3.4 μm exhibited a crack-free surface. Additionally, the twist component of the crystal quality improved with an increasing AlN thickness. Consequently, the mobility, sheet conductivity, and surface flatness improved. The FET fabricated from the sample with a thicker AlN layer achieved a higher drain current of 279 mA mm−1 at a gate bias of V G = 3 V.