Dynamic Applications Research Articles

Investigation of the Effect of Physical Ability on the Fall Mitigation Motion Using the Combination of Experiment and Simulation

The simulation of fall plays a critical role in estimating injuries caused by fall. However, implementing human fall mitigation motions on a simulator proves challenging due to the complexity and variability of fall movement. Our simulator estimates fall motion by extrapolating the motion observed in fall experiments. By incorporating actual fall motion data for the upper limbs, we enhanced the realism of the fall simulation. The application of forward dynamics control to the lower limbs allowed for the adjustment of mitigation motions, taking into account individual physical capabilities. In this study, fall simulations were conducted under the constraints of maximum joint torque and maximum torque change rate, emulating the physical capabilities of both the elderly and young adults. Our results successfully demonstrated the mitigation motion facilitated by the stance leg reduced the descent velocity of the center of mass by 0.75 m/s for elderly individuals and by 1.25 m/s for young adults, compared to a zero torque condition. This indicates that our study introduced a novel method for quantifying the impact of the lower limbs’ physical capabilities on fall velocity. Such a method represents a significant advancement in understanding how mitigation motions can influence fall injury simulations.

A new chaotic system and its practical applications in substitution box and random number generator

AbstractOne of the successful practical applications of chaos theory and nonlinear dynamics is chaos-based cryptology studies. In this study, a new chaotic system is proposed. The proposed chaotic system generator model has a simple algorithmic structure. In addition to, generated chaotic systems have rich dynamics. It has been examined whether this system has potential advantages, especially for information security applications. As a result of the analysis and experimental studies, it is seen that the study makes many contributions to the literature. The simple mathematical generator structure has the potential to reduce computational complexity, which is an important problem for cryptology applications. One of the most comprehensive studies has been realized for chaos-based substitution box designs. 70,000 different substitution box structures have been generated. It has been known that the highest nonlinearity value that can be achieved for designs that transform chaotic system outputs into substitution box structures is 106.75 today. It has been shown that this value may be higher. The new highest nonlinearity value is calculated as 107. Side channel analysis has been examined for substitution box structures with the highest nonlinearity value among the substitution boxes generated in the study. Analysis results show that the proposed new substitution box structures may have an advantage for application-oriented attacks. Among the generated substitution box structures, 289 substitution box structures with a nonlinearity value of 106 and above are presented as a public dataset for researchers to use. In addition, 3 different random sequences with a length of 1 million-bit were produced with the proposed map, and the NIST SP 800–22 test was applied. Test results for all datasets were successful. In this way, the proposed map can also be used as a random number generator.

Meso-scale size effects of material heterogeneities on crack propagation in brittle solids: Perspectives from phase-field simulations

Brittle solids are often toughened by adding a second-phase material. This practice often results in composites with material heterogeneities on the meso scale: large compared to the scale of the fracture process zone but small compared to that of the application. The specific configuration (both geometrical and mechanical) of this mesoscale heterogeneity is generally recognized as important in controlling crack propagation behavior and, subsequently, the (effective) toughness of the composite. Here, we systematically investigate how dynamic brittle fracture navigates through a linear array of mesoscale inclusions. Using a variational phase-field (PF) approach, we compute the apparent crack speed and fracture energy dissipation rate to compare crack propagation (and the resulting toughening) under Mode-I loading for various configurations of inclusions. We identify an interplay between the size of inclusion and that of the K-dominant zone in the presence of elastic heterogeneity: matching these two sizes gives rise to the best toughening outcome for a given area fraction of inclusions. We discuss mechanisms that rationalize this observation and the importance of the length scale parameter used in PF models in interpreting simulation results. Our work sheds physical insight into the interaction between size effects and material properties, thereby opening a venue for the rational design of functional (architected) composites for dynamic fracture applications.

Monte Carlo tree search control scheme for multibody dynamics applications

There is considerable interest in applying reinforcement learning (RL) to improve machine control across multiple industries, and the automotive industry is one of the prime examples. Monte Carlo Tree Search (MCTS) has emerged and proven powerful in decision-making games, even without understanding the rules. In this study, multibody system dynamics (MSD) control is first modeled as a Markov Decision Process and solved with Monte Carlo Tree Search. Based on randomized search space exploration, the MCTS framework builds a selective search tree by repeatedly applying a Monte Carlo rollout at each child node. However, without a library of available choices, deciding among the many possibilities for agent parameters can be intimidating. In addition, the MCTS poses a significant challenge for searching due to the large branching factor. This challenge is typically overcome by appropriate parameter design, search guiding, action reduction, parallelization, and early termination. To address these shortcomings, the overarching goal of this study is to provide needed insight into inverted pendulum controls via vanilla and modified MCTS agents, respectively. A series of reward functions are well-designed according to the control goal, which maps a specific distribution shape of reward bonus and guides the MCTS-based control to maintain the upright position. Numerical examples show that the reward-modified MCTS algorithms significantly improve the control performance and robustness of the default choice of a constant reward that constitutes the vanilla MCTS. The exponentially decaying reward functions perform better than the constant value or polynomial reward functions. Moreover, the exploitation vs. exploration trade-off and discount parameters are carefully tested. The study’s results can guide the research of RL-based MSD users.

Dynamic nature of analytical soliton solutions of the nonlinear ZKBBM and GZKBBM equations

A solitary wave, characterized as a localized perturbation in a medium, emerges as a result of a delicate equilibrium between nonlinear and dispersive phenomena. Solitons, a subtype of solitary waves, exhibit persistent shape and velocity during propagation, representing a fundamental phenomenon observed widely in natural systems and possessing various applications in nonlinear dynamics. This investigation focuses on two nonlinear evolution equations (NEEs), specifically the Zakharov–Kuznetsov–Benjamin–Bona–Mahony (ZKBBM) equation and the generalized Zakharov–Kuznetsov–Benjamin–Bona–Mahony (GZKBBM) equation, which find relevance in domains such as fluid dynamics and ocean engineering. Utilizing an ansatz-based methodology, soliton solutions of both bright and dark characteristics are derived, alongside exploration of rogue wave-type solutions. Notably, the manifestation of dark, bright, and rogue waves aligns with the physical interpretation of the generated solitons. Computational simulations conducted using Wolfram Mathematica aim to provide a comprehensive description of the physical phenomena. The novelty of this study lies in its unreported investigation, contributing new insights into the solitonic dynamics within the considered models.

A social inference model of idealization and devaluation.

People often form polarized beliefs, imbuing objects (e.g., themselves or others) with unambiguously positive or negative qualities. In clinical settings, this is referred to as dichotomous thinking or "splitting" and is a feature of several psychiatric disorders. Here, we introduce a Bayesian model of splitting that parameterizes a tendency to rigidly categorize objects as either entirely "Bad" or "Good," rather than to flexibly learn dispositions along a continuous scale. Distinct from the previous descriptive theories, the model makes quantitative predictions about how dichotomous beliefs emerge and are updated in light of new information. Specifically, the model addresses how splitting is context-dependent, yet exhibits stability across time. A key model feature is that phases of devaluation and/or idealization are consolidated by rationally attributing counter-evidence to external factors. For example, when another person is idealized, their less-than-perfect behavior is attributed to unfavorable external circumstances. However, sufficient counter-evidence can trigger switches of polarity, producing bistable dynamics. We show that the model can be fitted to empirical data, to measure individual susceptibility to relational instability. For example, we find that a latent categorical belief that others are "Good" accounts for less changeable, and more certain, character impressions of benevolent as opposed to malevolent others among healthy participants. By comparison, character impressions made by participants with borderline personality disorder reveal significantly higher and more symmetric splitting. The generative framework proposed invites applications for modeling oscillatory relational and affective dynamics in psychotherapeutic contexts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Adaptive sampling physics-informed neural network method for high-order rogue waves and parameters discovery of the (2 + 1)-dimensional CHKP equation.

Rogue waves are important physical phenomena, which have wide applications in nonlinear optics, hydrodynamics, Bose-Einstein condensates, and oceanic and atmospheric dynamics. We find that when using the original PINNs to study rogue waves of high dimensional PDEs, the prediction performance will become very poor, especially for high-order rogue waves due to that the randomness of selection of sample points makes insufficient use of the physical information describing the local sharp regions of rogue waves. In this paper, we propose an adaptive sampling physics-informed neural network method (ASPINN), which renders the points in local sharp regions to be selected sufficiently by a new adaptive search algorithm to lead to a prefect prediction performance. To valid the performance of our method, the (2+1)-dimensional CHKP equation is taken as an illustrative example. Experimental results reveal that the original PINNs can hardly be able to predict dynamical behaviors of the high-order rogue waves for the CHKP equation, but the ASPINN method can not only predict dynamical behaviors of these high-order rogue waves, but also greatly improve the prediction efficiency and accuracy to four orders of magnitude. Then, the data-driven inverse problem for the CHKP equation with different levels of corrupted noise is studied to show that the ASPINN method has good robustness. Moreover, some main factors affecting the neural network performance are discussed in detail, including the size of training data, the number of layers of the neural network, and the number of neurons per layer.

Numerical study on magnetic control of boundary layers in non-Newtonian flows over stretching cylinders using Keller box analysis

This article presents an analysis of the magnetic field’s effects on two-dimensional, two-directional, incompressible, and steady third-grade fluid flow over a stretched circular cylinder. A mathematical model describing the behavior of third-grade fluid in the cylindrical coordinate system is developed, accounting for nonlinear differential conditions. To simplify the analysis, appropriate transformations are applied to convert the fractional differential conditions into ordinary differential conditions. The resulting nonlinear differential framework is solved using the Keller Box method. The influences of several novel parameters on the velocity are depicted and examined. Furthermore, the expression for the skin-friction coefficient is computed and provided. The comparison of the obtained results with existing literature is made and found in good accordance. Through comprehensive numerical simulations and analytical derivations, this study contributes to the understanding of magnetic field control in boundary layers of third-grade fluid over stretching cylinders, with implications for a wide range of practical applications in engineering and fluid dynamics. The stronger influence of the magnetic field, indicating an increase in the Hartmann number, corresponds to suppression of thermal and solutal transport, thereby leading to a decrease in the temperature and concentration gradients. Conversely, the velocity profile exhibits an increase, indicating enhanced fluid motion under the influence of the magnetic field. This behavior is consistent with the magnetohydrodynamic effects, where the Lorentz force induced by the magnetic field alters the fluid flow, resulting in changes in the velocity distribution while impacting temperature and concentration gradients.

Dynamic characteristics and application of dual throat fluidic thrust vectoring nozzle

Thrust vectoring technology plays an important role in improving the maneuverability of aircraft. In order to overcome the disadvantages of mechanical thrust vectoring nozzles, such as complications of structure and significant increases in weight and cost, fluidic thrust vectoring nozzles are proposed. Dual Throat fluidic thrust vectoring Nozzle (DTN) has received wide attention due to its excellent thrust vectoring efficiency and minimal thrust loss. In this study, three-dimensional unsteady numerical simulations of a single axisymmetric DTN are conducted first to analyze its dynamic response. Then the pitch and yaw control characteristics of DTN equipped on a flying-wing aircraft are investigated. It is found that the dynamic response will experience three stages: rapid-deflecting stage, oscillating stage, and steady stage. A complete recirculation zone forms at the end of the rapid-deflecting stage, which pushes the primary flow to attach to the wall opposite the secondary injection. Meanwhile, the exhaust flow is deflected. In terms of DTN’s application, the DTN equipped on the flying-wing aircraft is capable of providing effective pitch and yaw moments at all angles of attack and Mach numbers. In addition, continuous pitch and yaw moments can be obtained by adjusting the secondary mass flow ratios. The control moment is generated due to the asymmetrical pressure distribution of nozzle surface, which is mainly contributed by the pressure decrease on the secondary injection surface. Moreover, the DTN equipped on the flying-wing aircraft has a relatively high thrust vectoring efficiency of around 5°/% and a thrust coefficient of around 0.95 when nozzle pressure ratio equals 4. These results provide an important theoretical basis for the practical application of DTN.

433 Orientation of the Lumbar Spine During Dynamic Compression Influences Fracture Characteristics

INTRODUCTION: Characteristics of lumbar spine fractures dictate patient outcomes and clinical treatment decisions. Understanding injury biomechanics associated with different fracture types can assist injury prediction and development of safety enhancements. METHODS: Twenty human lumbar spines (T12-L5) were isolated and attached to novel whole-column experimental dynamic compressive loading apparatus. Specimens were attached to the lower platform on a drop tower and a decoupled upper platform held a mass to simulate the torso inertially loading the lumbar spine as the lower platform was decelerated at the drop tower base. Each specimen was tested at increasing deceleration magnitudes until failure. Pre-test x-rays were used for the measurements to quantify spinal orientation. Fractures were classified using post-test x-rays and CT scans. RESULTS: Fractures were classified into six categories [Wedge (AO Spine A1): n = 8, Burst (AO Spine A3/A4): n = 7, Hyperextension (AO Spine B2): n = 5]. Spines that sustained hyperextension fractures had significantly larger Cobb angles compared to spines that sustained burst fracture (p = 0.03). Spines that sustained wedge fracture had significantly larger lumbar spine angles (i.e., closer to vertical orientation) compared to spines that sustained burst (p = 0.03) and hyperextension (p = 0.02) fracture. Spines that sustained wedge fracture also had a nearly significant longer horizontal distance between the T12 centroid and the load location compared to spines that sustained burst fracture (p = 0.07). CONCLUSIONS: This study demonstrated that orientation of the lumbar spine at the time of dynamic load application affected fracture characteristics and, therefore, post-injury spinal stability and patient outcomes. The clinical importance of these findings includes a better understanding of injury mechanisms and a recognition that a wide variety of injury outcomes (stable versus unstable; anterior versus middle versus posterior column) can result from identical loading situations with different spinal orientations.

Computational fluid dynamics (CFD) modeling and application for cleaning of food‐contact surfaces: A review

AbstractIn the food industry, ensuring clean production equipment is essential for maintaining product quality and safeguarding consumer health. For optimal geometric cleaning, it is crucial to consider the specific interactions between flow, dirt, and cleaning fluids. Computational fluid dynamics (CFD) is a powerful tool for simulating fluid flow behavior, especially in terms of momentum, mass, and energy transfer. It has been widely used to model elimination processes in food sanitation. The construction of CFD model and the cleaning of food contact surface were explored. Additionally, an overview of existing CFD studies on fouling and cleaning mechanisms was provided in this manuscript. The goal of this review is to discuss the present state of CFD modeling in cleaning soil from surfaces and to offer a valuable reference for food cleaning process.

Predefined-Time Noise Immunity ZNN Model for Dynamic Quaternion Least Squares Problem and Application to Synchronization of Hyperchaotic Systems

Predefined-Time Noise Immunity ZNN Model for Dynamic Quaternion Least Squares Problem and Application to Synchronization of Hyperchaotic Systems

Exploring the aqueous solubility and intermolecular interactions of diclofenac Diethylammonium: A molecular modeling study in solid state and solvation processes

Diclofenac, a widely used non-steroidal anti-inflammatory drug with analgesic, anti-inflammatory, and antipyretic properties, presents a challenge in oral administration due to its low aqueous solubility and subsequent low bioavailability. This issue, primarily attributed to the drug's high hydrophobicity, can lead to adverse side effects. To address this, our study delves into the aqueous solvation process of diclofenac through molecular modeling, employing both Carr-Parrinello molecular dynamics and Density Functional Theory. This combined approach enables a comprehensive investigation of the molecular interactions that influence solubility, encompassing non-polar interactions (van der Waals dispersion forces), dipole interactions, and hydrogen bonding. Our results highlight the formation of intermolecular interactions with water molecules through OH⋯Owater and CO⋯Hwater hydrogen bonds. Additionally, we observe intramolecular hydrogen bonds within diclofenac (NH⋯OH and NH⋯Cl) and weaker interactions involving CH⋯Cl. Notably, the protonation of diclofenac contributes to molecular rigidity, hindering hydrogen bonding with adjacent water molecules further. These insights offer a deeper understanding of the solubility challenges associated with diclofenac, paving the way for improved drug formulations that enhance bioavailability. The application of Carr-Parrinello molecular dynamics and density functional theory in this study highlights their valuable role in addressing complex drug solubility and design issues.

Optothermal Microparticle Oscillator Induced by Marangoni and Thermal Convection.

The light-fueled microparticle oscillator, exemplifying sustained driving in a static light source, potentially holds applications in fundamental physics, cellular manipulation, fluid dynamics, and various other soft-matter systems. The challenges of photodamage due to laser focusing on particles and the control of the oscillation direction have always been two major issues for microparticle oscillators. Here, we present an optical-thermal method for achieving a 3D microparticle oscillator with a fixed direction by employing laser heating of the gold film surface. First, the microparticle oscillation without direction limitation is studied. The photothermal conversion originates from the laser heating of a gold film. The oscillation mechanism is the coordination of the forces exerted on the particles, including the thermal convective force, thermophoresis force, and gravity. Subsequently, the additional Marangoni convection force, generated by the temperature gradient on the surface of a microbubble, is utilized to control the oscillation direction of the microparticle. Finally, a dual-channel oscillation mode is achieved by utilizing two microbubbles. During the oscillation process, the microparticle is influenced by flow field forces and temperature gradient force, completely avoiding optical damage to the oscillating microparticle.

Overview on GRS CFD activities related to containment applications

Abstract In the case of a severe accident in a nuclear power plant, hydrogen could be produced in the superheated reactor core. The hydrogen could flow through a leak in the primary circuit into the containment and a hydrogen deflagration could occur. A consequence of such an event was the Fukushima accident in 2011. For the analysis of such events, different phenomena in the reactor containment must be simulated correctly such as the distribution of steam and hydrogen, steam condensation, thermal radiation, conjugated heat transfer, hydrogen recombination and deflagration. The application of Computational Fluid Dynamics (CFD) methods for such analyses is becoming more relevant. Selected models needed to simulate the different containment phenomena with OpenFOAM are currently being tested, validated or improved and further developed by a network of German research institutes and universities.

SIMULATION-DRIVEN STRATEGIES FOR ENHANCING WATER TREATMENT PROCESSES IN CHEMICAL ENGINEERING: ADDRESSING ENVIRONMENTAL CHALLENGES

Water treatment processes in chemical engineering play a critical role in addressing environmental challenges and ensuring the sustainability of water resources. This paper examines simulation-driven strategies aimed at enhancing water treatment processes within the domain of chemical engineering. By leveraging advanced simulation techniques and methodologies, engineers can optimize the design, operation, and performance of water treatment systems, thereby mitigating environmental impacts and improving overall efficiency. The review highlights the importance of addressing environmental challenges through innovative approaches in water treatment processes. It underscores the role of simulation-driven strategies in chemical engineering to achieve sustainable solutions for water management. Through a comprehensive review of simulation techniques and case studies, this paper elucidates how simulation-driven approaches can enhance the effectiveness and sustainability of water treatment processes. Furthermore, the review emphasizes the interdisciplinary nature of this research, bridging chemical engineering principles with environmental science and technology. By integrating simulation tools with knowledge of water chemistry, fluid dynamics, and process engineering, engineers can develop robust strategies for optimizing water treatment processes while minimizing environmental footprints. Key topics covered include the application of computational fluid dynamics (CFD), process simulation software, and advanced modeling techniques in the analysis and design of water treatment systems. Case studies illustrating the successful implementation of simulation-driven strategies in various water treatment applications are presented to provide practical insights and demonstrate the potential benefits. Overall, this paper underscores the pivotal role of simulation-driven strategies in advancing water treatment processes in chemical engineering. It advocates for the adoption of innovative approaches to address environmental challenges and promote sustainability in water management practices within the oil and gas industry and other sectors reliant on chemical engineering processes.
 Keywords: Water treatment, Environmental Challenges, Chemical Engineering, Processes, Simulation Driven Strategies.

3D Geomechanical Finite Element Analysis for a Deepwater Faulted Reservoir in the Eastern Mediterranean

AbstractHydrocarbon reservoir structures are subjected to tectonic forces along the geological time that cause rock deformation and break into faulted zones. Faulted reservoirs, enclose certain complexity in terms of the distributed effective stresses, rock plastic alteration, slipping and fault block displacement. In this study, we develop a three-dimensional (3D) geomechanical reservoir model with faulted and compartmentalized geometry, located in the offshore deepwater environment of the Levantine basin in the Eastern Mediterranean, based on non-linear finite element analysis (FEA). A regional structural and stress map was also constructed, integrating various data sources, to present the regional stress setting to enhance this work. The assessment of the geomechanical impacts on the reservoir provides important information in reservoir studies, that can analyze potential stability issues during the depletion to optimize the field production planning. Stress–strain evolution in the reservoir is primarily affected by the in situ stresses, the geometry of faults, and the degree of compartmentalization. The results demonstrate clearly the mechanism of stress transfer transmission and the impact between the fault block compartments in the reservoir. Fault contacts exhibit a higher tendency for rock displacements and deformations. Plastic yielding develops at a narrow extent along the faults. The risk of fault slipping depends on the depletion strategy, but it is low in all cases. No significant reduction in permeability was found at the end of reservoir depletion. Overall, geomechanics integration enriches and improves the dynamic reservoir models and applications.

Transformer-based spatial–temporal detection of apoptotic cell death in live-cell imaging

Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial–temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial–temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial–temporal regulation of this process.

Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging.

Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

U-SeqNet: learning spatiotemporal mapping relationships for multimodal multitemporal cloud removal

ABSTRACT Optical remotely sensed time series data have various key applications in Earth surface dynamics. However, cloud cover significantly hampers data analysis and interpretation. Despite synthetic aperture radar (SAR)-to-optical image translation techniques emerging as a promising solution, their effectiveness is diminished by their inability to adequately account for the intertwined nature of temporal and spatial dimensions. This study introduces U-SeqNet, an innovative model that integrates U-Net and Sequence-to-Sequence (Seq2Seq) architectures. Leveraging a pioneering spatiotemporal teacher forcing strategy, U-SeqNet excels in adapting and reconstructing data, capitalizing on available cloud-free observations to improve accuracy. Rigorous assessments through No Reference and Full Reference Image Quality Assessments (NR – IQA and FR – IQA) affirm U-SeqNet’s exceptional performance, marked by a Natural Image Quality Evaluator (NIQE) score of 5.85 and Mean Absolute Error (MAE) of 0.039. These results underline U-SeqNet’s exceptional capabilities in image reconstruction and its potential to improve remote sensing analysis by enabling more accurate and efficient multimodal and multitemporal cloud removal techniques.