Forward Process Research Articles

Non-linear super-resolution computed tomography imaging algorithm based on a discrete X-ray source focal spot model

Spatial resolution is one of the critical metrics for evaluating the performance of a computed tomography (CT) system. Traditional methods often neglected the influence of the focal spot size of the X-ray source, leading to data inconsistency and degrading the spatial resolution of the reconstructed images. Thus, this study introduces what we believe to be a novel non-linear super-resolution CT reconstruction method based on the characteristics of the X-ray source’s focal spot. The proposed method employed a discrete focal spot model and utilized measured focal spot information to formulate a non-linear mathematical model for CT imaging. Building on this model, a high-precision iterative solution method was developed. The proposed approach achieved improved data consistency during the forward projection process and employed a highly accurate solution method in the inversion process. As a result, this approach reconstructed images of higher quality compared to other methods, revealing more detailed structural information.

Enhancing biological system engineering realisation through advanced feature transfer and extenics: a bionic design approach

During the process of bionic design, the discrepancies between biological systems and engineering systems often result in designers being unable to handle the knowledge involved in biological systems effectively, thereby leading to a low utilisation rate of biological knowledge. This study proposes a model for the transformation of biological systems to engineering systems on the basis of feature transfer. First, the basic system is determined according to user requirements, followed by the search for suitable biological systems and the performance of similarity analysis. Second, the element model of extenics is used to represent biological systems, and the extension analysis method is used to extend biological systems. Then, on the basis of substance-field theory in TRIZ combined with the extension transformation method, three approaches for transforming biological systems into engineering systems are proposed: no processing, forward processing, and reverse processing. After transformation, the features of the engineering system are extracted and incorporated into the basic system to complete the structural design. The container bag outlet folding device is taken as an engineering case to validate the effectiveness of the proposed model.

Cultural adaptation and validation of the Sinhala version of the spiritual needs assessment for patients (S-SNAP) questionnaire

BackgroundSpiritual support for patients and caregivers of critically ill patients is associated with improved quality of life. This aspect, however, is not incorporated into the current care pathways in Sri Lanka. The Spiritual Needs Assessment for Patients (SNAP) questionnaire, comprised of 3 domains: psychosocial, spiritual and religious, gives a platform for clinicians to assess the spiritual needs of those patients. This study presents the results of validation of the Sinhala version of the SNAP (S-SNAP) questionnaire.MethodsThe SNAP was translated from English to Sinhala using the standard forward and backward translation process. After verifying the content validity, unambiguity and clarity of items in a focused group discussion, and a pilot study, the pre-final version was tested among 267 volunteers with cancer selected from three state-run cancer care institutions. Data were analysed for internal consistency and item-total correlations. Factor analysis was done using Varimax rotation with Kaiser normalization. A Scree plot was also made to determine the number of factors.ResultsThe mean (SD) age of subjects was 63.2 (11.4) years. The total S-SNAP score ranged from 22 to 88 (maximum 88). The overall Cronbach’s alpha was 0.94 while item-total correlations varied from 0.26 to 0.87. Total SNAP score showed inverse correlations with age, Charleson Comorbidity index and Barthel index while a positive correlation was seen with the Karnofsky performance status scale (p < 0.05). Kaiser-Meyer-Olkein value of 0.92 (P = < 0.001) for Bartlett’s test indicated adequate sampling and non-linearity of factors. The scree plot showed a four-factor structure explaining 76% variation. Meaning of life and relationship with a supernatural being and religious rituals are loaded as 2 different factors. Worries, fears and forgiveness are grouped as the third factor while relaxation, coping and sharing feelings are loaded separately.ConclusionsThe S-SNAP is a reliable and valid tool to assess spiritual suffering among patients with cancers conversant in the Sinhala language.

STGlow: A Flow-Based Generative Framework With Dual-Graphormer for Pedestrian Trajectory Prediction.

The pedestrian trajectory prediction task is an essential component of intelligent systems. Its applications include but are not limited to autonomous driving, robot navigation, and anomaly detection of monitoring systems. Due to the diversity of motion behaviors and the complex social interactions among pedestrians, accurately forecasting their future trajectory is challenging. Existing approaches commonly adopt generative adversarial networks (GANs) or conditional variational autoencoders (CVAEs) to generate diverse trajectories. However, GAN-based methods do not directly model data in a latent space, which may make them fail to have full support over the underlying data distribution. CVAE-based methods optimize a lower bound on the log-likelihood of observations, which may cause the learned distribution to deviate from the underlying distribution. The above limitations make existing approaches often generate highly biased or inaccurate trajectories. In this article, we propose a novel generative flow-based framework with a dual-graphormer for pedestrian trajectory prediction (STGlow). Different from previous approaches, our method can more precisely model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors. Besides, our method has clear physical meanings for simulating the evolution of human motion behaviors. The forward process of the flow gradually degrades complex motion behavior into simple behavior, while its reverse process represents the evolution of simple behavior into complex motion behavior. Furthermore, we introduce a dual-graphormer combined with the graph structure to more adequately model the temporal dependencies and the mutual spatial interactions. Experimental results on several benchmarks demonstrate that our method achieves much better performance compared to previous state-of-the-art approaches.

Machine learning assisted crystallographic reconstruction from atom probe tomographic images

Atom probe tomography (APT) is a powerful technique for three-dimensional (3D) atomic-scale imaging, enabling the accurate analysis on the compositional distribution at the nanoscale. How to accurately reconstruct crystallographic information from APT data, however, is still a great challenge due to the intrinsic nature of the APT technique. In this paper, we propose a novel approach that consists of the modified forward simulation process and the backward machine learning process to recover the tested crystal from APT data. The high-throughput forward simulations on Al single crystals of different orientations generate 10 000 original 3D images and data augmentation is implemented on the original images, resulting in 100 000 3D images. The big data allows the development of deep learning models and three deep learning algorithms of Convolutional Neural Network (CNN), Vision Transformer (ViT), and Variational Autoencoder (VAE) are used in the backward process. After training, the ViT model performs superior than the CNN and VAE models, which can recover the crystalline orientation outstandingly, as evaluated by the coefficient of determinationR2and the Mean Percent Error (MPE), viz.,R2= 0.93 and MPE = 0.43%,R2= 0.97 and MPE = 0.35%, andR2= 0.93 and MPE = 0.77% for the rotation anglesϕ,ψandθ, respectively, on the test dataset. The present work clearly demonstrates the capability of deep learning models in the recovery of the tested crystals from APT data, thereby paving the way for the further development of large artificial intelligent models of APT.

Design of model fusion learning method based on deep bidirectional GRU neural network in fault diagnosis of industrial processes

This paper proposes an end-to-end model fusion feature learning method based on deep bidirectional gated recurrent unit (MCNN-DBiGRU) for fault diagnosis in industrial processes. First, a feature-aligned multi-scale feature extraction model (MCNN) is designed by analyzing the working principles of convolutional and pooling layers of convolutional neural networks. Secondly, a deep bidirectional mechanism is proposed to better extract the time series features in the process data. This mechanism makes the recurrent neural network not only present the forward processing input features from the past to the future, but also the reverse processing from the future to the past. By integrating these features, the diagnostic performance of the network model is improved. To verify that the proposed model has effective diagnostic accuracy for fault diagnosis, we conduct simulation experiments on the Tennessee-Eastman (TE) process and a chemical coking furnace, and compare with several conventional network models. In the end, not only the effectiveness of the model is proved, but it is also confirmed that the model is superior to other conventional neural networks in both diagnostic accuracy and feature robustness.

Multi-receptive-field physics-informed neural network for complex electromagnetic media

Acquiring the electromagnetic response of intricate media at the nanoscale constitutes a pivotal phase in the design intricacies of nanophotonic apparatuses. Conventional numerical algorithms often necessitate intricate and specialized treatments to accommodate the unique properties of the medium, coupled with substantial computational time and resource demands. In recent years, the advent of deep learning technology has heralded numerous advancements in the domain of computational electromagnetics, albeit with a scarcity of solvers tailored for versatile complex media. Consequently, this study introduces an innovative multi-receptive-field physics-informed neural network (MRF-PINN) designed to tackle nano optical scattering predicaments inherent in media exhibiting dispersion, inhomogeneity, anisotropy, nonlinearity, and chirality. This framework adeptly captures electromagnetic perturbations surrounding scatterers via variable-scale receptive fields, thereby enhancing prediction precision. Within the training regimen, a scale balancing algorithm is proposed to expedite network convergence. Empirical findings demonstrate that a fully trained MRF-PINN proficiently reconstructs electromagnetic field distributions within complex nanomaterials within a mere tens of milliseconds of inference time. Such quasi real-time capabilities herald a novel approach to supplant the arduous forward calculation processes inherent in nanomaterial design workflows.

Beam Orbital Parameter Prediction Based on the Deployment of Cascaded Neural Networks at Edge Intelligence Acceleration Nodes

During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcement learning algorithms to achieve automatic beam calibration. This study introduces a system architecture that employs edge intelligent acceleration nodes based on deep learning acceleration techniques. We designed a system to predict BPM parameters using a cascaded backpropagation neural network (CBPNN) that is informed by the physical structure. This system serves as an environmental map for reinforcement learning, aiding beam current correction. The CBPNN was implemented on the acceleration node to hasten the forward inference process, leveraging sparsification, quantization algorithms, and pipelining techniques. Our experimental results demonstrated that the simulated inference speed reached 28 μs with FPGA hardware as the edge acceleration node, achieving forward inference speeds 35.66 and 12.66 times faster than those of the CPU and GPU. The energy efficiency ratio was 10.582 MOPS/W, which was 989 and 410 times that of the CPU and GPU, respectively. This confirms the designed architecture’s energy efficiency and low latency attributes.

Transforming HR through Analytics: A Study of Performance Improvement Initiatives Driven by Data

ABSTRACT This study examines the transformative impact of facts analytics on Human Resources (HR) practices, especially focusing on overall performance development tasks. As agencies more and more apprehend the value of data-pushed choice-making, HR analytics has emerged as a strategic tool for reinforcing talent control, worker engagement, and ordinary organizational overall performance. By studying case research and empirical data, this studies identifies the important thing blessings of HR analytics, such as stepped forward recruitment processes, better employee retention, and heightened engagement ranges. However, the take a look at also addresses the challenges related to implementing analytics in HR, together with facts nice, change management, and talent gaps. Ultimately, the findings spotlight the need for HR professionals to include a facts-driven culture and invest in analytics abilities to reap sustained performance upgrades. This paper serves as a manual for companies in search of to leverage analytics for more suitable HR effects and strategic alignment.

A dual-domain cone beam computed tomography sparse-view reconstruction method based on generative projection interpolation

To propose a dual-domain CBCT reconstruction framework (DualSFR-Net) based on generative projection interpolation to reduce artifacts in sparse-view cone beam computed tomography (CBCT) reconstruction. The proposed method DualSFR-Net consists of a generative projection interpolation module, a domain transformation module, and an image restoration module. The generative projection interpolation module includes a sparse projection interpolation network (SPINet) based on a generative adversarial network and a full-view projection restoration network (FPRNet). SPINet performs projection interpolation to synthesize full-view projection data from the sparse-view projection data, while FPRNet further restores the synthesized full-view projection data. The domain transformation module introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes. The image restoration module includes an image restoration network FIRNet that fine-tunes the domain-transformed images to eliminate residual artifacts and noise. Validation experiments conducted on a dental CT dataset demonstrated that DualSFR-Net was capable to reconstruct high-quality CBCT images under sparse-view sampling protocols. Quantitatively, compared to the current best methods, the DualSFR-Net method improved the PSNR by 0.6615 and 0.7658 and increased the SSIM by 0.0053 and 0.0134 under 2-fold and 4-fold sparse protocols, respectively. The proposed generative projection interpolation-based dual-domain CBCT sparse-view reconstruction method can effectively reduce stripe artifacts to improve image quality and enables efficient joint training for dual-domain imaging networks in sparse-view CBCT reconstruction.

Challenges of forward osmosis desalination processes using hydrogels as draw agents

The present study aims to investigate the properties, identification, and comprehension of the obstacles encountered in the forward osmosis process when utilizing a hydrogel drawing agent (FO-HG). Furthermore, a comparison is made between the FO-HG system and conventional forward osmosis systems employing a salt solution drawing agent. The comparison and evaluation of the swelling process of hydrogel and the kinetics of water penetration are conducted in both un-constrained and constrained states. Furthermore, the investigation and analysis are carried out to determine the presence or absence of internal concentration polarization (ICP) and external concentration polarization (ECP) phenomena. These phenomena are studied in situations with and without mixing, as well as in different orientations of the membrane (FO-mode and PRO-mode). The impact of these phenomena on the water flux of the systems FO-HG and FO-NaCl is also examined. An evaluation is conducted to determine the influence of the amount and size of hydrogel used as a draw agent in the FO-HG system on the water flux. The results of the study reveal that smaller hydrogel particles in the FO-HG system exhibit a higher flux compared to larger particles. Additionally, it is observed that the water flux in PRO-mode is unexpectedly higher when salt water is used as feed solution. This phenomenon can be ascribed to a counter-osmotic effect, originating from the FO state. Despite the high water absorption capacity of hydrogel and its potential as an ideal drawing agent in the forward osmosis process, the results demonstrate that the flux of the FO-HG system is inferior to that of the FO-NaCl system. Finally, the focus is on resolving the low flux issue by suggesting a process involving multiple cycles throughout day and night. We investigate the influence of hydrogel particle size, membrane surface, hydrogel layer thickness, as well as swelling and deswelling time in one cycle. The swelling time displays a peak at an optimal absorption duration, while the deswelling time does not show a similar optimal point, highlighting the difference between swelling and deswelling phenomena. Therefore, the hydrogels' high absorption capacity alone is insufficient for achieving desalination success. The research findings emphasize the high importance of synthesis of a membrane with minimal resistance, enabling a high water flux and suitable selectivity.

On Lubrication Regime Changes during Forward Extrusion, Forging, and Drawing

The tribological phenomena concerning the lubrication regime change (LRC) during bulk metal forming are comprehensively studied. A multi-step cold forward extrusion process shows the evolution of LRC and reveals the shortcomings of the traditional Coulomb friction law. The previous works of the specific author’s research group on friction are reviewed, focusing on the LRC during bulk metal forming. Various LRC phenomena from various examples are revealed. It has been found that the drawing and forward extrusion processes are vulnerable to LRC because of significant sliding motion at the material–die interface, and that when the strain hardening of the material is slight, the influence of friction increases, and as a result, the influence of LRC increases excessively. The new findings also include the impact of LRC on the macroscopic phenomena of the process and the reason for the sharp increase in friction coefficient via LRC, which is validated by the work of Wilson. This paper aims to make engineers and researchers think much of the tribology with lubricant in bulk metal forming with a focus on the dependence of tribological phenomena on the state of the lubricants and the irrationality of traditional friction law, especially in the forging of materials with a low strain hardening capability.

Bearing fault diagnostic framework under unknown working conditions based on condition-guided diffusion model

Fault diagnosis is of vital importance in ensuring the safety of smart manufacturing. Current diagnostic methodologies require data spanning various working conditions. However, industrial settings offer scarce bearing failure data, leading to failure or degradation of performance of traditional intelligent diagnostic methods. Obtaining a complete sample of industrial environments is intensive and unrealistic. Acquiring a comprehensive sample of industrial environments is both resource-intensive and impractical. To handle this situation, an unknown condition diagnosis framework based on diffusion model (DiffUCD) is proposed, effectively integrating the generation capability of the diffusion model and learning from the condition-guided information (CGI), Specifically, signals under limited working conditions are gradually convert to noise through a forward noising process. Then, CGDiffusion reconstructs signals from the noise by a reverse denoising process. In addition, a condition-guided embedding UNet (CGE-UNet) structure is designed to extract CGI for noise level prediction during the reverse process. Moreover, an Unsupervised Clustering Filter (UCFilter) is constructed to select the qualified signals after generation. Signals under unknown working condition can be generated with specialized CGI. Ultimately, extensive experiments and validations on two public bearing datasets (SDUST and PU) are carried out, which validate the effectiveness of our method compared with the state-of-the-art baselines and the hyperparameter analysis confirms the advances of DiffUCD.

Digital background calibration algorithm for pipelined ADC based on time-delay neural network with genetic algorithm feature selection

This paper presents a novel background calibration method for pipelined analog-to-digital converters (ADCs) using a time-delay neural network (TDNN), which is optimized through genetic algorithm (GA) techniques. The proposed technique leverages TDNN to create enhanced feature sets, significantly improving the calibration of nonlinear errors exhibiting memory effects. It harnesses the GA's global optimization capabilities for feature selection, effectively reducing the feature dimension and consequently alleviating the NN's computational burden. A parallel pipeline architecture is devised for the calibration circuit, with its implementation realized on FPGA to facilitate forward inference processing. The inference circuit is synthesized using TSMC's 90 nm CMOS process, achieving a power consumption of 40.11 mW and an area of 0.45 mm2. Simulations based on MATLAB for a 14-bit Pipelined ADC demonstrate that the proposed calibration method significantly improves the SFDR from 59.77 dB to 165.52 dB, and ENOB from 8.79 bits to 19.23 bits, surpassing the target ADC's specifications. Moreover, the dimensionality of features is effectively reduced by up to 34 % without compromising the calibration performance.

Ultrasonic characterization of defects in polycrystalline materials based on TFM image reconstruction using denoising diffusion probabilistic models

This paper presents a refined ultrasonic total focusing method (TFM) for accurately characterizing key defect parameters including size, orientation, and location in complex materials. The proposed approach aims to accurately reconstruct the defect shape in images compromised by grain interference. First, a TFM image database was generated with varying defect parameters and grain noise levels by adopting a custom forward modeling process based on superposition of defect and grain scattering data. Second, a new architecture called TFM-DDPM was then proposed which incorporates TFM and the conditional denoising diffusion probabilistic model (DDPM). Third, pixel-level uncertainty can be quantified through repeated sampling of noise which follows a standard normal distribution. An acceptance criterion for the reconstruction was further established by analyzing the quantified uncertainty. In simulation, the median Dice coefficient between the de-noised TFMs of 30° cracks and their corresponding ground truth images increased from 0.5585 to 0.9137 using the proposed approach, and the median IoU metric rose from 0.3875 to 0.8408. By applying the 6-dB drop approach to the reconstructed images, the root mean squared errors (RMSEs) of size, angle and location were decreased by 93.59%, 86.90% and 86.25%, respectively. It is also demonstrated that our method can reject 69.19% of incorrect characterization results caused by high levels of noise and/or images with steeply inclined (e.g., 45°) cracks, while accepting 97.29% accurate results. Experimental results obtained on nickel-based alloy K403 specimens also exhibited significant improvements. For 2–3 mm cracks, the average sizing error reduced from 0.46 mm to 0.13 mm, and the error in crack angle decreased from 28.63° to 1.50°. Comparable performance enhancements were observed for 4–5 mm cracks using the proposed approach.

Mitigating Adversarial Attacks in Object Detection through Conditional Diffusion Models

The field of object detection has witnessed significant advancements in recent years, thanks to the remarkable progress in artificial intelligence and deep learning. These breakthroughs have significantly enhanced the accuracy and efficiency of detecting and categorizing objects in digital images. Nonetheless, contemporary object detection technologies have certain limitations, such as their inability to counter white-box attacks, insufficient denoising, suboptimal reconstruction, and gradient confusion. To overcome these hurdles, this study proposes an innovative approach that uses conditional diffusion models to perturb adversarial examples. The process begins with the application of a random chessboard mask to the adversarial example, followed by the addition of a slight noise to fill the masked area during the forward process. The adversarial image is then restored to its original form through a reverse generative process that only considers the masked pixels, not the entire image. Next, we use the complement of the initial mask as the mask for the second stage to reconstruct the image once more. This two-stage masking process allows for the complete removal of global disturbances and aids in image reconstruction. In particular, we employ a conditional diffusion model based on a class-conditional U-Net architecture, with the source image further conditioned through concatenation. Our method outperforms the recently introduced HARP method by 5% and 6.5% in mAP on the COCO2017 and PASCAL VOC datasets, respectively, under non-APT PGD attacks. Comprehensive experimental results confirm that our method can effectively restore adversarial examples, demonstrating its practical utility.

The Psychometric Properties of the Persian Version of Emotional Abuse Questionnaire

Objective: Intimate partner emotional abuse is a serious issue that can lead to catastrophic outcomes for victims. Emotional abuse involves psychological tactics to control, manipulate, and degrade a person within an intimate relationship. This research aimed to translate the Emotional Abuse Questionnaire (EAQ) developed by Jacobson and Gottman into Persian for use among Iranian university students. Method: The translation of the 66-item EAQ involved a meticulous forward and backward translation process, linguistic matching, and a pilot review. In this cross-sectional study, 346 university students from Rasht, Iran, completed the EAQ. The mean age of participants was 26.78 ± 8.10 years, with most being female (89.0%). Reliability was evaluated using Cronbach's alpha and test-retest analysis while content and face validity were assessed by a panel of experts. Construct validity was examined through confirmatory factor analysis (CFA) and internal consistency measures. Divergent validity was assessed by comparing the EAQ with the ENRICH Marital Satisfaction Scale (EMS). Results: Impact scores for face validity ranged from 2.33 to 3.92, based on respondents' ratings of frequency and importance. Content validity assessment led to removing four items with a CVR below 0.62, resulting in 62 valid items. The EAQ showed strong internal consistency, with a Cronbach’s alpha of 0.97, exceeding the acceptable threshold of 0.70. CFA results confirmed the validity of the second-order factor model of the EAQ (χ2/df = 4.34, CFI = 0.95, RMSEA = 0.098, SRMR = 0.077). The EAQ demonstrated a strong correlation with EMS measures, confirming divergent validity. Conclusion: The Persian version of the EAQ is a reliable instrument for assessing emotional abuse among Iranian university students. Future research should explore the cultural sensitivity of the questionnaire and investigate associations between emotional abuse and other variables of interest, such as mental health outcomes or relationship dynamics. These avenues promise valuable insights into the frequency and effects of emotional abuse across diverse cultural contexts.

Inverse design of cellular structures with the geometry of triply periodic minimal surfaces using generative artificial intelligence algorithms

Triply periodic minimal surfaces (TPMS) exhibit excellent mechanical and energy absorption properties due to their structural advantages. However, existing porous TPMS structural design methods are constrained to a forward process from structural parameters to mechanical properties. This study proposed an inverse design method that combines bidirectional generative adversarial networks (BiGAN) and mechanical performance targets, resulting in a combined TPMS structure of Primitive and IWP types with superior buffering and energy absorption capabilities. The results show that under a single load value target condition of the designed structure, the minimum deviation index (R2) between the load value corresponding to the displacement point and the target load value is only 0.987, and the maximum mean absolute percentage error (MAPE) is only 5.92 %. When considering the elastic modulus target, the approach successfully conducts two sets of combined structural designs meeting the requirements of both high and low elastic moduli. When targeting the specified load-displacement curve conditions, specifically when combining high elastic modulus with ascending plasticity, the designed structures exhibit an error of only 2.2 % compared to the target property. Moreover, the quasi-static uniaxial compression experiments conducted on additively manufactured designed structures confirm that the experimental curves match the target curves in terms of deformation trends and load value ranges. The success of this inverse design approach for cellular TPMS structures has the potential to expedite new structural material development processes.

Deep neural helmholtz operators for 3D elastic wave propagation and inversion

Summary Numerical simulations of seismic wave propagation in heterogeneous 3D media are central to investigating subsurface structures and understanding earthquake processes, yet are computationally expensive for large problems. This is particularly problematic for full waveform inversion, which typically involves numerous runs of the forward process. In machine learning there has been considerable recent work in the area of operator learning, with a new class of models called neural operators allowing for data-driven solutions to partial differential equations. Recent works in seismology have shown that when neural operators are adequately trained, they can significantly shorten the compute time for wave propagation. However, the memory required for the 3D time domain equations may be prohibitive. In this study, we show that these limitations can be overcome by solving the wave equations in the frequency domain, also known as the Helmholtz equations, since the solutions for a set of frequencies can be determined in parallel. The 3D Helmholtz neural operator is 40 times more memory-efficient than an equivalent time-domain version. We employ a Helmholtz neural operator for 2D and 3D elastic wave modeling, achieving two orders of magnitude acceleration compared to a baseline spectral element method. The neural operator accurately generalizes to variable velocity structures and can be evaluated on denser input meshes than used in the training simulations. We also show that when solving for wavefields strictly on the surface, the accuracy can be significantly improved via a graph neural operator layer. In leveraging automatic differentiation, the proposed method can serve as an alternative to the adjoint-state approach for 3D full-waveform inversion, reducing the computation time by a factor of 350.

Questionnaire Translation and Validation of Problem-Focused Coping in Increasing Work to Family Enrichment in Context of Laos

This study aims to translate and validate a questionnaire of problem-focused coping in increasing work to family enrichment in the context of Laos. The questionnaire is translated by using forward and backward translation process with two Lao bilingual translations: then this questionnaire is evaluated through three validity criteria (content validity, face validity, and construct validity) and reliability process. The findings show that the questionnaire translation is available locally, culturally acceptable, and conceptual equivalence for Lao language. Lao questionnaire version is fulfilled acceptable standards of validity and reliability analysis. This study suggests that the Lao questionnaire version is locally accepted to proceed for further actual study in Lao context.