Processing Strategies Research Articles

Laser shock-enabled optical–thermal–mechanical coupled welding method for silver nanowires

Silver nanowires (AgNWs) are recognized as highly promising materials for flexible and transparent electrode applications. However, existing material-processing methods fail to achieve uniform and reliable AgNWs junctions. In this study, we propose a new method using the laser shock effect combined with the laser heating effect, for creating AgNW junctions within thin films. We explored the welding mechanism of AgNWs through optic-thermal welding, laser shock-enabled mechanical welding, and laser-shock-enabled optical-thermal-mechanical (LS-OTM) experiments, as well as numerical simulations, and the results demonstrate that the innovative mechanism of the LS-OTM process lies in its utilization of laser shock to adjust the gap between the nanowire junctions, which in turn achieves a fine control of the thermal effect of the heating laser localised surface plasmon resonance, and the atomic diffusion in the solid state at intermediate temperature under the action of the impact force is the mechanism of the formation of high-quality junctions. We prepared flexible transparent conductive films and studied their transmittance, conductivity, and thermal properties, the results show that the flexible transparent conductive films prepared by LS-OTM welding method have excellent transmittance, conductivity, and thermal properties, this verifies the feasibility and effectiveness of this processing strategy. The LS-OTM method is a viable solution for manufacturing transparent, conductive films from AgNWs for emerging applications such as flexible heated films, flexible displays, and wearable medical devices.

Optimization of financial data processing strategy based on LSTM-GCN

In the process of training LSTM-GCN mixed model with labeled data, a common practice is to divide the data set into a training set and a validation set, and cross-validation techniques are frequently used to ensure the effective partitioning of data. The training phase mainly involves the selection of optimization methods such as gradient descent to update the model parameters, so that the model can correctly identify the feature patterns in the data. In this process, we first define a loss function to quantify the difference between the model's predicted value and the actual value. For example, when dealing with classification problems, the cross-entropy loss function is often used; Whereas in regression problems, the mean square error loss function or the absolute error loss function is more common. Then, the loss gradient of each parameter is calculated by backpropagation algorithm, and optimization strategies such as gradient descent are applied to adjust the parameter values. Through repeated iterations, these algorithms aim to minimize the loss function as a core goal for training the model. In addition, the details of model training, such as setting the appropriate learning rate, taking the appropriate regularization measures, and choosing the appropriate weight initialization strategy, are also key steps that cannot be ignored. This paper conforms to the research and development trend of the industry, focusing on how to use the LSTM-GCN model to capture temporal and spatial characteristics, process multi-modal data, process nonlinear data and dynamic weight allocation, etc., providing new ideas for Internet finance companies in data processing. This model combines the advantages of LSTM and GCN, and has prominent advantages such as timeliness, strong representation learning ability, associability, and interpretability. It can process both time series data and graphic data, and can analyze and mine the correlation and regularity in large-scale time series data, so as to process financial data more accurately. It greatly reduces the risk of investment decision.

Mobile measurement background quantification methods for determining local traffic emissions and background source contributions to ammonia

On-road ammonia (NH3) could be critical for public health in high-population areas due to its contribution to fine particulate matter (PM2.5) formation. Therefore, quantifying the impact of vehicular NH3 emissions on pollution levels is vital from both policy and exposure perspectives. In this study, we conducted night- and daytime mobile measurements during a pollution episode in March 2021, explored common data processing strategies for background concentration estimation and calculated the vehicle contribution in urban Beijing. Percentile and time–frequency methods were evaluated and applied to separate local vehicle and background concentrations. The spatial distributions differed between the night- and daytime periods. However, vehicular emissions were an important source of NH3 both at night and during the day. In the pollution accumulation process, the vehicle contribution increased to 27.4%. Even though local traffic emissions notably impacted NH3 levels, the background concentration, including regional transport, was the largest contributor to NH3 at the city scale. This study has implications for local and regional NH3 control strategies aimed at reducing pollution in densely populated urban areas.

Optimized Stokes imaging for highly resolved optical speckle fields, Part II: optimal acquisition and estimation strategies

In this second paper of a three-paper series focusing on Stokes polarimetry of optical speckle fields resolved at the individual speckle grain scale, a theoretical study based on numerical simulations is presented in order to establish the optimum sensing, estimation, and processing strategies that guarantee the best precision, accuracy, and robustness for Stokes polarimetry in this specific context. In particular, it is demonstrated that the so-called state of polarization analysis by full projection on the Poincaré space (SOPAFP) approach can be optimized in order to ensure best estimation performance. These numerical simulations also make it possible to establish that the SOPAFP approach provides better results in terms of robustness to residual experimental imperfections of the setup when compared to classical Stokes polarimetry approaches.

Natural Language Understanding for Navigation of Service Robots in Low-Resource Domains and Languages: Scenarios in Spanish and Nahuatl

Human–robot interaction is becoming increasingly common to perform useful tasks in everyday life. From the human–machine communication perspective, achieving effective interaction in natural language is one challenge. To address it, natural language processing strategies have recently been used, commonly following a supervised machine learning framework. In this context, most approaches rely on the use of linguistic resources (e.g., taggers or embeddings), including training corpora. Unfortunately, such resources are scarce for some languages in specific domains, increasing the complexity of solution approaches. Motivated by these challenges, this paper explores deep learning methods for understanding natural language commands emitted to service robots that guide their movements in low-resource scenarios, defined by the use of Spanish and Nahuatl languages, for which linguistic resources are scarcely unavailable for this specific task. Particularly, we applied natural language understanding (NLU) techniques using deep neural networks and transformers-based models. As part of the research methodology, we introduced a labeled dataset of movement commands in the mentioned languages. The results show that models based on transformers work well to recognize commands (intent classification task) and their parameters (e.g., quantities and movement units) in Spanish, achieving a performance of 98.70% (accuracy) and 96.96% (F1) for the intent classification and slot-filling tasks, respectively). In Nahuatl, the best performance obtained was 93.5% (accuracy) and 88.57% (F1) in these tasks, respectively. In general, this study shows that robot movements can be guided in natural language through machine learning models using neural models and cross-lingual transfer strategies, even in low-resource scenarios.

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes.

Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing the number of surface electromyography (sEMG) electrodes or adding other sensing mechanisms. While many prescribed myoelectric hands still adopt two-electrode sEMG systems, detailed studies on signal processing and classification performance are still lacking. In this study, nine able-bodied participants were recruited to perform six typical hand actions, from which sEMG signals from two electrodes were acquired using a Delsys Trigno Research+ acquisition system. Signal processing and machine learning algorithms, specifically, linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines (SVM), were used to study classification accuracies. Overall classification accuracy of 93 ± 2%, action-specific accuracy of 97 ± 2%, and F1-score of 87 ± 7% were achieved, which are comparable with those reported from multi-electrode systems. The highest accuracies were achieved using SVM algorithm compared to LDA and KNN algorithms. A logarithmic relationship between classification accuracy and number of features was revealed, which plateaued at five features. These comprehensive findings may potentially contribute to signal processing and machine learning strategies for commonly prescribed myoelectric hand systems with two sEMG electrodes to further improve functionality.

Source apportionment of soil PTE in a northern industrial county using PMF model: Partitioning strategies and uncertainty analysis

Positive matrix factorization (PMF) has commonly been applied for source apportionment of potentially toxic elements (PTE) in agricultural soil, however, spatial heterogeneity of PTE significantly undermines the accuracy and reliability of PMF results. In this study, a representative industrial-agricultural hub in North China (Xuanhua district, Zhangjiakou City) was selected as the research subject, multiple partition processing (PP) strategies and uncertainty analyses were integrated to advance the PMF modeling and associated algorithm mechanisms were comparatively discussed. Specifically, we adopted three methods to split the research area into several subzones according to industrial density (PP-1), population density (PP-2), and the ecological risk index (PP-3) respectively, to rectify the spatial bias phenomenon of PTE concentrations and to achieve a more interpretable result. Our results indicated that the obvious enrichment of Cd, Pb, and Zn was found in the agricultural soil, with Hg and Cd accounted for 83.49% of the overall potential ecological risk. Combining proper PP with PMF can significantly improve the modelling accuracy. Uncertainty analysis showed that interval ratios of tracer species (Cd, Pb, Hg, and Zn) calculated by PP-3 were consistently lower than that of PP-1 and PP-2, indicating that PP-3 coupled PMF can afford the optimal modeling results. It suggested that natural sources, fertilizers and pesticides, atmosphere deposition, mining, and smelting were recognized as the major contributor for the soil PTE contamination. The contribution of anthropogenic activities, specifically fertilizers and pesticides, and atmosphere deposition, increased by 1.64% and 5.91% compared to PMF results. These findings demonstrate that integration of proper partitioning processing into PMF can effectively improve the accuracy of the model even at the case of soil PTE contamination with high heterogeneity, offering support to subsequently implement directional control strategies.

Evaluating the influence of various friction stir processing strategies on surface integrity of hybrid nanocomposite Al6061

To fundamentally investigate the influence of different friction stir processing (FSP) strategies, namely raster, spiral, and parallel in various passes on the surface integrity of hybrid aluminum nanocomposites reinforced by titanium oxide (TiO2), silicon carbide (SiC), and zirconium oxide (ZrO2) nanoparticles, various examinations were conducted. The surface integrity, comprising microstructural characterization, elemental composition, surface topography, roughness, waviness, and microhardness was studied by different analyses, including scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), optical microscopy (OM), atomic force microscopy (AFM), and Vickers microhardness machine in different zones. Results demonstrated that surface integrity and quality are dependent on the type of FSP strategy. SEM images revealed that a homogeneous distribution of the nanoparticles in the matrix is obtainable by the parallel and raster FSP strategies. Roughness and waviness measurements illustrated that the surface topography of the hybrid nanocomposite was symmetrical and improved by raster strategy and TiO2 + ZrO2 nanoparticle reinforcement. Furthermore, the two-pass FSP improved the arithmetic average surface value (Ra) such that the Ra of two passes decreased by 32.5% compared to a single one. The mean microhardness in the spiral, raster, and parallel pass strategies increased by ~ 45%, 37%, and 31%, respectively.

Evaluating the recycling potential and economic benefits of end-of-life power batteries in China based on different scenarios

Recycling of end-of-life (EOL) power batteries has emerged as a vital strategy to mitigate resource depletion and minimize environmental pollution. This study utilizes the Stanford model and scenario analysis to project the EOL and reuse quantities of electric vehicle (EV) batteries in China from 2023 to 2035. Additionally, this study examines the potential for recycling and the economic advantages associated with echelon utilization and recovery utilization of lithium-ion batteries (LIBs). Results show that: (1) In 2035, China's EOL LIBs volume will potentially reach 11.77 million tons. And the battery reused volume will be up to 4.17 million tons, which can meet 55 % of the demand. (2) In 2035, up to 1468 Gwh of batteries could be allocated for echelon utilization, yielding economic benefits of $40.5 billion in load shifting and peak shaving, $25.1 billion in China Tower savings, and 4.57 million new batteries saved in low-speed vehicles. (3) Among the critical materials recycled in batteries, cobalt(Co) holds the highest economic value at $8.2 billion, followed by lithium(Li) at $4.3 billion. (4) The amount of nickel (Ni), Co, manganese(Mn), and graphite recovered in 2035 could satisfy battery manufacturing requirements. This study offers guidance to government and enterprises in formulating a circular economy strategy for battery recycling and processing.

Linking Pathways from Perceived Absolute and Comparative Risk to Colorectal Cancer Screening Intention: Towards an Extended Cognitive Mediation Model

ABSTRACT Digital media platforms are crucial in providing health information to the public, including learning about cancer prevention strategies. Although the Cognitive Mediation Model (CMM) is a sound theoretical framework for explaining the underpinning process of health knowledge acquisition, one important remaining gap is how individuals are motivated to process health information through different discussion-generated elaboration strategies. To better understand the knowledge acquisition process, this paper extends the CMM in the context of colorectal cancer prevention. We introduced perceived absolute risk and perceived comparative risk as two distinct risk assessments as the antecedents facilitating individuals’ attention to cancer information on digital media, two types of interpersonal communication as discussion-generated elaboration strategies, and two dimensions of knowledge assessment in our extended CMM. Based on a nationally representative online survey among 965 Chinese public, we found that two types of risk perception were positively associated with paying attention to colorectal cancer information on digital media, which in turn facilitated both types of interpersonal communication. However, information processing strategies differed in leading to cancer knowledge and screening intention. Only subjective knowledge was positively associated with colorectal screening intention. Our findings offered implications for the literature and health promotion practices.

Femtosecond laser fabrication of 3D vertically aligned micro-pore network on thick-film Li4Ti5O12 electrode for high-performance lithium storage

The development of energy storage devices with high energy density relies heavily on thick film electrodes, but it is challenging due to the limited ion transport kinetics inherent in thick electrodes. Here, we report on the preparation of a directional vertical array of micro-porous transport networks on LTO electrodes using a femtosecond laser processing strategy, enabling directional ion rapid transport and achieving good electrochemical performance in thick film electrodes. Various three-dimensional (3D) vertically aligned micro-pore networks are innovatively designed, and the structure, kinetics characteristics, and electrochemical performance of the prepared ion transport channels are analyzed and discussed by multiple characterization and testing methods. Furthermore, the rational mechanisms of electrode performance improvement are studied experimentally and simulated from two aspects of structural mechanics and transmission kinetics. The ion diffusion coefficient, rate performance at 60 C, and electrode interface area of the laser-optimized 60-15% micro-porous transport network electrodes increase by 25.2 times, 2.2 times, and 2.15 times, respectively than those of untreated electrodes. Therefore, the preparation of 3D micro-porous transport networks by femtosecond laser on ultra-thick electrodes is a feasible way to develop high-energy batteries. In addition, the unique micro-porous transport network structure can be widely extended to design and explore other high-performance energy materials.

Pembuatan Alat Pengolahan Limbah Tekstil di Kecamatan IV Nagari Bayang Utara Painan, Pesisir Selatan

Batik Jembatan Akar Small and Medium Industry (IKM) endeavors to produce Root Bridge batik fabric, which is a defining feature of the IV Nagari Bayang Utara sub-district. In its production process, IKM Batik Jembatan Akar still relies on rudimentary and traditional equipment and lacks the means to handle textile waste, leading to its disposal in nearby ditches. While the current waste output may not be significant, its continuation poses a threat to the local environment. Insufficient awareness of the environmental impact of textile waste can result in environmental pollution issues and disrupt community harmony around IKM Jembatan Akar. Additionally, IKM Batik Jembatan Akar struggles to stimulate consumer interest in traditional batik amidst the abundance of low-priced printed alternatives. The limited demand for production means that this SME has yet to invest in technology and waste processing equipment for its batik cloth production. Politeknik ATI Padang Community Service initiative seeks to educate all IKM Batik Jembatan Akar employees on the importance of waste processing and online marketing strategies, while providing training on donated textile waste processing equipment to enhance sales value and mitigate environmental harm. The approaches employed in this community service endeavor include (1) comprehensive activity planning, encompassing location surveys, production assessments, and inventory checks; (2) the fabrication and testing of waste processing machinery; (3) educational workshops covering waste management in batik production and its environmental implications, alongside practical training in equipment usage; (4) evaluation of activity outcomes. The outcomes of this Community Service initiative include the provision of textile waste processing equipment and a heightened awareness of environmental conservation, facilitating the integration of waste processing equipment into batik production at IKM Batik Jembatan Akar.

Exploring L2 learners’ processing of unknown words during subtitled viewing through self-reports

Abstract Studies have shown the benefits of subtitled viewing for incidental vocabulary learning, but the effects of different subtitling types varied across studies. The effectiveness of different types of subtitled viewing could be related to how unknown vocabulary is processed during viewing. However, no studies have investigated L2 learners’ processing of unknown words in viewing beyond exploring learners’ attention allocation. The present research followed a qualitative approach to explore L2 learners’ processing of unknown words during subtitled viewing under three conditions (i.e., captions, L1 subtitles, and bilingual subtitles) by tapping into learners’ reported awareness of the unknown words and the vocabulary processing strategies used to engage with unknown words. According to stimulated recall data (elicited by eye-tracking data) from 45 intermediate-to-advanced-level Chinese learners of English, captions led to increased awareness of the unknown words. Moreover, the types of strategies learners used to cope with unknown vocabulary were determined by subtitling type.

Eye blinks as a visual processing stage

Humans blink their eyes frequently during normal viewing, more often than it seems necessary for keeping the cornea well lubricated. Since the closure of the eyelid disrupts the image on the retina, eye blinks are commonly assumed to be detrimental to visual processing. However, blinks also provide luminance transients rich in spatial information to neural pathways highly sensitive to temporal changes. Here, we report that the luminance modulations from blinks enhance visual sensitivity. By coupling high-resolution eye tracking in human observers with modeling of blink transients and spectral analysis of visual input signals, we show that blinking increases the power of retinal stimulation and that this effect significantly enhances visibility despite the time lost in exposure to the external scene. We further show that, as predicted from the spectral content of input signals, this enhancement is selective for stimuli at low spatial frequencies and occurs irrespective of whether the luminance transients are actively generated or passively experienced. These findings indicate that, like eye movements, blinking acts as a computational component of a visual processing strategy that uses motor behavior to reformat spatial information into the temporal domain.

Robotic Disassembly Platform for Disassembly of a Plug-In Hybrid Electric Vehicle Battery: A Case Study

Efficient processing of end-of-life lithium-ion batteries in electric vehicles is an important and pressing challenge in a circular economy. Regardless of whether the processing strategy is recycling, repurposing, or remanufacturing, the first processing step will usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. This work was conducted as a case study to demonstrate the concept of the autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported, and future research directions are suggested.

Auditory and Visual Gratings Elicit Distinct Gamma Responses.

Sensory stimulation is often accompanied by fluctuations at high frequencies (>30 Hz) in brain signals. These could be "narrowband" oscillations in the gamma band (30-70 Hz) or nonoscillatory "broadband" high-gamma (70-150 Hz) activity. Narrowband gamma oscillations, which are induced by presenting some visual stimuli such as gratings and have been shown to weaken with healthy aging and the onset of Alzheimer's disease, hold promise as potential biomarkers. However, since delivering visual stimuli is cumbersome as it requires head stabilization for eye tracking, an equivalent auditory paradigm could be useful. Although simple auditory stimuli have been shown to produce high-gamma activity, whether specific auditory stimuli can also produce narrowband gamma oscillations is unknown. We tested whether auditory ripple stimuli, which are considered an analog to visual gratings, could elicit narrowband oscillations in auditory areas. We recorded 64-channel electroencephalogram from male and female (18 each) subjects while they either fixated on the monitor while passively viewing static visual gratings or listened to stationary and moving ripples, played using loudspeakers, with their eyes open or closed. We found that while visual gratings induced narrowband gamma oscillations with suppression in the alpha band (8-12 Hz), auditory ripples did not produce narrowband gamma but instead elicited very strong broadband high-gamma response and suppression in the beta band (14-26 Hz). Even though we used equivalent stimuli in both modalities, our findings indicate that the underlying neuronal circuitry may not share ubiquitous strategies for stimulus processing.

Cyclic Ion Mobility for Hydrogen/Deuterium Exchange-Mass Spectrometry Applications.

Hydrogen/deuterium exchange-mass spectrometry (HDX-MS) has emerged as a powerful tool to probe protein dynamics. As a bottom-up technique, HDX-MS provides information at peptide-level resolution, allowing structural localization of dynamic changes. Consequently, the HDX-MS data quality is largely determined by the number of peptides that are identified and monitored after deuteration. Integration of ion mobility (IM) into HDX-MS workflows has been shown to increase the data quality by providing an orthogonal mode of peptide ion separation in the gas phase. This is of critical importance for challenging targets such as integral membrane proteins (IMPs), which often suffer from low sequence coverage or redundancy in HDX-MS analyses. The increasing complexity of samples being investigated by HDX-MS, such as membrane mimetic reconstituted and in vivo IMPs, has generated need for instrumentation with greater resolving power. Recently, Giles et al. developed cyclic ion mobility (cIM), an IM device with racetrack geometry that enables scalable, multipass IM separations. Using one-pass and multipass cIM routines, we use the recently commercialized SELECT SERIES Cyclic IM spectrometer for HDX-MS analyses of four detergent solubilized IMP samples and report its enhanced performance. Furthermore, we develop a novel processing strategy capable of better handling multipass cIM data. Interestingly, use of one-pass and multipass cIM routines produced unique peptide populations, with their combined peptide output being 31 to 222% higher than previous generation SYNAPT G2-Si instrumentation. Thus, we propose a novel HDX-MS workflow with integrated cIM that has the potential to enable the analysis of more complex systems with greater accuracy and speed.

Mitigation of the Brighter-fatter Effect in the LSST Camera

Thick, fully depleted charge-coupled devices are known to exhibit nonlinear behavior at high signal levels due to the dynamic behavior of charges collecting in the potential wells of pixels, called the brighter-fatter effect (BFE). The effect results in distorted images of bright calibration stars, creating a flux-dependent point-spread function that if left unmitigated, could make up a large fraction of the error budget in Stage IV weak-lensing (WL) surveys such as the Legacy Survey of Space and Time (LSST). In this paper, we analyze image measurements of flat fields and artificial stars taken at different illumination levels with the LSST Camera (LSSTCam) at SLAC National Accelerator Laboratory in order to quantify this effect in the LSSTCam before and after a previously introduced correction technique. We observe that the BFE evolves anisotropically as a function of flux due to higher-order BFEs, which violates the fundamental assumption of this correction method. We then introduce a new method based on a physically motivated model to account for these higher-order terms in the correction, and then we test the modified correction on both data sets. We find that the new method corrects the effect in flat fields better than it corrects the effect in artificial stars, which we suggest is the result of sub-pixel physics not included in this correction model. We use these results to define a new metric for the full-well capacity of our sensors and advise image processing strategies to further limit the impact of the effect on LSST WL science pathways.

Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applications

PurposeThis study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.Design/methodology/approachThis paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.FindingsTo effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.Originality/valueThis study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

Innovative Approaches for Dynamic System Monitoring: Signal Processing and Parameter Estimation Strategies

Dynamic system monitoring is essential for ensuring the optimal performance and reliability of various systems across multiple domains. This Abstract introduces innovative approaches focusing on signal processing and parameter estimation strategies for dynamic system monitoring. Signal processing techniques such as wavelet transform and adaptive filtering are utilized for noise reduction and feature extraction from sensor data. Additionally, parameter estimation strategies including Kalman filtering and Bayesian inference aid in accurately estimating system parameters and states in real-time. These advanced methods, integrating machine learning and statistical inference, promise enhanced monitoring capabilities, facilitating proactive maintenance and fault detection in complex dynamic systems. Through case studies and simulation results, the effectiveness and versatility of these approaches in addressing real-world challenges are demonstrated, illustrating their potential for advancing the field of dynamic system monitoring.