Current Processing Methods Research Articles

Distinguishing evaluation of plastic deformation and fatigue damage using pulsed eddy current testing

The vessels and pipes of nuclear power structures are subjected to complex forces. The structures suffer sudden loads that lead to plastic deformation and cyclic loads that result in fatigue damage. In practice, two types of microscopic damage may occur simultaneously. The effect of plastic deformation on material fatigue properties remains unclear, and there is a lack of effective methods to distinguish plastic deformation and fatigue damage. To address these issues, the fatigue life curve of 304 stainless steel is experimentally measured, and the influence of fatigue life due to the presence of plastic deformation under a certain load is investigated. It's identified that preset plastic deformation below 15% will increase the fatigue life of the structure. Additionally, a pulsed eddy current signal processing and reconstruction method based on the empirical modal decomposition (EBD) method is proposed. Finally, the pulsed eddy current signal characteristics of plastic deformation and fatigue damage are analyzed, and a distinguishing evaluation method of fatigue damage with preset plastic deformation is established.

Let's put a smile on that face-A positive facial expression improves aesthetics of portrait photographs.

In today's age of social media and smartphones, portraits-such as selfies or pictures of friends and family-are very frequently produced, shared and viewed images. Despite their prevalence, the psychological factors that characterize a 'good' photo-one that people will generally like, keep, and think is especially aesthetically pleasing-are not well understood. Here, we studied how a subtle change in facial expression (smiling) in portraits determines their aesthetic image value (beyond a more positive appearance of the depicted person). We used AI-based image processing tools in a broad set of portrait photographs and generated neutral and slightly smiling versions of the same pictures. Consistent across two experiments, portraits with a subtle smile increased both spontaneous aesthetic preferences in a swiping task as well as improving more explicit aesthetic ratings after prolonged viewing. Participants distinguished between aspects associated with image beauty and the depicted person's attractiveness, resulting in specific interactions between variables related to participant traits, image content, and task. Our study confirms that a subtle-and in this case fully artificial-smile reliably increases the aesthetic quality of portraits, illustrating how current image processing methods can target psychologically important variables and thereby increase the aesthetic value of photographs.

Circularity in Materials: A Review on Polymer Composites Made from Agriculture and Textile Waste

Agriculture and textiles have the highest production yields among all sectors to meet mankind’s basic needs, i.e., feeding and clothing; however, they are top contributors to environmental pollution and global waste generation. Their wastes and byproducts are precious organic materials, they have great potential as raw materials for the manufacturing of valuable products. This review sheds light on various textile and agricultural wastes, waste management issues, and their existing utilization. Current waste processing methods are mostly based on waste-to-energy routes or material reclamation; however, both methods are hazardous for the environment and are inefficient. During the past decade, many researchers have utilized agriculture and textile wastes in the fabrication of composites. Textile and agricultural wastes and byproducts can be efficiently used for composite fabrication and can be suitable alternatives to existing raw materials. Using textiles and agricultural wastes for composite manufacturing can not only address waste management issues and replace non-eco-friendly materials in the composite industry but also significantly improve composite properties.

Optimization of the Simple One-Step Stool Processing Method to Diagnose Tuberculosis: Evaluation of Robustness and Stool Transport Conditions for Global Implementation.

Stool is recommended as an alternative specimen for the diagnosis of tuberculosis (TB) in young children, as they cannot easily produce sputum. The Simple One-Step (SOS) stool processing method is a new and simple stool processing method for the detection of Mycobacterium tuberculosis (MTB) using Xpert MTB/RIF Ultra (Xpert-Ultra). We determined the robustness of the SOS stool processing method and stool specimen transport conditions in participants with confirmed TB. We processed stool using the standard protocol after simulated "transport," varying time, and temperature, and experimented with slightly modified processing steps. We included 2,963 Xpert-Ultra test results from 132 stool specimens of 47 TB participants, including 11 children aged <10 years. We compared Xpert-Ultra processing errors and MTB positivity rates between standard and modified procedures. Minor deviations from the standard SOS protocol did not significantly impact the Xpert-Ultra test outcomes. The rate of Xpert-Ultra processing errors significantly increased with noncold-chain transport, exposure of stool to sample reagent at room temperature or beyond 12 h, and adding >0.8 g of stool. We found that almost all steps in the current SOS stool processing method provide optimal Xpert-Ultra results but recommend an adjustment to use a wider range of stool amounts (0.3 to 0.8 g) than advised previously (0.8 g). With this adaptation, stool-based diagnosis of TB using the SOS stool processing method can be scaled-up. IMPORTANCE The manuscript will support the global implementation and scale-up of the SOS stool method in routine settings. It also provides important insights on the optimal stool transport conditions and robustness of the SOS method, which can be used for bacteriological diagnosis of TB in children at the lowest levels of the healthcare system, avoiding lengthy healthcare-seeking pathways and additional costs.

A topic-aware classifier based on a hybrid quantum-classical model

In the era of Large Language Models, there is still potential for improvement in current Natural Language Processing (NLP) methods in terms of verifiability and consistency. NLP classical approaches are computationally expensive due to their high-power consumption, computing power, and storage requirements. Another computationally efficient approach to NLP is categorical quantum mechanics, which combines grammatical structure and individual word meaning to deduce the sentence meaning. As both quantum theory and natural language use vector space to describe states which are more efficient on quantum hardware, QNLP models can achieve up to quadratic speedup over classical direct calculation methods. In recent years, there is significant progress in utilizing quantum features such as superposition and entanglement to represent linguistic meaning on quantum hardware. Earlier research work has already demonstrated QNLP’s potential quantum advantage in terms of speeding up search, enhancing classification tasks’ accuracy and providing an exponentially large quantum state space in which complex linguistic structures can be efficiently embedded. In this work, a QNLP model is used to determine if two sentences are related to the same topic or not. By comparing our QNLP model to a classical tensor network-based one, our model improved training accuracy by up to 45% and validation accuracy by 35%, respectively. The QNLP model convergence is also studied when varying: first, the problem size, second, parametrized quantum circuits used for model’s training, and last, the backend quantum simulator noise model. The experimental results show that strongly entangled ansatz designs result in fastest model convergence.

A feasible process for recycling valuable metals from LiNi0.5Co0.2Mn0.3O2 cathode materials of spent Li-ion batteries

ABSTRACT The recycling of cathode materials for spent NCM has always been a major concern for the energy industry. However, among the current processing methods, the general leaching efficiency of Li is between 85% and 93%, with much room for improvement. The recovery of Ni, Co and Mn requires a high cost of secondary purification. In this study, to recycle the NCM cathode material, a route of sulphated reduction roasting – selective Li water leaching – efficiency acid leaching of Ni, Co, Mn – extraction separation – crystallisation was adopted. The results showed that after roasting (a temperature of 800 °C, a reaction time of 90 min, a carbon content of 26%, and a sulphuric acid addition of nH2SO4:nLi = 0.85), Li water leaching efficiency was 98.6%, followed by acid leaching of Ni, Co and Mn at around 99%. Mn, Co were extracted with Di-(2-ethylhexyl) phosphoric acid and 2-Ethylhexyl phosphonic acid mono-2-ethylhexyl ester respectively to obtain Ni, Co, Mn solutions, which eventually were crystallized for manganese sulphate, cobalt sulphate, lithium carbonate and nickel sulphate products, with high purity of 99.40%, 98.95%, 99.10%, and 99.95%. The results of this study improved the leaching efficiency of Li and were closely linked to the actual industrial preparation of Ni, Co and Mn sulphates, providing a feasible and promising basis for spent NCM cathode materials industrial recovery.

Data Processing Method of Photoelectric Effect Experiment Based on Grey Prediction

In the Planck teaching experiment of photoelectric effect measurement, Due to the limitation of experimental equipment, only a few wavelengths of data can be measured, and the current data processing methods used in experimental teaching are not suitable for the situation of small data capacity, which leads to large errors in experimental results. In this paper, a data processing method of Planck constant based on grey prediction model is proposed, which can effectively improve the measurement accuracy of experiments.

Research on Processing the Feature Model of Converter Station Based on Machine Learning

The current traditional converter station feature model processing method uses switching functions to model converter station equipment, which leads to poor processing results because it ignores the dynamic coupling characteristics between the second harmonics inside the converter station. In this regard, a machine-learning-based switching station feature model processing method is proposed. By combining different terminals to determine their port parameters, constructing the characteristic impedance model of the converter station, using time-domain recursive convolution to calculate the voltage levels at each key point of the AC system, and finally calculating the magnitude as well as the phase angle constants, the time-varying model of the multi-harmonic converter can be fixed. In the experiments, the computational accuracy of the proposed method is verified. The analysis of the experimental results shows that the proposed method has a high component amplitude and excellent computational performance when the characteristic model of the converter station is processed.

A method of improving inversion accuracy for ultra-low signal-to-noise ratio echo train of nuclear magnetic resonance logging

In tight reservoirs, the signal-to-noise ratio (SNR) of the echo train of nuclear magnetic resonance (NMR) logging is very low, which leads to inaccurate inversion results using the current echo train processing method. Therefore, a new processing method for improving inversion accuracy was developed, which includes four steps: echo-preferring, filtering, signal superimposition, and inversion. First, the echo-preferring method, which chooses high-quality echo trains, was studied in detail by analyzing low SNR NMR measurements. The SVD filtering method was then improved, and the echo train decomposed and filtered. Next, the echo trains of the adjacent depth points were stacked to further improve the SNR. Finally, the Butler–Reeds–Dawson (BRD) method was used to perform the inversion. Through numerical simulation experiments, the effects of echo optimization and high-efficiency filtering on inversion accuracy were studied, and the reliability of the method was verified. A case study of the gas reservoir in the Daqing oilfield shows that the porosity obtained by the new method agrees favorably with the core porosity, and the T2 distribution is also more reasonable. The new method improves inversion accuracy and is equivalent to improving the original echo train of NMR logging, which lays the foundation for the calculation of movable and bound fluid volumes in tight reservoirs.

Effect of post-harvest processing methods on the microbial safety of edible seaweed

The American seaweed industry is growing, primarily into the edible sector, and more seaweed products are available for human consumption. It is necessary to evaluate the safety of industry’s current post-harvest storage and processing methods to ensure the risk of foodborne pathogens on edible seaweed remains low. We evaluated the pathogen load of edible kelp post-harvest under three different storage temperatures (4˚C, 10˚C, 20˚C) and two different drying methods (air- and freeze-drying). The focal pathogens for this research included: Listeria monocytogenes, Salmonella enterica, Staphylococcus aureus, pathogenic Escherichia coli, Vibrio vulnificus and Vibrio parahaemolyticus. We tested all six pathogens under each treatment condition on both sugar kelp (Saccharina latissima) and rockweed (Ascophyllum nodosum) as these are the most commonly farmed and wild-harvested species in Maine, respectively. We inoculated a known concentration of pathogen onto freshly harvested kelp, treated it under a storage temperature or drying method, and sampled it over time to determine the impact of treatment on pathogen load. Our results showed that storage at 20˚C can lead to replication while storage at 4˚C and 10˚C halted the replication of focal pathogens. Both air-drying and freeze-drying produced significant log scale reductions in surface pathogen load for all focal pathogens. Additionally, air-drying reduced pathogen load more than freeze-drying for a majority of pathogens and storing dried kelp for 6-weeks further reduced pathogen load across all cases. These results are promising for industry as they corroborate historical evidence that current post-harvest storage and processing conditions are producing products safe for human consumption.

Miper-MVS: Multi-scale iterative probability estimation with refinement for efficient multi-view stereo

Multi-view stereo reconstruction aims to construct 3D scenes from multiple 2D images. In recent years, learning-based multi-view stereo methods have achieved significant results in depth estimation for multi-view stereo reconstruction. However, the current popular multi-stage processing method cannot solve the low-efficiency problem satisfactorily owing to the use of 3D convolution and still involves significant amounts of calculation. Therefore, to further balance the efficiency and generalization performance, this study proposed a multi-scale iterative probability estimation with refinement, which is a highly efficient method for multi-view stereo reconstruction. It comprises three main modules: 1) a high-precision probability estimator, dilated-LSTM that encodes the pixel probability distribution of depth in the hidden state, 2) an efficient interactive multi-scale update module that fully integrates multi-scale information and improves parallelism by interacting information between adjacent scales, and 3) a Pi-error Refinement module that converts the depth error between views into a grayscale error map and refines the edges of objects in the depth map. Simultaneously, we introduced a large amount of high-frequency information to ensure the accuracy of the refined edges. Among the most efficient methods (e.g., runtime and memory), the proposed method achieved the best generalization on the Tanks & Temples benchmarks. Additionally, the performance of the Miper-MVS was highly competitive in DTU benchmark. Our code is available at https://github.com/zhz120/Miper-MVS.

Toxic metals and aflatoxins occurrence in smoked-dried fish and their health risks assessment

This study investigated the microbiological and toxic heavy metals contamination in smoked-dried fish, and evaluates their health risk implications. Fifteen widely consumed fish species were sampled randomly, and their aflatoxins, mercury “Hg,” cadmium “Cd” and lead “Pb” concentrations were evaluated though standard guidelines. The results revealed that fish flesh Hg level ranged from 0.085–0.258 mg/kg, Cd concentration ranged from 0.092–0.204 mg/kg, and Pb level varied from 0.173–0.329 mg/kg; the mean toxic metals level in the freshwater fish tissue was lower, when compared to the levels in the saltwater fish (seafood). Regarding toxic metal toxicity, the average Hg, Cd and Pb estimated daily intake values for children and adult were 0.1200 and 1.63 × 10−5 mg/kg bw/day, 2.13 × 10−4 and 7.57 × 10−5 mg/kg bw/day, and 2.48 × 10−4 and 1.24 × 10−4 mg/kg bw/day respectively. Likewise, children targeted hazard quotients (THQ) values were Hg (0.585), Cd (0.151) and Pb (0.071), while adult THQ values were Hg (0.293), Cd (0.076) and Pb (0.035). Children and adult hazard index (HI) values were 0.949 and 0.404 respectively; while Carcinogenic Risk (CR) values indicated that Cd had higher chances of cancer risks when compared to Pb. Additionally, the aflatoxins level in the fish species ranged from 0.564–9.422 ppb, with the seafood having lower aflatoxins level when compared to the freshwater fish species. Aflatoxins estimated daily intakes (EDI) exposure was 4.81 × 10−3 and 2.41 × 10−3 μg/kg bw/day for the children and adults respectively; while the aflatoxins HI value for children was 7.300 × 10−4, and for adults was 7.293 × 10−4. Health risks assessment of heavy metal accumulation in the fish, for both children and adults portrayed insignificant adverse health effects (THQ &lt; 1), through the oral consumption of the fishes. Aflatoxins level detected in the all fishes was within acceptable limits (10 ppb) as approved by the World Health Organization; however, some of the values for freshwater fishes were above the allowable limit (2 ppb) approved by the European Union. Though, the results depicted a very low risk of toxic metals and aflatoxins poisoning, there is the need for proper evaluation of current fish processing and storage methods to prevent human exposure to toxic metals and aflatoxins introduced through smoked-dried fish consumption.

High-order Synchrosqueezing Superlets Transform and its application to mechanical fault diagnosis

Under the condition of variable speed, the vibration signal of mechanical transmission components present the typical non-stationary characteristics. Nevertheless, some problems, such as insufficient time–frequency energy concentration and frequent noise disturbance, exist in the current signal processing methods during the extraction of time-varying fault characteristics. Different from the frequently-used time–frequency analysis (TFA) methods such as Continuous Wavelet Transform (CWT) and Synchrosqueezing Transform (SST), High-order Synchrosqueezing Superlets Transform (HSSLT) is proposed in this paper in order to obtain a clearer and more centralized time–frequency representation (TFR). Firstly, multiple groups of wavelets with increased bandwidth are combined into a super wavelet set, and then the Superlets Transform (SLT) is proposed. The single wavelet mother function in the traditional CWT is replaced by this superlets set, which has better wavelet diversity and analytical ability for complex multi-component signals. On this basis, in order to further improve the accuracy of instantaneous frequency (IF) estimation and the energy concentration of TFA, the superlets coefficients calculated by SLT are used for the high-order IF estimation and time–frequency energy rearrangement, thereby HSSLT is presented in this paper. It has obvious advantages of high-resolution TFR and strong noise robustness. Finally, through the rolling bearing fault diagnosis of test bench and the 850 kW offshore wind turbine, the good application potential of the proposed HSSLT in the mechanical fault diagnosis under variable speed conditions is verified.

Artifact-free fat-water separation in Dixon MRI using deep learning

Chemical-shift encoded MRI (CSE-MRI) is a widely used technique for the study of body composition and metabolic disorders, where derived fat and water signals enable the quantification of adipose tissue and muscle. The UK Biobank is acquiring whole-body Dixon MRI (a specific implementation of CSE-MRI) for over 100,000 participants. Current processing methods associated with large whole-body volumes are time intensive and prone to artifacts during fat-water separation performed by the scanner, making quantitative analysis challenging. The most common artifacts are fat-water swaps, where the labels are inverted at the voxel level. It is common for researchers to discard swapped data (generally around 10%), which is wasteful and may lead to unintended biases. Given the large number of whole-body Dixon MRI acquisitions in the UK Biobank, thousands of swaps are expected to be present in the fat and water volumes from image reconstruction performed on the scanner. If they go undetected, errors will propagate into processes such as organ segmentation, and dilute the results in population-based analyses. There is a clear need for a robust method to accurately separate fat and water volumes in big data collections like the UK Biobank. We formulate fat-water separation as a style transfer problem, where swap-free fat and water volumes are predicted from the acquired Dixon MRI data using a conditional generative adversarial network, and introduce a new loss function for the generator model. Our method is able to predict highly accurate fat and water volumes free from artifacts in the UK Biobank. We show that our model separates fat and water volumes using either single input (in-phase only) or dual input (in-phase and opposed-phase) data, with the latter producing superior results. Our proposed method enables faster and more accurate downstream analysis of body composition from Dixon MRI in population studies by eliminating the need for visual inspection or discarding data due to fat-water swaps.

3-D Forward Modeling of Transient EM Field in Rough Media Using Implicit Time-Domain Finite-Element Method

In a heterogeneous medium (usually called a rough medium) with fractured formations, the propagation of an electromagnetic (EM) field is a type of subdiffusion. Current mainstream geophysical EM data processing methods cannot be applied to data acquired on heterogeneous Earth, as they are not governed by the classic diffusion theory. To evaluate the influence of roughness on the transient EM (TEM) signal for a complex model and contribute to data inversion, we proposed a novel three-dimensional (3-D) forward modeling scheme for TEM in rough media. First, we derived the governing equation with a fractional-order time derivative for the subdiffusion of EM waves in rough media. Then, we proposed a novel time discretization using an unequal step length for the Caputo operator, which significantly reduces the total number of time steps. Finally, an implicit time-domain finite-element method using unstructured tetrahedron discretization was adopted to solve the 3-D forward problem. Furthermore, an efficient time segmentation strategy combined with parallel RHS construction was proposed to accelerate modeling. The numerical results prove that the proposed method is accurate and efficient, and will be a powerful numerical method for analyzing TEM wave propagation and processing TEM data in areas with multiscale fractures or porosity.

3D printed continuous fiber reinforced composite lightweight structures: A review and outlook

Continuous fiber reinforced composite lightweight structures (CFRSs) are widely applied to aerospace, automobile, marine and other fields due to their high specific strength and modulus, low density and fatigue resistance. Innovations in the additive manufacturing technique of continuous fiber reinforced composites (CFRC) opened new perspectives for the integrated design and fabrication of composite lightweight structures with customized performance and acceptable cost. This paper reviewed the state-of-the-art 3D printing CFRSs and elaborated their future perspectives and potential applications. Specifically, different existing CFRSs printing techniques were reviewed, and the limitations of current processing methods were discussed in details. Then, the CFRSs including 2D and 3D types were discussed in terms of path planning, structural design and optimization methods. Further, the mechanical properties and novel applications in shape morphing and self-monitoring of CFRSs were presented. Finally, this paper concluded with an outlook on future research opportunities on 3D printing CFRSs from design, fabrication to final applications.

Overview of Research Status of DC Bias and Its Suppression in Power Transformers

In this article, the sources of DC bias and its effects on power transformers are first summarized. Secondly, the article classifies and summarizes the current DC bias calculation problems of power transformers, and puts forward some interesting viewpoints on the research logic of related calculations. The current processing methods of DC bias effect are classified and discussed, their advantages and disadvantages are compared, and the logic flow of DC bias effect processing is proposed. Finally, the current research on DC bias voltage of power transformers is summarized, and the progress and deficiencies of current research are pointed out, which has certain reference value for future research on DC bias voltage and its suppression.

Microfluidic device-based sperm preparation methods in Preimplantation Genetic Testing for Aneuploidy (PGT-A)

Sperm DNA Fragmentation is correlated with poor embryo development, implantation, euploidy and ongoing pregnancy rates. The current standard sperm processing method, density gradient selection (DGS), does not lower the DNA fragmentation Index (DFI) due to the centrifugation steps leading to creation of reactive oxygen species. It has been reported that microfluidic based sperm separation devices (MFSS) provide an alternative to centrifugation, selecting for the most motile and morphologically normal sperm with a decreased DFI and potentially improving euploidy and implantation rate in PGT-A cycles.

Integration of Train Control and Online Rescheduling for High-Speed Railways in Case of Emergencies

The high-speed train control system is essential to the safety and efficiency of train operation. With the rapid increase of high-speed railway (HSR) operating mileage and development of information technology, the disposal flow and methods in emergency response are still based on dispatchers and drivers’ experience within the “layered” architecture of current system. There is a certain gap between current processing methods and effective resolution, which may even cause the spread of delay along with the railway networks. Therefore, we propose an integration system of operation control and online rescheduling to improve the recovery ability of HSR carrying capacity. We first describe the framework, information flow, and disposal process of the current system and analyze the shortcomings in handling emergencies. Then, the basic concept, system structure, and framework of the integration system are introduced. Finally, taking temporary speed restriction caused by strong wind as an example, we also analyze the principle of why and how the integration system can promote the recovery ability of HSR carrying capacity.

A novel backflushing method for improving machining quality in electrochemical discharge drilling

Film cooling holes work in an extremely high temperature, high pressure, and alternating load. The recast layer is not allowed on the wall side. Electrochemical discharge drilling (ECDD) is a promising nontraditional method in aerospace manufacturing to obtain a high surface quality, offering high efficiency in theory. However, a shortage of working fluid near the hole exit after penetration is inevitable, leading to poor stability and surface quality, and the current processing methods make it difficult to drill small holes with high quality. Hence, a novel backflushing ECDD (BF-ECDD) method has been proposed. Backflushing is a certain conductivity liquid. It flows continuously along the backside of the workpiece during the entire process. When the hole is penetrated, the backflushing liquid flows from the exit to the entrance along the inter-electrode gap, improving the flow field and effectively solving the problem of liquid solution shortage. It helps to strengthen the electrochemical dissolution near the hole exit and improve the hole quality. Here, the method is shown to be feasible, and a conductivity matching relationship between the working and backflushing liquids is studied with simulation and comparison experiments on a nickel-based single crystal material. The results show that BF-ECDD can reduce the occurrence of short circuits by 68 % and enhance the stability of the penetration stage. The flow rate in the machining gap is more than doubled, and the taper angle is eliminated. Finally, it meets the extreme manufacturing needs of the entire hole without recast layer with high efficiency.