Increase In Processing Time Research Articles

Leveraging Distributed Processing for Enhanced Data Cleaning and Mining: Principles, Techniques, and Case Studies

Abstract. In the era of big data, the volume, variety, and velocity of data generated pose significant challenges for data cleaning and mining processes. Traditional approaches to data cleaning and mining often struggle to handle large datasets efficiently, leading to increased processing time and reduced accuracy. Leveraging distributed processing techniques can significantly enhance the efficiency and effectiveness of these processes. This paper explores the principles behind distributed processing, particularly in the context of data cleaning and mining. It delves into various techniques, including MapReduce, distributed databases, and parallel processing, highlighting their advantages in managing large datasets. Furthermore, the paper presents case studies that illustrate the application of distributed processing in real-world scenarios, demonstrating how these techniques can be employed to achieve cleaner, more accurate data and more insightful mining results. Through these case studies, the paper also discusses the challenges and considerations associated with implementing distributed processing systems, such as data distribution, fault tolerance, and the need for specialized hardware and software. The findings suggest that while distributed processing offers substantial benefits, careful planning and execution are required to fully realize its potential in data cleaning and mining.

INFLUENCE OF PLASMA ELECTROLYTIC OXIDATIONPARAMETERS ON TRIBOLOGICAL PROPERTIES OF LAYERSPRODUCED ON AZ31B MAGNESIUM ALLOY

The article discusses the effect of Plasma Electrolytic Oxidation (PEO) on the tribological properties of oxidelayers produced on the AZ31B magnesium alloy. The layers were formed using PEO (AC+DC) in an alkalineelectrolyte with a current density of 10 A/dm. The oxidation parameters (process time, cathodic voltage) wereselected based on a research plan. The tribological properties of the samples were tested on a T-17 tester in apin-on-plate test under technically dry friction conditions, using a pin made of PEEK-HPV polymer. The testsallowed the determination of the coefficient of friction, linear wear, and changes in the mass of the sampleand pin. Profilometric measurements were also made before and after the friction process. An increase in theoxidation time and cathodic voltage led to an increase in the thickness and roughness of the oxide layer. Thesample oxidized under the highest parameters exhibited the greatest thickness, as well as the highest amplitudeand Abbott–Firestone bearing ratio parameters. It was found that with increasing process time and cathodicvoltage, the coefficient of friction (μ) and polymer pin wear increased. Sample C, which had the highestprocessing parameters, showed the best tribological properties, with a coefficient of friction of 0.253 and a linearwear of 0.057 mm. Due to friction, a significant amount of opaque tribofilm was deposited on sample C, resultingin the highest mass wear of the pin (5.33 mg) and an increase in the mass of the sample by 0.74 mg.

Evaluation of an Infinite-Level Inverter Operation Powered by a DC–DC Converter in Open and Closed Loop

This paper evaluates the open- and closed-loop DC–DC converter operation within a DC coupling multilevel inverter architecture to obtain an infinite-level stepped sinusoidal voltage. Adding a cascade controller to the DC–DC converter should reduce the settling time and increase the number of levels in the output voltage waveform; it could decrease the speed error and phase shift concerning the sinusoidal reference signal. The proposed methodology consists of implementing an experimental multilevel inverter with DC coupling through a single-phase bridge inverter energized from a BUCK converter. Trigger signals for the two converters are obtained from a control circuit based in an ATMEGA644P microcontroller to explore its capabilities in power electronics applications. A digital controller is also implemented to evaluate the operation of the BUCK converter in open and closed loop and observe its influence in the stepped sinusoidal output voltage. The evaluation is performed to energize a resistive load with common output voltage in multilevel inverters, i.e., 3, 5, 7, 11, and infinity levels. Results show that during the design stage, fast dynamic elements, like the storage capacitor, can be used to obtain a minimum THD because the settling time is sufficiently fast, the speed error remains small, and there is no need for a controller. A digital controller requires processing time, and although in theory it can reduce the settling time to a minimum, the processor introduces latency in the control signals generation, producing the opposite effect. Controller complexity of the digital controller must be considered because it increases processing time and influences the efficiency of the closed-loop operation.

Filtering Useful App Reviews Using Naïve Bayes—Which Naïve Bayes?

App reviews provide crucial feedback for software maintenance and evolution, but manually extracting useful reviews from vast volumes is time-consuming and challenging. This study investigates the effectiveness of six Naïve Bayes variants for automatically filtering useful app reviews. We evaluated these variants on datasets from five popular apps, comparing their performance in terms of accuracy, precision, recall, F-measure, and processing time. Our results show that Expectation Maximization-Multinomial Naïve Bayes with Laplace smoothing performed best overall, achieving up to 89.2% accuracy and 0.89 F-measure. Complement Naïve Bayes with Laplace smoothing demonstrated particular effectiveness for imbalanced datasets. Generally, incorporating Laplace smoothing and Expectation Maximization improved performance, albeit with increased processing time. This study also examined the impact of data imbalance on classification performance. Our findings suggest that these advanced Naïve Bayes variants hold promise for filtering useful app reviews, especially when dealing with limited labeled data or imbalanced datasets. This research contributes to the body of evidence around app review mining and provides insights for enhancing software maintenance and evolution processes.

Privacy-Preserving Data Analytics in Internet of Medical Things

The healthcare sector has changed dramatically in recent years due to depending more and more on big data to improve patient care, enhance or improve operational effectiveness, and forward medical research. Protecting patient privacy in the era of digital health records is a major challenge, as there could be a chance of privacy leakage during the process of collecting patient data. To overcome this issue, we propose a secure, privacy-preserving scheme for healthcare data to ensure maximum privacy of an individual while also maintaining their utility and allowing for the performance of queries based on sensitive attributes under differential privacy. We implemented differential privacy on two publicly available healthcare datasets, the Breast Cancer Prediction Dataset and the Nursing Home COVID-19 Dataset. Moreover, we examined the impact of varying privacy parameter (ε) values on both the privacy and utility of the data. A significant part of this study involved the selection of ε, which determines the degree of privacy protection. We also conducted a computational time comparison by performing multiple complex queries on these datasets to analyse the computational overhead introduced by differential privacy. The outcomes demonstrate that, despite a slight increase in query processing time, it remains within reasonable bounds, ensuring the practicality of differential privacy for real-time applications.

Optimizing prediction of stainless steel mechanical properties with random forest: a comparison of feature selection methods

In machine learning, predicting the mechanical properties of stainless steel, such as Yield Strength (YS), Ultimate Tensile Strength (UTS), and Elongation (EL), requires many input variables, such as chemical composition, type of heat treatment, heating duration, and cooling method. However, the complexity and number of these variables can increase processing time and reduce model accuracy. This study aims to explore the impact of selecting the most influential input variables to improve prediction accuracy. We compared two feature selection techniques: Recursive Feature Elimination (RFE), which systematically removes less important features, and Information Gain (IG), which measures the contribution of each variable to the target prediction. Both techniques were implemented using the random forest algorithm, chosen for its robustness in handling large datasets and its ability to capture complex interactions between variables. Parameter optimization was performed using a grid search. The analysis showed that the RFE-based model outperformed both the IG-based model and the model without feature selection. In predicting YS, RFE identified 13 out of 21 influential variables, achieving a Mean Absolute Error (MAE) of 9.91, Root Mean Square Error (RMSE) of 14.20, and R-squared value of 0.89. For UTS, RFE identified 8 out of 21 variables, with an MAE of 12.89, RMSE of 16.97, and R-squared of 0.97. In predicting EL, RFE identified 14 out of 21 variables, with an MAE of 3.82, RMSE of 6.10, and an R-squared value of 0.85. The high R-squared values (0.85) across all properties indicate the model’s strong predictive capabilities, making it suitable for practical applications in predicting the mechanical properties of stainless steel.

Modelling the Contributions of Riparian Vegetation and Topography to Stream Shade Using LiDAR and Conventional Digital Elevation Data

ABSTRACTStream temperature is widely considered the master variable in stream ecosystems. One of the key drivers of diel and seasonal stream temperature variability is the solar radiation received at the stream surface, which can be influenced by shading associated with both larger scale topographic features and riparian vegetation. In this study, a stream shade model was developed that uses LiDAR point cloud data to model shading by riparian vegetation, including canopy overhang, and conventional elevation data to model stream shading by topography. The model was applied to a dominantly north–south oriented river flowing in a floodplain within a mountain valley. When compared with shade interpreted from PlanetScope visual imagery, the model predicted stream shade at the point scale with 92% agreement. Sources of error were attributed to pixel and azimuth band size, which can be refined within the model arguments, although at the cost of increased processing time. The shade model was re‐run after virtually rotating the reach by 90° and 270° clockwise to evaluate the effect of valley orientation. Peak reach‐wide sunlight exposure occurred approximately 2 h later in the day when the stream reach was rotated 90°, and produced greater shading from mid‐morning to mid‐afternoon. Further work should test the model on smaller streams using ground‐based oblique or drone‐based photography to provide ground‐truthing, particularly to assess the accuracy of predicted shade below over‐hanging vegetation.

Design and Evaluation of Real-Time Data Storage and Signal Processing in a Long-Range Distributed Acoustic Sensing (DAS) Using Cloud-Based Services.

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1-110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system's performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets.

Chemical strengthening of glass powder particles

It is well known that chemical strengthening of glass brings the benefit of increased fracture strength. Despite extensive research on processing and mechanics at the macroscale, the effectiveness of chemical strengthening on glass elements with all three dimensions in the micrometer regime remains largely unexplored. Here, we develop a novel process for chemical strengthening of micrometer-sized spherical glass powder particles and study the fracture behavior of these particles with in-situ particle compression tests inside a scanning electron microscope. Cross-sectional microscopy and energy dispersive spectroscopy measurements confirm ion exchange and show an increase in diffusion depth with an increase in processing time and temperature. We report a higher fracture strength for chemically strengthened powder particles compared with the as-received ones. We show that the increase in fracture strength is associated to the compressive residual stress resulting from ion exchange during chemical strengthening.

Multi-View Graph Learning for Path-Level Aging-Aware Timing Prediction

As CMOS technology continues to scale down, the aging effect—known as negative bias temperature instability (NBTI)—has become increasingly prominent, gradually emerging as a key factor affecting device reliability. Accurate aging-aware static timing analysis (STA) at the early design phase is critical for establishing appropriate timing margins to ensure circuit reliability throughout the chip lifecycle. However, traditional aging-aware timing analysis methods, typically based on Simulation Program with Integrated Circuit Emphasis (SPICE) simulations or aging-aware timing libraries, struggle to balance prediction accuracy with computational cost. In this paper, we propose a multi-view graph learning framework for path-level aging-aware timing prediction, which combines the strengths of the spatial–temporal Transformer network (STTN) and graph attention network (GAT) models to extract the aging timing features of paths from both timing-sensitive and workload-sensitive perspectives. Experimental results demonstrate that our proposed framework achieves an average MAPE score of 3.96% and reduces the average MAPE by 5.8 times compared to FFNN and 2.2 times compared to PNA, while maintaining acceptable increases in processing time.

Hyperspectral Image Denoising and Compression Using Optimized Bidirectional Gated Recurrent Unit

The availability of a higher resolution fine spectral bandwidth in hyperspectral images (HSI) makes it easier to identify objects of interest in them. The inclusion of noise into the resulting collection of images is a limitation of HSI and has an adverse effect on post-processing and data interpretation. Denoising HSI data is thus necessary for the effective execution of post-processing activities like image categorization and spectral unmixing. Most of the existing models cannot handle many forms of noise simultaneously. When it comes to compression, available compression models face the problems of increased processing time and lower accuracy. To overcome the existing limitations, an image denoising model using an adaptive fusion network is proposed. The denoised output is then processed through a compression model which uses an optimized deep learning technique called "chaotic Chebyshev artificial hummingbird optimization algorithm-based bidirectional gated recurrent unit" (CCAO-BiGRU). All the proposed models were tested in Python and evaluated using the Indian Pines, Washington DC Mall and CAVE datasets. The proposed model underwent qualitative and quantitative analysis and showed a PSNR value of 82 in the case of Indian Pines and 78.4 for the Washington DC Mall dataset at a compression rate of 10. The study proved that the proposed model provides the knowledge about complex nonlinear mapping between noise-free and noisy HSI for obtaining the denoised images and also results in high-quality compressed output.

Study of optical characteristics of microdischarges in the micro-arc oxidation process

Micro-arc oxide coatings of aluminum alloy AD31 samples were formed in an electrolyte containing 2 g/l NaOH and 9 g/l Na2SiO3 for 2, 4, 8, 16 min in the anode and anode-cathode modes. During the processing, the forming curve, the time dependences of the charge passed through the galvanic cell, and the optical parameters of the microdischarges were measured using the optical synchronization method developed by the authors with subsequent image recognition. The thickness of the coatings was measured using a point autofocus probe surface texture measuring instrument Mitaka PF-60, the surface morphology was studied using a VEGA3 scanning electron microscope using SBH surface topography. The elemental composition of the coatings was determined using a scanning electron microscope JSM-6610LV. The analysis of the elemental composition and morphology of the surface revealed that the inner layer of synthesized coatings consists of Al2O3, the outer layer consists of Al2O3·SiO2 mullite, and the ratio of phases Al2O3 and mullite changes with changes in current density and oxidation mode. The formation of mullite is due to the presence of Na2SiO3 in the electrolyte. It is shown that with increasing processing time and current density, the thickness, roughness and porosity of coatings increase. The interrelation of the optical parameters of microdischarges with the morphology of the surface and the elemental composition of the formed coatings is substantiated; it is shown that the ratio of illuminated and non-illuminated sections of the sample surface by microdischarges can be used as a rough estimate of the ratio of electron and ion currents corresponding to microplasma and electrochemical processes. A mathematical model describing the dependence of the coating thickness on the oxidation time based on Faraday's laws for electrolysis and the results of measuring the optical parameters of microdischarges and the electrical parameters of the MAO process without taking into account microplasma processes that do not lead to an increase in the thickness of coatings is proposed. The error of adequacy of the proposed model does not exceed ±10 %. The results of the study can be used in the development of a digital twin of the micro-arc oxidation process.

Comparison with Polygonatum cyrtonema Hua steaming with huangjiu or honey based on UPLC-Q-Exactive MS/MS analysis combined with multi-component variation

Polygoantum cyrtonema Hua is widely used as food and medicine. It is usually processed before being edible due to the irritability and numb feeling. huangjiu and honey are used to process Polygoantum cyrtonema Hua in this study. The changes in appearance, function components and the comparison of specific ingredients of Polygoantum cyrtonema Hua processed with huangjiu and honey were explored. The results showed that the Polygoantum cyrtonema Hua became dark from bright yellow with the increase in processing time. The polysaccharide content was decreased from 7.42 ± 0.11 g/100g to 3.12 ± 0.19 g/100g (huangjiu-steaming processed, p < 0.05) and 4.41 ± 0.11 g/100g (honey-steaming processed, p < 0.05), while the contents of saponins, phenolic and flavonoids were increased. Besides, the in vitro antioxidant activities of Polygoantum cyrtonema Hua showed a similar change trend with the change of phenolic and flavonoid contents. In the meantime, UPLC-Q-Exactive MS/MS was used to identify the non-volatile ingredients of raw Polygoantum cyrtonema Hua (S), huangjiu-steaming processed Polygoantum cyrtonema Hua (J16), and honey-steaming processed Polygoantum cyrtonema Hua (M20), identifying 138, 74 and 53 substances, respectively.

Closed-loop Laser Volume Ablation with Adaptive Scan Paths

This research focuses on closed-loop control in laser volume ablation, also known as laser milling. Such process control enables precise ablation results on workpieces with much wider tolerances regarding the initial surface geometry, internal structure, or its response to the incident laser beam, compared to conventional open-loop processing. However, state of the art closed-loop ablation systems incorporate the process control at the cost of increased processing time. The two main causes are the alternating between processing and measuring, and the use of static scan paths that do not adapt continuously to the evolving geometry of the workpiece during processing. This study addresses this issue by proposing a parallelized work flow of processing, measuring the surface topography and adaptive path planning, eliminating interruptions and achieving faster processing through continuously optimized scan paths. The realized machining system achieved a mean reduction in processing time of 29%, 36%, and 52% on three different test geometries compared to the state of the art.

Resilience mechanisms of Trichopria drosophilae (Hymenoptera: Diapriinae) under global extreme cooling: insights into parasitic response and physiological adaptation.

Global climate warming and frequent extreme low-temperature events have made it essential to investigate the impact of low temperatures on parasitic wasps to protect and strengthen farmland biodiversity, which in turn enhances the biological control potential of natural enemies such as parasitic wasps. We systematically examined how low-temperature stress affects the parasitic functional response of Trichopria drosophilae to Drosophila suzukii (Diptera: Drosophilidae) pupae. Our findings indicate that the parasitic behavior of T. drosophilae towards D. suzukii pupae aligns with the Holling II functional response model following exposure to different temperatures. Within the temperature range of 8 °C to -8 °C, lower temperatures correlated decreased instantaneous attack rate of T. drosophilae and an increase in processing time. The search constant Q initially increased and then decreased with declining temperatures. Short-term low-temperature stress negatively impacted the parasitic and searching abilities of T. drosophilae but did not alter its parasitic functional response model. Notably, short-term low-temperature stress had minimal effects on the water content, protein content, and total sugar content of male and female T. drosophilae adults. However, as temperatures decreased, the activities of key enzymes, including GAPDH, SOD, T-AOC, and malondialdehyde (MDA), exhibited an initial increase followed by a decrease. Conversely, the activities of LDH and HOAD decreased, while the activities of CAT and POD increased. Further study on the effect of short-term low temperature on T. drosophilae can provide a research basis for the large-scale production and low-temperature refrigeration technology of T. drosophilae, and provide a scientific basis for its efficient use in the field.

Valorization of brewer's spent grains (BSG) through alkaline hydrogen peroxide processing: Effect on composition, structure and rheological properties

This study investigated the impact of alkaline processing with hydrogen peroxide (AHP) on the chemical composition, structure, and rheological properties of brewer's spent grains (BSG). Suspensions with different concentrations of BSG (2–8 %) and AHP (1–8 %) were subjected to different processing times (0–12 h). The BSG chemical composition, morphology, crystallinity, modifications of functional groups, and rheological behavior were evaluated over processing. Increasing the concentration of AHP in the suspension and the processing time improved the removal of proteins, lignin, and extractives from BSG into the suspension and, consequently, increased the cellulose and hemicellulose content in the processed BSG. On the other hand, higher concentrations of BSG in the suspension slightly reduced the removal efficiency of these components. AHP processing also induced thinning of the cell wall and changes in particle shape. These changes together with the increase in crystallinity of the processed BSG indicated the material destructuring. FTIR spectra showed reduced intensity of lignin and protein post-processing, indicating their removal, while peaks related to cellulose and hemicellulose increased in processed BSG. The flow curves of the suspensions were adjusted to the Herschel-Bulkley model, exhibiting non-Newtonian behavior with flow yield stress (1.529 Pa < τ0 < 4.646 Pa) and pseudoplasticity (0.830 < n < 0.969) in all conditions. Flow resistance increased with increasing concentration of AHP, BSG, and processing time. Notably, the increase in processing time resulted in greater removal of BSG components, especially proteins, lignin, and extractives, which significantly contributed to the increase in both the flow yield stress and the consistency index of the suspensions. All this information is useful and will support the design of equipment and processes, especially those involving the extraction of proteins and the conversion of the BSG lignocellulose fraction into biofuels.

Italy's Post-COVID-19 Stroke Network: Has It Returned to Pre-Pandemic Standards?

The COVID-19 pandemic strongly transformed the healthcare system in the Lombardy region (Italy), forcing a rapid reorganization of hospital structures. The emergency medical service (EMS) system and emergency departments (EDs) were among the most affected departments. Several studies have shown a change in the epidemiology of time-dependent pathologies, such as stroke, during the pandemic's peak. However, there is little scientific evidence regarding the interpandemic phase. The regional register for ED accesses (EUOL) was analyzed, taking into consideration all accesses for stroke and stroke-like syndromes during the years 2019, 2020, and 2021. The analysis shows a significant difference in the average number of diagnoses per month [2092 vs. 1815 vs. 2015, respectively (p < 0.05)] and an increase in the percentage of transports carried out by EMS vehicles to EDs [17% vs. 25% vs. 22%, respectively (p < 0.05)]. The length of stay (LOS) increased for both discharged patients (9.0 vs. 10.1 vs. 11.2 h, respectively; p < 0.005) and hospitalized patients (7.5 vs. 9.7 vs. 10.6 h, respectively; p < 0.005). During the COVID-19 pandemic, the overall number of stroke diagnoses decreased, while the percentage of patients transported to EDs by EMS vehicles increased. Furthermore, an increased processing time in EDs was highlighted.

Polishing performance and material removal mechanism in the solid-phase Fenton reaction based polishing process of SiC wafer using diamond gel disc

The use of SiC wafer is widespread in many fields, especially in aerospace, energy, 5 G communications, and microelectronics. Chemical-mechanical polishing (CMP) is the primary method used for achieving an ultra-smooth surface on SiC wafers. However, CMP suffers from low efficiency, leading to increased processing time and costs. To address this issue, we developed a novel diamond gel polishing disc that incorporates SiO2/Fe3O4 (S/F) powder. The disc enhances polishing efficiency through a solid-phase Fenton reaction between the disc and SiC. The research investigates the reaction mechanism and the material removal model of the polishing process using SEM, TEM, and XPS analysis. Experimental studies are conducted to assess the polishing performance and validate the effectiveness of the theoretical model. The findings indicate that SiC undergo a solid-phase Fenton reaction with polishing disc mixed S/F powder (SG-S/F disc) during polishing. The Fenton reaction generates hydroxyl radicals (·OH), which break the Si-C and Si-Si bonds in the crystal structure, leading to the formation of a softer nanoscale amorphous oxide layer on the SiC surface. The cyclic generation and removal of this oxide layer enable highly efficient polishing of SiC wafers. Compared to a gel disc without S/F (SG disc), SiC polished with the SG-S/F disc exhibits superior surface quality. Additionally, the material removal rate (MRR) of the SG-S/F disc reaches 1.42 μm/h, representing a 51.1 % improvement over that of the SG disc. These results clearly demonstrate that the solid-phase Fenton reaction significantly enhances the polishing performance of the gel polishing disc.

Photoacoustic processing of decellularized extracellular matrix for biofabricating living constructs

The diverse biomolecular landscape of tissue-specific decellularized extracellular matrix (dECM) biomaterials provides a multiplicity of bioinstructive cues to target cells, rendering them highly valuable for various biomedical applications. However, the isolation of dECM biomaterials entails cumbersome xenogeneic enzymatic digestions and also additional inactivation procedures. Such, increases processing time, increments costs and introduces residues of non-naturally present proteins in dECM formulations that remain present even after inactivation. To overcome these limitations, herein we report an innovative conjugation of light and ultrasound-mediated dECM biomaterial processing for fabricating dECM biomaterials. Such approach gathers on ultrasound waves to facilitate dECM-in-liquid processing and visible light photocrosslinking of tyrosine residues naturally present in dECM biomaterials. This dual step methodology unlocked the in-air production of cell laden dECM hydrogels or programmable dECM hydrogel spherical-like beads by using superhydrophobic surfaces. These in-air produced units do not require any additional solvents and successfully supported both fibroblasts and breast cancer cells viability upon encapsulation or surface seeding. In addition, the optimized photoacoustic methodology also enabled a rapid formulation of dECM biomaterial inks with suitable features for biofabricating volumetrically defined living constructs through embedded 3D bioprinting. The biofabricated dECM hydrogel constructs supported cell adhesion, spreading and viability for 7 days. Overall, the implemented photoacoustic processing methodology of dECM biomaterials offers a rapid and universal strategy for upgrading their processing from virtually any tissue. Statement of significanceLeveraging decellularized extracellular matrix (dECM) as cell instructive biomaterials has potential to open new avenues for tissue engineering and in vitro disease modelling. The processing of dECM remains however, lengthy, costly and introduces non-naturally present proteins in the final biomaterials formulations. In this regard, here we report an innovative light and ultrasound two-step methodology that enables rapid dECM-in-liquid processing and downstream photocrosslinking of dECM hydrogel beads and 3D bioprinted constructs. Such photoacoustic based processing constitutes a universally applicable method for processing any type of tissue-derived dECM biomaterials.

Plant pathology identification using local-global feature level based on transformer

Deep learning plays a crucial role in addressing the challenge of plant disease identification in the field of agriculture. Detecting diseases in plants requires extensive effort, along with a comprehensive understanding of various plant diseases and increased processing time. Balancing both speed and accuracy in predicting leaf diseases in plants can significantly improve crop production and reduce environmental damage. In this paper, we examined deseases on popular plants in agriculture. We proposed a novel model to predict crop pathology on a feature space of global-local based on transformer aggregation. Paticular, we use refined feature of different layer to correlate semantics from high-level feature and low-level feature. Besides, to capture the extended temporal scale across the entire image, we employ a transformer to discern long-range dependencies among frames. Subsequently, the enhanced features incorporating these dependencies are inputted into a classifier for preliminary crop pathology prediction. The plant village dataset and VietNam strawberry disease (VNStr) dataset were utilized for training and disease classification in the experiments. Extensive experiments show that the proposed method outperforms by 99.18% and 94.05% accuracy in plant village and VNStr, respectivly. The model after being judged was applied on Android devices and therefore is easy to use.