Processing Software Research Articles

Research on the impact of streamers’ linguistic emotional valence on live streaming performance in live streaming shopping environments

As an emerging retail model, live streaming shopping involves real-time interactions between streamers and viewers, and among the viewers themselves. In live streaming interactions, the comments posted by the viewers reflect their emotional states, which may have been influenced by the streamers’ emotional expression. This study utilized the S-O-R model and emotional contagion theory as its theoretical foundation to investigate how the emotional valence of streamer speech and real-time comment influence live streaming performance (sales growth/fans growth) and explored the moderating effect of the live streaming atmosphere cues. With Python software and natural language processing technology of Baidu AI platform, sentiment analysis was conducted on 124,085 streamer speech sentences and 24,3767 viewer real-time comments. Our findings reveal that both positive and negative emotional valence of streamer speech positively affect live streaming performance; in addition to the negative impact of neutral emotional valence on sales growth and of negative emotional valence on fans growth, real-time comment emotional valence has a positive correlation with live streaming performance; finally, the study examined the moderating effect of atmosphere cues and demonstrated the complex dynamics of atmospheric cues affecting emotional contagion and sales in live streaming. The findings provide a scientific reference for live marketing managers to optimize the emotional expression of streamer speech and their live streaming operation mode.

A Feasibility Study of HoloLens Ear Image Guidance for Ear Reconstruction.

An ideal 3D cartilage framework and accurate anatomical location are the most important factors to carry out a satisfactory reconstruction of the ear. To streamline this process, we developed an augmented reality assistance system, HoloLens Ear Image Guidance, which is based on computed tomography (CT) data, tailor-made navigation kits to guide reconstruction, data processing software, and HoloLens hardware. The objective of this study is to verify its feasibility in a clinical setting. This study first validated our system in healthy controls and then extrapolated data to test on patients with microtia. First, three healthy volunteers were recruited, and reconstructive navigation kits were made using 3D printing. CT data were collected for the head and neck and imported into the HoloLens Ear Image Guidance Application to generate a personalized 3D virtual ear image. Volunteers then wore the navigation kits while researchers observed them through the HoloLens to check accuracy, track delay, and view the ear image guide.Ten patients with unilateral microtia were recruited and CT data were collected, and reconstructive navigation kits were made to assist with surgery. The procedure was monitored to record the surgeon's experience wearing the HoloLens, the patients' complications associated with wearing navigation kit, and to measure the symmetry between the reconstructed ear and the reference counterpart. In control patients, the deviation between the virtual image and the real ear was less than 2.4% (±0.22%); the tracking delay was less than 1.26 s (±0.09 s), the display effect was good, and surgeons did not report discomfort or dizziness while wearing the HoloLens. Volunteers did not report any pain from holding the navigation reference in their mouth during the test. Following validation, the HoloLens-assisted procedures were not associated with surgeon discomfort or dizziness. No complications were noted in patients including injury to the oral mucosa. Symmetry between the reconstructed ear and the contralateral ear was noted to be satisfactory in HoloLens-assisted surgery. The HoloLens Ear Image Guidance initially met clinical demands in registration accuracy, tracking speed, and subjective user experience, which can be used as the basis for continual software improvements and clinical application.

An accurate identification and spatial characterization method for the development degree of preferential flow paths in water-flooded reservoir

Water flooding is one of the most important methods for oil field development, It accounts for more then 70% of China's crude oil production. However, in the progress of water flooding, preferential flow paths often formed between oil and water wells, which seriously restricts production rate. How to effectively identify the preferential flow paths has become the key to improving the effect of water flooding. In this paper, to solve the problem of difficult identification of preferential flow paths in reservoirs, through the change of core seepage law with high pore volume water flooding experiment, parameters such as multiple of water flux, change value of permeability, and water saturation were selected for analysis. The weight coefficient for each parameter was determined by the variation coefficient method of objective weight. Subsequently, a comprehensive reservoir identification index was obtained by weighting each parameter, which was used to describe the development degree of the preferential flow paths. Finally, quantitative criteria of preferential flow paths were given. The spatial characterization of preferential flow paths was realized by post processing of the Eclipse software. The new method for identifying preferential flow paths fully considers the changes in physical properties and fluid mobility of water-flooded reservoirs. The results of the new applied to a typical water-flooded reservoir in the Bohai Bay Basin show that the preferential flow paths calculated by the new method were highly consistent with the judgment results of tracers. It can accurately and quickly identify the preferential flow paths. This study provides a scientific basis for adjusting measures of water-flooded reservoirs to further enhance oil recovery. Moreover, the new method holds broader prospects for application in the field of porous media transport.

Disparities in Documentation: Evidence of Race-Based Biases in the Electronic Medical Record.

Personal implicit biases may contribute to inequitable health outcomes, but the mechanisms of these effects are unclear at a system level. This study aimed to determine whether stigmatizing subjective terms in electronic medical records (EMR) reflect larger societal racial biases. A cross-sectional study was conducted using natural language processing software of all documentation where one or more predefined stigmatizing words were used between January 1, 2019 and June 30, 2021. EMR from emergency care and inpatient encounters in a metropolitan healthcare system were analyzed, focused on the presence or absence of race-based differences in word usage, either by specific terms or by groupings of negative or positive terms based on the common perceptions of the words. The persistence ("stickiness") of negative and/or positive characterizations in subsequent encounters for an individual was also evaluated. Final analyses included 12,238 encounters for 9135 patients, ranging from newborn to 104years old. White (68%) vs Black/African American (17%) were the analyzed groups. Several negative terms (e.g., noncompliant, disrespectful, and curse words) were significantly more frequent in encounters with Black/African American patients. In contrast, positive terms (e.g., compliant, polite) were statistically more likely to be in White patients' documentation. Independent of race, negative characterizations were twice as likely to persist compared with positive ones in subsequent encounters. The use of stigmatizing language in documentation mirrors the same race-based inequities seen in medical outcomes and larger sociodemographic trends. This may contribute to observed healthcare outcome differences by disseminating one's implicit biases to unknown future healthcare providers.

Return on Asset, Return on Equity, Gross Profit Margin and Net Profit Margin of manufacturing companies in the Consumer Sector Listed on the Indonesia Stock Exchange and its Impact on Earnings Growth from 2015 to 2022

With regard to manufacturing businesses in the consumption sector listed on the Indonesia Stock Exchange between 2015 and 2022, the purpose of this study is to ascertain the impact of profitability measures, which include Return on Equity, Gross Profit Margin, and Net Profit Margin, on Profit Growth. An approach to verification and description is combined with a quantitative methodology in this study. A variety of statistical techniques are employed in the testing process, including the partial and simultaneous hypothesis testing (f and t tests), multiple linear regression, Pearson product moment correlation coefficient, determination coefficient, and classical assumption test. For this investigation, the sample was calculated using a combination of nonprobability and purposeful sampling methods. The consumption subsector's 48 manufacturing enterprises' financial reports provide the sample data. The data processing software utilized is SPSS version 26.0. Partially and concurrently, the study's findings indicate that profit growth is unaffected by return on equity, return on assets, gross profit margin, and net profit margin

Automated, Near Real-Time Ground-Motion Processing at the U.S. Geological Survey

Abstract We describe automated ground-motion processing software named gmprocess that has been developed at the U.S. Geological Survey (USGS) in support of near-real-time earthquake hazard products. Because of the open-source development process, this software has benefitted from the involvement and contributions of a broad community and has been used for a wider range of applications than was initially envisioned. Here, we give an overview and introduction to the software, including how it has leveraged other open-source libraries. We highlight some key features that gmprocess provides, compare response spectra calculated with the automated processing approach of gmprocess to the response spectra provided by the Next Generation Attenuation projects, and summarize projects that have utilized gmprocess. These use-cases demonstrate that this software development effort has been successfully leveraged in earthquake research activities both within and outside the USGS.

Fiber-integrated quantum microscopy system for cells.

Quantum entanglement serves as an essential resource across various fields, including quantum communication, quantum computing, and quantum precision measurement. Quantum microscope, as one of the significant applications in quantum precision measurement, could bring revolutionary advancements in both signal-to-noise ratio (SNR) and spatial resolution of imaging. Here, we present a quantum microscopy system that relies on a fully fiber-integrated high-performance energy-time entangled light source operating within the near-infrared II (NIR-II) window. Complemented by tailored real-time data acquisition and processing software, we successfully demonstrate the quantum imaging of a standard target, achieving a SNR of 131.51 ± 6.74 and a spatial resolution of 4.75 ± 0.27 µm. Furthermore, we showcase quantum imaging of cancer cells, unveiling the potential of quantum entanglement in biomedical applications. Our fiber-integrated quantum microscope, characterized by high imaging SNR, instantaneous image capture, and analysis capabilities, marks an important step toward the practical application in life sciences.

A Cadaveric Comparison of Discectomy Performance During Transforaminal Lumbar Interbody Fusion Approach Using an Endoscopic Technique versus a Minimally Invasive Tubular Approach.

Cadaveric study. Compare discectomy performance between transforaminal lumbar interbody fusion (TLIF) done via an endoscopic versus a tubular technique. Performance of an adequate discectomy is essential to lumbar fusion when performing a TLIF. Previous cadaveric studies comparing open and minimally invasive techniques have reported 36.6%-80% discectomy. There is controversy whether an endoscopic TLIF (E-TLIF) can allow for an adequate discectomy. An E-TLIF was performed on 14 discs (T12-L5) and a minimally invasive tubular TLIF (T-TLIF) was performed on 15 discs (T12-L4, L5-S1). Fellowship trained surgeons performed the TLIFs. Each disc was transected after discectomy and a digital image was analyzed using an imaging processing software to determine the percent of discectomy. Each quadrant of the discectomy was compared. Quadrant one was defined as the left posterior-ipsilateral quadrant of the disc, with each quadrant numbered 2-4 clockwise around the disc. The time to perform the discectomy was compared. Pedicle screws were placed contralaterally to the TLIF and the change in interpedicular distance was compared between techniques after expandable cage implantation as a marker for indirect decompression. A student's t-test was used to determine statistical significance. There was no difference in discectomy performance between techniques (48.86%+/-6.98% T-TLIF vs. 50.26%+/-7.38% E-TLIF, P=0.61). There was no statistical difference between T-TLIF vs E-TLIF at quadrants 1, 3 and 4. There was a difference in discectomy performance at quadrant 2 (39.02%+/-10.18% T-TLIF vs 50.13%+/-14.00% E-TLIF, P=0.02). There was no statistical difference between interpedicular distance created (2.20mm+/-1.97mm T-TLIF vs 1.36mm+/-1.82mm E-TLIF, P=0.24). E-TLIF took less time than MIS-TLIF (20.00min+/-7.12min vs 15.22min+/-4.42min, P=0.048). Our cadaveric study demonstrates that an adequately performed E-TLIF discectomy may be comparable to a T-TLIF discectomy. Further research is required to maximize the efficiency and instrumentation of this technique.

Development of geospatial parallel processing software tool for large-scale geospatial exposure assessment

Development of geospatial parallel processing software tool for large-scale geospatial exposure assessment

Evaluating the impacts of digital ECG denoising on the interpretive capabilities of healthcare professionals.

Electrocardiogram (ECG) interpretation is an essential skill across multiple medical disciplines; yet, studies have consistently identified deficiencies in the interpretive performance of healthcare professionals linked to a variety of educational and technological factors. Despite the established correlation between noise interference and erroneous diagnoses, research evaluating the impacts of digital denoising software on clinical ECG interpretation proficiency is lacking. Forty-eight participants from a variety of medical professions and experience levels were prospectively recruited for this study. Participants' capabilities in classifying common cardiac rhythms were evaluated using a sequential blinded and semi-blinded interpretation protocol on a challenging set of single-lead ECG signals (42 × 10 s) pre- and post-denoising with robust, cloud-based ECG processing software. Participants' ECG rhythm interpretation performance was greatest when raw and denoised signals were viewed in a combined format that enabled comparative evaluation. The combined view resulted in a 4.9% increase in mean rhythm classification accuracy (raw: 75.7% ± 14.5% vs. combined: 80.6% ± 12.5%, P = 0.0087), a 6.2% improvement in mean five-point graded confidence score (raw: 4.05 ± 0.58 vs. combined: 4.30 ± 0.48, P < 0.001), and 9.7% reduction in the mean proportion of undiagnosable data (raw: 14.2% ± 8.2% vs. combined: 4.5% ± 2.4%, P < 0.001), relative to raw signals alone. Participants also had a predominantly positive perception of denoising as it related to revealing previously unseen pathologies, improving ECG readability, and reducing time to diagnosis. Our findings have demonstrated that digital denoising software improves the efficacy of rhythm interpretation on single-lead ECGs, particularly when raw and denoised signals are provided in a combined viewing format, warranting further investigation into the impact of such technology on clinical decision-making and patient outcomes.

High Throughput Quality Control Tools for Carbon-Related Materials: A Raman Approach

Carbon-based materials play a pivotal role in battery technology, serving as negative electrode materials, conductive additives, and coating layers for both positive and negative electrode materials. Traditional characterization methods, such as transmission electron microscopy (TEM) and X-ray diffraction (XRD), fall short in providing comprehensive quality control, especially when distinguishing the structure of amorphous carbon materials. Raman spectroscopy as a convenient, simple tool is widely used to study the structure of carbon materials. It provides insights into the degree of crystallization, doping degree, and subtle structural differences arising from various preparation processes. By quantifying Raman spectrum testing results through statistical methods and providing relevant statistical outcomes, we demonstrate the potential of Raman spectroscopy as a quality control method for carbon material characterization. With extensive sample testing and automation, along with standardized spectral data processing and analysis software, we propose Raman spectroscopy as a suitable tool for high-throughput quality control in industrial production.

Nine-grid Area Division Method: A New Ideal Bone Puncture Region for Percutaneous Vertebroplasty in Lumbar Spine.

Percutaneous vertebroplasty (PVP) is widely recognized as an efficacious intervention for alleviating low back pain resulting from osteoporotic vertebral compression fractures. The ideal bone puncture point is conventionally situated at the projection "left 10 points, right 2 points" of the pedicle in the lumbar spine. Determining the optimal bone puncture point represents a critical and complex challenge. The accuracy of percutaneous vertebroplasty (PVP) is primarily influenced by the proficiency of the operating surgeons and the utilization of multiple fluoroscopes during the conventional procedure. Incidences of puncture-related complications have been documented globally. In an effort to enhance the precision of the surgical technique and reduce the occurrence of puncture-related complications, our team applied the "Nine-grid Area Division Method" for PVP in the lumbar spine to modify the traditional procedure. There is potential to decrease the number of puncture times, the radiation exposure dosage, and the duration of surgical procedures. This protocol introduces the definition of the "Nine-grid Area Division Method" and describes the process of modeling target vertebrae DICOM imaging data within medical imaging processing software, simulating operations within a 3-D model, refining the 3-D model using reverse engineering production software, reconstructing the vertebral engineering model within 3-D modeling design software, and utilizing surgical data to determine safe entry regions for pedicle projection. By employing this methodology, surgeons can effectively identify appropriate puncture points with precision and ease, thereby reducing the intricacies associated with puncturing and enhancing the overall accuracy of surgical procedures.

Forecasting Stock Market Performance: An Ensemble Learning-Based Approach

There is tremendous value in the ability to predict stock market trends and outcomes. The public sentiment surrounding a stock is unquestionably a vital factor contributing to the rise or fall of a stock price. This paper aims to detail how data from public sentiment can be integrated into traditional stock analyses and how these analyses can then be used to make predictions of stock price trends. Headlines from seven news publications and conversations from Yahoo! Finance’s conversations forum were processed by the Valence Aware Dictionary and sEntiment Reasoner (VADER) natural language processing package to determine numerical polarities that represent a positive, negative, or neutral public sentiment around a stock ticker. The resulting polarities were paired with popular stock-table metrics to create a dataset for a Logistic Regression machine learning model. The model was trained on approximately 4400 major stocks to determine a binary “Buy” (1) or “Not Buy” (0) recommendation for each stock. We present approaches for a variety of machine learning models – Logistic Regression, Random Forest Classifiers, and Extreme Gradient Boosting models and do a statistical comparison on the balanced accuracies of each machine learning model. This paper aims to detail our understanding of how one can leverage the public sentiment of a stock to gauge its future value.

High-throughput image processing software for the study of nuclear architecture and gene expression

High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.

Erosion wear performance of HVOF sprayed WC-10Co-4Cr + 2% TiO2 coating on SS-409 using slurry jet erosion tester

Erosion is a phenomenon and is the primary cause of failure in numerous slurry transportation equipment such as pump impellers, pipeline fittings, etc. It reduces the working life of the equipment and is thus an essential factor in the design, choice, and operation of any system. Pipeline materials used to convey slurry often experience severe wear from erosive forces. Researchers are interested in minimizing erosion wear using different coatings to increase equipment life. The High-Velocity Oxy-Fuel (HVOF) spray technique was employed to deposit WC-10Co-4cr + 2% TiO2 coating on the SS409. The erosion wear was evaluated using a slurry jet erosion tester of uncoated and coated materials with sand as the erodent. The impact of impingement angle, velocity, time, and other parameters was studied. On the erosion wear of uncoated and coated materials, it was found that the influence of jet velocity was greater than the impact of other parameters. The deteriorated surfaces were analyzed using a scanning electron microscope (SEM). The erosion wear resistance of SS 409 was significantly improved by the WC-10Co-4cr + 2% TiO2 coating. The coating demonstrated a brittle mechanism of erosion wear. Furthermore, image processing software investigated the mechanism of erosion wear.

Optimizing SEM parameters for segmentation with AI – Part 1: Generating a training set

Extracting significant quantitative results from SEM images requires feature segmentation with image processing software. The efficiency of segmentation algorithms depends on the image quality, determined by the parameters set on the microscope during acquisitions. By integrating AI within SEM acquisition workflows, it is possible to suggest microscope parameters that will produce images where the features to quantify will be easily segmented. Specifically, a model is trained to automatically suggest the beam energy and probe current to set on the microscope during acquisitions. This paper is the first of two parts, describing workflows for generating a complete training set. The training set is carefully designed, consisting of both simulated data and real data acquired on the SEM by varying the energy and current. Separate workflows are required for generating simulated and acquired training examples. Simulated data generation is accomplished with the MC X-ray simulator in Dragonfly, where multiple virtual samples are created to intentionally diversify the training set. Acquiring data on the SEM for training is a time-consuming process when compared to generating simulations and would ideally be avoided but is included here to determine the degree to which it is required. Using only simulated data for adequate training, we show that our data generation workflow can be fully automated and produces a considerable amount of high quality data rapidly and with minimal effort.

Analisis Sistem Informasi Manajemen Berbasis Teknologi di MTS YPAK Cigugur

The purpose of this study was to analyze the management information system used in MTS YPAK Cigugur. The method applied in this study is qualitative descriptive analysis. Data collection techniques were carried out by interviewing the operator staff of MTS YPAK Cigugur. In addition, the researcher also conducted a literature study on a number of scientific journals related to the topic to be analyzed. The results of the study indicate that the management information system has been implemented well in MTS YPAK Cigugur. The management information system model used in MTS YPAK Cigugur is the education management information system or EMIS developed by the Ministry of Religion to assist Islamic educational institutions in managing data related to student data, educator and education data, facilities and infrastructure, and other institutional interest data. In its implementation, there are various aspects that influence it, including the procurement of adequate software such as computers and laptops, the availability of software for data processing and adequate networks to support the smooth running of the data processing process, and human resources as users who are capable and understand technology-based management information systems.

Health data management practice and associated factors among health professionals working in public health facilities in Oromia Special Zone, Amhara, Ethiopia: a cross-sectional study

ObjectiveData and information are vital to the decision-making process surrounding health sector reform. In spite of the vast amounts of capital invested in the development of health information systems, health...

A robust method for measuring aminoacylation through tRNA-Seq

Current methods to quantify the fraction of aminoacylated tRNAs, also known as the tRNA charge, are limited by issues with either low throughput, precision, and/or accuracy. Here, we present an optimized charge transfer RNA sequencing (tRNA-Seq) method that combines previous developments with newly described approaches to establish a protocol for precise and accurate tRNA charge measurements. We verify that this protocol provides robust quantification of tRNA aminoacylation and we provide an end-to-end method that scales to hundreds of samples including software for data processing. Additionally, we show that this method supports measurements of relative tRNA expression levels and can be used to infer tRNA modifications through reverse transcription misincorporations, thereby supporting multipurpose applications in tRNA biology.

A robust method for measuring aminoacylation through tRNA-Seq.

Current methods to quantify the fraction of aminoacylated tRNAs, also known as the tRNA charge, are limited by issues with either low throughput, precision, and/or accuracy. Here, we present an optimized charge transfer RNA sequencing (tRNA-Seq) method that combines previous developments with newly described approaches to establish a protocol for precise and accurate tRNA charge measurements. We verify that this protocol provides robust quantification of tRNA aminoacylation and we provide an end-to-end method that scales to hundreds of samples including software for data processing. Additionally, we show that this method supports measurements of relative tRNA expression levels and can be used to infer tRNA modifications through reverse transcription misincorporations, thereby supporting multipurpose applications in tRNA biology.