Processing Software Research Articles

A robust, deep learning-based analysis of time-domain signals for NMR spectroscopy

When analyzing the Free Induction Decay (FID) signal produced by nuclear magnetic resonance (NMR) spectroscopy, Fourier transforms (FT) are used to decompose time-domain signals arising from nuclear interactions. This transformation enables the extraction of frequency-domain information, allowing for the recognition of patterns within the generated NMR spectra. Most modern NMR processing software applies FT to generate the final spectra. Researchers process FID using various techniques, such as phase correction, windowing, and FT, to enhance the interpretation of the obtained spectra. This processing step requires careful consideration of the characteristics of the original data and can also be influenced by the researchers' experience, often making it time-consuming to produce reliable results. However, recent advancements in artificial intelligence, particularly deep learning, have demonstrated superior pattern recognition capabilities compared to humans in complex scenarios. These developments have been successfully applied to various aspects of NMR spectroscopy. In this study, we demonstrate that neural networks can replace FT in NMR spectroscopy, enabling robust and rapid prediction of spectra and peak lists from FID signals. Our results confirm that deep learning can efficiently process NMR data to generate final spectra. As a proof of concept, we present the resulting spectra, along with peak lists predicted by supplying only FID input to the deep learning algorithm. The generated peak lists can be considered as spectra with infinite resolution.

Determinants of Village Fund Management Accountability

Investigating and determining the impacts of village officials' duties, fund managers' skills, and information technology use on the accountability of village fund management is the aim of this study, both partially and simultaneously. The descriptive analysis method employs a quantitative approach to data analysis. Data collection was implemented by distributing questionnaires and using SPSS software for processing. This research found that the significance value of the partial test findings (t-test), specifically with a value of 0.002 < 0.05, suggests that village authorities' participation impacts the accountability of local fund management. The significance value of the partial test findings (t-test), specifically with a value of 0.000 < 0.05, indicates that the competency of village fund managers affects the accountability of village fund management. The significance value of the partial test findings (t-test), specifically with a value of 0.004 < 0.05, indicates that the usage of information technology affects the accountability of village fund administration. The use of information technology, the function of village officials, and the proficiency of village fund managers all affect the accountability of village fund management

Pengaruh Current Ratio (Cr) dan Debt To Equity Ratio (Der) Terhadap Laba Bersih Pada PT Tempo Scan Pasific Tbk Periode 2012-2022

This study aims to determine the effect of Current Ratio (CR) and Debt to Equity Ratio (DER) on Net Profit at PT Tempo Scan Pasific Tbk for the 2012-2022 period, both partially and simultaneously. This type of research is quantitative and the data sources used are secondary data in the form of financial reports (annual reports), namely balance sheets and income statements. The data analysis technique for this study uses SPSS (Statistical Program For Social Science) Version 24 data processing software. The samples used in this study are balance sheets and income statements published by PT Tempo Scan Pasific Tbk for the 11-year period 2012-2022. Based on the results of the hypothesis test, it shows that the Current Ratio (CR) variable on Net Profit is tcount 3.268> ttable 2.306, meaning that the Ho1 hypothesis is rejected and Ha1 is accepted, thus there is an influence between the Current Ratio (CR) on Net Profit at PT Tempo Scan Pasific Tbk for the 2012-2022 period. Meanwhile, for the variable Debt to Equity Ratio (DER) to Net Profit is tcount 3.378> ttable 2.306 meaning that the Ho2 hypothesis is rejected and Ha2 is accepted, thus there is an influence between Debt to Equity Ratio (DER) to Net Profit at PT Tempo Scan Pasific Tbk for the period 2012-2022. Simultaneously, that Current Ratio (CR) and Debt to Equity Ratio (DER) have a significant effect on Net Profit at PT Tempo Scan Pasific Tbk for the period 2012-2022 with an Fcount value of 6.209> Ftable 4.26. The results of the determination coefficient test show that Current Ratio (CR) and Debt to Equity Ratio (DER) contribute 60.80%. So the remaining 39.20% is influenced by other variables not examined in this study.

ECLStat: A robust machine learning based visual imaging tool for electrochemiluminescence biosensing.

Visual electrochemiluminescence (ECL) has emerged as a prominent diagnostic method for accurately quantifying various disease markers even at point of care setting with high sensitivity and accuracy. It does not employ complicated instruments such as potentiostat and expensive imaging microscopy for quantifying trace amounts of molecules. The ECL system offers significant advantages over other detection processes, such as high sensitivity, selectivity, rapid response, multiplexing, and miniaturization capabilities, making it well-suited for future commercialization. However, the current ECL system lacks standardization and accuracy in the resulting output data due to the manual measurement of ECL signal response using open-source image processing software, which often limits the efficiency of the ECL process in real-time applications. To address the shortcomings of the existing approach and advance the ECL detection process, a fully automated machine learning-assisted standalone graphical user interface (GUI) application was developed for dedicated measurement and management of ECL-emitted light signals. The working performance of the developed program is evaluated for its real-time utility by detecting hydrogen peroxide, which is an important reactive oxygen species, and glucose, which is a significant biomarker of diabetes. The obtained results show the detection limit of 0.024mM and 0.035mM for H2O2 and glucose, with a quantification limit of 0.074mM and 0.10mM, respectively. The ultimate objective of the developed application is to improve accuracy by enabling users to apply machine learning algorithms to raw image data seamlessly without deeply comprehending the underlying computational processes and establish a standard protocol for ECL signal measurements. Moreover, the developed application can be used in other optical detection approaches such as chemiluminescence, colorimetric, and fluorescence.

A novel method for assessing the shedding of fibre in forensic science: Investigating the effects of washing.

The evaluation of the shedding capacity of a garment is crucial in forensic analysis to understand fibre transfer mechanisms during contact activities. While adhesive tapes are commonly used, the lack of standardised pressure application -often done manually- poses a challenge. In addition, while previous studies have examined the effects of washing on fibre evidence, there is a notable absence in the literature regarding its impact on garment shedding capacity. This study aims to address these gaps by proposing a practical method to assess garment shedding capacity. Conventional tape lifting experiments, involving manual pressure application, were conducted for comparison with the novel method proposed in this study. Controlled conditions for reproducible experiments were achieved using a cost-effective friction tester and automated data collection through photography and ImageJ image processing software. Through controlled simulations, this study seeks to examine the relationship between garment shedding capacity, fibre transfer dynamics, and the effects of textile washing during laundry cycles.

MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data

MLinvitroTox is an automated Python pipeline developed for high-throughput hazard-driven prioritization of toxicologically relevant signals detected in complex environmental samples through high-resolution tandem mass spectrometry (HRMS/MS). MLinvitroTox is a machine learning (ML) framework comprising 490 independent XGBoost classifiers trained on molecular fingerprints from chemical structures and target-specific endpoints from the ToxCast/Tox21 invitroDBv4.1 database. For each analyzed HRMS feature, MLinvitroTox generates a 490-bit bioactivity fingerprint used as a basis for prioritization, focusing the time-consuming molecular identification efforts on features most likely to cause adverse effects. The practical advantages of MLinvitroTox are demonstrated for groundwater HRMS data. Among the 874 features for which molecular fingerprints were derived from spectra, including 630 nontargets, 185 spectral matches, and 59 targets, around 4% of the feature/endpoint relationship pairs were predicted to be active. Cross-checking the predictions for targets and spectral matches with invitroDB data confirmed the bioactivity of 120 active and 6791 nonactive pairs while mislabeling 88 active and 56 non-active relationships. By filtering according to bioactivity probability, endpoint scores, and similarity to the training data, the number of potentially toxic features was reduced by at least one order of magnitude. This refinement makes the analytical confirmation of the toxicologically most relevant features feasible, offering significant benefits for cost-efficient chemical risk assessment.Scientific Contribution:In contrast to the classical ML-based approaches for toxicity prediction, MLinvitroTox predicts bioactivity for HRMS features (i.e., distinct m/z signals) based on MS2 fragmentation spectra rather than the chemical structures from the identified features. While the original proof of concept study was accompanied by the release of a MLinvitroTox v1 KNIME workflow, in this study, we release a Python MLinvitroTox v2 package, which, in addition to automation, expands functionality to include predicting toxicity from structures, cleaning up and generating chemical fingerprints, customizing models, and retraining on custom data. Furthermore, as a result of improvements in bioactivity data processing, realized in the concurrently released pytcpl Python package for the custom processing of invitroDBv4.1 input data used for training MLinvitroTox, the current release introduces enhancements in model accuracy, coverage of biological mechanistic targets, and overall interpretability.

Parallelized Event Data Management System Based on MT-SNiPER Framework and PODIO

Software framework serves as a skeleton for the offline data processing software for many high energy physics (HEP) experiments. The event data management, including the event data model (EDM), transient event store and data input/output, implements the core functionalities of the framework, and has a great impact on the performance of the entire offline software. Future HEP experiments are generating increasingly large amounts of data, bringing challenges to offline data processing. To address this issue, a common event data management system that supports efficient parallelized data processing applications has been developed based on Software for Non-collider Physics ExpeRiments (SNiPER) common software framework as well as PODIO, a common EDM toolkit for future HEP experiments. In this paper, the implementation of a parallelized event data management (PEDM) system is introduced, including the integration with MT-SNiPER and PODIO, as well as the implementation of GlobalStore to support multi-threaded event processing. Finally, the application and performance evaluation of the data management system in OSCAR (offline software of Super Tau Charm Facility) is presented.

Design of ERP Production Planning Module to Optimize the use of System Application and Processing (SAP) Software Using Business Process Improvement and Activate SAP Methodology at PT. XYZ

The management of a company always tries to find a solution on how to survive and be able to align company resources with the development or increasing business demands. Increasing business demands, especially in managing company resources, require a systematic and integrated system. One way is to improve the Enterprise Resource Planning (ERP) system that is currently owned by changing or increasing its status by upgrading to ERP that is able to help company employees in managing company assets, production needs, customer management, accounting and financial systems. One type of ERP that is widely used by large companies is the System Application Product (SAP). In addition, to optimize its operational performance by reimplementing SAP ERP because in its operations there are still several business processes that have not implemented SAP ERP to improve Business Process Improvement, cost efficiency through integrated digitalization and corporate value.

Novel automated quality indicators for echocardiography and their importance for left ventricular strain

Abstract Background Until now, no method is available for automatic extraction of indicators of image quality and view standardization from echocardiographic recordings. Thus, description of data quality has been indirect, often by description of operators experience and the methodology used. Peak systolic global longitudinal strain (GLS) is a sensitive measure of left ventricular (LV) function, still hampered by random variability due to suboptimal standardization of recordings and variability in data processing by the operators. Data on how acquisitions and data processing influence GLS and its variability are scarce. Objectives Aim was to study the importance of image acquisition and data processing by experienced operators for GLS in a large healthy population by automatically extracting quality indicators using novel deep learning based image analysis software and vendor specific metadata. Secondly, we aimed to study how these characteristics influenced the reference ranges and GLS variability. Methods Participants from a large echocardiographic study were included. Echocardiography was performed according to current recommendations. Two experienced operators and two expert cardiologists read and re-read all GLS recordings (1,412 paired analyses). Acquisition- and data processing characteristics were extracted using deep learning and GLS software metadata providing specific data on position of landmarks in the view, as well as rotation and tilt of the cut-plane according to the best possible standard. From these measurements several quality indicators were provided (Table 1). Results Mean age for the 1,412 participants was 58±12 years and 56% were women. Averaged apical LV foreshortening in the recordings was ≤2 mm and the view specific recordings were well standardized, with high alignment with the preferred cut-plane for tilt and rotation across the three apical views (Figure 1). Most quality indicators influenced GLS and absolute GLS was lower with longer LVs (-0.2% per 1 cm, p=0.003) and longer distance from the transducer to the LV apex (-0.3% per 1 cm, p=0.02) in the recordings, and also for wider regions of interest during data processing (-0.7% per 1 mm, p <0.001). Absolute GLS was 0.7% higher per mm systolic foreshortening of the apical region of interest, and when more knots were placed under initialization of the ROI (0.1% per knot, p=0.004). In test-retest analyses, the results for agreement between operators followed the above presented data. Conclusions Novel quality indicators were successfully extracted using novel DL software and strain specific metadata. Even though acquisitions and data processing were well standardized, several quality indicators from both steps influenced GLS. Deep learning software and data processing metadata may provide quality indicators of the echocardiographic databases of importance to interpret the study outcomes and motivate researchers to optimize echocardiographic acquisitions and analyses.

Enhancing Data Collection Time Intervals and Modeling the Structural Behavior of Bridges in Response to Temperature Variations

The impact of temperature on bridges represents one of the main long-term challenges of structural health monitoring (SHM). Temperature is an environmental variable that changes both throughout the day and between different seasons, and its variations can induce thermal loads on bridges, potentially resulting in considerable displacements and deformations. Therefore, it is essential to obtain current data on the impact of daily and seasonal temperature variations on bridge displacements. Unfortunately, the maintenance costs associated with using precise estimates of thermal loads in a bridge design are quite high. The introduction of more accessible structural monitoring services is imperative to increase the number of observed structures. Viable solutions to make SHM more efficient include minimizing the costs of equipment, sensors, data loggers, data transmission systems, or monitoring data processing software. This research aims to improve the time intervals for collecting data on external temperature variations measured on a bridge structure through a sensor-based detection system and the integration of results into a regression analysis model. The paper aims to determine the appropriate interval for capturing and transmitting the structural response influenced by temperature variations over a year and to develop a behavioral mathematical model for the concrete structural components of a monitored bridge. The structural behavior was modeled using the statistical software TableCurve 2D, v.5.01. The results indicate that extending the data collection periods from 15 min to 4 h, in a static regime, maintains the accuracy of the regression model; instead, the effects of this integration are a significant reduction in the costs of data collection, transmission, and processing. The practical implications of this study consist of improving the monitoring of the structural behavior of bridges and the prediction under thermal stress, aiding in the design of more resilient structures, and enabling the implementation of efficient maintenance strategies.

Rapid structural analysis of bacterial ribosomes in situ

Rapid structural analysis of purified proteins and their complexes has become increasingly common thanks to key methodological advances in cryo-electron microscopy (cryo-EM) and associated data processing software packages. In contrast, analogous structural analysis in cells via cryo-electron tomography (cryo-ET) remains challenging due to critical technical bottlenecks, including low-throughput sample preparation and imaging, and laborious data processing methods. Here, we describe a rapid in situ cryo-ET sample preparation and data analysis workflow that results in the routine determination of sub-nm resolution ribosomal structures. We apply this workflow to E. coli, producing a 5.8 Å structure of the 70S ribosome from cells in less than 10 days and facilitating the discovery of a minor population of 100S-like disomes. We envision our approach to be widely applicable to related bacterial samples.

Core software for the super tau-charm facility

The Super Tau Charm Facility (STCF) is a new generation electron–positron collider, proposed to study various physics topics in the tau charm energy region. STCF will operate within a center-of-mass energy spanning 2 to 7[Formula: see text]GeV, with a peak luminosity of [Formula: see text] [Formula: see text]cm[Formula: see text]s[Formula: see text], which means STCF will produce two orders of magnitude more data compared to BESIII each year, posing great challenges for the data processing software. To support the intricate requirements from the offline data processing tasks, as well as to tackle this immense challenge caused by the huge data volume, the offline software for STCF (OSCAR) is designed and developed, based on the state-of-the-art technology and toolkits in the high energy physics community. This paper provides an overview of the design and implementation of OSCAR, highlighting its event data management system, parallel data processing capabilities leveraging SNiPER and TBB, and its geometry management framework powered by DD4hep. Presently, OSCAR is fully operational, effectively supporting the conceptual design of the STCF detector and exploring its vast physics potential.

Emiproc: A Python package for emission inventory processing

Emiproc: A Python package for emission inventory processing

Adaptive Recognition of English Translation Errors Based on Improved Machine Learning Methods

The accurate identification of errors in machine-translated English textual content is both necessary and tough, with massive implications for natural language processing packages. current device translation structures, while an increasing number of sophisticated, nevertheless fall prey to quite a number of errors that may compromise which means and fluency. This paper addresses these shortcomings through offering a greater system mastering method for the adaptive recognition of translation errors. We gift a singular framework that integrates the Transformer version, famed for its efficacy in shooting contextual relationships within text sequences, with a robust attention mechanism that prioritizes salient data at some stage in translation. The innovation of this studies lies in the incorporation of meta-getting to know techniques, allowing the model to self-modify in response to various classes of mistakes, therefore refining the precision of errors recognition. We outline the technique in detail, emphasizing the systematic steps taken to enrich the model’s adaptability. The efficacy of our approach is substantiated through comparative experiments, which demonstrate extremely good upgrades in errors identification over existing techniques. The results suggest the capacity of our adaptive mechanism to decorate the excellent of machine translation, paving the method for greater, reliable and nuanced language translation tools.

Socio-demografske karakteristike zaposlenih i fleksibilni aranžmani rada - dokazi iz Srbije

The turbulent environment that influences the contemporary business has caused a reconfiguration of operations, where organizations have developed flexible work arrangements that involve the modification of traditional work. The goal of applying various forms of flexible work arrangements is to attract and retain talent, develop employee skills and capabilities, and become more innovative in terms of job organization. The aim of the research is to investigate differences in the use of flexible work arrangement according to socio-demographic characteristics of employees in the Republic of Serbia. The research was conducted on a sample of 582 employees, using the independent sample T-test and univariate analysis of variance (ANOVA) by using the IBM SPSS software for data processing. The research results indicated that there are statistically significant differences between employees who use flexible work arrangements according to gender, age, and level of education, and accordingly, the research hypothesis was confirmed. The scientific contribution is focused on a key segment related to the lack of scientific research on this topic in the Republic of Serbia and beyond. The results represent significant propositions for future research in this field and provide a basis for amending labor laws.

Models for Processing Nighttime Light Data and Software Development

Models for Processing Nighttime Light Data and Software Development

Analytical sensitivity factors from distributions of time of flight of photons for near-infrared spectroscopy studies in multilayered turbid media.

In the last years, time-resolved near-infrared spectroscopy (TD-NIRS) has gained increasing interest as a tool for studying tissue spectroscopy with commercial devices. Although it provides much more information than its continuous wave counterpart, accurate models interpreting the measured raw data in real time are still lacking. We introduce an analytical model that can be integrated and used in TD-NIRS data processing software and toolkits in real time. This is based on the so-called sensitivity factors (SFs) of the distributions of time of flight (DTOFs) of photons measured in optically turbid and semi-infinite multilayered media, such as the human head. We derived analytical expressions for the SFs that link changes in the absorption coefficient of each layer to changes in the statistical moments of DTOFs acquired in a reflectance configuration. This was later validated with results from Monte Carlo (MC) simulations, which stand as the gold standard in terms of photon migration in biological tissue. Next, we designed a couple of simulated experiments depicting how the analytical SFs can be used to retrieve absorption changes in the particular case of a five-layered medium. Comparison between theory and simulations in 2-, 5-, and 10-layered media showed very good agreement (in most cases with weighted mean absolute percentage errors below 10%). Moreover, our derivations could be run in a few milliseconds (except for the extreme case of the variance SF in the 10-layered medium), which means a speedup of up to 10,000× with respect to MC simulations, with a much better spatial resolution and without their typically associated stochastic noise. In summary, our method achieves performances similar to those given by MC simulations, but orders of magnitude faster, which makes it very suitable for its implementation in real-time applications.

GDA: a GNSS data processing software for quality analysis

GDA: a GNSS data processing software for quality analysis

The design of IMB bus data processing software

The design of IMB bus data processing software

Flexible, self-healing and portable supramolecular visualization smart sensors for monitoring and quantifying structural damage.

Visually monitoring micro-crack initiation and corrosion failure evolution is crucial for early diagnosis of structural health and ensuring safe operation of infrastructures. However, existing damage detecting approaches are subject to the limited-detection of heterogeneous structures, intolerance of harsh environments, and challenge of quantitative analysis, impeding applications in structural health monitoring (SHM). Herein, we present a stretchable, semi-quantitative, instrument-free, supramolecular SHM sensor by integrating a polyurea elastomer with sensitive corrosion-probes, enabling localized corrosion monitoring and quantification of failure dynamics. Initially, a correlation between visual monitoring signals and structural health status is proposed, and sensor-based image processing software that accurately quantifies structural failure indicators (crack scale, corrosion reactivity and deterioration status) is proposed. Moreover, this sensor can be fabricated as multiple derivatives: a coating or patch covered on metallic substrates and an ionic-responsive test strip, ensuring real-time detection of the initiation of pitting, degradation events of metallic components and convenient monitoring of ion concentrations in corrosive media. Furthermore, the inherent geometric plasticity and dynamic hydrogen-bonded network validates the reliability for heterogeneous components and stability under extreme environments of sensors. This portable, smart SHM strategy established the channel-transformation model from corrosion dynamics to visual signals, exhibiting prospects for structural monitoring in offshore energy-harvesting equipment.