Data Processing Research Articles

The prevalence of gas exchange data processing methods: a semi-automated scoping review.

Cardiopulmonary exercise testing involves collecting variable breath-by-breath data, sometimes requiring data processing of outlier removal, interpolation, and averaging before later analysis. These data processing choices, such as averaging duration, affect calculated values such as VO2max. However, assessing the implications of data processing without knowing popular methods worth comparing is difficult. In addition, such details aid study reproduction. We conducted a semi-automated scoping review of articles with exercise testing that collected data breath-by-breath from three databases. Of the 8,344 articles, 376 (mean 4.5%, 95% CI: 4.1-5.0%) and 581 (7.0%, 6.4-7.5%) described outlier removal and interpolation, respectively. A random subset of 1,078 articles revealed 60.9% (57.9-63.7%) reported averaging methods. Commonly documented outlier cutoffs were ± 3 or 4 SD (39.1% and 51.6%, respectively). The dominating interpolation duration and procedure were one second (93.9%) and linear interpolation (92.5%). Averaging methods commonly described were 30 (30.9%), 60 (12.4%), 15 (11.6%), 10 (11.0%), and 20 (8.1%) second bin averages. This shows that studies collecting breath-by-breath data often lack detailed descriptions of data processing methods, particularly for outlier removal and interpolation. While averaging methods are more commonly reported, improved documentation across all processing steps will enhance reproducibility and facilitate future research comparing data processing choices.

Development of intelligent traction machine for tensioning and paying off construction of transmission lines

Monitoring the traction force and speed of the traction machine is crucial for timely discovering construction problems and avoiding accidents during the construction process of tension wire laying. The existing monitoring methods have low accuracy, are susceptible to environmental interference, and cannot achieve real-time monitoring. Therefore, this article has developed an intelligent traction machine suitable for tension line construction.The design scheme includes three modules: data acquisition and processing, data transmission, and software design. The data acquisition and processing module is responsible for real-time monitoring of traction force and traction speed, and converting the monitored data into digital signals through pressure sensors and speed sensors, and then processing them through a microcontroller. The data transmission module transmits monitoring data to the construction site terminal equipment and remote command center through LoRa wireless transmission technology, displays monitoring data on the terminal equipment, and realizes warning and control functions. At present, the intelligent traction machine has been applied in the tension wire laying construction of a 750 kV transmission line project in Guangdong, China and has achieved good results. Through continuous optimization and improvement, intelligent traction machines have practical conditions and broad application prospects.

A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.

To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators. Tabular data with missing laboratory indicators were collected from 393 patients with ovarian cancer and 1951 control patients. The missing ovarian cancer laboratory indicator features were projected to the latent space to obtain a classification model using the representational learning classification model based on discriminative learning and mutual information coupled with feature projection significance score consistency and missing location estimation. The proposed constraint term was ablated experimentally to assess the feasibility and validity of the constraint term by accuracy, area under the ROC curve (AUC), sensitivity, and specificity. Cross-validation methods and accuracy, AUC, sensitivity and specificity were also used to evaluate the discriminative performance of this classification model in comparison with other interpolation methods for processing of the missing data. The results of the ablation experiments showed good compatibility among the constraints, and each constraint had good robustness. The cross-validation experiment showed that for identification of ovarian cancer with missing laboratory indicators, the AUC, accuracy, sensitivity and specificity of the proposed multi-constraints representation-based learning classification model was 0.915, 0.888, 0.774, and 0.910, respectively, and its AUC and sensitivity were superior to those of other interpolation methods. The proposed model has excellent discriminatory ability with better performance than other missing data interpolation methods for identification of ovarian cancer with missing laboratory indicators.

An adaptive ensemble learning capacity prediction method with dual-modal data in lithium-ion battery manufacturing

ABSTRACT To measure the capacity of lithium-ion batteries in manufacturing, it is necessary to go through the formation, standing, and grading processes. The existing data-driven capacity prediction methods can only reduce a portion of the grading process and do not comprehensively use both time series data and statistical feature data. In response to the above problems, this paper proposes an adaptive ensemble learning method, which does not go through the grading process and only uses the statistical features and time series of dual-modal data in the formation process to predict the capacity. The LightGBM algorithm is used to predict statistical feature data, while the multi-channel GRU and multi-head self-attention network are used to predict time series data. Then, the two parts are fused through adaptive ensemble learning to estimate the capacity. Furthermore, the proposed method could be flexibly applied to the prediction of single-modal dirty data as required. The method is verified using actual industrial production line data. The experimental results show that the MAE and MAPE of the test set are 0.948 Ah and 0.324%, respectively. For single-modal dirty data (accounting for about 4%), MAPE of its prediction results is less than 0.65%.

Compact Quantum Cascade Laser-Based Noninvasive Glucose Sensor Upgraded with Direct Comb Data-Mining.

Mid-infrared spectral analysis has long been recognized as the most accurate noninvasive blood glucose measurement method, yet no practical compact mid-infrared blood glucose sensor has ever passed the accuracy benchmark set by the USA Food and Drug Administration (FDA): to substitute for the finger-pricking glucometers in the market, a new sensor must first show that 95% of their glucose measurements have errors below 15% of these glucometers. Although recent innovative exploitations of the well-established Fourier-transform infrared (FTIR) spectroscopy have reached such FDA accuracy benchmarks, an FTIR spectrometer is too bulky. The advancements of quantum cascade lasers (QCLs) can lead to FTIR spectrometers of reduced size, but compact QCL-based noninvasive blood glucose sensors are not yet available. This work reports on two compact sensor system designs, both reaching the FDA accuracy benchmark. Each design commonly comprises a mid-infrared QCL for emission, a multiple attenuation total reflection prism (MATR) for data acquisition, and a computer-controlled infrared detector for data analysis. The first design translates the comb-like signals into conventional spectra, and then data-mines the resultant spectra to yield blood glucose concentrations. When a pressure actuator is employed to press the patient's hypothenar against the MATR, the sensor accuracy is considered to reach the FDA accuracy benchmark. The second design abandons the data processing step of translating combs-to-spectra and directly data-mines the "first-hand" comb signal. Beyond increasing the measurement accuracy to the FDA accuracy benchmark, even without a pressure actuator, direct comb data-mining upgrades the sensor system with speed and data integrity, which can impact the healthcare of diabetic patients. Specifically, the sensor performance is validated with 492 glucose absorption scans in the time domain, each with 20 million datapoints measured from four subjects with glucose concentrations of 3.9-7.9 mM. The sensor data-mines 164 sets of critical singularity strengths, each comprising 4 critical singularity strengths directly from the 9840 million raw signal datapoints, and the 656 critical singularity strengths are subjected to a machine-learning regression model analysis, which yields 164 glucose concentrations. These concentrations are correlated with those measured with a standard finger-pricking glucometer. An accuracy of 99.6% is confirmed from the 164 measurements with errors not more than 15% from the reference of the standard glucometer.

Exploring Environmental Microfungal Diversity Through Serial Single Cell Screening.

Known for its remarkable diversity and ecological importance, the fungal kingdom remains largely unexplored. In fact, the number of unknown and undescribed fungi is predicted to exceed the number of known fungal species by far. Despite efforts to uncover these dark fungal taxa, we still face inherent sampling biases and methodological limitations. Here, we present a framework that combines taxonomic knowledge, molecular biology and data processing to explore the fungal biodiversity of enigmatic aquatic fungal lineages. Our work is based on serial screening of environmental fungal cells to approach unknown fungal taxa. Microscopic documentation is followed by DNA analysis of laser micro-dissected cells, coupled with a ribosomal operon barcoding step realised by long-read sequencing, followed by an optional whole genome sequencing step. We tested this approach on a range of aquatic fungal cells mostly belonging to the ecological group of aquatic hyphomycetes derived from environmental samples. From this initial screening, we were able to identify 60 potentially new fungal taxa in the target dataset. By extending this methodology to other fungal lineages associated with different habitats, we expect to increasingly characterise the molecular barcodes of dark fungal taxa in diverse environmental samples. This work offers a promising solution to the challenges posed by unknown and unculturable fungi and holds the potential to be applied to the diverse lineages of undescribed microeukaryotes.

A digital platform for automated analysis of 1H NMR data: prototype framework of digital reference standard

This study introduces an innovative approach to automatically analyzing 1H nuclear magnetic resonance (NMR) data, integrating a quantum mechanical spectral analysis (QMSA) to enhance efficiency over manual data analysis in NMR. This proposed digital platform features a standardized quantitative NMR (qNMR) procedure, digital Reference Spectra within a spectral library, and a visualized interactive interface for streamlined, automated NMR data analysis. This digital platform significantly reduces the need for manual intervention and shows promise in using spectral fitting to compare the calculated spectrum derived from a digital Reference Spectrum with an experimental spectrum. The digital platform excels in processing data of both single chemical and mixture, delivering accurate results, and positioning it as an essential tool for automatic 1H qNMR analyses. Case studies highlight the digital platform’s effectiveness, demonstrating its wide-ranging applicability. This shows that the proposed digital platform, based on a qNMR-based QMSA approach, offers reliable qualitative and quantitative analyses and paves the way for integrating digital reference standards into future compendial tests.Graphical

Astronomical Image Denoising Using AttentionGAN

Denoising astronomical images is a significant challenge in the field of astronomical data processing. Image data acquired from astronomical sources typically contains noise from various sources. The study aims to investigate the denoising of astronomical images using an image-to-image translation approach with AttentionGAN method. This method combines attention-guided techniques with a Generative Adversarial Network (GAN) model to improve the quality of noisy astronomical images. Attention-guided technique allows the model to learn the most important features of the image and guide the image generation process. This approach has been tested on several images in different domains, each with varying levels of noise. The results shows that AttentionGAN method produces denoised images with better and sharper quality than several other denoising methods. Two databases, The Panoramic Survey Telescope and Rapid Response System (PAN-STARRS) and the Sloan Digital Sky Survey (SDSS), were used in this research. Images acquired from PAN-STARRS contain noise, while images acquired from SDSS are clean. Overall, this research contributes to improving the quality of astronomical images by demonstrating the effectiveness of the AttentionGAN method in denoising noisy astronomical images. We employed denoising techniques using CycleGAN and AttentionGAN and evaluated them using metrics such as PSNR, SSIM, and FID. The analysis showed that the AttentionGAN model outperformed CycleGAN. We also conducted ablation studies to further investigate the components of the AttentionGAN model. This study provides a foundation for future research in the field of astronomical data processing, which has the potential to enhance image quality.

Tantangan dan Peluang Pengimplementasian Kurikulum Merdeka di SD/MI Sumatera Selatan

This study aims to analyze the implementation process of the Merdeka Curriculum in MI/SD in South Sumatra. Several issues arise during the implementation process, including teacher readiness, limited training, inadequate infrastructure, and community understanding. This research is a qualitative study with a field approach, employing data collection techniques such as interviews, observations, and documentation. The informants in this study consist of 25 individuals, including educators, school principals, and students. The process of data processing and analysis is conducted in three stages: data reduction, data presentation, and conclusion drawing. Data validity is ensured through triangulation methods, which include source, technique, and time triangulation. Based on the results and discussion, the curriculum provides opportunities to enhance learning through a flexible, student-centered approach and the development of character and 21st-century skills. Several schools in South Sumatra have implemented project-based learning, differentiated instruction, and technology to create active and innovative learning environments. Despite the challenges, the Merdeka Curriculum has a positive impact on improving the quality of education and preparing a creative and independent younger generation

Multi-gate neuron-like transistors based on ensembles of aligned nanowires on flexible substrates

The intriguing way the receptors in biological skin encode the tactile data has inspired the development of electronic skins (e-skin) with brain-inspired or neuromorphic computing. Starting with local (near sensor) data processing, there is an inherent mechanism in play that helps to scale down the data. This is particularly attractive when one considers the huge data produced by large number of sensors expected in a large area e-skin such as the whole-body skin of a robot. This underlines the need for biological skin like processing in the e-skin. Herein, we present multi-gate field-effect transistors (v-FET) having capacitively coupled floating gate (FG) to mimic some of the neural functions. The v-FETs are obtained by deterministic assembly of ZnO nanowires on a flexible substrate using contactless dielectrophoresis method, followed metallization using conventional microfabrication steps. The spatial summation of two presynaptic inputs (applied at multiple control gates) of the transistor confirm their neuron-like response. The temporal summation (such as paired-pulse facilitation) by presented v-FETs further confirm their neuron-like mimicking with one presynaptic input. The temporal and spatial summation functions, demonstrated by the v-FET presented here, could open interesting new avenues for development of neuromorphic electronic skin (v-skin) with possibility of biological-skin like distributed computing.

The Concept Of Legal Norms Of Personal Data Protection Related To Data Processing In The Form Of Artificial Intelligence

Issues examined in this study: first, How is the protection of personal data related to data processing in the form of artificial intelligence in Indonesia. Second, what is the concept of the existing legal norms of personal data protection related to data processing in the form of artificial intelligence. The method of research is carried out in literature with the type of normative legal research, according to dogmatic issues related to the emptiness of norms. Findings in this study found the protection of personal data related to data processing in the form of artificial intelligence in Indonesia there are two views. In this regard, the protection of personal data in data processing through artificial intelligence in Indonesia has no legal certainty to be protected. Because legal certainty refers to the application of clear, fixed, consistent and consequent laws whose implementation cannot be influenced by subjective circumstances. The concept of existing legal norms for personal data protection related to data processing in the form of artificial intelligence special arrangements stipulated in Law Number 27 of 2022 can be said to have accommodated personal rights. However, the existing condition of artificial intelligence norm is only legally domiciled as a legal object, because it is needed by the party responsible for the actions of artificial intelligence. Legal liability for the actions of artificial intelligence can be charged or imposed on the organizers of artificial intelligence.

Zero-Trust Access Control Mechanism Based on Blockchain and Inner-Product Encryption in the Internet of Things in a 6G Environment.

Within the framework of 6G networks, the rapid proliferation of Internet of Things (IoT) devices, coupled with their decentralized and heterogeneous characteristics, presents substantial security challenges. Conventional centralized systems face significant challenges in effectively managing the diverse range of IoT devices, and they are inadequate in addressing the requirements for reduced latency and the efficient processing and analysis of large-scale data. To tackle these challenges, this paper introduces a zero-trust access control framework that integrates blockchain technology with inner-product encryption. By using smart contracts for automated access control, a reputation-based trust model for decentralized identity management, and inner-product encryption for fine-grained access control, the framework ensures data security and efficiency. Firstly, smart contracts are employed to automate access control, and software-defined boundaries are defined for different application domains. Secondly, through a trust model based on a consensus algorithm of node reputation values and a registration-based inner-product encryption algorithm supporting fine-grained access control, zero-trust self-sovereign enhanced identity management in the 6G environment of the Internet of Things is achieved. Furthermore, the use of multiple auxiliary chains for storing data across different application domains not only mitigates the risks associated with data expansion but also achieves micro-segmentation, thereby enhancing the efficiency of access control. Finally, empirical evidence demonstrates that, compared with the traditional methods, this paper's scheme improves the encryption efficiency by 14%, reduces the data access latency by 18%, and significantly improves the throughput. This mechanism ensures data security while maintaining system efficiency in environments with large-scale data interactions.

Noise-agnostic quantum error mitigation with data augmented neural models

Quantum error mitigation, a data processing technique for recovering the statistics of target processes from their noisy version, is a crucial task for near-term quantum technologies. Most existing methods require prior knowledge of the noise model or the noise parameters. Deep neural networks have the potential to lift this requirement, but current models require training data produced by ideal processes in the absence of noise. Here we build a neural model that achieves quantum error mitigation without any prior knowledge of the noise and without training on noise-free data. To achieve this feature, we introduce a quantum augmentation technique for error mitigation. Our approach applies to quantum circuits and to the dynamics of many-body and continuous-variable quantum systems, accommodating various types of noise models. We demonstrate its effectiveness by testing it both on simulated noisy circuits and on real quantum hardware.

The impact of dance on enhancing social skills and emotional intelligence through creativity.

This research aims to analyze the impact of dance experience on the development of social and emotional competence in individuals, as well as to determine the influence of dance on the development of social interaction skills, communication, and perception of others. Additionally, through experimental means, effective strategies for utilizing dance to improve levels of social and emotional competence are identified. The study was conducted using online surveys and questionnaires. For data processing and analysis, a multivariate analysis of variance (MANOVA) was employed, enabling the assessment of differences between the control and experimental groups across multiple dependent variables. Participants in the experimental group who participated in the program achieved a mean score of 75, which was higher than the control group's mean score of 70. The MANOVA confirmed the statistical significance of the obtained results, with an F-statistic of 7.412 and a p-value of <0.05. Furthermore, the experimental group demonstrated greater consistency in achieving program goals, as evidenced by a smaller standard deviation (10) compared to the control group (12). Therefore, the dance program can be recognized as an effective tool for developing social and emotional competence in youth. The practical significance of this article lies in the confirmation that participants in the experimental group, engaged in the program, achieved higher mean scores compared to the control group without such experience. Statistical analysis underscores the effectiveness of the program.

Designing Face Detection Systems with Gray Wolf Optimization

The main objective of this paper project was to create a state-of-the-art face identification technique that can handle the various difficulties caused by changes in illumination, occlusions, and facial emotions. Face detection is a cornerstone of computer vision, facilitating diverse applications ranging from surveillance systems to human-computer interaction. Throughout this paper, the comprehensive exploration of advancing face detection methodologies has been undertaken, culminating in developing and evaluating a novel approach. The challenges posed by variations in facial expressions, lighting conditions, and occlusions necessitated a multifaceted solution. Our proposed method, which consists of interconnected steps, works quite well to overcome these challenges. Using deep learning architectures to increase feature extraction and discrimination was beneficial in the initial stage of fine-tuning Residual Networks (ResNet-50) to serve as the Region-based Convolutional Neural Network (Faster R-CNN) framework classifier. The process of gradually optimizing thresholds, such as batch size, learning rate, and detection threshold, involved using the Gray Wolf optimization technique (GWO). The conversion process was accelerated and improved overall detection process efficiency and accuracy using a clever fusion of machine learning and metaheuristic optimization techniques. A key component of our methodology is the careful data processing, which was necessary to ensure. The suggested method was carefully examined on a particular dataset, and the 94% training accuracy that was attained together with an identical test dataset accuracy highlights the method's resilience. These findings support the effectiveness of our approach in reducing false positives and negatives, resulting in unmatched recall and precision in the detection system. The discovery has significant significance as it can potentially improve face detection systems' performance and reliability in various real-world applications, such as human-computer interaction and surveillance. Convolutional neural networks, deep learning architectures, and metaheuristic optimization approaches were synergized to produce a new and reliable solution.

Microsoft Azure vs. Google Cloud Platform: An In-Depth Review of Services, Pricing, and Performance

This comparative review paper aims to provide a comprehensive analysis of two leading cloud platforms: Microsoft Azure and Google Cloud Platform (GCP). Cloud computing has become pivotal in modern technology infrastructure, offering scalability, cost-efficiency, and a wide range of services. Both Azure and GCP have established themselves as dominant players in this domain, each with its unique strengths and service offerings. The core services of both platforms, including compute, storage, and networking, are evaluated to understand their performance, flexibility, and ease of use. Azure’s Virtual Machines and Kubernetes Service are compared with GCP’s Compute Engine and Kubernetes Engine, highlighting their capabilities in handling various workloads (Jones, 2020, p. 15). On the storage front, Azure Blob Storage and Data Lake are examined alongside GCP’s Cloud Storage and Filestore for their scalability and integration capabilities (Smith, 2021, p. 25). Specialized services such as AI and machine learning, big data analytics, and database solutions are also compared. Azure’s Machine Learning and Cognitive Services are measured against GCP’s AI Platform and AutoML, showcasing their impact on data-driven decision-making (Miller &amp; Davis, 2022, p. 30). For big data, Azure Synapse Analytics and HDInsight are juxtaposed with GCP’s BigQuery and Dataflow to discuss their efficiency in handling large-scale data processes (Clark, 2019, p. 19). Pricing models and cost management tools are critical for enterprises, making it essential to compare Azure’s Pay-as-you-go and Reserved Instances with GCP’s On-demand and Sustained Use Discounts (Brown, 2021, p. 42). Additionally, security and compliance are paramount, thus the security frameworks and certifications of both platforms are rigorously evaluated (White, 2022, p. 28). Usability factors such as user interfaces, documentation, training resources, and support plans are analyzed to understand the ease of adoption and customer support experiences. Case studies and user feedback provide real-world insights into each platform’s performance and reliability, drawing on enterprise-level and SMB use cases (Green, 2020, p. 37). Finally, the review discusses the innovation trajectories and future directions of both Azure and GCP, highlighting recent feature updates and predicting future trends in the cloud computing landscape (Taylor, 2022, p. 50). The paper concludes with a summary of findings, offering tailored recommendations based on specific use cases, thereby aiding organizations in making informed decisions about their cloud strategy.

A privacy-enhanced framework with deep learning for botnet detection

A botnet is a group of hijacked devices that conduct various cyberattacks, which is one of the most dangerous threats on the internet. Organizations or individuals use network traffic to mine botnet communication behavior features. Network traffic often contains individual users’ private information, such as website passwords, personally identifiable information, and communication content. Among the existing botnet detection methods, whether they extract deterministic traffic interaction features, use DNS traffic, or methods based on raw traffic bytes, these methods focus on the detection performance of the detection model and ignore possible privacy leaks. And most methods are combined with machine learning and deep learning technologies, which require a large amount of training data to obtain high-precision detection models. Therefore, preventing malicious persons from stealing data to infer privacy during the botnet detection process has become an issue worth pondering. Based on this problem, this article proposes a privacy-enhanced framework with deep learning for botnet detection. The goal of this framework is to learn a feature extractor. It can hide the private information that the attack model tries to infer from the intermediate anonymity features, while maximally retaining the interactive behavior features contained in the original traffic for botnet detection. We design a privacy confrontation algorithm based on a mutual information calculation mechanism. This algorithm simulates the game between the attacker trying to infer private information through the attack model and the data processor retaining the original content of the traffic to the maximum extent. In order to further ensure the privacy protection of the feature extractor during the training process, we train the feature extractor in the federated learning training mode. We extensively evaluate our approach, validating it on two public datasets and comparing it with existing methods. The results show that our method can effectively ensure detection accuracy on the basis of removing private information. For the CTU-13 dataset, the detection framework achieves the best detection performance; for the ISCX-2014 dataset, the accuracy of the framework is less than 1% lower than the best effect.

Are psychological attitudes towards vaccination an expression of personality? A cross-sectional study on COVID-19 vaccination in France

BackgroundThe capacity of the 7C model’s psychological antecedents, which include confidence in vaccines, complacency, convenience, calculation, collective responsibility, confidence in the wider system, and social conformism, to explain variance in COVID-19 vaccine intentions and behaviours has been documented. However, it remains unclear whether the attitudes represented by the 7C psychological antecedents are specific to vaccination or if they are, in fact, an expression of underlying personality traits.MethodsFrom February to June 2022, French adults completed self-administered questionnaires assessing COVID-19 vaccination history, the 7C antecedents, and personality traits (“ComCor” and “Cognitiv” studies). Vaccination behaviours were studied through three outcomes: at-least-one-dose vaccination status by 2022 (N = 49,019), up-to-date vaccination status (N = 46,566), and uptake speed of first dose (N = 25,998). Personality traits were evaluated using the French version of the Big Five Inventory (BFI-Fr). Multivariable logistic regressions and Cox models predicting vaccine behaviours were run with the 7C antecedents, both with and without personality traits.ResultsAmong the 49,019 participants, 95.0% reported receipt of at least one dose and 89.8% were up to date with recommendations. All 7C antecedents were significantly associated with the outcomes. The inclusion of personality traits did not substantially alter the effect estimates of the association between the 7C antecedents and vaccination behaviours, with differences between effect sizes of models with and without personality traits being < 5%.ConclusionsOur results suggest that the 7C psychological antecedents of vaccination are not the mere expression of personality and that their impact on vaccine behaviours is independent of personality traits. As such, the 7C antecedents may be modifiable by appropriate information and vaccine promotion.Trial registrationThe “ComCor” study received ethical approval by the Comité de Protection des Personnes Sud Ouest et Outre Mer 1 on 21/09/2020. The addition of the “Cognitiv” questionnaire received ethical approval by the Comité de Protection des Personnes Sud Ouest et Outre Mer 1 on 01/02/2022. The data protection authority Commission Nationale de l’Informatique et des Libertés (CNIL) authorised the processing of data on 21/10/2020. The study is registered with ClinicalTrials.gov under the identifier NCT04607941.

Differential absorption laser spectroscopy at 8 kHz using precompensated current modulation

We present a differential laser absorption spectroscopy (DLAS) system operating at 1550 nm for rapid and sensitive gas concentration measurements. A dual-wavelength toggling mechanism is presented, which significantly reduces data processing, hence supporting a high update rate and data robustness against fast-changing environmental conditions. We showcase the ability to toggle between two wavelengths separated by 90 pm in 14 μs and with minimal chirp (∼1 pm), facilitating sensitive DLAS measurements at 8 kHz update rate. This performance is achieved by driving a 1550 nm diode laser with a modified square-wave current pulse, overcoming the thermal time constant limited wavelength-modulation response of the diode laser. A sensitive feedback mechanism ensures excellent long-term wavelength stability better than 1.4 pm peak-to-peak at 8 kHz toggling over 20 h. As a performance test, we measured the volumetric ratio (VMR) of hydrogen cyanide (HCN) gas in a fiber-coupled gas cell with less than 0.2% peak-to-peak variation over 20 h at 40 Hz. A best sensitivity in VMR of 8×10−6 was achieved at 25 ms integration time. The simplicity and high update rate of our system make it well-suited for gas monitoring in dynamic atmospheric and industrial environments. Further, it offers potential utility in applications requiring precise wavelength control, such as injection seeding of pulsed lasers. A simple analytical model is derived, which, in detail, supports the experimental results, hence offering a tool for future design optimization.

Predicting Risks in Healthcare Claims Using Advanced Data Processing and Machine Learning Techniques

Predicting Risks in Healthcare Claims Using Advanced Data Processing and Machine Learning Techniques