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Looking at caregivers' faces is important for early social development, and there is a concomitant increase in neural correlates of attention to familiar versus novel faces in the first 6 months. However, by 12 months of age brain responses may not differentiate between familiar and unfamiliar faces. Traditional group‐based analyses do not examine whether these 'null' findings stem from a true lack of preference within individual infants, or whether groups of infants show individually strong but heterogeneous preferences for familiar versus unfamiliar faces. In a preregistered proof‐of‐principle study, we applied Neuroadaptive Bayesian Optimisation (NBO) to test how individual infants' neural responses vary across faces differing in familiarity. Sixty‐one 5–12‐month‐olds viewed faces resulting from gradually morphing a familiar (primary caregiver) into an unfamiliar face. Electroencephalography (EEG) data from fronto‐central channels were analysed in real‐time. After the presentation of each face, the Negative central (Nc) event‐related potential (ERP) amplitude was calculated. A Bayesian Optimisation algorithm iteratively selected the next stimulus until it identified the stimulus eliciting the strongest Nc for that infant. Attrition (15%) was lower than in traditional studies (22%). Although there was no group‐level Nc‐difference between familiar versus unfamiliar faces, an optimum was predicted in 85% of the children, indicating individual‐level attentional preferences. Traditional analyses based on infants' predicted optimum confirmed NBO can identify subgroups based on brain activation. Optima were not related to age and social behaviour. NBO suggests the lack of overall familiar/unfamiliar‐face attentional preference in middle infancy is explained by heterogeneous preferences, rather than a lack of preference within individual infants. [ABSTRACT FROM AUTHOR]

Methods from Machine Learning (ML) and Computer Vision (CV) have proven powerful tools for quickly and accurately analyzing behavioral recordings. The computational complexity of these techniques, however, often precludes applications that require real-time analysis: for example, experiments where a stimulus must be applied in response to a particular behavior or samples must be collected soon after the behavior occurs. Here, we describe SARTAB (Scalable Automated Real-Time Analysis of Behavior), a system that achieves automated real-time behavior detection by continuously monitoring animal positions relative to behaviorally relevant Regions Of Interest (ROIs). We then show how we used this system to detect infrequent courtship behaviors in Pseudotropheus demasoni (a species of Lake Malawi African cichlid fish) to collect neural tissue samples from actively behaving individuals for multiomic profiling at single nucleus resolution. Within this experimental context, we achieve high ROI and animal detection accuracies (mAP@ [.5 :.95] of 0.969 and 0.718, respectively) and 100% classification accuracy on a set of 32 manually selected behavioral clips. SARTAB is unique in that all analysis runs on low-cost, edge-deployed hardware, making it a highly scalable and energy-efficient solution for real-time experimental feedback. Although our solution was developed specifically to study cichlid courtship behavior, the intrinsic flexibility of neural network analysis ensures that our approach can be adapted to novel species, behaviors, and environments. [ABSTRACT FROM AUTHOR]

This study addressed the quality assessment of photovoltaic (PV) panels by analyzing their efficiency and electrical power under varying environmental conditions. A sophisticated algorithm was developed to extract and analyze PV panel voltage and current data in a complex manner, allowing the identification of key parameters such as open circuit voltage (Voc), short circuit current (Isc), and peak electrical power. These parameters were compared to the input power to accurately determine the panel efficiency. The novelty of this approach lies in its real-time implementation using a DC/DC (buck-boost) converter equipped with precise voltage and current sensors and running on a TMS320f379D board, linking theoretical knowledge with practical results. Experimental results demonstrated that temperature and irradiation significantly influence PV performance. With a 10°C increase in temperature, it resulted in a 5-10% decrease in output power, while a 100 W/m² increase in irradiation resulted in a 10-15% increase in output power. The study highlights the importance of considering both temperature and irradiation variations to optimize PV system design and operation, providing a robust method to assess PV panel quality in real-time. [ABSTRACT FROM AUTHOR]

Emotion prediction is a subset of sentiment analysis that aims to extract emotions from text, speech, or images. The researchers posit that emotions determine human behavior, making the development of a method to recognize emotions automatically crucial for use during global crises, such as the COVID-19 pandemic. In this paper, a real-time system is developed that identifies and predicts emotions conveyed by users in Arabic tweets regarding COVID-19 into standard six emotions based on the big data platform, Apache Spark. The system consists of two main stages: (1) Developing an offline model and (2) Online emotion prediction pipeline. For the first stage, two different approaches: The deep Learning (DL) approach and the Transfer Learning-based (TL) approach to find the optimal classifier for identifying and predicting emotion. For DL, three classifiers are applied: Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). For TL, five models are applied: AraBERT, ArabicBERT, ARBERT, MARBERT, and QARiB. For the second stage, create a Transmission Control Protocol (TCP) socket between Twitter's API and Spark used to receive streaming tweets and Apache Spark to predict the label of tweets in real-time. The experimental results show that the QARiB model achieved the highest Jaccard accuracy (65.73%), multi-accuracy (78.71%), precision-micro (78.71%), recall-micro (78.71%), f-micro (78.71%), and f-macro (78.55%). The system is available as a web-based application that aims to provide a real-time visualization of people's emotions during a crisis. [ABSTRACT FROM AUTHOR]

Large-scale hydro-steel structures (LS-HSSs) are vital to hydraulic engineering, supporting critical functions such as water resource management, flood control, power generation, and navigation. However, due to prolonged exposure to severe environmental conditions and complex operational loads, these structures progressively degrade, posing increased risks over time. The absence of effective structural health monitoring (SHM) systems exacerbates these risks, as undetected damage and wear can compromise safety. This paper presents an advanced SHM framework designed to enhance the real-time monitoring and safety evaluation of LS-HSSs. The framework integrates the finite element method (FEM), multi-sensor data fusion, and Internet of Things (IoT) technologies into a closed-loop system for real-time perception, analysis, decision-making, and optimization. The system was deployed and validated at the Luhun Reservoir spillway, where it demonstrated stable and reliable performance for real-time anomaly detection and decision-making. Monitoring results over time were consistent, with stress values remaining below allowable thresholds and meeting safety standards. Specifically, stress monitoring during radial gate operations (with a current water level of 1.4 m) indicated that the dynamic stress values induced by flow vibrations at various points increased by approximately 2 MPa, with no significant impact loads. Moreover, the vibration amplitude during gate operation was below 0.03 mm, confirming the absence of critical structural damage and deformation. These results underscore the SHM system's capacity to enhance operational safety and maintenance efficiency, highlighting its potential for broader application across water conservancy infrastructure. [ABSTRACT FROM AUTHOR]

Six hundred million cases of disease and roughly 420,000 deaths occur globally each year due to foodborne pathogens. Current methods to screen and identify pathogens in swine, poultry, and cattle products include immuno-based techniques (e.g., immunoassay integrated biosensors), molecular methods (e.g., DNA hybridization and PCR assays), and traditional culturing. These methods are often used in tandem to screen, quantify, and characterize samples, prolonging real-time comprehensive analysis. Next-generation sequencing (NGS) is a relatively new technology that combines DNA-sequencing chemistry and bioinformatics to generate and analyze large amounts of short- or long-read DNA sequences and whole genomes. The goal of this project was to evaluate the quantitative capabilities of the real-time NGS Oxford Nanopore Technologies' MinION sequencer through a shotgun-based sequencing approach. This investigation explored the correlation between known amounts of the analyte (lambda DNA as a pathogenic bacterial surrogate) with data output, in both the presence and absence of a background matrix (Bos taurus DNA). A positive linear correlation was observed between the concentration of analyte and the amount of data produced, number of bases sequenced, and number of reads generated in both the presence and absence of a background matrix. In the presence of bovine DNA, the sequenced data were successfully mapped to the NCBI lambda reference genome. Furthermore, the workflow from pre-extracted DNA to target identification took less than 3 h, demonstrating the potential of long-read sequencing in food safety as a rapid method for screening, identification, and quantification. [ABSTRACT FROM AUTHOR]

Credit card fraud is a huge challenge in the financial sector, causing huge losses every year. The problem is exacerbated by increased marketing and sophisticated fraudulent activities. This study addresses the important issue of accurate real-time detection of fraudulent transactions to minimize financial losses and enhance transactional security. The main objective of this study is to develop a comprehensive fraud detection algorithm using deep learning techniques, specially designed to address the complexity and volume of modern credit card transactions. Key contributions of this research include the presentation of a new deep learning algorithm optimized for credit card fraud detection, the integration of feature engineering techniques to improve the performance of the model, and a potential scalable solution analysis in real-time Significant improvement in proven rates. The results show that the proposed deep learning-based model achieves higher accuracy and lower false positive rate, giving financial institutions a significant advantage in protecting against fraudulent activities about the character. This study highlights the power of deep learning in reforming fraud detection systems, and lays the foundation for future developments in this important area. [ABSTRACT FROM AUTHOR]

To provide applications with the ability to analyze fresh data and eliminate the time-consuming ETL workflow, hybrid transactional and analytical (HTAP) systems have been developed to serve online transaction processing and online analytical processing workloads in a single system. In recent years, HTAP systems have attracted considerable interest from both academia and industry. Several new architectures and technologies have been proposed. This paper provides a comprehensive overview of these HTAP systems. We review recently published papers and technical reports in this field and broadly classify existing HTAP systems into two categories based on their data formats: monolithic and hybrid HTAP. We further classify hybrid HTAP into four sub-categories based on their storage architecture: row-oriented, column-oriented, separated, and hybrid. Based on such a taxonomy, we outline each stream's design challenges and performance issues (e.g., the contradictory format demand for monolithic HTAP). We then discuss potential solutions and their trade-offs by reviewing noteworthy research findings. Finally, we summarize emerging HTAP applications, benchmarks, future trends, and open problems. [ABSTRACT FROM AUTHOR]

Modifying the chemical environment of active surfaces with ionic liquids (IL) is an emerging strategy for tailoring novel electrocatalytic systems, including carbon dioxide reduction (CO2RR). Although copper (Cu) catalysts have recently gained more attention in this field, their modification with ILs is yet to be investigated. This work tested a range of common hydrophobic ILs impregnated into carbon‐supported Cu catalysts, following the "solid catalyst with ionic liquid layer" (SCILL) approach. The latter was used to showcase the applicability of real‐time product detection for CO2RR employing electrochemical mass spectrometry. The observed patterns of C1 to C3 product selectivity offered valuable insights into the intricate reaction mechanism. In addition, increasing the size of the IL cation showed an opposite and significant effect on the reaction selectivity. The obtained qualitative results were partially compared with conventional long‐term experiments. [ABSTRACT FROM AUTHOR]

An X-ray fluorescence handheld spectrometer (hh-XRF) is adapted for real-time qualitative and quantitative elemental analysis in scanning mode for applications in cultural heritage. Specifically, the Tracer-5i (Bruker) is coupled with a low-cost constructed computer-controlled x–y target stage that enables the remote control of the target's movement under the ionizing X-ray beam. Open-source software synchronizes the spectrometer's measuring functions and handles data acquisition and data analysis. The spectrometer's analytical capabilities, such as sensitivity, energy resolution, beam spot size, and characteristic transition intensity as a function of the distance between the spectrometer and the target, are evaluated. The XRF scanner's potential in real-time imaging, object classification, and quantitative analysis in cultural heritage-related applications is explored and the imaging capabilities are tested by scanning a 19th-century religious icon. The elemental maps provide information on used pigments and reveal an underlying icon. The scanner's capability to classify metallic objects was verified by analyzing the measured raw spectra of a coin collection using Principal Components Analysis. Finally, the handheld's capability to perform quantitative analysis in scanning mode is demonstrated in the case of precious metals, applying a pre-installed quantification routine. [ABSTRACT FROM AUTHOR]

Railway infrastructure safety is a paramount concern, with bolt integrity being a critical component. In the realm of railway maintenance, the detection of missing bolts is a vital task that ensures the stability and safety of tracks. Traditionally, this task has been approached through manual inspections or conventional automated methods, which are often time-consuming, costly, and prone to human error. Addressing these challenges, this paper presents a state-of-the-art solution with the development of a lightweight convolutional neural network (CNN) featuring an integrated attention mechanism. This novel model is engineered to be computationally efficient while maintaining high accuracy, making it particularly suitable for real-time analysis in resource-constrained environments commonly found in railway inspections. The proposed CNN utilises a distinctive architecture that synergises the speed of lightweight networks with the precision of attention-based mechanisms. By integrating an attention mechanism, the network selectively concentrates on regions of interest within the image, effectively enhancing the model's capability to identify missing bolts with remarkable accuracy. Comprehensive testing showcases a remarkable 96.43% accuracy and an impressive 96 F1-score, substantially outperforming existing deep learning frameworks in the context of missing bolt detection. Key contributions of this research include the model's innovative attention-integrated approach, which significantly reduces the model complexity without compromising detection performance. Additionally, the model offers scalability and adaptability to various railway settings, proving its efficacy not just in controlled environments but also in diverse real-world scenarios. Extensive experiments, rigorous evaluations, and real-time deployment results collectively underscore the transformative potential of the presented CNN model in advancing the domain of railway safety maintenance. [ABSTRACT FROM AUTHOR]

We consider the challenge of efficiently detecting changes within a network of sensors, where we also need to minimise communication between sensors and the cloud. We propose an online, communication-efficient method to detect such changes. The procedure works by performing likelihood ratio tests at each time point, and two thresholds are chosen to filter unimportant test statistics and make decisions based on the aggregated test statistics respectively. We provide asymptotic theory concerning consistency and the asymptotic distribution if there are no changes. Simulation results suggest that our method can achieve similar performance to the idealised setting, where we have no constraints on communication between sensors, but substantially reduce the transmission costs. [ABSTRACT FROM AUTHOR]

Online changepoint detection algorithms that are based on (generalised) likelihood-ratio tests have been shown to have excellent statistical properties. However, a simple online implementation is computationally infeasible as, at time T, it involves considering O(T) possible locations for the change. Recently, the FOCuS algorithm has been introduced for detecting changes in mean in Gaussian data that decreases the per-iteration cost to O (log T) . This is possible by using pruning ideas, which reduce the set of changepoint locations that need to be considered at time T to approximately log T . We show that if one wishes to perform the likelihood ratio test for a different one-parameter exponential family model, then exactly the same pruning rule can be used, and again one need only consider approximately log T locations at iteration T. Furthermore, we show how we can adaptively perform the maximisation step of the algorithm so that we need only maximise the test statistic over a small subset of these possible locations. Empirical results show that the resulting online algorithm, which can detect changes under a wide range of models, has a constant-per-iteration cost on average. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous high‐throughput sequencing studies, there is a growing need for analysis tools that are not just performant but also comprehensive and user‐friendly enough to cater to both novice and advanced users. This article introduces SeqKit2, the next iteration of the widely used sequence analysis tool SeqKit, featuring expanded functionality, performance optimizations, and support for additional compression methods. Retaining a pragmatic subcommand architecture, SeqKit2 represents substantial enhancement through the inclusion of 19 additional subcommands, expanding its overall repertoire to a total of 38 in eight categories. The new subcommands add functionality such as amplicon processing and robust, error‐tolerant parsing of sequence records. In addition, three subcommands designed for real‐time analysis are added for periodic monitoring of properties of FASTQ and Binary Alignment/Map alignment records and real‐time streaming from multiple sequence files. The performance of SeqKit2 is benchmarked against the old version of SeqKit, Bioawk, Seqtk, and SeqFu tools. SeqKit2 consistently outperforms its predecessor, albeit with marginally higher memory usage, while maintaining competitive runtimes against other tools. With its broad functionality, proven usability, and ongoing development driven by user feedback, we hope that bioinformaticians will find SeqKit2 useful as a "Swiss army knife" of sequence and alignment processing—equally adept at facilitating ad hoc analyses and seamlessly integrating into larger pipelines. Highlights: SeqKit2 expands its capabilities, doubling the number of subcommands from 19 to 38, and adding support for three more compression file formats.SeqKit2 outperforms its predecessor and maintains competitive with other tools.SeqKit2 improves user‐friendliness with new features, like, autocompletion, progress bars, and enhanced error handling. [ABSTRACT FROM AUTHOR]

Arsenic contamination poses a significant public health risk worldwide, with chronic exposure leading to various health issues. Detecting and monitoring arsenic exposure accurately remains challenging, necessitating the development of sensitive detection methods. In this study, we introduce a novel approach using fast-scan cyclic voltammetry (FSCV) coupled with carbon-fiber microelectrodes (CFMs) for the electrochemical detection of As3+. Through an in-depth pH study using tris buffer, we optimized the electrochemical parameters for both acidic and basic media. Our sensor demonstrated high selectivity, distinguishing the As3+ signal from those of As5+ and other potential interferents under ambient conditions. We achieved a limit of detection (LOD) of 0.5 μM (37.46 ppb) and a sensitivity of 2.292 nA/μM for bare CFMs. Microscopic data confirmed the sensor's stability at lower, physiologically relevant concentrations. Additionally, using our previously reported double-bore CFMs, we simultaneously detected As3+-Cu2+ and As3+-Cd2+ in tris buffer, enhancing the LOD of As3+ to 0.2 μM (14.98 ppb). To our knowledge, this is the first study to use CFMs for the rapid and selective detection of As3+ via FSCV. Our sensor's ability to distinguish As3+ from As5+ in a physiologically relevant pH environment showcases its potential for future in vivo studies. [ABSTRACT FROM AUTHOR]

The current mainstream image semantic segmentation networks often suffer from mis-segmentation, segmentation discontinuity, and high model complexity, which limit their application in real-time processing scenarios. The work established a lightweight neural network model for semantic segmentation to address this issue. The network used a dual-branch strategy to solve low semantic boundary segmentation accuracy in semantic segmentation tasks. The semantic branch applied the characteristics of the deeplabv3 + model structure. Besides, dilated convolutions with different dilation rates in the encoder were used to expand the receptive field of convolutional operations and enhance the ability to capture local features. The boundary refinement branch extracted second-order differential features of the input image through the Laplace operator, and it gradually refined the second-order differential features through a feature refinement extraction module to obtain advanced semantic features. A convolutional block attention module was introduced to filter the features from both the channel and spatial dimensions and finally fused with the semantic branch to achieve constrained segmentation boundary effects. Based on this, a multi-channel attention fusion module was proposed to aggregate features from different stages. Low-resolution features were first up-sampled and then fused with high-resolution features to enhance the spatial information of high-level features. Proposed network's effectiveness was demonstrated through extensive experiments on the MaSTr1325 dataset, the MID dataset, the Camvid dataset, and the PASCAL VOC2012 dataset, with mIoU of 98.1, 73.1, and 81.1% and speeds of 111.40, 100.36, and 111.43 fps on a single NVIDIA RTX 3070 GPU, respectively. [ABSTRACT FROM AUTHOR]

The proposed Quantum Neural Networks (QNN) perform better than traditional machine learning models. The escalating complexity of malware poses a significant challenge to cybersecurity, necessitating innovative approaches to keep pace with its rapid evolution. Contemporary malware analysis techniques underscore the urgent need for solutions that can adapt to the dynamic functionalities of evolving malware. In this context, Quantum Neural Networks (QNNs) emerge as a cutting-edge and distinctive approach to malware analysis, promising to overcome the limitations of conventional methods. Our exploration of QNNs focuses on uncovering their valuable applications, particularly in real-time malware research. We meticulously examine the advantages of QNNs in contrast to conventional machine-learning methods employed in malware detection and classification. The proposed QNN showcases its unique capability to handle complex patterns, emphasizing its potential to achieve heightened levels of accuracy. Our contribution extends to introducing a dedicated framework for QNN-based malware analysis, harnessing the formidable computational capabilities of quantum computing for real-time malware analysis. This framework is structured around three pivotal components, Malware Feature Extraction utilizes quantum feature extraction techniques to identify relevant features from malware samples. Malware Classification employs a QNN classifier to categorize malware samples as benign or malicious. Real-Time Analysis enables the instantaneous examination of malware samples by integrating feature extraction and classification within a streaming data pipeline. Our proposed methodology undergoes comprehensive evaluation using a benchmark dataset of malware samples. The Proposed Quantum Neural Networks (QNNs) demonstrated a high accuracy of 0.95, outperforming other quantum models such as Quantum Support Vector Machines (QSVM) and Quantum Decision Trees (QDT), as well as classical models like Random Forest (RF), Support Vector Machines (SVM), and Decision Trees (DT) on the Malware DB dataset. The results affirm the framework's exceptional accuracy rates and low latency, establishing its suitability for real-time malware analysis. These findings underscore the potential for QNNs to revolutionize malware evaluation and strengthen real-time defenses against cyberattacks. While our research demonstrates promising outcomes, further exploration and development in this domain are imperative to fully exploit the extensive viability that QNNs offer for cybersecurity applications. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Model predictive control (MPC), an extensively developed rolling optimization control method, is widely utilized in the industrial field. While some researchers have incorporated predictive control into underactuated unmanned surface vehicles (USVs), most of these approaches rely primarily on theoretical simulation research, emphasizing simulation outcomes. A noticeable gap exists regarding whether predictive control adequately aligns with the practical application conditions of underactuated USVs, particularly in addressing real-time challenges. This paper aims to fill this void by focusing on the application of MPC in the path following of USVs. Using the hydrodynamic model of USVs, we examine the details of both linear MPC (LMPC) and nonlinear MPC (NMPC). Several different paths are designed to compare and analyze the simulation results and time consumption. To address the real-time challenges of MPC, the calculation time under different solvers, CPUs, and programming languages is detailed through simulation. The results demonstrate that NMPC exhibits superior control accuracy and real-time control potential. Finally, we introduce an enhanced A* algorithm and use it to plan a global path. NMPC is then employed to follow that path, showing its effectiveness in tracking a common path. In contrast to some literature studies using the LMPC method to control underactuated USVs, this paper presents a different viewpoint based on a large number of simulation results, suggesting that LMPC is not fit for controlling underactuated USVs. [ABSTRACT FROM AUTHOR]

Neurotoxic heavy metals, such as Cd2+, pose a significant global health concern due to their increased environmental contamination and subsequent detrimental health hazards they pose to human beings. These metal ions can breach the blood-brain barrierblood–brain barrier, leading to severe and often irreversible damage to the central nervous system and other vital organs. Therefore, developing a highly sensitive, robust, and rapid in vivo detection method for these hazardous heavy metal ions is of the utmost importance for early detection, thus initiating timely therapeutics. Detecting ultra-low levels of toxic metal ions in vivo and obtaining accurate speciation information remains a challenge with conventional analytical techniques. In this study, we fabricated a novel carbon carbon-fiber microelectrode (CFM)-based sensor that can detect Cd2+ ions using fast-scan cyclic voltammetry by electrodepositing gold nanoparticles (AuNP). We optimized electrochemical parameters that generate a unique cyclic voltammogram (CV) of Cd2+ at a temporal resolution of 100 ms with our novel sensor. All our experiments were performed in tris buffer that mimics the artificial cerebellum fluid. We established a calibration curve resulting in a limit of detection (LOD) of 0.01 µM with a corresponding sensitivity of 418.02 nA/ µM. The sensor's selectivity was evaluated in the presence of other metal ions, and it was noteworthy to observe that the sensor retained its ability to produce the distinctive Cd2+ CV, even when the concentration of other metal ions was 200 times higher than that of Cd2+. We also found that our sensor could detect free Cd2+ ions in the presence of complexing agents. Furthermore, we analyzed the solution chemistry of each of those Cd2+–ligand solutions using a geochemical model, PHREEQC. The concentrations of free Cd2+ ions determined through our electrochemical data align well with geochemical modeling data, thus validating the response of our novel sensor. Furthermore, we reassessed our sensor's LOD in tris buffer based on the concentration of free Cd2+ ions determined through PHREEQC analysis, revealing an LOD of 0.00132 µM. We also demonstrated the capability of our sensor to detect Cd2+ ions in artificial urine samples, showcasing its potential for application in actual biological samples. To the best of our knowledge, this is the first AuNP-modified, CFM-based Cd2+ sensor capable of detecting ultra-low concentrations of free Cd2+ ions in different complex matrices, including artificial urine at a temporal resolution of 100 ms, making it an excellent analytical tool for future real-time, in vivo detection, particularly in the brain. [ABSTRACT FROM AUTHOR]

The analysis of chemical compounds present at trace levels in liquids is important not only for environmental measurements but also, for example, in the health sector. The reference technique for the analysis of Volatile Organic Compounds (VOCs) in liquids is GC, which is difficult to use with an aqueous matrix. In this work, we present an alternative technique to GC to analyze VOCs in water. A tubular oven is used to completely vaporize the liquid sample deposited on a gauze. The oven is heated in the presence of a dinitrogen flow, and the gas is analyzed at the exit of the oven by a chemical ionization mass spectrometer developed in our laboratory. It is a low magnetic field Fourier Transform Ion Cyclotron Resonance (FT-ICR) optimized for real-time analysis. The Proton Transfer Reaction (PTR) used during the Chemical Ionization event results in the selective ionization of the VOCs present in the gas phase. The optimization of the desorption conditions is described for the main operating parameters: temperature ramp, liquid quantity, and nitrogen flow. Their influence is studied using a 100 ppmv aqueous toluene solution. The analytical method is then tested on a mixture of seven VOCs. [ABSTRACT FROM AUTHOR]

Applying a spatio-temporal endemic–epidemic forecasting model, we evaluate different perspectives on the adequacy of COVID-19 containment policies. Using Germany's early containment policy as an example, we show that containment policies judged as rational based on the real-time perspective of policymakers may be deemed unnecessary or ineffective in ex-post evaluations. We also demonstrate that one-size-fits-all policies implemented in Germany early in the pandemic are likely suboptimal. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Mass Spectrometry Society is the property of Journal of Chinese Mass Spectrometry Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Because microstructure plays an important role in the mechanical properties of structural materials, developing the capability to quantify microstructures rapidly is important to enabling high‐throughput screening of structural materials. Electron backscatter diffraction (EBSD) is a common method for studying microstructures and extracting information such as grain size distributions (GSDs), but is not particularly fast and thus could be a bottleneck in high‐throughput systems. One approach to accelerating EBSD is to reduce the number of points that must be scanned. In this work, we describe an iterative method for reducing the number of scan points needed to measure GSDs using incremental low‐discrepancy sampling, including on‐the‐fly grain size calculations and a convergence test for the resulting GSD based on the Kolmogorov–Smirnov test. We demonstrate this method on five real EBSD maps collected from magnesium AZ31B specimens and compare the effectiveness of sampling according to two different low discrepancy sequences, the Sobol and R2 sequences, and random sampling. We find that R2 sampling is able to produce GSDs that are statistically very similar to the GSDs of the full density grids using, on average, only 52% of the total scan points. For EBSD maps that contained monodisperse GSDs and over 1000 grains, R2 sampling only required an average of 39% of the total EBSD points. [ABSTRACT FROM AUTHOR]

Ultrathin three-dimensional (3D) interconnected porous carbon nanosheets (PCNs) derived from Moringa oleifera barks (commonly known as the drumstick tree) with electrochemical (EC) sensor performance was employed as the carbon precursor. In this study, we described a simple one-step pyrolysis technique to generate hierarchical PCN using a high-temperature pyrolysis and KOH activation procedure. Using the microwave-solvothermal approach, platinum–nickel bimetallic octahedral nanoparticles were decorated on ultrathin 3D PCN (referred to as Pt–Ni@PCN). The morphological, structural, and EC properties of the Pt–Ni@PCN nanocomposite were well characterized. Using cyclic voltammetry and linear sweep voltammetry, the dihydroxybenzene (DHB) isomers hydroquinone, catechol, and resorcinol were identified individually as well as simultaneously. The screen-printed carbon electrode-modified nanocomposite [Pt–Ni@PCN/screen-printed carbon electrode (SPCE)] served as an outstanding electron (e–)-transfer mediator for DHB isomer oxidation, yielding limits of detection of 0.0093, 0.0263, and 0.0529 μM, respectively. Also, the Pt–Ni@PCN/SPCE sensor has exceptional selectivity, long-term durability, reproducibility, repeatability, and anti-interference. Furthermore, the proposed sensor can be exploited to detect simultaneously environmental pollutants in river, lake, and tap water samples with good recovery rates. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) has attracted significant attention over the past two decades due to its ability to provide real-time insight into the condition of structures. Despite the development of several SHM systems for long-span bridges, which play a crucial role in the assessment of these structures, studies focusing on short- or middle-span bridges remain scarce. This research paper presents an efficient and practical bridge monitoring and warning system established on a middle-span bridge, a key crossroad bridge located in Shenzhen. The monitoring system consists of sensors and measuring points that collect a substantial amount of data, enabling the close monitoring of various operational indicators to facilitate the early detection of threshold exceedances. Based on this system, the subtle condition of the bridge can be evaluated, and the operational condition of the bridge can be studied through the comparative analysis of the collected data. Over four months of monitoring, data including the strain and creep of the main beam, the strain and settlement of piers and the crack width of the bridge body are observed. Furthermore, the real-time operational status of the bridge is analyzed and evaluated through the combination of the collected data and the structural finite element model. [ABSTRACT FROM AUTHOR]

The availability of spatial and spatiotemporal big data is increasing rapidly. Spatially and temporally high resolved data are especially gathered via the Internet of Things. This data can often be accessed as data streams that push new data tuples continuously and make the data available in real time. Such real-time spatiotemporal data have great potential for new analysis approaches based on modern data processing technologies. The ability to retrieve spatial big data in real time, as well as process it in real time, demands new analysis methodologies that catch up with the instantaneous and continuous character of today's spatiotemporal data. In this work, we present an evaluation of a high-frequent dynamic spatiotemporal autocorrelation approach. This approach allows for geostatistical analysis of streaming spatiotemporal data in real time and can provide insights into spatiotemporal processes while they are still ongoing. To evaluate this new approach, it was applied to mobility data from New York City. The results show that a high-frequent dynamic spatiotemporal autocorrelation approach provides comparable and meaningful results. In this way, high-frequent geostatistical analyses in real time can become an addition to retrospective analyses based on historical data. [ABSTRACT FROM AUTHOR]

Bioethanol's importance as a renewable energy carrier led to the development of new devices for the high-throughput screening (HTS) of ethanol-producing microorganisms, monitoring ethanol production, and process optimization. This study developed two devices based on measuring CO2 evolution (an equimolar byproduct of microbial ethanol fermentation) to allow for a fast and robust HTS of ethanol-producing microorganisms for industrial purposes. First, a pH-based system for identifying ethanol producers (Ethanol-HTS) was established in a 96-well plate format where CO2 emission is captured by a 3D-printed silicone lid and transferred from the fermentation well to a reagent containing bromothymol blue as a pH indicator. Second, a self-made CO2 flow meter (CFM) was developed as a lab-scale tool for real-time quantification of ethanol production. This CFM contains four chambers to simultaneously apply different fermentation treatments while LCD and serial ports allow fast and easy data transfer. Applying ethanol-HTS with various yeast concentrations and yeast strains displayed different colors, from dark blue to dark and light green, based on the amount of carbonic acid formed. The results of the CFM device revealed a fermentation profile. The curve of CO2 production flow among six replications showed the same pattern in all batches. The comparison of final ethanol concentrations calculated based on CO2 flow by the CFM device with the GC analysis showed 3% difference which is not significant. Data validation of both devices demonstrated their applicability for screening novel bioethanol-producer strains, determining carbohydrate fermentation profiles, and monitoring ethanol production in real time. [ABSTRACT FROM AUTHOR]

In this work, a new microfluidic paper-based analytical device (µPAD) was developed for on-hand creatinine quantification in urine samples. When compared to conventional methods, this innovative paper device is more accessible and portable, it provides low-cost analysis (cost of consumables of 40 cents), and it is applicable to non-invasive biological fluids. Furthermore, the paper-based approach is used within an environmentally friendly assembly with no need for wax printing and small amounts of reagents resulting in low waste production and easy disposal by incineration. Its assembly method includes cutting paper discs arranged into several reading units within a plastic pouch, enabling effective creatinine quantification with accuracy based on a vertical flow approach. The method is based on the colourimetric reaction between creatinine and alkaline picric acid, where the solution colour changes from yellow to orange/red. Under optimal conditions, the developed method allowed creatinine quantification in the dynamic range of 2.20–35.0 mg/dL, with a limit of detection (LOD) of 0.66 mg/dL and a limit of quantification (LOQ) of 2.20 mg/dL. The colour intensity developed was processed in ImageJ software, based on digital image scanning, performed in 20 min (up to 4 h) after the sample insertion. The device is stable for up to one week when stored in a vacuum at 4 °C. The method was validated by comparing the results with a batch-wise procedure, where there were no statistically significant differences between both methods. [ABSTRACT FROM AUTHOR]

Introduction: Red blood cells (RBCs) are among the simplest, yet physiologically relevant biological specimens, due to their peculiarities, such as their lack of nucleus and simplified metabolism. Indeed, erythrocytes can be seen as biochemical machines, capable of performing a limited number of metabolic pathways. Along the aging path, the cells' characteristics change as they accumulate oxidative and non-oxidative damages, and their structural and functional properties degrade. Methods: In this work, we have studied RBCs and the activation of their ATPproducing metabolism using a real-time nanomotion sensor. This device allowed time-resolved analyses of the activation of this biochemical pathway, measuring the characteristics and the timing of the response at different points of their aging and the differences observed in favism erythrocytes in terms of the cellular reactivity and resilience to aging. Favism is a genetic defect of erythrocytes, which affects their ability to respond to oxidative stresses but that also determines differences in the metabolic and structural characteristic of the cells. Results: Our work shows that RBCs from favism patients exhibit a different response to the forced activation of the ATP synthesis compared to healthy cells. In particular, the favism cells, compared to healthy erythrocytes, show a greater resilience to the aging-related insults which was in good accord with the collected biochemical data on ATP consumption and reload. Conclusion: This surprisingly higher endurance against cell aging can be addressed to a special mechanism of metabolic regulation that permits lower energy consumption in environmental stress conditions. [ABSTRACT FROM AUTHOR]

Comprehensive analytical validation studies of a developed ion molecule reaction – mass spectrometer (IMR-MS) were undertaken for the real-time determination of volatile organic compounds (VOCs) in air. The instrument was developed with a focus on promoting chemical ionization (CI) in the reaction chamber by direct sample loading and enhancing maintenance efficiency and reliability of the results. Instrument stability was assessed through a system check and pre-performance check process, and consequently, the instrumental and analytical conditions including the plasma generation, pressure, temperature, and flow rate were successfully optimized. Relevant performance characteristics, such as mass resolution, mass detection range, accuracy, and precision were also investigated by VOC standards composed of benzene, toluene, perfluorotoluene, propylbenzene, and octane. To evaluate whether the performance of the technology is comparable to already accepted techniques, the quantitative results of the IMR-MS were compared with those of a commercial mass spectrometer. This evaluation was successful and suggests the applicability of the technology for spillage accidents of hazardous chemicals and identification of odor-causing substances as well as for real-time gas analysis. [ABSTRACT FROM AUTHOR]

Environmental pollution has arisen from releasing pollutants into water sources in many parts of the world, endangering human health and marine environments. Chemical discharge may come from various places, including wastewater treatment plants, agriculture, manufacturing, and stormwater overflows. As a result, monitoring pollution including, heavy metals, pesticides, toxic gases, and other contaminants in environmental samples such as water (e.g., groundwater, surface water, and drinking water), air, soil, and vegetables is critical to eliminating or reducing their risk and toxicity. Real-time analysis may also have an effect on reducing consumption of a variety of harsh chemicals and reagents, with the additional benefit of on-site contaminant composition assessment prior to discharge into the setting. Electrochemical biosensors have received a lot of interest in solving this issue as a result of recent technological breakthroughs. This review presents the types and properties of carbon-based nanomaterials and their applications in electrochemical biosensors for environmental toxicants over the past five years. We emphasize the sensing performances of electrochemical biosensors in terms of limit of detection, linear range, and their applicability in real samples. This review would be helpful in raising awareness and understanding of the role of electrochemical biosensors in sustaining the environment. [ABSTRACT FROM AUTHOR]

The development of high-throughput field phenotyping, which uses modern detection technologies and advanced data processing algorithms, could increase productivity and make in-field phenotypic evaluation more efficient by collecting large amounts of data with no or minimal human assistance. Moreover, high-throughput plant phenotyping systems are also very effective in selecting crops and characterizing germplasm for drought tolerance and disease resistance by using spectral sensor data in combination with machine learning. In this study, an affordable high-throughput phenotyping platform (phenomobile) aims to obtain solutions at reasonable prices for all the components that make up it and the many data collected. The goal of the practical innovation in field phenotyping is to implement high-performance precision phenotyping under real-world conditions at accessible costs, making real-time data analysis techniques more user-friendly. This work aims to test the ability of a phenotyping prototype system constituted by an electric phenomobile integrated with a MAIA multispectral camera for real in-field plant characterization. This was done by acquiring spectral signatures of F1 hybrid Elisir (Olter Sementi) tomato plants and calculating their vegetation indexes. This work allowed to collect, in real time, a great number of field data about, for example, the morphological traits of crops, plant physiological activities, plant diseases, fruit maturity, and plant water stress. [ABSTRACT FROM AUTHOR]

COVID-19 is a contagious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARSCoV-2). The disease has spread worldwide, leading to an ongoing pandemic. The most common symptom of COVID-19 is fever which can be detected using various manual screening techniques that have the risk of exposing the personnel. Since the virus has globally spread, a reliable system to detect COVID-19-infected people, especially before entering any premises and buildings, is in high demand. The most common symptom that can be detected is fever, even though people with fever might not have COVID-19. Thus, a real-time analytic face thermal recognition system integrated with email notification that has the capability to scan the person's temperature and simultaneously analyze the measured temperature with the recorded/stored information/data is presented in this paper. The proposed system is also able to send an email notification to the relevant authorities during the real-time analytical process. Besides that, this information is also recorded in the system database for continuous monitoring of the respective person's health status. The development of the proposed system is integrated with a Thermal Module AMG8833, Pi camera, and Raspberry Pi Zero Wireless. The proposed system has been tested and the captured results successfully accomplished the development objectives. [ABSTRACT FROM AUTHOR]

We develop a novel approach integrating epidemiological and economic models that allows data-based simulations during a pandemic. We examine the economically optimal opening strategy that can be reconciled with the containment of a pandemic. The empirical evidence is based on data from Germany during the SARS-CoV-2 pandemic. Our empirical findings reject the view that there is necessarily a conflict between health protection and economic interests and suggest a non-linear U-shape relationship: it is in the interest of public health and the economy to balance non-pharmaceutical interventions in a manner that further reduces the incidence of infections. Our simulations suggest that a prudent strategy that leads to a reproduction number of around 0.75 is economically optimal. Too restrictive policies cause massive economic costs. Conversely, policies that are too loose lead to higher death tolls and higher economic costs in the long run. We suggest this finding as a guide for policy-makers in balancing interests of public health and the economy during a pandemic. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Mass Spectrometry Society is the property of Journal of Chinese Mass Spectrometry Society and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Real-time gait event detection (GED) using inertial sensors is important for applications such as remote gait assessments, intelligent assistive devices including microprocessor-based prostheses or exoskeletons, and gait training systems. GED algorithms using acceleration and/or angular velocity signals achieve reasonable performance; however, most are not suited for real-time applications involving clinical populations walking in free-living environments. The aim of this study was to develop and evaluate a real-time rules-based GED algorithm with low latency and high accuracy and sensitivity across different walking states and participant groups. The algorithm was evaluated using gait data collected from seven able-bodied (AB) and seven lower-limb prosthesis user (LLPU) participants for three walking states (level-ground walking (LGW), ramp ascent (RA), ramp descent (RD)). The performance (sensitivity and temporal error) was compared to a validated motion capture system. The overall sensitivity was 98.87% for AB and 97.05% and 93.51% for LLPU intact and prosthetic sides, respectively, across all walking states (LGW, RA, RD). The overall temporal error (in milliseconds) for both FS and FO was 10 (0, 20) for AB and 10 (0, 25) and 10 (0, 20) for the LLPU intact and prosthetic sides, respectively, across all walking states. Finally, the overall error (as a percentage of gait cycle) was 0.96 (0, 1.92) for AB and 0.83 (0, 2.08) and 0.83 (0, 1.66) for the LLPU intact and prosthetic sides, respectively, across all walking states. Compared to other studies and algorithms, the herein-developed algorithm concurrently achieves high sensitivity and low temporal error with near real-time detection of gait in both typical and clinical populations walking over a variety of terrains. [ABSTRACT FROM AUTHOR]

The mechanical performance analysis of the members is the primary basis for evaluating the hoisting quality and safety of the valve hall grid structure. Ordinarily, manual analysis of monitoring data and on-site experience inspection are employed to structural judgment, but it is challenging to evaluate the correlation of the various members and the overall safety of a valve hall. In this paper, an intelligent correlation real-time analysis method based on a BPNN (Back Propagation Neural Network) for the mechanical properties of members is proposed to intelligently control the safety of valve hall grid structure hoisting. The correlation between the mechanical properties of multi-points in the grid structure is used to model the target measuring points. In addition, an intelligent real-time analysis system is used to manage and apply the mechanical property correlation and abnormality of members in real-time. Then, the model is applied to a super-span valve hall in South China, and the application effect is good. The mechanical property correlation model can accurately reflect the mechanical state of the valve hall grid structure hoisting process. Simultaneously, it can effectively pinpoint hidden dangers and locate risk members. It provides a new reference for the normal operation and maintenance of a super-span valve hall grid. [ABSTRACT FROM AUTHOR]

Climate change is a major threat to agriculture production among small-scale farms worldwide. Climate-smart agriculture (CSA) is one of the technologies and strategies to sustain agriculture growth in a changing climate. Researchers are finding ways to collect big data, which are required to clarify local climate change and its impacts on agriculture to pinpoint the farming strategies for the practice of CSA. The honeybee (Hymenoptera: Apidae) hives around the world which are equipped with digital devices for continuously monitoring the status of colonies for precise beekeeping, accumulate huge amounts of data that can be used to address some questions about CSA. In this paper, we confer an overview of the big beehive data (BBD) and data science and identifies their potential applications to support CSA, as well as several challenges confronted by this approach. Here, we also outline that how can we predict the bee-plant interaction based on monitoring dynamics in honey production using novel and technological approaches. Numerous approaches including big data analytics, IoT, Wireless sensor network (WSN)-based monitoring systems, machine learning, and AI algorithms are being considered as a power source to assist in delivering novel insights and explication to the problems. We put in examples where all these approaches have been employed for monitoring and analyzing BBD. Moreover, we predict their role to aid in apiary management with the perspective of CSA. [ABSTRACT FROM AUTHOR]

Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments. [ABSTRACT FROM AUTHOR]

This review emphasizes the importance of classification of surface reservoirs represented by building and indoor materials. Models developed in the past 45 years to study mainly non-reactive thermodynamic partitioning of air pollutants to surfaces as well as their validation through analytical measurements are discussed. Analytical techniques aiming at surface characterization have been summarized. Applications relevant to surface chemistry performed indoors in the last decade are also given. Finally, techniques for microbiological sampling to characterize the hygienic state of surfaces have also been compiled. Readers will be introduced to the state-of-the-art of analytical techniques that are being used indoors for (physico)chemical and microbiological characterization of surfaces. The focus is on real-time in situ instrumentation used to understand chemical processes rather than air quality monitoring indoors. Our aim was to give an overview of useful instrumentation and a tutorial on analytical approaches and challenges represented by surfaces. [ABSTRACT FROM AUTHOR]

Computer vision and image processing techniques have been extensively used in various fields and a wide range of applications, as well as recently in surface treatment to determine the quality of metal processing. Accordingly, digital image evaluation and processing are carried out to perform image segmentation, identification, and classification to ensure the quality of metal surfaces. In this work, a novel method is developed to effectively determine the quality of metal surface processing using computer vision techniques in real time, according to the average size of irregularities and caverns of captured metal surface images. The presented literature review focuses on classifying images into treated and untreated areas. The high computation burden to process a given image frame makes it unsuitable for real-time system applications. In addition, the considered current methods do not provide a quantitative assessment of the properties of the treated surfaces. The markup, processed, and untreated surfaces are explored based on the entropy criterion of information showing the randomness disorder of an already treated surface. However, the absence of an explicit indication of the magnitude of the irregularities carries a dependence on the lighting conditions, not allowing to explicitly specify such characteristics in the system. Moreover, due to the requirement of the mandatory use of specific area data, regarding the size of the cavities, the work is challenging in evaluating the average frequency of these cavities. Therefore, an algorithm is developed for finding the period of determining the quality of metal surface treatment, taking into account the porous matrix, and the complexities of calculating the surface tensor. Experimentally, the results of this work make it possible to effectively evaluate the quality of the treated surface, according to the criterion of the size of the resulting irregularities, with a frame processing time of 20 ms, closely meeting the real-time requirements. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a monthly economic activity indicator is constructed for the Turkish economy for the period of 1988–2020. A dynamic factor modeling framework is utilized in the process. The variables are first categorized into five types as activity (hard data), activity (survey-based data or soft data), trade, employment, and financial variables. After determining the candidate variables for each category, data selection is finalized by using the hard-thresholding method. Results indicate that our monthly economic activity indicator is successful in detecting the past recessionary periods of the Turkish economy and providing timelier information about the course of economic activity. [ABSTRACT FROM AUTHOR]

In recent times, we have seen a massive rise in vision-based applications, such as video anomaly detection, motion detection, object tracking, people counting, etc. Most of these tasks are well defined, with a clear idea of the goal, along with proper datasets and evaluation procedures. However, perimeter intrusion detection (PID), which is one of the major tasks in visual surveillance, still needs to be formally defined. A perimeter intrusion detection system (PIDS) aims to detect the presence of an unauthorized object in a protected outdoor site during a certain time. Existing works vaguely define a PIDS, and this has a direct impact on the evaluation of methods. In this paper, we mathematically define it. We review the existing methods, datasets and evaluation protocols based on this definition. Furthermore, we provide a suitable evaluation protocol for real-life application. Finally, we evaluate the existing systems on available datasets using different evaluation schemes and metrics. [ABSTRACT FROM AUTHOR]

Biological agents used in biological warfare or bioterrorism are also present in bioaerosols. Prompt identification of a biological weapon and its characteristics is necessary. Herein, we optimized an environmentally adaptive detection algorithm that can better reflect changes in the complex South Korean environment than the current models. The algorithm distinguished between normal and biological particles using a laser-induced fluorescence-based biological particle detector capable of real-time measurements and size classification. We ensured that the algorithm operated with minimal false alarms in any environment by training based on experimental data acquired from an area where rainfall, snow, fog and mist, Asian dust, and water waves on the beach occur. To prevent time and money wastage due to false alarms, the detection performance for each level of sensitivity was examined to enable the selection of multiple sensitivities according to the background, and the appropriate level of sensitivity for the climate was determined. The basic sensitivity was set more conservatively than before, with a 3% alarm rate at 20 agent-containing particles per liter of air (ACPLA) and a 100% alarm rate at 63 ACPLA. The reliability was increased by optimizing five variables. False alarms did not occur in situations where no alarm was unnecessary. [ABSTRACT FROM AUTHOR]

Real-time online data processing is quickly becoming an essential tool in the analysis of social media for political trends, advertising, public health awareness programs and policy making. Traditionally, processes associated with offline analysis are productive and efficient only when the data collection is a one-time process. Currently, cutting edge research requires real-time data analysis that comes with a set of challenges, particularly the efficiency of continuous data fetching within the context of present NoSQL and relational databases. In this paper, we demonstrate a solution to effectively adsress the challenges of real-time analysis using a configurable Elasticsearch search engine. We are using a distributed database architecture, pre-build indexing and standardizing the Elasticsearch framework for large scale text mining. The results from the query engine are visulized in almost real-time. [ABSTRACT FROM AUTHOR]

Objectives: We desired an automated approach to expedite ordering additional antibody panels in our clinical flow cytometry lab. This addition could improve turnaround times, decrease time spent revisiting cases, and improve consistency.Methods: We trained a machine learning classifier to use our screening B-cell panel to predict whether we should order an additional panel to distinguish chronic lymphocytic lymphoma from mantle cell lymphoma. We used data from 2016 to 2018 for training and validation, and cases were restricted to the first case per patient (9,635 cases, 887 with the additional panel). We applied the model in real time over approximately 2.5 months in 2020 to 376 sequential cases, with automated email notifications for positive predictions.Results: Using 80% of the data from 2016 to 2018 to train and 20% for validation, we achieved 95% area under the receiving operating characteristic curve (AUROC) and 94% accuracy in the validation set. Applying the classifier in real time achieved 89% AUROC and 94% real-time prediction accuracy (precision [positive predictive value] = 51%, recall [sensitivity] = 78%, and F1 score = 0.62). Fourteen of the 17 false positives had prior diagnoses to which the algorithm was not privy.Conclusions: As an observational, not interventional study, our system performed well on testing within our laboratory for identifying cases to be flagged but cannot be used without laboratory-specific modifications. [ABSTRACT FROM AUTHOR]

The real-time analysis of Big Data streams is a terrific resource for transforming data into value. For this, Big Data technologies for smart processing of massive data streams are available, but the facilities they offer are often too raw to be effectively exploited by analysts. RAM 3 S (Real-time Analysis of Massive MultiMedia Streams) is a framework that acts as a middleware software layer between multimedia stream analysis techniques and Big Data streaming platforms, so as to facilitate the implementation of the former on top of the latter. RAM 3 S has been proven helpful in simplifying the deployment of non-parallel techniques to streaming platforms, such as Apache Storm or Apache Flink. In this paper, we show how RAM 3 S has been updated to incorporate novel stream processing platforms, such as Apache Samza, and to be able to communicate with different message brokers, such as Apache Kafka. Abstracting from the message broker also provides us with the ability to pipeline several RAM 3 S instances that can, therefore, perform different processing tasks. This represents a richer model for stream analysis with respect to the one already available in the original RAM 3 S version. The generality of this new RAM 3 S version is demonstrated through experiments conducted on three different multimedia applications, proving that RAM 3 S is a formidable asset for enabling efficient and effective Data Mining and Machine Learning on multimedia data streams. [ABSTRACT FROM AUTHOR]

The Regional Connector Transit Corridor (RCTC) project consists of an 89-m long, 17.7-m wide, and 11-m high span crossover cavern constructed beneath critical infrastructure using the sequential excavation method (SEM) at relatively shallow depth (15 m below ground surface) in downtown Los Angeles. The cavern construction involved a complex three-drift, two-sidewall configuration and was excavated in the Fernando formation, a weak clayey siltstone. This paper presents a comprehensive case study of the cavern excavation and initial support construction. The performance of the urban SEM was assessed and complex ground behavior was examined once per excavation step during the construction process. The overall average surface settlement was 20 mm and the largest single-stage incremental movements occurred in the center bench and invert excavation, accounting for 30%–40% of the total deformation. The subsurface experienced less differential settlement during the side drift excavations due to the ovate excavation profile and the restriction from surrounding infrastructure. The measured center crown vertical displacement varied between 5 and 10 mm and the center invert initial support was recorded to heave 8–12 mm. A systematic procedure was developed to apply a deliberate observational method. The lessons learned support that appropriate real-time analysis improves the quality of regular data review, continuously updating the knowledge of risk and enabling prompt adaptions and optimizations of the design and construction along the SEM tunneling process. [ABSTRACT FROM AUTHOR]