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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]

The accurate and efficient measurement of flicker severity levels is crucial for evaluating and compensating power system flicker in electric power systems. This paper proposes a simple and fast measurement algorithm for power system flicker severity evaluation. The algorithm utilizes an equivalent measurement model and employs the Hilbert transform for effective envelope extraction. Subsequently, the extracted envelope is processed using windowed interpolation fast Fourier transform to accurately determine the flicker parameters. Finally, flicker assessment indicators are obtained. By simplifying the measurement procedure recommended by IEC Standard 61000-4-15, the proposed algorithm reduces the calculation process of flicker indicators, making it suitable for real-time flicker analysis in embedded applications. The accuracy and effectiveness of the algorithm are validated through simulation and practical test results. [ABSTRACT FROM AUTHOR]

Aircraft envelope expansion during the installation of new underwing stores presents significant challenges, particularly due to the aeroelastic flutter phenomenon. Accurate modeling of aeroelastic behavior often necessitates flight testing, which poses risks due to the potential catastrophic consequences of reaching the flutter point. Traditional methods, like frequency sweeps, are effective but require prolonged exposure to flutter conditions, making them less suitable for transonic flight validations. This paper introduces a robust deep learning approach to process sine dwell signals from aeroelastic flutter flight tests, characterized by short data lengths (less than 5 s) and low frequencies (less than 10 Hz). We explore the preliminary viability of different deep learning networks and compare their performances to existing methods such as the PRESTO algorithm and Laplace Wavelet Matching Pursuit estimation. Deep learning algorithms demonstrate substantial accuracy and robustness, providing reliable parameter identification for flutter analysis while significantly reducing the time spent near flutter conditions. Although the results with the networks trained show less accuracy than the PRESTO algorithm, they are more accurate than the Laplace Wavelet estimation, and the results are promising enough to justify extended investigation on this area. This approach is validated using both synthetic data and real F-18 flight test signals, which highlights its potential for real-time analysis and broader applicability in aeroelastic testing. [ABSTRACT FROM AUTHOR]

Handwritten text recognition (HTR) technology has brought about a revolution in the way handwritten data is converted and analyzed. This proposed work focuses on developing a HTR system using deep learning through advanced deep learning architecture and techniques. The aim is to create a model for real-time analysis and detection of handwritten texts. The proposed deep learning architecture that is convolutional neural networks (CNNs), is investigated and implemented with tools like OpenCV and TensorFlow. The model is trained on large handwritten datasets to enhance recognition accuracy. The system’s performance is evaluated based on accuracy, precision, real-time capabilities, and potential for deployment on platforms like Raspberry Pi. The actual outcome is a robust HTR system that can convert handwritten text to digital formats accurately. The developed system has achieved a high accuracy rate of 91.58% in recognizing English alphabets and digits and outperformed other models with 81.77% mAP, 78.85% precision, 79.32% recall, 79.46% F1-Score, and 82.4% receiver operating characteristic (ROC). This research contributes to the advancement of HTR technology by enhancing its precision and utility. [ABSTRACT FROM AUTHOR]

Information about dairy cow ruminating is closely associated with the health status of dairy cows. Therefore, it is of great significance to recognize and make statistics of dairy cows’ ruminating and feeding behavior. Concerning conventional recognition methods which are dependent on contact type devices, they have some defects of poor instantaneity and strong stress responses. As for recognition based on machine vision, it needs to transmit masses of data and raises high requirements for the cloud server and network performance. According to principles of edge computing, the model is deployed via Tensorflow.js in an edge device in the present study, constructing a recognition and statistical system for ruminating and feeding behavior of dairy cows. Through the application programming interface (API) of the browser, an edge device is able to invoke a camera and acquire dairy cow images. Then, the images can be inputted in the SSD MobileNet V2 model, which is followed by inference based on browser hashrate. Moreover, the edge device merely uploads recognition results to the cloud server for statistics, which features high instantaneity and compatibility. In terms of recognizing ruminating and feeding behavior of dairy cows, the proposed system has a precision ratio of 96.50%, a recall rate of 91.77%, an F1-score of 94.08%, specificity of 91.36%, and accuracy of 91.66%. This suggests that the proposed method is effective in recognizing dairy cow behavior.

Abstract 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.

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]

In the contemporary landscape of healthcare, the demand for efficient and proactive monitoring solutions is paramount. Digital healthcare monitoring systems, leveraging cutting-edge technologies such as the Internet of Things (IoT), have emerged as transformative tools in revolutionizing health data collection, analysis and utilization. This paper presents a comprehensive overview of a Digital Healthcare Monitoring System with Real-Time Analysis, aimed at providing continuous, real-time monitoring of vital health parameters through a custom-built mobile application. The system integrates various sensors including the DHT11 for temperature and humidity, the MAX30102 for heart rate and SpO and the AD8232 for ECG monitoring, facilitated by the NodeMCU microcontroller. The collected data is processed 2 and transmitted to a mobile application for visualization and analysis in real-time, enabling timely interventions and improved patient outcomes. Key objectives of the system include real-time monitoring, multi-sensor integration, remote accessibility, alerts and notifications, data analytics, user-friendly interface, scalability and interoperability. Through rigorous methodology encompassing hardware and software integration, testing, and calibration, the system ensures accuracy, reliability and user engagement. The initiative underscores the transformative potential of digital health technologies in fostering proactive and personalized healthcare, ultimately leading to better health outcomes and quality of life. [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]

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.

Nanopores play essential roles in biological processes, such as ion channels and pumps in cellular membranes, and in technological applications such as DNA sequencing. Advancements in nanofabrication techniques have enabled the routine integration of nanopores into solid-state devices, resulting in a plethora of analytical applications. This review explores recent developments in nanopore-enabled electrochemical systems, which have transcended traditional resistive pulse sensing to offer novel capabilities in single-entity studies, stimulus-responsive gating, and point-of-care diagnostics. We highlight recent studies on the design and utility of nanopore electrode arrays, which serve as nanocontainers capable of isolating and analyzing single entities, and extend the discussion to hierarchically organized, stimulus-responsive systems that regulate species transport across nanopores, enriching analytes for ultrasensitive detection. In addition, we review the utilization of probe-assisted nanopore sensing, demonstrating its efficacy in selectively binding and detecting target molecules and ions. Finally, we outline future directions for nanopore-based systems to enhance robustness, achieve high-throughput analysis, and incorporate artificial intelligence for materials design and data analysis, promising transformative impacts on diagnostics and biological research.

Developing a dynamic, personalized user interface that changes in real-time in response to user behavior is the goal. This paper supplies a modern method to beautify consumers enjoy using Reinforcement Learning (RL) and a Deep Q Network (DQN). Through support examination, the task objectives are to upgrade buyer connections and increment commitment, delight, and undertaking of consummation rates. Users who utilize traditional user interfaces get a common experience because they're impersonal and unflexible. The potential for higher engagement and happiness levels is limited in the absence of real-time changes based on individual preferences and behaviors. To overcome this problem, the study suggests a cunning technique for a getting-to-comprehend layout that may constantly analyze and modify patron communications. This evaluation is new as it provides a blended RL and DQN framework that modifies person interfaces grade by grade. Dissimilar to conventional methodologies, the proposed form adjusts the utilization of well-known, over-the-top prize moves with the development of the most recent ones through an investigation double-dealing system. EventType, contentId, personId, sensorId, and timestamp are instances of timestamped insights handles that give a thorough skill of client conduct and license planned and nuanced changes.

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.

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]

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]

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]

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]

Developing a dynamic, personalized user interface that changes in real-time in response to user behavior is the goal. This paper supplies a modern method to beautify consumers enjoy using Reinforcement Learning (RL) and a Deep Q Network (DQN). Through support examination, the task objectives are to upgrade buyer connections and increment commitment, delight, and undertaking of consummation rates. Users who utilize traditional user interfaces get a common experience because they're impersonal and unflexible. The potential for higher engagement and happiness levels is limited in the absence of real-time changes based on individual preferences and behaviors. To overcome this problem, the study suggests a cunning technique for a getting-to-comprehend layout that may constantly analyze and modify patron communications. This evaluation is new as it provides a blended RL and DQN framework that modifies person interfaces grade by grade. Dissimilar to conventional methodologies, the proposed form adjusts the utilization of well-known, over-the-top prize moves with the development of the most recent ones through an investigation double-dealing system. EventType, contentId, personId, sensorId, and timestamp are instances of timestamped insights handles that give a thorough skill of client conduct and license planned and nuanced changes. [ABSTRACT FROM AUTHOR]

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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]

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]

This paper analyzes the effect of different types of dust on the reduction of PV systems' performance. The PV system installed on the field gives maximum output. However, in a real-time scheme, the PV system fails to generate the desired power output due to various realistic and unavoidable factors like soiling. Different dust particles accumulating on photovoltaic (PV) modules result in shadowing and reduced irradiance, impacting power production. The study investigates eight dust samples (ash, dirt, cement, brick powder, putty, wood dust, sand, and salt) in indoor and outdoor environments. It evaluates the decrease in power at solar irradiance levels of 500, 700, and 1000 W/m². The soiling experiments conducted indoors show that the maximum% reduction in power is 39.21 % in the case of Ash, while a minimum of 8.32 % in the case of red soil. The outdoor experiments suggest that red soil, sand, and brick dust can create huge power losses in the PV system, followed by ash powder, wood dust, putty dust, salt, and cement powder. The advantages of this research lie in its thorough analysis of several forms of dust and their impact on photovoltaic (PV) systems under different circumstances. Furthermore, the study emphasizes the crucial need for implementing dust control measures to maximize system performance, thereby providing insightful information for system design and maintenance procedures. The novelty of this study is to investigate the performance impact of various kinds of dust on PV systems, offering significant insights for improving system efficiency in real-world circumstances. The research enhances the reliability and applicability of its results by thoroughly assessing outdoor and indoor conditions, bridging the gap between laboratory results and practical applications.

Abstract 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.

Electrochemical interfaces are the core of various important fields, such as energy conversion and storage, biochemistry, sensors, and corrosion. The investigations of the structure-performance relationship of electrochemical solid-liquid interfaces have become a hot topic yet extremely challenging due to the fact that the interfaces are ultrathin, highly dynamic and extremely complex. Mass spectrometric techniques coupled with electrochemistry are powerful and have been widely applied in investigations of mechanisms of electrochemical reactions. However, traditional mass spectrometry (MS) is difficult to characterize the electrode-electrolyte interfaces in an in situ manner due to inherent limitations existing in their ionization processes. In recent years, the state-of-the-art in situ liquid time-of-flight secondary ion MS (ToF-SIMS) based on high-vacuum compatible microfluidic devices has been developed to tackle with this challenge. This review mainly reviewed the principle, characteristics and rapid development of in situ liquid ToF-SIMS in real-time and in situ investigations of electrochemical solid-liquid interfaces during the past decade. In situ liquid ToF-SIMS possesses shallow information depth (nm), high temporal resolution (μs) and high detection sensitivity (10-6-10-9). Besides, it ionizes the electrochemical interfaces in a truly in situ manner and provides direct molecular evidences of chemical evolution of both electrode/electrocatalyst surfaces and reactants/intermediates/products in electrolytes at the interfaces simultaneously. Being attributed to its uniqueness, in situ liquid ToF-SIMS has become a powerful and versatile molecular "eye" in in situ and real-time tracking dynamic electrochemical solid-liquid interfaces, such as capturing electrochemical reaction intermediates, identification of electrocatalytic active sites, probing fine structures of electrochemical double layers, and unraveling the formation chemistry of solid-electrolyte interphases in batteries. Further innovations of microfluidic electrochemical devices and ToF-SIMS instruments are desired to promote the enhanced performance and wider applications of in situ liquid ToF-SIMS in the electrochemical field, and in situ liquid ToF-SIMS will make significant contributions to the understanding of the structure-performance relationship of interfaces in complex electrochemical assays and guide the engineering of better electrochemical interfaces in important fields, such as electrocatalysis and batteries.