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Purpose: Clinical needle insertion into tissue, commonly assisted by 2D ultrasound imaging for real-time navigation, faces the challenge of precise needle and probe alignment to reduce out-of-plane movement. Recent studies investigate 3D ultrasound imaging together with deep learning to overcome this problem, focusing on acquiring high-resolution images to create optimal conditions for needle tip detection. However, high-resolution also requires a lot of time for image acquisition and processing, which limits the real-time capability. Therefore, we aim to maximize the US volume rate with the trade-off of low image resolution. We propose a deep learning approach to directly extract the 3D needle tip position from sparsely sampled US volumes. Methods: We design an experimental setup with a robot inserting a needle into water and chicken liver tissue. In contrast to manual annotation, we assess the needle tip position from the known robot pose. During insertion, we acquire a large data set of low-resolution volumes using a 16 × 16 element matrix transducer with a volume rate of 4 Hz. We compare the performance of our deep learning approach with conventional needle segmentation. Results: Our experiments in water and liver show that deep learning outperforms the conventional approach while achieving sub-millimeter accuracy. We achieve mean position errors of 0.54 mm in water and 1.54 mm in liver for deep learning. Conclusion: Our study underlines the strengths of deep learning to predict the 3D needle positions from low-resolution ultrasound volumes. This is an important milestone for real-time needle navigation, simplifying the alignment of needle and ultrasound probe and enabling a 3D motion analysis. [ABSTRACT FROM AUTHOR]

Real-time time transfer is the basis for the time synchronization and establishment and maintenance of time scales. In this contribution, we present a regional multi-station real-time time transfer approach, in which observations from multi-global navigation satellite system (GNSS) received at regional multi-station are integrated to conduct an undifferenced network solution. Consequently, the simultaneous estimation of receiver clock offsets for multiple stations becomes possible, enabling the execution of real-time time transfer. One week of observation data from five multi-GNSS Experiment (MGEX) stations located in Europe are selected to generate four links (distances from 919.7 to 2153.4 km), and four schemes are designed, i.e., GPS-only, BDS-3-only, Galileo-only, and GPS/BDS-3/Galileo (GCE) solutions, respectively. Experiment results show that the mean number of observations, time dilution of precision (TDOP) values, and standard deviation (STD) of clock offset difference time series between the Center for Orbit Determination in Europe (CODE) and the estimated solutions for four time links are 90, 76, 72, 239 and 0.34, 0.38, 0.39, 0.21 and 0.039, 0.050, 0.043, 0.036 ns for GPS-only, BDS-3-only, Galileo-only, and GCE solutions, respectively. When the averaging time is shorter than 7680 s, the frequency stability of GCE solutions shows the best performance. The mean frequency stability of four time links at 7680 s is 6.59 × 10–15,7.25 × 10–15, 6.88 × 10–15 and 5.96 × 10–15 for GPS-only, BDS-3-only, Galileo-only, and GCE solutions, respectively. The GCE solution shows the best performance among the four schemes in terms of the number of observations, TDOP, STD, and frequency stability. The experiment results demonstrate that this approach is suitable for regional multi-station real-time time transfer. [ABSTRACT FROM AUTHOR]

Low Earth Orbit (LEO) satellites can augment the traditional GNSS-based positioning, navigation and timing services, which require real-time high-precision LEO satellite clock products. As the complicated systematic effects contained in the LEO satellite clock estimates limit their high-precision mid- to long-term prediction, high-frequency LEO satellite clocks need to be estimated within a Kalman filter, resulting in a short prediction time for real-time applications. Compared to the clock estimation using Batch Least-Squares (BLS) adjustment, filter-based clock estimation experiences a lower precision. Increasing the model strength by introducing external orbital information, thus, de-correlating the orbital and clock parameters, will benefit real-time clock precision. In this contribution, reduced-dynamic LEO satellite orbits are first estimated using BLS adjustment in near real-time and predicted in the short term. The predicted orbits are then constrained during the Kalman-filter-based clock estimation process. The variance–covariance matrix of the introduced orbital errors is tested for different sets of values in the radial, along-track and cross-track directions when constraining orbits of different prediction times. One week of GPS data from the Sentinel-3B satellite in 2018 was used for validation of the proposed method. When weakly constraining high-accuracy predicted orbits within a prediction time of 20 min, i.e., with a standard deviation of the constraint set to 2–3 dm in the radial and cross-track directions, and 4–6 dm in the along-track direction, the estimated clock accuracy can be improved from about 0.27 to 0.23 ns, with a 13.4% improvement. Depending on the prediction period of the introduced orbits, the Signal-In-Space Range Error (SISRE) of the LEO satellite to Earth can also be improved, from about 9.59 cm without constraints, to 7.38–8.07 cm after constraining the predicted orbits, with an improvement of 16–23%. The improvements in the SISRE also indicate a better consistency between the real-time clock and orbital estimates. [ABSTRACT FROM AUTHOR]

The tropospheric delay is difficult to be modeled accurately resulting from the high variability of atmospheric water vapor, especially under the conditions of sparse station distribution and large elevation differences, which poses challenges for real-time precise positioning. In this contribution, a real-time high-resolution (0.01° × 0.01°) zenith tropospheric delay (ZTD) model considering sparse stations and topography variations (named GFNSS) is established by integrating the information from the Global Forecast System (GFS) and Global Navigation Satellite System (GNSS). GNSS observations and GFS forecasts in the Hong Kong area are selected for the experiments. The performance of ZTDs derived from GFNSS is assessed and validated with the real-time GNSS ZTDs obtained by the precise point positioning method and the IGS post-processed ZTD products. Results show that the root mean square error (RMSE) of GFNSS ZTDs is 5.5 mm and 12.8 mm when validated with real-time and post-processed ZTD, while those for ZTD derived from the low-order surface model (LSM) are 8.8 mm and 19.0 mm, presenting a reduction of 37.5% and 32.6%, respectively. The sensitivity of model performance to the number of modeling stations and elevation differences is also evaluated. The results reveal that the GFNSS model is resistant to station number and presents high accuracy and stability with the RMSE values varying between 4.0 and 6.0 mm as the modeling station number decreases from 13 to 4, while the RMSE for the LSM model increases dramatically from 4.0 to 27.4 mm. Meanwhile, the GFNSS model achieves an RMSE value of 5.8 mm when the elevation differences are over 300 m, indicating a notable 84.9% reduction compared to that of LSM (RMSE of 38.5 mm). [ABSTRACT FROM AUTHOR]

Confocal laser scanning microscopy is one of the most widely used tools for high-resolution imaging of biological cells. However, the imaging resolution of conventional confocal technology is limited by diffraction, and more complex optical principles and expensive optical-mechanical structures are usually required to improve the resolution. This study proposed a deep residual neural network algorithm that can effectively improve the imaging resolution of the confocal microscopy in real time. The reliability and real-time performance of the algorithm were verified through imaging experiments on different biological structures, and an imaging resolution of less than 120 nm was achieved in a more cost-effective manner. This study contributes to the real-time improvement of the imaging resolution of confocal microscopy and expands the application scenarios of confocal microscopy in biological imaging. [ABSTRACT FROM AUTHOR]

Initiating communication and conveying critical information to the human operator is a key problem in human-automation collaboration. This problem is particularly pronounced in time-constrained safety critical domains such as in Air Traffic Management. A visual representation should aid operators understanding why the system initiates the communication, when the operator must act, and the consequences of not responding to the cue. Data glyphs can be used to present multidimensional data, including temporal data in a compact format to facilitate this type of communication. In this paper, we propose a glyph design for communication initialization for highly automated systems in Air Traffic Management, Vessel Traffic Service, and Train Traffic Management. The design was assessed by experts in these domains in three workshop sessions. The results showed that the number of glyphs to be presented simultaneously and the type of situation were domain-specific glyph design aspects that needed to be adjusted for each work domain. The results also showed that the core of the glyph design could be reused between domains, and that the operators could successfully interpret the temporal data representations. We discuss similarities and differences in the applicability of the glyph design between the different domains, and finally, we provide some suggestions for future work based on the results from this study.

Abstract Background Blood cultures are essential for diagnosing bloodstream infections, but current phenotypic tests for antimicrobial resistance (AMR) provide limited information. Oxford Nanopore Technologies introduces nanopore sequencing with adaptive sampling, capable of real-time host genome depletion, yet its application directly from blood cultures remains unexplored. This study aimed to identify pathogens and predict AMR using nanopore sequencing. Methods In this cross-sectional genomic study, 458 positive blood cultures from bloodstream infection patients in central Taiwan were analyzed. Parallel experiments involved routine microbiologic tests and nanopore sequencing with a 15-h run. A bioinformatic pipeline was proposed to analyze the real-time sequencing reads. Subsequently, a comparative analysis was performed to evaluate the performance of species identification and AMR prediction. Results The pipeline identified 76 species, with 88 Escherichia coli, 74 Klebsiella pneumoniae, 43 Staphylococcus aureus, and 9 Candida samples. Novel species were also discovered. Notably, precise species identification was achieved not only for monomicrobial infections but also for polymicrobial infections, which was detected in 23 samples and further confirmed by full-length 16S rRNA amplicon sequencing. Using a modified ResFinder database, AMR predictions showed a categorical agreement rate exceeding 90% (3799/4195) for monomicrobial infections, with minimal very major errors observed for K. pneumoniae (2/186, 1.1%) and S. aureus (1/90, 1.1%). Conclusions Nanopore sequencing with adaptive sampling can directly analyze positive blood cultures, facilitating pathogen detection, AMR prediction, and outbreak investigation. Integrating nanopore sequencing into clinical practices signifies a revolutionary advancement in managing bloodstream infections, offering an effective antimicrobial stewardship strategy, and improving patient outcomes.

Background: The hepatitis B test can be conducted using the real-time PCR method. In chronic cases, the test is performed by detecting one of the Hepatitis B Virus (HBV) specific genes, HBcAg. The real-time PCR method requires optimization to obtain optimal results. Purpose: This research aims to determine the optimal temperature and time for denaturation in the hepatitis B test using the real-time PCR method. Method: The research method used was a quasi-experiment involving variations in temperature (94 °C, 95 °C, and 96 °C) and time (10 seconds, 15 seconds, and 20 seconds) for denaturation. Data processing resulted in static group comparisons based on 27 primary data from the Cycle Threshold (CT) value. Result: Variations in temperature conditions, specifically at 94 °C, 95 °C, and 96 °C, combined with a denaturation time of 10 seconds, yielded the mean CT values of 26.495, 26.355, and 26.003, respectively. When the temperature conditions were maintained at 94 °C, 95 °C, and 96 °C, with a denaturation time of 15 seconds, yielded the mean CT values of 25.962, 25.641, and 25.396. Similarly, under temperature conditions of 94 °C, 95 °C, and 96 °C with a denaturation time of 20 seconds, yielded the mean CT values of 26.544, 26.505, and 25.830 were obtained. Conclusion: Optimal results in this study are obtained through the acquisition of the smallest CT value, namely at temperature conditions of 96 °C and 15 seconds.

Background: The adoption of sixth generation (6G) networks is expected to revolutionize various sectors like health care by providing unprecedented data transfer capabilities and reliability. This transition is particularly applicable to real-time health monitoring where the speed and consistency of data transmission matters. Objective: The article explores the integration of Arduino Bluetooth modules, specifically the Bluetooth HC-05 module, with 6G technologies for betterment in remote health monitoring systems. By merging the ultra-reliable, low-latency communication (URLLC) characteristics of 6G with the relatively cheap and user-friendly nature of Arduino platforms, a more intelligent health monitoring system could be established. Methodology: The article proposes Arduino sensor modules with a 6G-based real-time uplink to transmit high-resolution biometric data. The study also uses machine learning and edge computing methodologies to improve data processing for patient diagnosis. Factors such as power usage, scalability, and network integration are thoroughly explored to reach a holistic strategy. Results: The results suggest that the use of Arduino Bluetooth and 6G together can enable much better monitoring of many categories in health metrics to meet very different patient profiles. In this collaboration, we are also going to include the two main things that need everyone's attention, data privacy and security. Moreover, the utilization of artificial intelligence (AI) and edge computing led to improved diagnoses and quicker responses. Conclusions: The study demonstrates the promise of Arduino Bluetooth for future healthcare monitoring over 6G networks, and serves as a benchmark toward such ends. It paves the way for cheaper, more available, and innovative healthcare options in the era of the Internet of Things (IoT) and beyond, transforming patient treatment.

Abstract This paper aims to propose a faster and more accurate network for human spatiotemporal action localization tasks. Like the YOWO model, we also use convolutional neural networks (CNNs) for feature extraction, but our model differs from YOWO in three significant ways: firstly, we don’t use the feature fusion strategy, we only use spatial features extracted by 2D CNNs for action localization and spatiotemporal features extracted by 3D CNNs for action recognition; secondly, we make an improvement to the 2D CNNs network by introducing a coordinate attention mechanism and utilize the CIoU loss instead of the coordinate offset loss for bounding box regression; thirdly, we provide a more lightweight and faster spatiotemporal action localization architecture, which reduces the number of parameters by 21.76 million and achieves a speed of 39 fps on 16-frame input clips compared to the YOWO model. We test our model’s performance on three public datasets: UCF-Sports, JHMDB-21 and UCF101-24. Compared with the YOWO model, we improve frame-mAP (@IoU 0.5) by 17.09% and 7.15% on the UCF-Sports and JHMDB-21 datasets, and for video-mAP, we improve by 2.7%, 8.7% and 14.4% at IoU thresholds of 0.2, 0.5 and 0.75 on the JHMDB-21 dataset.

Applications of terahertz (THz) imaging technologies have advanced significantly in the disciplines of biology, medical diagnostics, and nondestructive testing in the past several decades. Significant progress has been made in THz biomedical imaging, allowing for the label-free diagnosis of malignant tumors. Terahertz frequencies, which lie between those of the microwave and infrared, are highly sensitive to water concentration and are significantly muted by water. Terahertz radiation does not cause ionization of biological tissues because of its low photon energy. Recently, terahertz spectra, including spectroscopic investigations of cancer, have been reported at an increasing rate due to the growing interest in their biological applications sparked by these unique features. To improve cancer diagnosis with terahertz imaging, an appropriate differentiation technique is required to increased blood supply and localized rise in tissue water content that commonly accompany the presence of malignancy. Terahertz imaging has been found to benefit from structural alterations in afflicted tissues. This study provides an overview of terahertz technology and briefly discusses the use of terahertz imaging techniques in the detection of skin cancer. Research into the promise and perils of terahertz imaging will also be discussed. [ABSTRACT FROM AUTHOR]

Automatic extraction of coal flow region of coal mine belt conveyor plays an important role in coal flow monitoring, and real-time control of belt speed through real-time accurate monitoring of coal flow, which realizes the purpose of energy saving and consumption reduction of belt conveyor. In this paper, a real-time semantic segmentation network with detail enhancement for pixel-level coal flow monitoring, called DENet, is proposed. First, to ensure the strong real-time performance of the network, a two-branch coding structure is used to extract the semantic information and spatial detail information. Second, to improve the feature representation of spatial detail information, we design the Parameter-free Attention-Guided Enhancement Module (PF-AGEM) and the detail enhancement module (DEM), which fully integrate the semantic information features in the semantic branch into the detail branch and further enhance the detail features. Third, we design the multi-scale channel attention (MSCA) module in the semantic branch to extract the semantic information features of small targets earlier in the high-resolution feature maps, which solves the problem that the semantic information features of small targets are easily lost in the low-resolution feature maps. Finally, we propose a selective feature fusion module (FFM) to better realize the fusion of semantic information and spatial detail information. Experimental results show that the proposed DENet achieves a mean intersection over union (mIoU) of 96.23% at 87.1 frames per second (FPS) on the Coal Flow Segmentation (CFS) dataset and 74.9% mIoU at 207 FPS on the Camvid dataset, which is competitive with the state-of-the-art real-time semantic segmentation models. [ABSTRACT FROM AUTHOR]

Cross-docking is a logistics procedure implemented in a warehouse to achieve a competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. Today, one of the challenges related to cross-docking for both practitioners and researchers is handling the uncertainty. Robust cross-docking solutions bring a part of the answer to this challenge. This paper proposes an overview of robust and real-time models for cross-dock problems with a focus on scheduling problems, notably in the road-to-road cross-dock environment. To this end, the conducted systematic literature review addresses the collection, identification, screening, eligibility, and inclusion steps to extract the most relevant literature. The gaps in the literature are identified, and some perspectives to support future studies are proposed. [ABSTRACT FROM AUTHOR]

ECG signals and sleep monitoring parameters complement each other and can be used for qualitative diagnosis of sleep apnea syndrome and cardio-related diseases. However, due to the limitations of the instrument volume and the detection environment, it is often challenging to integrate these two functions in practical applications. In this paper, a 12-lead dynamic electrocardiograph integrated with sleep monitoring is designed. The system’s volume is reduced by combining the integrated ECG simulation front end with a miniature sensor. The system achieves the extraction, conditioning, and calculation of 12-lead ECG signals and sleep-related parameters and writes the data to a memory card in real time, which offers convenience for users and doctors in the diagnostic process.

Abstract Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance.

Deteriorating water quality poses a substantial risk to human health, with billions at risk of waterborne diseases due to contamination. Insufficient water quality data augment risks as conventional monitoring methods lack comprehensive coverage. Technologies like the Internet of Things and machine learning offer real-time water quality monitoring and classification. IoT nodes often provide point data insufficient for monitoring the quality of entire water bodies. Remote sensing, though useful, has limitations such as measuring only optically active parameters and being affected by climate and resolution issues. To address these challenges, an unmanned surface vehicle named `AquaDrone' has been developed. AquaDrone traverses water bodies, collecting data of four key parameters (pH, dissolved oxygen, electrical conductivity, and temperature) along with GPS coordinates. The data is transmitted to a web portal via LoRa communication and Wi-Fi, where visualizations like data tables, trendlines and color-coded heatmaps are generated. A multilayer perceptron classifies water quality into five categories, aiding in real-time classification. A comparative analysis of various oversampling techniques has been conducted in the context of water quality classification. The AquaDrone offers a feasible solution for monitoring quality of small to medium-sized water bodies, crucial for safeguarding public health. HIGHLIGHTS AquaDrone measures four physicochemical parameters of water along with GPS coordinates to monitor water quality in real-time.; A multilayer perceptron classifies water quality into five categories, with oversampling used to enhance model training.; AquaDrone wirelessly transmits data to the web portal, where trendlines, data table and a color-coded heatmap are generated to visualize water quality.; This ensures effective real-time monitoring in small to medium-sized water bodies, protecting both water sources and human health.;

High-Efficiency Video Coding (HEVC) is a video compression standard designed to improve video compression efficiency compared to its predecessor, Advanced Video Coding (AVC). HEVC provides approximately twice the compression efficiency compared to the AVC. In the HEVC system, the most computational complexity module is the Motion Estimation (ME). ME module helps reduce redundancy by compensating for motion during video compression. However, the computational complexity makes it a bottleneck in the design of high-resolution video encoders. This paper proposes an efficient architecture for the Integer ME (IME) module of the HEVC encoder. The proposed architecture introduces an efficient memory usage scheme to support the Full Search motion search algorithm. The full pipeline architecture includes 4096 Processing Elements (PE) and an adder tree to compute the Sum of Absolute Difference (SAD) for every Prediction Unit (PU) partition. The proposed architecture was designed and implemented on Xilinx Virtex-7 XC7VX550T FPGA. Our design achieved up to 275 FPS and approximately 10 FPS at 4 K video for the Search Regions of [Formula: see text] and [Formula: see text] pixels, respectively.

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance. [ABSTRACT FROM AUTHOR]

This paper introduces software patterns (registration, acquire-release, and cache awareness) and data structures (Petri net, finite state machine, and protocol flag array) to support the coordinated execution of software activities (also called "components" or "agents"). Moreover, it presents and tests an implementation for Petri nets that supports real-time execution in shared memory for deployment inside one individual robot and separates event firing and handling, enabling distributed deployment between multiple robots. Experimental validation of the introduced patterns and data structures is performed within the context of activities for task execution, control and perception, and decision making for an application on coordinated navigation. [ABSTRACT FROM AUTHOR]

The transmembrane nitrate reductase (Nar) is the first enzyme in the dissimilatory alternate anaerobic nitrate respiratory chain in denitrifying bacteria. To date, there has been no real-time method to determine its specific activity embedded in its native membrane; here, we describe such a new method, which is useful with the inside-out membranes of Paracoccus denitrificans and other denitrifying bacteria. This new method takes advantage of the native coupling of the endogenous NADH dehydrogenase or Complex I with the reduction of nitrate by Nar through the quinone pool of the inner membranes of P. denitrificans. This is achieved under previously reached anaerobic conditions. Inner controls confirming the specific Nar activity determined by this new method were made by the total inhibition of the Nar enzyme by sodium azide and cyanide, well-known Nar inhibitors. The estimation of the Michaelis–Menten affinity of Nar for NO3− using this so-called Nar-JJ assay gave a Km of 70.4 μM, similar to previously determined values. This new Nar-JJ assay is a suitable, low-cost, and reproducible method to determine in real-time the endogenous Nar activity not only in P. denitrificans, but in other denitrifying bacteria such as Brucella canis, and potentially in other entero-pathogenic bacteria. [ABSTRACT FROM AUTHOR]

GNSS precise point positioning (PPP) plays an important role in retrieving atmospheric water vapor values and performing numerical weather prediction. However, traditional PPP relies on real-time orbits and clocks, which require continuous internet or satellite communication. Improved broadcast ephemerides of GNSSs offer new opportunities for PPP with broadcast ephemerides (BE-PPP) instead of using precise ephemeride products. Therefore, we investigated the feasibility of utilizing BE-PPP for retrieving zenith tropospheric delay (ZTD) and precipitable water vapor (PWV) data. We processed the GPS/Galileo observations from 80 IGS stations during a 30-day experiment to retrieve ZTD values using both real-time PPP (RT-PPP) and BE-PPP solutions. Then, we processed observations from 20 EUREF Permanent GNSS Network (EPN) stations to retrieve PWV data. The IGS final tropospheric products were used to validate the ZTD, and radiosonde (RDS) and ERA5 data were used to validate the PWV. The results show that the real-time ZTD from BE-PPP agrees well with that from RT-PPP: the standard deviation (STD) of the ZTD is 1.07 cm when using BE-PPP and 0.6 cm when using RT-PPP. Furthermore, the STD of the PWV is 1.69 mm when using BE-PPP, and 0.96 mm when using RT-PPP, compared to the ERA5-PWV. Compared to the RDS-PWV, the STD is 3.09 mm when using BE-PPP and 1.39 mm when using RT-PPP. In conclusion, the real-time ZTD/PWV products obtained using the BE-PPP solution are consistent with those of RT-PPP and meet the requirements of NWP, so this method can be used as an effective complement to RT-PPP to expand its application potential. [ABSTRACT FROM AUTHOR]

In Industry 4.0, the focus of task scheduling is more on smart services such as robotic services under the paradigm of mobile edge computing, which are widely used to improve the efficiency of smart manufacturing. Existing scheduling research efforts on optimizing efficiency such as swarm algorithm, reinforcement learning, but with little real-time dynamic scheduling. Online learning and adaptation is a critical function when tackling real challenges in unpredictable and dynamically changing edge computing environments. Our goal is to propose algorithms that can meet the real-time dynamic task scheduling even reach the level of communication. We propose a Deep Reinforcement Learning (DRL)-based online real-time task scheduling method which has the new exploration and exploitation strategy using the improved sparrow search algorithm (ISSA). We replace the traditional strategy with a meta-heuristic algorithm, while introducing natural disturbances and modifying the population position update formula to converge to the optimal position. The algorithm has two layers. The first layer is scheduling generation layer, which is responsible for making decisions, and the second layer is scheduling policy update layer, which optimizes the strategy and finds the best set of hyperparameters. The experimental results show that the running time of the method to solve the problem can reach 0.0092 seconds, and the real-time performance reaches up to the millisecond communication level. At the same time other methods are compared, effectively reducing the running cost and meeting the requirements of production scheduling tasks. [ABSTRACT FROM AUTHOR]

In this work, a stochastic dynamic version of the container drayage problem is studied. The presented model incorporates uncertainty in the form of stochastic loading and unloading times at both terminals and customers, as well as stochastic travel times, conditionally dependent upon the departure time, allowing robust planning with respect to varying processing times. Moreover, the presented model is dynamic, allowing flexible orders and having the capability of re-solving the optimization problem in case of last-minute orders. Finally, the model also incorporates a truck appointment system operating at each terminal. First, a description of the general model is given, which amounts to a mixed integer non-linear program. In order to efficiently solve the optimization problem, and linearize both the objective and the conditional chance constraints, it is reformulated based on time window partitioning, yielding a purely integer linear program. As a test case, a large road carrier operating in the port of Antwerp is considered. We demonstrate that the model is efficiently solvable, even for instances of up to 300 orders. Moreover, the impact of incorporating stochastic information is clearly illustrated. [ABSTRACT FROM AUTHOR]

Digital twin represents the core technology to realize the dynamic monitoring of complex industrial systems. However, the human body, as the most complex system in the physical world, digital twin is rarely applied in it. In this study, we successfully demonstrated a digital twin in the human biomedical application by proposing a dynamic monitoring system of the upper limb force. In this system, the real upper limb drives the motion of the virtual one in real-time and dynamically updates the force. Meanwhile, the virtual upper limb feeds back the monitoring-results of the force to the controller of the real upper limb via immersive virtual reality interaction. Experimental results of the typical motions of the upper limb revealed that the proposed system functioned interactively in real-time in a non-invasive manner, while ensuring the accurate solving of the muscle force. In conclusion, our digital twin-driven system is of great importance for rehabilitation medicine, biomechanical scientific research and physical training, promoting the application of the digital twin in the human biomedical field. [ABSTRACT FROM AUTHOR]

Employee attendance is crucial for a company because it is one indicator for determining an employee's salary or wages. Implementing the attendance process requires thoroughness so that it helps work activities in the company and produces accurate and correct attendance reports. Many companies have utilized technology that implements facial recognition to verify employee attendance. However, some companies still need to use the traditional method, which takes longer. Therefore, facial recognition is beneficial in terms of verifying attendance in the attendance system to speed up the process of recording and verifying the person. Tests were carried out on a total of 20 employees who took the attendance test using face recognition, all of them succeeded in carrying out attendance, and attendance data was recorded on the attendance website of PT. Srivijaya 87. In this study, we developed a website-based attendance system integrated with a facial recognition system using a deep learning method that can work in real-time to record employee attendance. This system is proven to simplify the attendance process and can recognize all employee data stored in the database with an average accuracy of 93. [ABSTRACT FROM AUTHOR]

Wearing safety helmets at construction sites is a major measure to prevent safety accidents, so it is essential to supervise and ensure that workers wear safety helmets. This requires a high degree of real-time performance. We improved the network structure based on YOLOv7. To enhance real-time performance, we introduced GhostModule after comparing various modules to create a new efficient structure that generates more feature mappings with fewer linear operations. SE blocks were introduced after comparing several attention mechanisms to highlight important information in the image. The EIOU loss function was introduced to speed up the convergence of the model. Eventually, we constructed the efficient model EGS-YOLO. EGS-YOLO achieves a mAP of 91.1%, 0.2% higher than YOLOv7, and the inference time is 13.3% faster than YOLOv7 at 3.9 ms (RTX 3090). The parameters and computational complexity are reduced by 37.3% and 33.8%, respectively. The enhanced real-time performance while maintaining the original high precision can meet actual detection requirements. [ABSTRACT FROM AUTHOR]

The benefits of teacher-facing dashboards are incontestable, yet their evidence is finite in terms of long-term use, meaningful usability, and maturity level. Thus, this paper uses design science research and critical theory to design and develop TEADASH to support teachers in making decisions on teaching and learning. Three cycles of design science research and multiple small loops were implemented to develop the dashboard. The tool was then deployed and evaluated in real time with the authentic courses. Five courses from two Swedish universities were included in this study. The co-design with teachers is crucial to the applicability of this dashboard, while letting teachers use the tool during their courses is more important to help them to recognize the features they actually use and the tool's usefulness for their teaching practices. TEADASH can address the prior matters, align with the learning design, and meet teachers' needs. The technical and co-design aspects, as well as the advantages and challenges of applying TEADASH in practice, are also discussed here. [ABSTRACT FROM AUTHOR]

The application and utilization of technology to measure the level of comfort in mass-produced vehicles, including military vehicles, is constantly evolving. Currently, the testing of comfort parameters is carried out manually through human-driven test drives. Thus, the range of variability in measurements is extensive as it depends on the subjective experiential indications of experts. This research utilizes KY-037 sensor to measure noise level and BME280 sensor fusion to detect temperature, air pressure, humidity, and altitude. These parameters have a significant impact on passenger comfort inside the passenger cabin of military vehicles. This project included involves the development of LoRa-based communication medium using RFM95W technology. The system has extensive performance testing inside the passenger cabin of a military vehicle on various test area tracks. The test results indicate that the system is capable of accurately reading the KY-037 sensor, with a range of 80 - 141 dB depending on the tracks. The BME280 sensor consistently measures a temperature of 36,98°C, altitude readings ranging from 667-677 meter above sea level, maintaining a stable air pressure of 955.35 hPa, and measuring the lowest humidity level in the vehicle cabin at 24.34%. The LoRa technology possesses remarkable to extend the communication range, even in challenging environments, reaching distances over 2 kilometers. The response time for data sent in web-based applications consistently remains below 1 second. Thus, this system can assist experts in enhancing cabin passenger comfort standards by narrowing the range and making it more measurable. [ABSTRACT FROM AUTHOR]

The path tracking control of electric mining articulated vehicles (EMAVs), critical equipment commonly used for mining and transportation in underground mines, is a research topic that has received much attention. The path tracking control of EMAVs is subject to several system constraints, including articulation angle and articulation angular velocity. In light of this, many researchers have initiated studies based on model predictive control (MPC). The principal design schemes for existing MPC methods encompass linear MPC (LMPC) utilizing a single reference point, so named the single reference point LMPC (SRP-LMPC), and nonlinear MPC (NMPC). However, NMPC exhibits suboptimal real-time performance, while SRP-LMPC demonstrates inferior accuracy. To simultaneously improve the accuracy and real-time performance of the path tracking control of EMAV, based on the SRP-LMPC, a path tracking control method for EMAV based on nonlinear compensated multiple reference points LMPC (MRP-LMPC) is proposed. The simulation results demonstrate that MRP-LMPC simultaneously exhibits a commendable degree of accuracy and real-time performance. In all simulation results, the displacement error amplitude and heading error amplitude of MRP-LMPC do not exceed 0.2675 m and 0.1108 rad, respectively. Additionally, the maximum solution time in each control period is 5.9580 ms. The accuracy of MRP-LMPC is comparable to that of NMPC. However, the maximum solution time of MRP-LMPC can be reduced by over 27.81% relative to that of NMPC. Furthermore, the accuracy of MRP-LMPC is significantly superior to that of SRP-LMPC. The maximum displacement and heading error amplitude can be reduced by 0.3075 m and 0.1003 rad, respectively, representing a reduction of 65.51% and 73.59% in the middle speed and above scenario. [ABSTRACT FROM AUTHOR]

The autonomous lane change maneuver is a critical component in the advancement of intelligent transportation systems (ITS). To enhance safety and efficiency in dynamic traffic environments, this study introduces a novel autonomous lane change strategy leveraging a quintic polynomial function. To optimize the trajectory, we formulate an objective function that balances the time required for lane changes with the peak acceleration experienced during the maneuver. The proposed method addresses key challenges such as driver discomfort and prolonged lane change durations by considering the entire lane change process rather than just the initiation point. Utilizing a fifth-order polynomial for trajectory planning, the strategy ensures smooth and continuous vehicle movement, reducing the risk of collisions. The effectiveness of the method is validated through comprehensive simulations and real-world vehicle tests, demonstrating significant improvements in lane change performance. Despite its advantages, the model requires further refinement to address limitations in mixed traffic conditions. This research provides a foundation for developing intelligent vehicle systems that prioritize safety and adaptability. [ABSTRACT FROM AUTHOR]

There is a growing need in public health to conduct large-scale epidemiological studies to investigate the health effects of fine particulate matter (PM2.5) exposure levels. In response to this need, we developed a real-time personal PM2.5 exposure monitoring system (PEPS: Personal Exposure PM2.5 System), which is capable of monitoring personal exposure concentration and uploading data in real time. The air quality self-labelling device, specifically customized for the PEPS, can be worn on the body and features functions for real-time data automatic upload, data storage, data export, and localization. This system enables researchers to obtain the big data of personal PM2.5 exposure concentration at low cost, with minimal manpower and technical requirements. It has been utilized to investigate the personal exposure levels of PM2.5 among college students in Beijing, China, providing a substantial volume of valuable data for indoor air quality and related epidemiological study. The maximum difference between the monitored daily average exposure concentration and the outdoor concentration was 265 µg/m3, corresponding to a relative error of 1579.5%. The correlation analysis of 11 factors showed that the correlation between exposure concentration and outdoor concentration was as high as 0.66 (p < 0.001), and the correlation between exposure concentration and other certain factors was in the range of [−0.11, −0.03]. [ABSTRACT FROM AUTHOR]

Purpose: Develop a true real‐time implementation of MR signature matching (MRSIGMA) for free‐breathing 3D MRI with sub‐200 ms latency on the Elekta Unity 1.5T MR‐Linac. Methods: MRSIGMA was implemented on an external computer with a network connection to the MR‐Linac. Stack‐of‐stars with partial kz sampling was used to accelerate data acquisition and ReconSocket was employed for simultaneous data transmission. Movienet network computed the 4D MRI motion dictionary and correlation analysis was used for signature matching. A programmable 4D MRI phantom was utilized to evaluate MRSIGMA with respect to a ground‐truth translational motion reference. In vivo validation was performed on patients with pancreatic cancer, where 15 patients were employed to train Movienet and 7 patients to test the real‐time implementation of MRSIGMA. Dice coefficients between real‐time MRSIGMA and a retrospectively computed 4D reference were used to evaluate motion tracking performance. Results: Motion dictionary was computed in under 5 s. Signature acquisition and matching presented 173 ms latency on the phantom and 193 ms on patients. MRSIGMA presented a mean error of 1.3–1.6 mm for all phantom experiments, which was below the 2 mm acquisition resolution along the motion direction. The Dice coefficient over time between MRSIGMA and reference contours was 0.88 ± 0.02 (GTV), 0.87 ± 0.02(duodenum‐stomach), and 0.78 ± 0.02(small bowel), demonstrating high motion tracking performance for both tumor and organs at risk. Conclusion: The real‐time implementation of MRSIGMA enabled true real‐time free‐breathing 3D MRI with sub‐200 ms imaging latency on a clinical MR‐Linac system, which can be used for treatment monitoring, adaptive radiotherapy and dose accumulation mapping in tumors affected by respiratory motion. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems rely heavily on accurate traffic sign detection (TSD) to enhance road safety and traffic management. Various methods have been explored in the literature for this purpose, with deep learning methods consistently demonstrating superior accuracy. However, existing research highlights the persistent challenge of achieving high accuracy rates while maintaining non-destructive and real-time requirements. In this study, we propose a deep learning model based on the YOLOv8 architecture to address this challenge. The model is trained and evaluated using a custom dataset, and extensive experiments and performance analysis demonstrate its ability to achieve precise results, thus offering a promising solution to the current research challenge in deep learning-based TSD. [ABSTRACT FROM AUTHOR]

Meeting the rising global demand for healthcare diagnostic tools is crucial, especially with a shortage of medical professionals. This issue has increased interest in utilizing deep learning (DL) and telemedicine technologies. DL, a branch of artificial intelligence, has progressed due to advancements in digital technology and data availability and has proven to be effective in solving previously challenging learning problems. Convolutional neural networks (CNNs) show potential in image detection and recognition, particularly in healthcare applications. However, due to their resource-intensiveness, they surpass the capabilities of general-purpose CPUs. Therefore, hardware accelerators such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and graphics processing units (GPUs) have been developed. With their parallelism efficiency and energy-saving capabilities, FPGAs have gained popularity for DL networks. This research aims to automate the classification of normal and abnormal (specifically Diabetic Foot Ulcer—DFU) classes using various parallel hardware accelerators. The study introduces two CNN models, namely DFU_FNet and DFU_TFNet. DFU_FNet is a simple model that extracts features used to train classifiers like SVM and KNN. On the other hand, DFU_TFNet is a deeper model that employs transfer learning to test hardware efficiency on both shallow and deep models. DFU_TFNet has outperformed AlexNet, VGG16, and GoogleNet benchmarks with an accuracy 99.81%, precision 99.38% and F1-Score 99.25%. In addition, the study evaluated two high-performance computing platforms, GPUs and FPGAs, for real-time system requirements. The comparison of processing time and power consumption revealed that while GPUs outpace FPGAs in processing speed, FPGAs exhibit significantly lower power consumption than GPUs. [ABSTRACT FROM AUTHOR]

Most real-time semantic segmentation networks use shallow architectures to achieve fast inference speeds. This approach, however, limits a network's receptive field. Concurrently, feature information extraction is restricted to a single scale, which reduces the network's ability to generalize and maintain robustness. Furthermore, loss of image spatial details negatively impacts segmentation accuracy. To address these limitations, this paper proposes a Multiscale Context Pyramid Pooling and Spatial Detail Enhancement Network (BMSeNet). First, to address the limitation of singular semantic feature scales, a Multiscale Context Pyramid Pooling Module (MSCPPM) is introduced. By leveraging various pooling operations, this module efficiently enlarges the receptive field and better aggregates multiscale contextual information. Moreover, a Spatial Detail Enhancement Module (SDEM) is designed, to effectively compensate for lost spatial detail information and significantly enhance the perception of spatial details. Finally, a Bilateral Attention Fusion Module (BAFM) is proposed. This module leverages pixel positional correlations to guide the network in assigning appropriate weights to the features extracted from the two branches, effectively merging the feature information of both branches. Extensive experiments were conducted on the Cityscapes and CamVid datasets. Experimental results show that the proposed BMSeNet achieves a good balance between inference speed and segmentation accuracy, outperforming some state-of-the-art real-time semantic segmentation methods. [ABSTRACT FROM AUTHOR]

In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles' characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network's ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements. [ABSTRACT FROM AUTHOR]

Summary: Fog computing, an emerging technology, extends Cloud computing services to the network's edge in the proximity of the application request. This extension yields improvement in Bandwidth (BW) utilization, faster responses to Real‐Time (RT) and Internet of Things (IoT) requests, and the provision of the heterogeneous resource services. While extensive work has been conducted on resource allocation for RT and Non‐Real‐Time (NRT) requests separately in Fog as well as Cloud computing, there is limited focus on resource provisioning for mixed RT and NRT requests in the Fog‐integrated Cloud (FiC) environment. Moreover, the majority of the existing provisioning methods primarily consider parameters from the system's perspective, overlooking crucial user aspects such as deadline and request size. To address the gap, this work introduces a resource provisioning method named "Parameter Matching of Realtime and Non‐Realtime Applications (PMRNA)," which considers user parameters and resource information, gathered by the broker. The performance evaluation of the proposed model is done in CloudSim, using various combinations of RT and NRT requests along with diverse Fog and Cloud resource configurations. Evaluation metric includes average Execution Time (ET), average Waiting Time (WT), average Turn Around Time (TAT), and resource utilization. The experimental results demonstrate a significant reduction in both average WT and average TAT for the diverse pool of RT and NRT requests in the FiC compared to the Cloud‐only environment. [ABSTRACT FROM AUTHOR]

Forest fires pose a catastrophic threat to Earth's ecology as well as threaten human beings. Timely and accurate monitoring of forest fires can significantly reduce potential casualties and property damage. Thus, to address the aforementioned problems, this paper proposed an unmanned aerial vehicle (UAV) based on a lightweight forest fire recognition model, Fire-Net, which has a multi-stage structure and incorporates cross-channel attention following the fifth stage. This is to enable the model's ability to perceive features at various scales, particularly small-scale fire sources in wild forest scenes. Through training and testing on a real-world dataset, various lightweight convolutional neural networks were evaluated on embedded devices. The experimental outcomes indicate that Fire-Net attained an accuracy of 98.18%, a precision of 99.14%, and a recall of 98.01%, surpassing the current leading methods. Furthermore, the model showcases an average inference time of 10 milliseconds per image and operates at 86 frames per second (FPS) on embedded devices. [ABSTRACT FROM AUTHOR]

An inspection in tablet appearance integrity before bottling is regarded as a routine task in a pharmaceutical factory. Although some methods such as automated optical instrument, video or artificial intelligence (AI) are currently available in industry, it usually pays for a complex computational process as well as high cost. Based on the symmetry of tablet appearance in reality, this study develops a biaxial scanning slope symmetry algorithm to realize a dynamic real-time tablet defect detection with a simple arithmetic operation. First, the tablet is discretely scanned using image sensor in two axes, i.e. X and Y directions, simultaneously. Second, the analogy output signals generated from the sensor during the scanning process is discretely digitized and stored in an array. Third, the coordinate of center point in the series data array is identified from every line scanning. Fourth, every section slope between two nearby center points from the first to last lines is formulated and calculated sequentially. Finally, the square mean error (SME) is used to evaluate the shape defect situation according to all accumulated errors from every slope variation. The experimental results verify that the proposed algorithm can achieve both fast and accurate detection performance. [ABSTRACT FROM AUTHOR]

Photonic integrated circuits (PICs) are enabling breakthroughs in several areas, including quantum computing, neuromorphic processors, wearable devices, and more. Nevertheless, existing PIC measurement methods lack the spectral precision, speed, and sensitivity required for refining current applications and exploring new frontiers such as point‐of‐care or wearable biosensors. Here, the "sweeping optical frequency mixing method (SOHO)" is presented, surpassing traditional PIC measurement methods with real‐time operation, 30 dB higher sensitivity, and over 100 times better spectral resolution. Leveraging the frequency mixing process with a sweeping laser, SOHO excels in simplicity, eliminating the need for advanced optical components and additional calibration procedures. Its superior performance is demonstrated on ultrahigh‐quality factor (Q) fiber‐loop resonators (Q = 46 × 106), as well as microresonators, realized on a new optical waveguide platform. An experimental spectral resolution of 19.1 femtometers is demonstrated using an 85‐meter‐long unbalanced fiber Mach Zehnder Interferometer, constrained by noise resulting from the extended fiber length, while the theoretical resolution is calculated to be 6.2 femtometers, limited by the linewidth of the reference laser. With its excellent performance metrics, SOHO has the potential to become a vital measurement tool in photonics, excelling in high‐speed and high‐resolution measurements of weak optical signals. [ABSTRACT FROM AUTHOR]

Measuring heart rate is essential for evaluating a patient's health status. Non-contact heart rate measurement offers numerous advantages across various sectors, including medicine, hospital care, and sports, and is particularly valuable in remote medical practices. To serve as an effective alternative to current contact-based heart rate monitoring technologies, non-contact methods must achieve high accuracy, despite the challenge of heart rate variability within short time frames. In this paper, we suggest a camera-based heart rate monitoring system that detects objects using image processing methods such as the Haar cascade classifier. We use the Fast Fourier Transform (FFT) for analyzing frequency domain heart rate signals. [ABSTRACT FROM AUTHOR]

New Adaptive Nano-Scale Sensor Network (ANSN) can quickly feel nanoscale surroundings. ANSN uses data in many scenarios to improve networks, consume less energy, and gain more accurate data. ANSI essentials are covered in detail here. This group has numerous parts. Making service better, collecting data with less energy, sending data with Q-learning, merging sensor data to increase accuracy, controlling power dynamically, and protecting data using AES are examples. Energy collection and sensor use are key to this effort. Academic research has proven that ANSN outperforms other techniques in several areas. Improvements include speed, security, latency, sensor accuracy, and network stability. With these changes, ANSN may be suitable for small wireless sensor networks [ABSTRACT FROM AUTHOR]

With the growth of computer networks worldwide, there has been a greater need to protect local networks from malicious data that travel over the network. The increase in volume, speed, and variety of data requires a more robust, accurate intrusion detection system capable of analyzing a huge amount of data. This work proposes the creation of an intrusion detection system using stream classifiers and three classification layers—with and without a reduction in the number of features of the records and three classifiers in parallel with a voting system. The results obtained by the proposed system are compared against other models proposed in the literature, using two datasets to validate the proposed system. In all cases, gains in accuracy of up to 18.52% and 3.55% were obtained, using the datasets NSL-KDD and CICIDS2017, respectively. Reductions in classification time up to 35.51% and 94.90% were also obtained using the NSL-KDD and CICIDS2017 datasets, respectively. [ABSTRACT FROM AUTHOR]

Substations are vital elements of electrical infrastructure that necessitate continuous monitoring and maintenance to ensure optimal performance. This research advocates for the deployment and design of devices based on the Raspberry Pi 3 Model B to enhance substation reliability. The project involves developing hardware and software capable of real-time monitoring of substation conditions, utilizing sensors to measure critical parameters such as temperature, current, voltage, and humidity. The monitoring software is designed to collect, analyze, and report data, employing detection algorithms, including the Fuzzy Mamdani method, to ensure accurate sensor and frequency measurements and to identify potential disturbances or anomalies. Additionally, the system integrates automatic mechanisms for maintaining substation conditions, encompassing preventive measures and rapid responses to emergencies. Testing under various fault scenarios and operational conditions demonstrated the device's effectiveness in detecting issues and providing swift responses, thereby enhancing substation performance. The results show an average error of 0.14% for voltage measurements, 0.31% for current measurements, and 0.02% for data transmission frequency. This implementation is expected to positively impact substation management and maintenance, reduce the risk of system failures, and improve overall operational efficiency. Leveraging Raspberry Pi technology ensures a cost-effective solution that can be seamlessly integrated with existing substation monitoring systems. [ABSTRACT FROM AUTHOR]

In this paper a system to monitor road conditions, detect unsafe driving behaviours and determine the influence of rainfall on traffic safety in real time using different machine learning algorithms, has been proposed. The system developed consists of a mobile application that captures car movement using its in-built accelerometer and gyroscope sensors and a server that monitors weather conditions at 16 key locations in Mauritius using the OpenWeather API. Road conditions, pothole, speed bumps as well as driving events were analysed using the K-Nearest Neighbour (KNN) and Multi-Layer Perceptron (MLP) algorithms. Moreover, a mathematical model, which incorporates the predicted rainfall in the estimation of braking distance and recommended speed, has been proposed. An average accuracy of 80.9% was obtained for pothole detection, 70% for speed bumps and 85.5% for unsafe driving behaviours detection. The proposed model with rainfall data predicted the braking distance and recommended speed with a Mean Absolute Percentage Error (MAPE) of 14.7% and 0.735% respectively. [ABSTRACT FROM AUTHOR]

Despite sufficient studies performed in non-primate animal models, there exists scanty information obtained from pilot trials in non-human primate animal models, severely hindering nanomaterials moving from basic research into clinical practice. We herein present a pioneering demonstration of nanomaterials based optical imaging-guided surgical operation by using macaques as a typical kind of non-human primate-animal models. Typically, taking advantages of strong and stable fluorescence of the small-sized (diameter: ~ 5 nm) silicon-based nanoparticles (SiNPs), lymphatic drainage patterns can be vividly visualized in a real-time manner, and lymph nodes (LN) are able to be sensitively detected and precisely excised from small animal models (e.g., rats and rabbits) to non-human primate animal models (e.g., cynomolgus macaque (Macaca fascicularis) and rhesus macaque (Macaca mulatta)). Compared to clinically used invisible near-infrared (NIR) lymphatic tracers (i.e., indocyanine green (ICG); etc.), we fully indicate that the SiNPs feature unique advantages for naked-eye visible fluorescence-guided surgical operation in long-term manners. Thorough toxicological analysis in macaque models further provides confirming evidence of favorable biocompatibility of the SiNPs probes. We expect that our findings would facilitate the translation of nanomaterials from the laboratory to the clinic, especially in the field of cancer treatment. [ABSTRACT FROM AUTHOR]

As the scale of the networks continually expands, the detection of distributed denial of service (DDoS) attacks has become increasingly vital. We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network (DNN). The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible, thereby reducing data volume. Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic, enhancing the neural network's generalization capabilities. Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic. Compared with the benchmark methods, our method reaches 99.9% on low-rate DDoS (LDDoS), flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%, 31% and 8%. [ABSTRACT FROM AUTHOR]

The primary concern of modern technology is cyber attacks targeting the Internet of Things. As it is one of the most widely used networks today and vulnerable to attacks. Real-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things (IoT) networks, as devices can be monitored or service isolated from them and affect users in one way or another. Securing Internet of Things networks is an important matter, as it requires the use of modern technologies and methods, and real and up-to-date data to design and train systems to keep pace with the modernity that attackers use to confront these attacks. One of the most common types of attacks against IoT devices is Distributed Denial-of-Service (DDoS) attacks. Our paper makes a unique contribution that differs from existing studies, in that we use recent data that contains real traffic and real attacks on IoT networks. And a hybrid method for selecting relevant features, And also how to choose highly efficient algorithms. What gives the model a high ability to detect distributed denial-of-service attacks. the model proposed is based on a two-stage process: selecting essential features and constructing a detection model using the K-neighbors algorithm with two classifier algorithms (logistic regression and Stochastic Gradient Descent classifier (SGD), combining these classifiers through ensemble machine learning (stacking), and optimizing parameters through Grid Search-CV to enhance system accuracy. Experiments were conducted to evaluate the effectiveness of the proposed model using the CIC-IoT2023 and CIC-DDoS2019 datasets. Performance evaluation demonstrated the potential of our model in robust intrusion detection in IoT networks, achieving an accuracy of 99.965% and a detection time of 0.20 s for the CIC-IoT2023 dataset, and 99.968% accuracy with a detection time of 0.23 s for the CIC-DDoS 2019 dataset. Furthermore, a comparative analysis with recent related works highlighted the superiority of our methodology in intrusion detection, showing improvements in accuracy, recall, and detection time. [ABSTRACT FROM AUTHOR]

With pervasive network access, cloud computing offers on-demand access to a shared pool of computing resources. To guarantee high quality cloud services, cloud task scheduling algorithms need to be dependable and efficient. Cloud infrastructure, however, are vulnerable to errors and malfunctions that may affect how tasks are carried out. In this research, a metaheuristic approach for adaptive fault tolerant job scheduling in cloud systems is proposed. The method efficiently schedules work while optimizing fault tolerance by adaptively applying numerous fault tolerance strategies and meta-heuristic based optimization. Additionally, a thorough analysis of modern Meta-heuristic based job scheduling algorithms is presented in this work, with an emphasis on the algorithm's fault tolerance and adaptability. Through simulation tests using CloudSim toolkit, adaptability, scalability, energy efficiency, and reliability matrices are used to compare the suggested algorithm against existing approaches. The suggested methodology works better than current techniques, according to the results, which indicate increased task throughput, scalability, energy efficiency, and fault tolerance across a range of cloud workloads. An efficient method for ensuring consistent task execution in unstable cloud environments is offered by the innovative scheduling architecture that incorporates six metaheuristics, redundancy, checkpointing and migration based fault tolerance in an adaptable manner. [ABSTRACT FROM AUTHOR]

This study proposes an edge computing-based facial expression recognition system that is low cost, low power, and privacy preserving. It utilizes a minimally obtrusive cap-based system designed for the continuous and real-time monitoring of a user's facial expressions. The proposed method focuses on detecting facial skin deformations accompanying changes in facial expressions. A multi-zone time-of-flight (ToF) depth sensor VL53L5CX, featuring an 8 × 8 depth image, is integrated into the front brim of the cap to measure the distance between the sensor and the user's facial skin surface. The distance values corresponding to seven universal facial expressions (neutral, happy, disgust, anger, surprise, fear, and sad) are transmitted to a low-power STM32F476 microcontroller (MCU) as an edge device for data preprocessing and facial expression classification tasks utilizing an on-device pre-trained deep learning model. Performance evaluation of the system is conducted through experiments utilizing data collected from 20 subjects. Four deep learning algorithms, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN), are assessed. These algorithms demonstrate high accuracy, with CNN yielding the best result, achieving an accuracy of 89.20% at a frame rate of 15 frames per second (fps) and a maximum latency of 2 ms. [ABSTRACT FROM AUTHOR]