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Digital enabled agility reflects the transformative outset of an organisation to first upgrade its business processes digitally towards facilitating real-time information processing/capabilities. Second, tapping from real-time capabilities, digital enabled agility maximises an agile response's efficacy in enhancing the sophistication of rapidity, flexibility, and sensing involved. In this backdrop, driven by newer IT infrastructure and disruptive technology, this research accentuates on Industry 4.0 digitalisation to understand how a manufacturer can gain digital enabled agility. An empirical survey of manufacturers within the premise of Industry 4.0 is conducted. Drawn from the findings, the capabilities of traceability and visibility can be facilitated by Industry 4.0 that would improve information sharing and information quality in real-time. These capabilities are key propellants to unlock the digital extent of agility. This research however details further how this scope of agility is an extension to that of the scope of typical agility in terms of its ability to handle expansive information processing needs and dynamic coordination/resource orchestration. Insights are weaved under the purviews of (a) organisational information processing view and (b) the manufacturer who is coordinating contemporary dynamic production and allied value chain requirements. [ABSTRACT FROM AUTHOR]

Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved

The exponential growth of Internet of Things (IoT) devices underscores the need for robust security measures against cyber-attacks. Extensive research in the IoT security community has centered on effective traffic detection models, with a particular focus on anomaly intrusion detection systems (AIDS). This paper specifically addresses the preprocessing stage for IoT datasets and feature selection approaches to reduce the complexity of the data. The goal is to develop an efficient AIDS that strikes a balance between high accuracy and low detection time. To achieve this goal, we propose a hybrid feature selection approach that combines filter and wrapper methods. This approach is integrated into a two-level anomaly intrusion detection system. At level 1, our approach classifies network packets into normal or attack, with level 2 further classifying the attack to determine its specific category. One critical aspect we consider is the imbalance in these datasets, which is addressed using the Synthetic Minority Over-sampling Technique (SMOTE). To evaluate how the selected features affect the performance of the machine learning model across different algorithms, namely Decision Tree, Random Forest, Gaussian Naive Bayes, and k-Nearest Neighbor, we employ benchmark datasets: BoT-IoT, TON-IoT, and CIC-DDoS2019. Evaluation metrics encompass detection accuracy, precision, recall, and F1-score. Results indicate that the decision tree achieves high detection accuracy, ranging between 99.82 and 100%, with short detection times ranging between 0.02 and 0.15 s, outperforming existing AIDS architectures for IoT networks and establishing its superiority in achieving both accuracy and efficient detection times. [ABSTRACT FROM AUTHOR]

Summary: Optimizing energy consumption and maximizing throughput in multi‐core real‐time architectures through dynamic task scheduling is a critical design challenge. While significant attention has been devoted to addressing this challenge in the domain of real‐time multi‐core scheduling, the focus has primarily centered on considering periodic tasks as independent. However, the existing literature notably lacks comprehensive study of scheduling methodologies on multi‐core systems that consider dependent tasks, though typical real‐time systems execute tasks that share resources. Earlier studies have predominantly examined scenarios involving random new tasks and task instances (jobs), which are executed in different power levels. Each task (and job) has distinct execution time corresponding to each power level. By considering these parameters (power levels and execution times of jobs), various combinations of energy signatures have been found to attain an optimum system state. Building upon this prior research, our paper extends the scope to encompass task scheduling in multi‐core systems with task dependencies. We introduce a novel approach that categorizes dependent tasks into ASAP (as soon as possible) and ALAP (as late as possible) groups, prioritizing task execution based on task mobility—defined as the disparity between the last cycle the task can be scheduled in and the current cycle. Furthermore, our model demonstrates an approach for efficient scheduling of sporadic and aperiodic tasks within this framework. Through experimental validation using randomized task sets, our results indicate that the proposed model achieves a minimum of 5% reduction in normalized total energy consumption compared to existing methodologies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: To date, most intradialytic hypotension (IDH) studies have proposed technologies to comprehensively predict the occurrence of IDH using the patient's baseline information and ultrafiltration (UF) information, but it is difficult to apply the technologies while identifying the patient's condition in real time. Methods: In this study, we propose an IDH indicator that uses heart rate (HR) change information to identify the patient's condition in real time and visually shows whether IDH has occurred. The data used were collected from 40 dialysis patients in a clinical trial conducted in the Artificial Kidney Unit at Yeungnam University Medical Center, Korea, from 18 July to 29 November 2023. Results: The IDH indicator infers changes in the patient's blood pressure during dialysis by analyzing the upper and lower maximum HRs based on the real-time average HR. Medical staff can respond to IDH in real time by looking at the IDH indicator, which visually expresses changes in the patient's HR. In addition, we propose a multilayer perceptron structure that inputs upper and lower maximum HR information based on the average HR for the time interval accumulated in real time. In learning using 40 min of data up to 5 min before IDH occurred, models using two and five layers showed excellent performance, with accuracy of 88.6% and 85.2%, respectively. Conclusions: By combining IDH visual indicators and the multi-layer perceptron method, medical staff can effectively respond to IDH in real time. [ABSTRACT FROM AUTHOR]

Identifying traffic signs is an essential part of traffic safety and self-driving systems. In real life, the driving environment is changing, making detecting traffic signs wisely and economically vital. The traffic sign detection problem has several small objects and complex ambient interference. The detecting situation also requires a practical and lightweight detection model. This study proposes a new lightweight model, the enhanced Ghost-YOLOv8, based on lightweight modules GhostConv and C3Ghost, based on the YOLOv8 model. It used a light method to extract the features, significantly speeding up inference. In addition to small, medium, and large objects, the head was expanded to include a new multi-scale feature extraction module layer focused on x-small. The experiment results show that when using the German Traffic Sign Detection Benchmark (GTSDB) dataset with three classes, the enhanced Ghost-YOLOv8 has mAP (0.50) of 99.4%and has fewer computations than the YOLOv8 model by 155.2 GFLOPs and has 18.6 Mparameters, which represents only 27.3% from the parameters used in the base model. Also, we suggested a new dataset called the GTSDB-43 dataset, which expanded the number of classes on the GTSDB dataset from three or four main classes to 43 classes and mentioned their main category type simultaneously. Compared with notable algorithms, this method's accuracy and speed are competitive. [ABSTRACT FROM AUTHOR]

There are currently many in-field methods for estimating soil properties (e.g., pH, texture, total C, total N) available in precision agriculture, but each have their own level of suitability and only a few can be used for direct determination of plant-available nutrients. As promising approaches for reliable in-field use, this review provides an overview of electromagnetic, conductivity-based, and electrochemical techniques for estimating plant-available soil nutrients and pH. Soil spectroscopy, conductivity, and ion-specific electrodes have received the most attention in proximal soil sensing as basic tools for precision agriculture during the last two decades. Spectral soil sensors provide indication of plant-available nutrients and pH, and electrochemical sensors provide highly accurate nitrate and pH measurements. This is currently the best way to accurately measure plant-available phosphorus and potassium, followed by spectral analysis. For economic and practicability reasons, the combination of multi-sensor in-field methods and soil data fusion has proven highly successful for assessing the status of plant-available nutrients in soil for precision agriculture. Simultaneous operation of sensors can cause problems for example because of mutual influences of different signals (electrical or mechanical). Data management systems provide relatively fast availability of information for evaluation of soil properties and their distribution in the field. For rapid and broad adoption of in-field soil analyses in farming practice, in addition to accuracy of fertilizer recommendations, certification as an official soil analysis method is indispensable. This would strongly increase acceptance of this innovative technology by farmers. [ABSTRACT FROM AUTHOR]

BDS-3 geostationary orbit (GEO) satellites broadcast PPP-B2b real-time precise products to support real-time precise point positioning (RT-PPP) without dependence on the ground network communication. Since all BDS-3 satellites can provide multi-frequency signals, we investigate the effect of different multi-frequency integrations and the refinement of multi-frequency stochastic model for PPP-B2b RT-PPP. Given that the retrieval of PPP-B2b real-time precise corrections relies on the B1C and B2b frequencies, this study first investigates the kinematic uncombined (UC) RT-PPP performance of the B1C/B2b integration with a comparison to the conventional B1I/B3I integration. The results indicate that the convergence time (with a convergence threshold of 20 cm) of the RT-PPP with the B1C/B2b integration is shortened by 16 %, 15 %, and 12 % over the B1I/B3I integration in the east, north, and up directions, respectively. Further, this study compares the kinematic UC RT-PPP performance of the B1C/B2b dual-frequency integration, B1C/B2b/B3I triple-frequency integration, and B1C/B2b/B3I/B1I/B2a five-frequency integration. Compared with the dual-frequency integration, the positioning performance of the triple-frequency integration is slightly improved. By contrast, the performance improvement of the five-frequency integration is more significant, which can reach 6 %, 32 %, and 9 % for the convergence time, and 11 %, 9 %, and 6 % for the positioning accuracy in three directions, respectively. To further enhance the multi-frequency PPP performance, a refined stochastic model for the BDS-3 five-frequency integrated PPP-B2b RT-PPP is proposed by taking the inconsistency of signal quality among multi-frequency signals into account. After applying the refined stochastic model, the convergence time of the five-frequency kinematic RT-PPP is shortened by 10 %, 14 %, and 9 % to 32, 7, and 19 min in east, north, and up directions, respectively. The positioning accuracy (the root mean square of all the positioning errors excluding the first two hours) of the five-frequency RT-PPP with the refined stochastic model can reach the optimal level, which is 9.9, 6.5, and 14.0 cm in three directions, respectively. [ABSTRACT FROM AUTHOR]

In the application of image super-resolution (SR) based on field-programmable gate array (FPGA), depthwise separable convolution is widely utilized. However, existing network designs overly simplify the structures used for deep feature extraction to conserve on-chip memory resources, which compromises network performance. Despite this, they still consume relatively large amounts of hardware resources. This study adopts a hardware-software co-design approach, proposing a hardware-friendly algorithm based on recurrent convolution neural network (RCNN), and implemented on FPGA. The algorithm employs a lightweight information multi-distillation block (LIMDB) to deeply extract and distill feature information, enhancing the network’s receptive field and improving its capability to extract feature information. Concurrently, the network reduces the dimensions of hidden state, enhancing performance and decreasing data transmission. Experimental results demonstrate that our lightweight deep recurrent convolution network (LDRCN) significantly outperforms other methods on common datasets. At the hardware level, we designed an efficient pipelining structure that combines skip connection and line buffer shared storage with a lossless segmentation computational strategy to reduce on-chip memory usage. Additionally, a method for finding mixed-precision quantization bit widths was designed to significantly reduce computational resource consumption while ensuring accuracy. The synthesis results on the ZYNQ XCZU9EG platform show that, compared to similar RNN-based works, the computation speed increased by 24.3% and DSP usage decreased by 43.5%, achieving ultra-high-definition (UHD) video output at 4K 95 fps. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During disaster response, it is very important to obtain the information of collapsed building distribution accurately and quickly. However, limited by some practical factors, existed methods often suffer from the contradiction between the accuracy and efficiency of building damage extraction. This paper proposed a simple and effective framework to rapid recognize collapsed building objects using pre-disaster building distribution maps and post-disaster quasi-panchromatic remote sensing images. The proposed method is validated using several historical disasters in the xBD dataset and tested using three cases of earthquakes in terms of both effectiveness and efficiency. In addition, we have verified that the texture information of optical remote sensing images can be used as the main basis to judge whether a building is collapsed or not, so the panchromatic images are sufficient to enable the deep learning model to correctly recognize collapsed buildings. The experimental results indicate that using quasi-panchromatic images can alleviate the influence of style variations and diverse roof colors present in multi-spectral images on the model's generalization performance, resulting in an average overall accuracy improvement of 2.4%. Additionally, the reduced data volume leads to an improvement in inference efficiency. [ABSTRACT FROM AUTHOR]

Semantic segmentation is crucial in autonomous driving because of its accurate identification and segmentation of objects and regions. However, there is a conflict between segmentation accuracy and real-time performance on embedded devices. We propose an efficient lightweight semantic segmentation network (DRMNet) to solve these problems. Employing a streamlined bilateral structure, the model encodes semantic and spatial paths, cross-fusing features during encoding, and incorporates unique skip connections to coordinate upsampling within the semantic pathway. We design a new self-calibrated aggregate pyramid pooling module (SAPPM) at the end of the semantic branch to capture more comprehensive multi-scale semantic information and balance its extraction and inference speed. Furthermore, we designed a new feature fusion module, which guides the fusion of detail features and semantic features through attention perception, alleviating the problem of semantic information quickly covering spatial detail information. Experimental results on the CityScapes, CamVid, and NightCity datasets demonstrate the effectiveness of DRMNet. On a 2080Ti GPU, DRMNet achieves 78.6% mIoU at 88.3 FPS on the CityScapes dataset, 78.9% mIoU at 149 FPS on the CamVid dataset, and 53.5% mIoU at 160.4 FPS on the NightCity dataset. These results highlight the model’s ability to balance accuracy and real-time performance better, making it suitable for embedded devices in autonomous driving applications. [ABSTRACT FROM AUTHOR]

We present a hard-real-time multi-resolution mesh shape deformation technique for skeleton-driven soft-body characters. Producing mesh deformations at multiple levels of detail is very important in many applications in computer graphics. Our work targets applications where the multi-resolution shapes must be generated at fast speeds ("hard-real-time", e.g., a few milliseconds at most and preferably under 1 millisecond), as commonly needed in computer games, virtual reality and Metaverse applications. We assume that the character mesh is driven by a skeleton, and that high-quality character shapes are available in a set of training poses originating from a high-quality (slow) rig such as volumetric FEM simulation. Our method combines multi-resolution analysis, mesh partition of unity, and neural networks, to learn the pre-skinning shape deformations in an arbitrary character pose. Combined with linear blend skinning, this makes it possible to reconstruct the training shapes, as well as interpolate and extrapolate them. Crucially, we simultaneously achieve this at hard real-time rates and at multiple mesh resolution levels. Our technique makes it possible to trade deformation quality for memory and computation speed, to accommodate the strict requirements of modern real-time systems. Furthermore, we propose memory layout and code improvements to boost computation speeds. Previous methods for realtime approximations of quality shape deformations did not focus on hard real-time, or did not investigate the multi-resolution aspect of the problem. Compared to a "naive" approach of separately processing each hierarchical level of detail, our method offers a substantial memory reduction as well as computational speedups. It also makes it possible to construct the shape progressively level by level and interrupt the computation at any time, enabling graceful degradation of the deformation detail. We demonstrate our technique on several examples, including a stylized human character, human hands, and an inverse-kinematics-driven quadruped animal. [ABSTRACT FROM AUTHOR]

We present EgoHDM, an online egocentric-inertial human motion capture (mocap), localization, and dense mapping system. Our system uses 6 inertial measurement units (IMUs) and a commodity head-mounted RGB camera. EgoHDM is the first human mocap system that offers dense scene mapping in near real-time. Further, it is fast and robust to initialize and fully closes the loop between physically plausible map-aware global human motion estimation and mocap-aware 3D scene reconstruction. To achieve this, we design a tightly coupled mocap-aware dense bundle adjustment and physics-based body pose correction module leveraging a local body-centric elevation map. The latter introduces a novel terrain-aware contact PD controller, which enables characters to physically contact the given local elevation map thereby reducing human floating or penetration. We demonstrate the performance of our system on established synthetic and real-world benchmarks. The results show that our method reduces human localization, camera pose, and mapping accuracy error by 41%, 71%, 46%, respectively, compared to the state of the art. Our qualitative evaluations on newly captured data further demonstrate that EgoHDM can cover challenging scenarios in non-flat terrain including stepping over stairs and outdoor scenes in the wild. Our project page: https://handiyin.github.io/EgoHDM/ [ABSTRACT FROM AUTHOR]

In this generation, deep learning techniques are widely used for sign language prediction. In this paper, a deep learning model is proposed for American sign language detection using webcam images and transfer learning. The particular model is designed for a real-time sign language detection. The author claims 98% of accuracy for this designed model, when trained with a total of 15 images for each gesture. Jupyter notebook is used as the environment for working out this research. Cuda, cudnn graphic processor upgraders are also utilised in this research for training the model. To make real-time detections easy, a local environment is used rather than a cloud system for implementing the code. The main aim of this research work is to create a model in order to identify and detect the sign language alphabets and some very frequently used gestures. The model is designed based on deep learning by using the convolutional neural networks and single shot detector algorithm to surpass the difficulty that is faced by the speech impaired and normal people. [ABSTRACT FROM AUTHOR]

Three‐dimensional (3D) reconstruction based on structured light (SL) has wide applications in fields such as artificial intelligence, surveying and mapping, and precision manufacturing. An ideal 3D reconstruction system features both wide field‐of‐views (FOV), high‐accuracy and high‐speed reconstruction and ultracompact volume. However, these criteria have been achieved only partially with conventional SL techniques like optical projector and differentiate optical element, which will substantially limit their practical applications. In this study, an ultracompact SL and its 3D reconstruction system is designed and experimentally demonstrated, which exhibits super‐large FOV angles up to 210° × 210°, only 3.8‰ reconstruction errors, real‐time processing speeds up to 30 fps and SL dimensions as small as 0.3 mm × 0.3 mm. Specifically, the SL is realized with a resonant geometric metasurface, which can produce tens of thousands of random dots and project them into the whole 4π free space. The metasurface enabled SL is collaborated with a binocular stereoscopic camera and advanced 3D reconstruction algorism to form a practical 3D reconstruction system, which can provide robust hardware and software support for high‐accuracy and real‐time 3D scene reconstruction. [ABSTRACT FROM AUTHOR]

Advanced computational models and simulations to unravel the complexities of brain function have known a growing interest in recent years in the field of neurosciences, driven by significant technological progress in computing platforms. Multicompartment models, which capture the detailed morphological and functional properties of neural circuits, represent a significant advancement in this area providing more biological coherence than single compartment modeling. These models serve as a cornerstone for exploring the neural basis of sensory processing, learning paradigms, adaptive behaviors, and neurological disorders. Yet, the high complexity of these models presents a challenge for their real-time implementation, which is essential for exploring alternative therapies for neurological disorders such as electroceutics that rely on biohybrid interaction. Here, we present an accessible, user-friendly, and real-time emulator for multicompartment Hodgkin-Huxley neurons on SoC FPGA. Our system enables real-time emulation of multicompartment neurons while emphasizing cost-efficiency, flexibility, and ease of use. We showcase an implementation utilizing a technology that remains underrepresented in the current literature for this specific application. We anticipate that our system will contribute to the enhancement of computation platforms by presenting an alternative architecture for multicompartment computation. Additionally, it constitutes a step toward developing neuromorphic-based neuroprostheses for bioelectrical therapeutics through an embedded real-time platform running at a similar timescale to biological networks. [ABSTRACT FROM AUTHOR]

High-precision Low Earth Orbit (LEO) satellite clocks are essential for LEO-augmented Positioning, Navigation, and Timing (PNT) services. Nowadays, high-precision LEO satellite clocks can be determined in real-time using a Kalman filter either onboard or on the ground, as long as the GNSS observations collected onboard LEO satellites can be transmitted to the ground in real-time. While various real-time and high-precision GNSS products are available nowadays in the latter case, their continuity and latencies in engineering reality are not as perfect as expected and will lead to unignorable impacts on the precision of the real-time LEO satellite clocks. In this study, based on real observations of Sentinel-3B, the impacts of different latencies and continuity of the real-time GNSS products on LEO real-time clocks are determined and discussed for two scenarios, namely the "epoch estimation" and "arc estimation" scenarios. The former case refers to the traditional filter-based processing epoch-by-epoch, and the latter case connects LEO satellite clocks from different rounds of filter-based processing under a certain arc length. The two scenarios lead to the "end-loss" and "mid-gap" situations. Latencies of the real-time GNSS products are discussed for the cases of orbit-only latency, clock-only latency, and combined forms, and different handling methods for the missing GNSS satellite clocks are discussed and compared. Results show that the real-time LEO satellite clock precision is very sensitive to the precision of real-time GNSS satellite clocks, and prediction of the latter becomes essential in case of their latencies. For the "end-loss" situation, with a latency of 30 to 120 s for the GNSS real-time clocks, the LEO satellite clock precision is reduced from about 0.2 to 0.28–0.57 ns. Waiting for the GNSS products in case of their short latencies and predicting the LEO satellite clocks instead could be a better option. For "arc-estimation", when the gap of GNSS real-time products increases from 5 to 60 min, the real-time LEO clock precision decreases from 0.26 to 0.32 ns. [ABSTRACT FROM AUTHOR]

This article addresses the real‐time reachable set problem on nonlinear time‐delay switched singular systems with state jump, which is subject to initial state and peak‐bound disturbance. Initially, based on the system state decomposition method, a state jump lemma is proposed, which combines the nonlinear function and the system state at the switching instant. Then, a real‐time bounded lemma is developed, premised on using the idea of average dwell‐time and analyzing subinterval function changes. Afterwards, the sufficient conditions for estimating the real‐time and nonreal‐time reachable set are established, which ensures that the reachable state of system can be restricted within a real‐time or nonreal‐time closed set. Finally, simulation results of the typical Direct Current boost chopper are performed to exhibit the effectiveness of the devised estimation scheme for the real‐time reachable set of system. [ABSTRACT FROM AUTHOR]

The recognition of alphabet signs is pivotal for the educational, linguistic, and cognitive development of individuals. This paper delves into the application of Convolutional Neural Networks (CNNs) on the ArASL database, a compilation of Arabic alphabet sign language images. Several CNN architectures underwent testing for image classification, with the EfficientNet B7 model emerging as the most effective, achieving an outstanding test accuracy of 99.24%. Capitalizing on the success of this optimized model, we have developed a real-time application with a dual purpose: to serve as a user-friendly tool for learning Arabic sign language and to enhance communication for individuals who are deaf or hard of hearing. The application, designed for seamless integration into users' daily lives, facilitates an intuitive engagement with the Arabic sign language alphabet. Its real-time functionality empowers users to learn and communicate effortlessly, breaking down barriers and fostering inclusivity in communication. This research contributes not only to the field of computer vision and deep learning but also to the broader spectrum of inclusive education and accessibility. By harnessing cutting-edge technology, our application represents a significant step forward in leveraging artificial intelligence for the betterment of education. [ABSTRACT FROM AUTHOR]

A convolutional neural network (CNN) can be expected to grade the mango appearance quality for high accuracy. However, it is still challenging to implement CNN on low-cost and performance-constrained edge devices, due to the computation- and storage-intensive CNN algorithm. This has greatly limited the application and promotion of CNN for mango appearance quality grading. In this study, a low-cost grading device was proposed to balance the grading accuracy, speed, power consumption, and detection cost of mango appearance quality using FPGA-accelerated CNN. Firstly, a lightweight network called “ Compact MobileNet” (CMNet) was designed with a simple and efficient structure. The parameter and computational complexity of CMNet were significantly reduced to compress the network structure suitable for the deployment of edge devices with acceptable accuracy. Secondly, Batch normalization (BN) layer fusion and model quantization were used to further reduce the storage requirements and computational complexity of the model, in order to accelerate the execution speed of CMNet on cost- and performance-constrained edge devices. Meanwhile, an FPGA-based hardware accelerator was designed for CMNet. Since the basic function of the accelerator was achieved, the high-level synthesis (HLS) optimization, including “ unrolling the for-loop”, “ pipelining the for-loop”, and “ array partitioning”, were used to optimize the parallelism of the hardware accelerator. Finally, a grading device was developed for the appearance quality of mangoes using an FPGA development board, specifically the “ ZYNQ Z7 Lite 7020” model. An OV5640 camera module, the CMNet network acceleration circuit, and an HDMI interface were integrated to enable the mango image collection, real-time detection, and display of appearance quality. A series of experiments were conducted in a laboratory environment, in order to verify the performance of CMNet and the grading device. The Dosehri mango dataset downloaded from the internet was used to train and validate CMNet. The mango appearance quality was classified into three grades, according to China's agricultural industry standard for mango (NY/T 492-2002). The model comparison demonstrated that the CMNet shared a very lightweight model scale, only with 0.015M parameters and 7.2MFlops computations, with a high grading accuracy of 96.4%. Therefore, the CMNet was suitable for deployment on a resource-limited FPGA terminal. The assessment experiments on the FPGA-based accelerator of CMNet demonstrated that the accelerator significantly expedited the quality grading, with a speed of 0.072 s per frame. The accelerator was capable of detecting the mango appearance quality in real time. The performance comparison among various computing devices revealed that the power consumption of the FPGA-based grading device was 2.6 W, which was the lowest among the tested devices. The FPGA-based detection device offered substantial advantages, in terms of power consumption and portability, compared with the CPUs and GPUs. The FPGA excelled in the best unit cost performance, compared with the portable Raspberry Pi devices and Android smartphones. In summary, all experimental results indicate that a low-cost, low-power, high-accuracy, and high-speed grading device was suitable for assessing mango appearance quality. This device can also be effectively utilized for the real-time grading of mango quality in the field. Furthermore, this finding can provide a strong reference for similar agricultural applications, where artificial intelligence can be employed to rapidly extract information from video data. [ABSTRACT FROM AUTHOR]

As an important data source for monitoring the behavior and variations of the ionosphere, the accuracy of current real-time global ionospheric maps (RT-GIMs) in low-latitude regions and oceanic regions is usually poor, and the accuracy during geomagnetic storms is not ideal. Therefore, the ionospheric vertical total electron content (VTEC) short-term forecast results were integrated into the global ionospheric real-time modeling process to improve the accuracy of RT-GIMs. Firstly, the preliminary RT-GIMs were established by constructing a virtual grid and determining the number of ionospheric pierce points in the grid. Then, different strategies were used to determine the virtual VTEC observations and filled the preliminary RT-GIMs. Finally, the filled RT-GIMs were modeled using spherical harmonic expansion and generated the final RT-GIMs, XRTG. On this basis, three ways were selected to evaluate the accuracy of XRTG. The GPS dSTEC (differential slant total electron content) assessment results showed that the performance of XRTG was the closest to that of Centre for Orbit Determination in Europe’s final GIMs (CODG), and it outperformed other RT-GIMs during geomagnetic storm periods and low-latitude regions. Compared with Universitat Politècnica de Catalunya’s RT-GIMs (UADG) with better performance in other RT-GIMs, the maximum decrease in root mean square error (RMSE) of XRTG during the geomagnetic storm period exceeds 25%, and the maximum decrease in the overall average RMSE of the 20 stations in low latitudes exceeds 27%. The Jason-3 VTEC assessment results showed that the accuracy of XRTG was closer to that of UADG and CODG, and the performance of XRTG and UADG in the range of 22° N–22° S was significantly better than that of other RT-GIMs. The consistency between XRTG and Universitat Politècnica de Catalunya’s rapid GIMs, Chinese Academy of Sciences’ final GIMs, and CODG was good, and the VTEC deviations from each post-processing GIMs were mainly concentrated in the range of ± 5 TECU. [ABSTRACT FROM AUTHOR]

Numerical simulations are crucial for predicting outcomes in forging processes but often neglect dynamic interactions within forming tools and presses. This study proposes an approach for achieving accurate real-time prediction of forging outcomes. Initially, a simulation-based surrogate model is developed to replicate key process characteristics related to the billet, enabling prediction of geometry, deformation field, and forging load after an upsetting operation. Subsequently, this model is integrated with a mass-spring-damper model representing the behavior of forging machine and tools. This integration enables the prediction of blow efficiency and energy distribution after each blow, including plastic, elastic, damping, and frictional energy of the upsetting operation. The approach is validated by comparing predictions with experimental results. The coupled model outperformed Finite Element Method (FEM) predictions, exhibiting mean absolute errors (MAE) below 0.1 mm and mean absolute percentage errors (MAPE) below 1% in geometry predictions. Deformation field predictions showed errors below 0.05 mm/mm, and load-displacement curves closely matched experimental data. Blow efficiency predictions aligned well with experimental results, demonstrating a mean absolute error below 1.1%. The observed energy distribution correlated with literature findings, underscoring the model’s fidelity. The proposed methodology presents a promising approach for accurate real-time prediction of forging outcomes. [ABSTRACT FROM AUTHOR]

Material identification based on R_value (Transparency natural logarithm ratio of low‐energy to high‐energy.) of line‐scan dual‐energy X‐ray transmission (DE‐XRT) has a good prospect for industrial application. Unfortunately, the DE‐XRT signals before attenuation within the material range cannot be directly measured, whereas their precision has essential effect on R_value. Therefore, a vertical‐horizontal‐context‐based signal synthesis method was proposed to rebuilt incomplete/masked image, which takes the filtered signals outside the material range as the reference context, and takes into account the vertical (forward/column/Y) and horizontal (scanning/row/X) anisotropy. The vertical is a time series with continuity of signal trend; the horizontal is a spatial characteristic with the fluctuation synchronization within the same row signals. The vertical curves are synthesized one by one, thus extending to the whole surface. The special rigorous synthesis evaluations of curve synthesis difference and surface synthesis difference were also proposed. Experimental results show that the tow evaluations are both only around 0.0007, and it only takes 35 ms to complete the surface synthesis of 119 × 119 pixels on the CPU with 3.4 GHz main frequency. This high numerical precision can match the similarly filtered signals after attenuation so as to improve the accuracy of R_value. And this, together with calculation real‐time, can promote the application of industrial inline material identification. [ABSTRACT FROM AUTHOR]

Multicast has emerged as a primary communication pattern in datacenter networks due to the increasing demand for distributed data-parallel applications. To accelerate multicast traffic, the emerging reconfigurable circuit technology, which can establish circuit connections among switches, has been proposed as a promising paradigm for datacenter networks. This paper investigates how to accelerate the non-preemptive multicast flows in a demand-aware manner in reconfigurable datacenter networks. Firstly, the problem of scheduling circuit switches to minimize the average completion time is formulated and proved to be NP-hard. To address the conflicts between different multicast flows under the bandwidth constraint, a connection based hypergraph is constructed and then a two round matching algorithm is proposed under the bandwidth constraint. Additionally, to further reduce the average completion time, we introduce a method to utilize the remaining capacity of the ToR switches by splitting the unscheduled flows. The proposed algorithm is proved to have an approximation ratio of 2 2 n , where n represents the number of Top-of-Rack (ToR) switches. Finally, the extensive simulations demonstrate the effectiveness of the proposed algorithm in reducing the average completion time of flows compared to state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In recent years, early detection and warning of fires have posed a significant challenge to environmental protection and human safety. Deep learning models such as Faster R-CNN (Faster Region based Convolutional Neural Network), YOLO (You Only Look Once), and their variants have demonstrated superiority in quickly detecting objects from images and videos, creating new opportunities to enhance automatic and efficient fire detection. The YOLO model, especially newer versions like YOLOv10, stands out for its fast processing capability, making it suitable for low-latency applications. However, when applied to real-world datasets, the accuracy of fire prediction is still not high. This study improves the accuracy of YOLOv10 for real-time applications through model fine-tuning techniques and data augmentation. The core work of the research involves creating a diverse fire image dataset specifically suited for fire detection applications in buildings and factories, freezing the initial layers of the model to retain general features learned from the dataset by applying the Squeeze and Excitation attention mechanism and employing the Stochastic Gradient Descent (SGD) with a momentum optimization algorithm to enhance accuracy while ensuring real-time fire detection. Experimental results demonstrate the effectiveness of the proposed fire prediction approach, where the YOLOv10 small model exhibits the best balance compared to other YOLO family models such as nano, medium, and balanced. Additionally, the study provides an experimental evaluation to highlight the effectiveness of model fine-tuning compared to the YOLOv10 baseline, YOLOv8 and Faster R-CNN based on two criteria: accuracy and prediction time. [ABSTRACT FROM AUTHOR]

Objective. To develop a 2D MR acceleration method utilizing principal component analysis (PCA) in a hybrid fashion for rapid real-time applications. Approach. Retrospective testing was performed on 10 lung, 10 liver and 10 prostate 3T MRI data sets for image quality and target contourability. Sampling of k-space is performed by acquiring central (low-frequency) data in every frame while the high-frequency data is incoherently undersampled such that all of k-space is acquired in a pre-determined number of frames. Firstly, principal components (PCs) representative of intra-frame correlations between central and outer k-space data are used to estimate unsampled data in the frame of interest. Then to add further stability, PCs representative of time-domain fluctuations within a reconstruction window of the most recent frames are fit to outer k-space data (including above estimations) to obtain final estimates in the frame of interest. Accelerated reconstructions between 3x and 8x were tested for image quality and contourability along with the optimal number of PCs for fitting. Main results. It was found that at higher acceleration rates, image quality did not deteriorate significantly. Similarly, it was found that the images were of sufficient quality to contour a target using auto-contouring software at all tested acceleration rates and sites. SSIM values were found to be ⩾0.91 at all accelerations tested. Similarly dice coefficients at the different sites were found to be ⩾0.89 even at 8x accelerations which is on par with or better than intra-observer variation. Significance. This method appears to produce improved image quality and contourability compared to previous PCA methods while also allowing a greater number of PCs to be used in reconstruction. The method can be run using a simple single-channel coil and does not require significant computing power to meet real-time interventional standards (reconstruction times ∼60 ms/frame on Intel i5 CPU). [ABSTRACT FROM AUTHOR]

In the cigarette manufacturing industry, machine vision and artificial intelligence algorithms have been employed to improve production efficiency by detecting product defects. However, achieving both high accuracy and real-time defect detection for cigarettes with complex patterns remains a challenge. To address these issues, this study proposes a model based on RESNET18, combined with a feature enhancement algorithm, to improve detection accuracy. Additionally, a method is designed to deploy the model on a field-programmable gate array (FPGA) with high parallel processing capabilities to achieve high-speed detection. Experimental results demonstrate that the proposed detection model achieves a detection accuracy of 95.88% on a cigarette filter defect dataset with an end-to-end detection speed of only 9.38 ms. [ABSTRACT FROM AUTHOR]

The pressure decaying test is traditionally used to detect leakage in pneumatic actuators; however, it is time-consuming as it is performed between cycles of actuation. To date, there is a notable lack of real-time, data-driven machine learning methods to detect such leakages. Such leakage prediction is important to determine the lifecycle of the actuator and the exerting force to seal the vacuum chamber. This study aims to employ machine learning techniques to predict the categories of normal and leaked pressure loss ratio (PLR) in real-time during different number of pneumatic actuators cycles. The data was collected using a two-way factorial design with center points for the cycle test setting parameters. Seven parameters were recorded and undergo the training and test phase. Additionally, multiple model architecture was employed, each performing binary classification for each class to predict the PLR. Correlation analysis shows that pressure reading of each "open" and "close" inlets have better correlation for predicting the leakage with 0.216 and 0.183 respectively. The top performing model for each class is K-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP). For the model 1, the KNN model achieved the highest accuracy of 93.86%, with an AUC of 0.94 in ROC analysis. Meanwhile, for the Model 2, utilizing MLP model attained an accuracy of 89.36% and an 84.87% mean-harmonic score. This study demonstrates that real-time machine learning can effectively predict leakage in pneumatic actuators, with the potential for further improvement through the inclusion of more data and increased setting points in the cycle test. [ABSTRACT FROM AUTHOR]

This work presents the development of an embedded platform using Field Programmable Gate Arrays (FPGAs) for real-time simulation of dynamic systems in industrial plants. The platform, Real-Time Simulator for Dynamic Systems in FPGA (RTSDS-FPGA), is designed for industrial and academic applications. In industrial contexts, the RTSDS-FPGA facilitates the optimization and tuning of embedded control algorithms, while in academia, it supports research on new embedded solutions in automation and control. It is also an educational tool for undergraduate and postgraduate students developing embedded control system projects. Additionally, the simulator accelerates the simulation of slow dynamic systems, significantly reducing overall simulation time. Experimental results demonstrate the platform's capability to perform real-time simulations effectively, validating its accuracy and performance through comparative analyses with established software tools such as Matlab/Simulink. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Stereotactic arrhythmia radioablation (STAR) is a novel treatment approach for refractory ventricular tachycardia (VT). The risk of treatment‐induced toxicity and geographic miss can be reduced with online MRI‐guidance on an MR‐linac. However, most VT patients carry cardiac implantable electronic devices (CIED), which compromise MR images.Robust MR‐linac imaging sequences are required for cardiac visualization and accurate motion monitoring in presence of a CIED during MRI‐guided STAR. We optimized two clinically available cine sequences for cardiorespiratory motion estimation in presence of a CIED on a 1.5 T MR‐linac. The image quality, motion estimation accuracy, and geometric fidelity using these cine sequences were evaluated.Clinically available 2D balanced steady‐state free precession (bSSFP, voxel size = 3.0 ×$\times$ 3.0 ×$\times$ 10 mm3, Tscan = 96 ms, bandwidth (BW) = 1884 Hz/px) and T1${\rm T}_{1}$‐spoiled gradient echo (T1${\rm T}_{1}$‐GRE, voxel size = 4.0 ×$ \times$ 4.0 ×$ \times$ 10 mm3, Tscan = 97 ms, BW = 500 Hz/px) sequences were adjusted for real‐time cardiac visualization and cardiorespiratory motion estimation on a 1.5 T Unity MR‐linac (Elekta AB, Stockholm, Sweden), while complying with safety guidelines for MRI in presence of CIEDs (specific absorption rate <$ <$ 2 W/kg and dBdt<$\frac{dB}{dt}<$ 80 mT/s). Cine acquisitions were performed in five healthy volunteers, with and without an implantable cardioverter– defibrillator (ICD) placed on the clavicle, and a VT patient. Generalized divergence‐curl (GDC) deformable image registration (DIR) was used for automated landmark motion estimation in the left ventricle (LV). Gaussian processes (GP), a machine‐learning technique, was trained using GDC landmarks and deployed for real‐time cardiorespiratory motion prediction. B0$B_{0}$‐mapping was performed to assess geometric image fidelity in the presence of CIEDs.CIEDs introduced banding artifacts partially obscuring cardiac structures in bSSFP acquisitions. In contrast, the T1${\rm T}_{1}$‐GRE was more robust to CIED‐induced artifacts at the expense of a lower signal‐to‐noise ratio. In presence of an ICD, image‐based cardiorespiratory motion estimation was possible for 85% (100%) of the volunteers using the bSSFP (T1${\rm T}_{1}$‐GRE) sequence. The in‐plane 2D root‐mean‐squared deviation (RMSD) range between GDC‐derived landmarks and manual annotations using the bSSFP (T1‐GRE) sequence was 3.1–3.3 (3.3–4.1) mm without ICD and 4.6–4.6 (3.2–3.3) mm with ICD. Without ICD, the RMSD between the GP‐predictions and GDC‐derived landmarks ranged between 0.9 and 2.2 mm (1.3–3.0 mm) for the bSSFP (T1‐GRE) sequence. With ICD, the RMSD between the GP‐predictions and GDC‐derived landmarks ranged between 1.3 and 2.2 mm (1.2–3.2 mm) using the bSSFP (T1‐GRE) sequence resulting in an RMSD‐increase of 42%–143% (bSSFP) and −61%–142% (T1‐GRE). Lead‐induced spatial distortions ranged between −0.2 and 0.2 mm (−0.7–1.2 mm) using the bSSFP (T1${\rm T}_{1}$‐GRE) sequence. The 98th percentile range of the spatial distortions in the gross target volume of the patient was between 0.0 and 0.4 mm (0.0–1.8 mm) when using bSSFP (T1${\rm T}_{1}$‐GRE).Tailored bSSFP and T1${\rm T}_{1}$‐GRE sequences can facilitate real‐time cardiorespiratory estimation using GP trained with GDC‐derived landmarks in the majority of landmark locations in the LV despite the presence of CIEDs. The need for high temporal resolution noticeably reduced achievable spatial resolution of the cine MRIs. However, the effect of the CIED‐induced artifacts is device, patient and sequence dependent and requires specific assessment per case. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) seismology has witnessed enormous success in a variety of fields, especially in earthquake detection and P and S‐wave arrival picking. It has become widely accepted that DL techniques greatly help routine seismic monitoring by enabling more accurate picking than traditional pickers like STA/LTA. However, a completely automatic AI‐driven earthquake monitoring framework has not been reported due to the concerns of potential false positives using DL pickers. Here, we propose a novel AI‐facilitated near real‐time monitoring framework using a recently developed deep learning (DL) picker (EQCCT) that has been deployed in the Texas seismological network (TexNet). For the West Texas area, TexNet's seismic monitoring relies on the EQCCT picker to report earthquake events. For earthquakes with a magnitude above two, the picks are further validated by analysts to output the final TexNet catalog. Due to the fast‐increasing seismicity caused by continuing oil&gas production in West Texas, this AI‐facilitated framework significantly relieves the workload of TexNet analysts. We show the mean absolute error (MAE) of automatic magnitude estimation for the magnitude‐above‐two earthquakes is smaller than 0.15 in West Texas and MAEs of hypocenter locations within 2.6 km in both distance and depth estimates. This research provides more evidence that DL pickers can play a fundamental role in daily earthquake monitoring. Plain Language Summary: AI has been a significant part of almost every aspect of our life. However, it is not widely accepted that AI can be a game changer in geoscience. In this work, we provide a compelling example that AI can significantly lower the overload of earthquake analysts in everyday work and boost our earthquake detectability, thereby enhancing our understanding of earthquake statistics, physics, and potential nucleation mechanisms. In a nutshell, AI has already been demonstrated to be successful in earthquake analysis but never in real‐time monitoring, which requires a high success rate and zero tolerance to large‐earthquake detection errors. Here, we show that AI‐assisted earthquake monitoring workflow can be almost 100% accurate for moderate‐to‐large earthquakes. Key Points: We propose an AI‐facilitated near real‐time monitoring framework that has been deployed at TexNet using a recently developed deep learning (DL) pickerDue to the fast‐increasing seismicity in West Texas, this AI‐facilitated framework significantly relieves the workload of TexNet analystsThe mean absolute error (MAE) of automatic magnitude estimation for the magnitude‐above‐two earthquakes is smaller than 0.15 in West Texas [ABSTRACT FROM AUTHOR]

Flow sensing exhibits significant potential for monitoring, controlling, and optimizing processes in industries, resource management, and environmental protection. However, achieving wireless real‐time and omnidirectional sensing of gas/liquid flow on a simple, self‐contained device without external power support has remained a formidable challenge. In this study, a compact‐sized, fully self‐powered wireless sensing flowmeter (CSWF) is introduced with a small size diameter of down to less than 50 mm, which can transmit real‐time and omnidirectional wireless signals, as driven by a rotating triboelectric nanogenerator (R‐TENG). The R‐TENG triggers the breakdown discharge of a gas discharge tube (GDT), which enables flow rate wireless sensing through emitted electromagnetic waves. Importantly, the performance of the CSWF is not affected by the R‐TENG's varied output, while the transmission distance is greater than 10 m. Real‐time wireless remote monitoring of wind speed and water flow rate is successfully demonstrated. This research introduces an approach to achieve a wireless, self‐powered environmental monitoring system with a diverse range of potential applications, including prolonged meteorological observations, marine environment monitoring, early warning systems for natural disasters, and remote ecosystem monitoring. [ABSTRACT FROM AUTHOR]

PEMFCs play a key role in the energy transition scenarios thanks to the zero emissions, versatility, and power density. PEMFC performances are improved optimizing water management to ensure proper ion transport: it is well known that a well-balanced water content avoids either electrodes flooding or membrane drying, causing gas starvation at the active sites or low proton conductivity, respectively. In this paper, an analytical formulation for water transport dynamics within the membrane, derived from membrane water balance, is proposed to overcome the limitations of PEM dynamics model largely adopted in the literature. The dynamics is simulated thanks to the introduction of a characteristic time with a closed analytical form, which is general and easily implementable for any application where both low computational time and high accuracy are required. Furthermore, the net water molar fluxes at the membrane boundaries can be easily computed as well for a cell's simulation. The analytical formulation has a strong dependency on the operative conditions, as well as physical parameters of the membrane itself. From the proposed formulation, for a 200 µm membrane, the characteristic time can vary from 5 s up to 50 s; this example shows how control strategies must consider PEM dynamic behavior. [ABSTRACT FROM AUTHOR]

A new architectural approach to real-time embedded programming is described. A bare-metal program is written in C/C++ and combines event-driven technique with concurrency based on co-routines. Events are processed by soft real-time core at the priority level of software generated interrupts. Event is first posted to input queue of the core and then processed by invocation of its event handler. A special case of event is co-routine, its resumable function being a co-routine event handler. The co-routine that either yielded or needs to be resumed is queued for event processing once again. As a result, it is processed multiple times until execution of resumable function comes to the end of its operator sequence. Different levels of processing priority may be assigned to an event. Soft real-time core could be further expanded to run on symmetrical multiprocessor hardware. A combination of co-routines and basic events could easily be used in fork/join model. Concurrency constructs resemble those of Go and occam languages. Virtually all classic types of synchronization primitives could be implemented. The new approach was implemented for various ARM and Blackfin processors in C++ language as portable DORSECC library. This library was further used to program real-time embedded systems for mass-produced banknote sorting machines. One type of systems was used to recognize and validate banknote images by the method of cascade of one-class classifiers. The other system worked as a motion controller and used finite automata to control sensors and actuators. The total number of systems in operation is currently over 20000. The event and co-routine core in these systems provides average event processing time in the range of dozens of microseconds with sub-microsecond overhead time per each event. [ABSTRACT FROM AUTHOR]

Integrating the Internet of Things (IoT) with Social Networks (SN) has given rise to a new paradigm called Social IoT, which allows users and objects to establish social relationships. Nonetheless, trust issues such as attacks have emerged. These attacks can influence service discovery results. A trust management mechanism has become a major challenge in the Social IoT to prevent these attacks and ensure qualified services. A few studies have addressed trust management issues, especially those that prevent trust attacks in Social IoT environments. However, most studies have been dedicated to detect offline attacks with or without specifying the type of attack performed. These works will not be able to prevent attacks by aborting transactions between users because their primary purpose is to detect an offline attack. In addition, they do not consider security properties. This research paper aims to provide a detailed survey on trust management mechanism to handle trust attacks in Social IoT. In this research paper, we compared the techniques and technologies whose common point is attack prevention and demonstrated that blockchain technology can play a key role in developing a trust management mechanism that can prevent trust attacks while maintaining security properties. Then, we proposed combining the Apache Spark Framework with blockchain technology to provide real-time attack prevention. This combination can assist in creating upgraded trust management mechanisms in Social IoT environments. These mechanisms aim to prevent attacks in real-time through considering the security properties. Lack of survey papers in the area of trust attack prevention in real-time stands for an important motivational factor for writing this paper. The current research paper highlights the potential of the blockchain technology and Apache Spark in terms of developing an upgraded trust management able to prevent trust attacks in real-time.This paper provides a comprehensive survey on trust management mechanisms and approaches to handle trust attacks in Social IoT. Lack of such papers increases the significance of this paper. It also offers potential future research directions in terms of real-time trust attack prevention. [ABSTRACT FROM AUTHOR]

Symbolic generation of multibody systems equations of motion appeared in the 1980s. In addition to their computational advantage over their numerical counterparts, symbolic models can be very easily and straightforwardly interfaced with a wide range of software environments and hardware devices. These two features place this approach in a pole position to participate and intervene in the design of digital twins for systems such as vehicles, manipulators, walking robots or haptic devices. In this context, the first goal of this paper is to highlight the interest of symbolically generated multibody models – at the root of the ROBOTRAN program – in the form of a standalone set of equations calculating the dynamic model of multibody systems, for use as a computational component within a Digital-Twin-type process. The next goal is to embed realistic and complex multibody models within processes or devices whose functioning requires a synchronized real-time computation – or analysis – of their motion. An implementation (i) on specific hardware and (ii) on two extremely opposite but revealing applications (namely a railway vehicle and a digital piano) are presented to highlight the usefulness of symbolic models for the development of current and future multibody-based digital twins. [ABSTRACT FROM AUTHOR]

Due to the inherent unpredictability of marine trial conditions, safety issues in the launching and recovery process are commonplace. For these issues, digital twin (DT) maintenance offers a novel idea. In this paper, a blended-wing-body underwater glider skeleton is selected as a research object. And a simple demo is created to investigate the major technologies that may be involved in DT maintenance. According to this specimen, the necessary development environments are developed to obtain physical information and produce a DT model in cyberspace respectively. An attitude sensor is used as the physical data collector, while real-time structural field prediction and virtual reality visualisation are employed. By using the attitude angles and structural data, it is possible to achieve real-time monitoring of the skeleton strength. Through the means of this straightforward case study, the key technologies are supported and can be applied to far more complex inquiries. [ABSTRACT FROM AUTHOR]