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Emotional blackmail is a way of communication that forces people around them to listen to their requirements by emotional pressure. It can easily lead to negative emotions and even psychological problems of the other side, then affect the communication effect. In order to detect emotional blackmail utterance in daily communication scenarios and improve the communication effect, this paper developed an emotional blackmail utterance detection system based on Chinese text similarity evaluation. Firstly, this paper labelled the collected data based on Susan Forward's emotional blackmail theory, and construeted an emotional blackmail corpus and a test set. Secondly, this paper analyzed the expression modes of emotional blackmail, and designed text similarity evaluation methods based on part of speech and semantic words respectively, then formed an emotional blackmail utterance detection algorithm based on Chinese text similarity evaluation. Thirdly, this paper carried out experiments on the constructed datasets. The average recall and F1-score of the proposed algorithm are 95.21% and 79.95% respectively, which are better than the compared algorithms. Finally, this paper integrated an emotional blackmail utterance detection prototype system based on the proposed algorithm. Under different testing conditions, system test results show that the average recall is 87.24%, which shows good robustness and detection performance. [ABSTRACT FROM AUTHOR]

The quality of medical images is crucial for accurately diagnosing and treating various diseases. However, current automated methods for assessing image quality are based on neural networks, which often focus solely on pixel distortion and overlook the significance of complex structures within the images. This study introduces a novel neural network model designed explicitly for automated image quality assessment that addresses pixel and semantic distortion. The model introduces an adaptive ranking mechanism enhanced with contrast sensitivity weighting to refine the detection of minor variances in similar images for pixel distortion assessment. More significantly, the model integrates a structure-aware learning module employing graph neural networks. This module is adept at deciphering the intricate relationships between an image's semantic structure and quality. When evaluated on two ultrasound imaging datasets, the proposed method outshines existing leading models in performance. Additionally, it boasts seamless integration into clinical workflows, enabling real-time image quality assessment, crucial for precise disease diagnosis and treatment. [ABSTRACT FROM AUTHOR]

With the increasing awareness of environmental protection, the rotary hearth furnace system has emerged as a key technology that facilitates a win-win situation for both environmental protection and enterprise economic benefits. This is attributed to its high flexibility in raw material utilization, capability of directly supplying blast furnaces, low energy consumption, and high zinc removal rate. However, the complexity of the raw material proportioning process coupled with the rotary hearth furnace system's reliance on human labor results in a time-consuming and inefficient process. This paper innovatively introduces an intelligent formula method for proportioning raw materials based on online clustering algorithms and develops an intelligent batching system for rotary hearth furnaces. Firstly, the ingredients of raw materials undergo data preprocessing, which involves using the local outlier factor (LOF) method to detect any abnormal values, using Kalman filtering to smooth the data, and performing one-hot encoding to represent the different kinds of raw materials. Afterwards, the affinity propagation (AP) clustering method is used to evaluate past data on the ingredients of raw materials and their ratios. This analysis aims to extract information based on human experience with ratios and create a library of machine learning formulas. The incremental AP clustering algorithm is utilized to learn new ratio data and continuously update the machine learning formula library. To ensure that the formula meets the actual production performance requirements of the rotary hearth furnace, the machine learning formula is fine-tuned based on expert experience. The integration of machine learning and expert experience demonstrates good flexibility and satisfactory performance in the practical application of intelligent formulas for rotary hearth furnaces. An intelligent batching system is developed and executed at a steel plant in China. It shows an excellent user interface and significantly enhances batching efficiency and product quality. [ABSTRACT FROM AUTHOR]

Designing intelligent diagnosis of prostate diseases in intelligent medical assistance systems has gradually become a research hotspot. However, rectal ultrasound (TRUS) as the main diagnostic tool for prostate diseases remains a challenging issue. (1) Due to limited prostate TRUS imaging data, it is difficult to train a robust deep learning model. (2) In terms of visual features, ultrasound images of prostate cancer are similar to TRUS images of other tissues and organs, so it is difficult for a single neural network model to accurately learn the feature representation of the disease. To address the above problems, we first establish a high-quality dataset for prostate TRUS imaging, and then design multi teacher knowledge distillation to achieve accurate disease recognition. The experimental results show that, compared with knowledge distillation without a teacher model and a single teacher model, knowledge distillation using multiple teacher models can significantly improve the accuracy of prostate TRUS image cancer prediction. As the number of teacher models increases, the accuracy rate is further improved, which verifies the effectiveness of this method in intelligent systems. [ABSTRACT FROM AUTHOR]

Fatigued driving is a problem that every driver will face, and traffic accidents caused by drowsy driving often occur involuntarily. If there is a fatigue detection and warning system, it is generally believed that the occurrence of some incidents can be reduced. However, everyone's driving habits and methods may differ, so it is not easy to establish a suitable general detection system. If a customized intelligent fatigue detection system can be established, it may reduce unfortunate accidents. With its potential to mitigate unfortunate accidents, this study offers hope for a safer driving environment. Thus, on the one hand, this research hopes to integrate the information obtained from three different sensing devices (eye movement, finger pressure, and plantar pressure), which are chosen for their ability to provide comprehensive and reliable data on a driver's physical and mental state. On the other hand, it uses an autonomous learning architecture to integrate these three data types to build a customized fatigued driving detection system. This study used a system that simulated a car driving environment and then invited subjects to conduct tests on fixed driving routes. First, we demonstrated that the system established in this study could be used to learn and classify different driving clips. Then, we showed that it was possible to judge whether the driver was fatigued through a series of driving behaviors, such as lane drifting, sudden braking, and irregular acceleration, rather than a single momentary behavior. Finally, we tested the hypothesized situation in which drivers were experiencing three cases of different distractions. The results show that the entire system can establish a personal driving system through autonomous learning behavior and further detect whether fatigued driving abnormalities occur. [ABSTRACT FROM AUTHOR]

Existing control strategies, such as Real-time Optimization (RTO), Dynamic Real-time Optimization (DRTO), and Economic Model Predictive Control (EMPC) cannot enable optimal operation and control behavior in an optimal fashion. This work proposes a novel control strategy, named the efficiency-oriented model predictive control (MPC), which can fully realize the potential of the optimization margin to improve the global process performance of the whole system. The ideas of optimization margin and optimization efficiency are first proposed to measure the superiority of the control strategy. Our new efficiency-oriented MPC innovatively uses a nested optimization structure to optimize the optimization margin directly online. To realize the computation, a Periodic Approximation technique is proposed, and an Efficiency-Oriented MPC Type I is constructed based on the Periodic Approximation. In order to alleviate the strict constraint of Efficiency-Oriented MPC Type I, the zone-control-based optimization concept is used to construct an Efficiency-Oriented MPC Type II. These two well-designed efficiency-oriented controllers were compared with other control strategies over a Continuous Stirred Tank Reactor (CSTR) application. The simulation results show that the proposed control strategy can generate superior closed-loop process performance, for example, and the Efficiency-Oriented MPC Type I can obtain 7.11% higher profits than those of other control strategies; the effectiveness of the efficiency-oriented MPC was, thereby, demonstrated. [ABSTRACT FROM AUTHOR]

Complex practical problems are distinguished by their subjective and dynamic character. Such problems never repeat exactly; therefore, the method for their solution must be built each time. In view of this, development of intelligent systems that are dedicated and have the capability of acquiring, reproducing, and using the knowledge, whose functions are estimated from the point of view of ensuring stability, openness, and real-time operation is urgent. In this work, understanding of intelligent systems and their openness is considered, as well as approaches to their design. [ABSTRACT FROM AUTHOR]

The problem of improving the efficiency of decision making based on cases (case-based reasoning) in real-time intelligent systems is considered. Methods for preprocessing and storing temporal data are discussed. A method and algorithms for structuring the case base are proposed. The main stages of the method are the generalization of properties of dynamic parameters, the formation of classes of similar situations associated with cases from the case base, and the construction of decision trees to enable efficient search for solutions in each class. The results of a machine experiment are presented. To implement the proposed approach, a temporal database created using the Neo4j NoSQL graph database management system is used. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xihua University (Natural Science Edition) is the property of Xihua University 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.)

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) 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.)

Multi-sensor management and control technology generally constructs a reasonable objective function to solve the optimal control command set to control a limited number of sensors to obtain higher quality measurement information, thus obtaining better target tracking performance. In the process of multi-sensor information fusion, there is not only the problem of information redundancy but also obvious time delay. A sensor fusion algorithm combined with global optimization algorithm is innovatively proposed. According to the key frames saved in the previous steps, feature points in local maps, sensor information, and loop information, a global optimization algorithm based on graph optimization model is constructed to optimize the position and pose of intelligent hardware system and the position of spatial feature points. Moreover, this work studies and experiments on multi-sensor fusion simultaneous localization and mapping (SLAM) comprehensively and systematically, and the experimental results show that the algorithm proposed in this work is superior to common open-source SLAM algorithm in positioning accuracy and mapping effect under special circumstances. Therefore, the method proposed in this work can be applied to intelligent driving of vehicles, vision-assisted movement of robots and intelligent control of unmanned aerial vehicles, thus effectively improving the hardware control accuracy of intelligent systems.

Proximity sensing technology, which can obtain information without physical contact, has become an ideal choice in scenarios where physical contact is not feasible. Despite substantial advancements in tactile sensing, proximity sensing technology still holds great potential and has yet to be fully developed. Among numerous proximity sensing technologies, the fringing‐effect‐based capacitive proximity sensor (FE‐CPS) has garnered considerable attention due to its low cost, low power consumption, wide sensing range, and flexible and versatile structural design. However, research on FE‐CPS has not yet formed a complete system, and its development and intellectualization are still in their infancy, urgently requiring a systematic review to advance its development. This paper systematically summarizes the recent advances in FE‐CPS, from basic theory to practical applications. The working principle and typical structure of FE‐CPS are first introduced, followed by a discussion of methods for optimizing device performance. Furthermore, the application scenarios of FE‐CPS in intelligent pre‐alarm systems, intelligent control systems, and intelligent material perception systems are reviewed. Finally, the future development and challenges faced by FE‐CPS are prospected. [ABSTRACT FROM AUTHOR]

Background: Effective management of cancer pain critically depends on timely medication administration and adherence to precise medication guidelines. In the context of limited time and a busy healthcare environment, tailoring the optimal medication schedule for each patient with cancer pain presents a significant challenge for physicians and clinical pharmacists. Methods: To address this challenge, we conducted a comprehensive analysis of healthcare professionals’ needs in guiding cancer pain medication. By developing core features based on key user needs and continuously updating them, we have created the Universal Medication Schedule System (UMSS). We invited 20 physicians and pharmacists specializing in oncology or cancer pain to trial the system and assessed UMSS usage through distributed questionnaires. Results: We identified five key needs of healthcare professionals in cancer pain medication guidance. Based on these needs, we (1) constructed a comprehensive drug information database, including basic information for 1135 drugs, 130,590 drug interaction data entries, and 1409 individual medication timing constraints, and (2) developed a web-based system that provides essential reference information such as drug interactions and dietary restrictions. It can create medication schedules and provide medication education tailored to the patient’s daily routine. Participating evaluators unanimously agreed (100%) that the system aids in accurately assessing the risks of polypharmacy and quickly scheduling medication regimens. Conclusion: UMSS, by offering personalized medication schedule support, assists healthcare professionals in better managing patients’ medication treatment plans. However, further improvements are needed in the automation of database updates and maintenance, as well as in integrating it with electronic health records. [ABSTRACT FROM AUTHOR]

Coal is the cheapest source of energy. It is much cheaper than nuclear and petroleum. However, spontaneous combustion is inherent in coal seams that lead to fires at their place of storage. Therefore, early detection is required to ensure safety. This article aims to develop an intelligent system for the classification of coal seams based on their spontaneous heating on the Internet of Things (IoT) paradigm. It utilizes an artificial intelligence (AI) approach by utilizing a two-stage neural network architecture to render an accurate and robust categorization of coal samples. Three commonly available proximate analysis parameters of the coal samples have been used as input to this system. These inherent parameters are found to be sufficient to classify coal samples into different categories according to their combustion susceptibility. Three publicly available datasets are involved in the assessment purpose. Our proposed method has outperformed previous approaches. The coal seams classification results agree with the real field experiences. [ABSTRACT FROM AUTHOR]

Due to the absence of more efficient diagnostic tools, the spread of mpox continues to be unchecked. Although related studies have demonstrated the high efficiency of deep learning models in diagnosing mpox, key aspects such as model inference speed and parameter size have always been overlooked. Herein, an ultrafast and ultralight network named Fast‐MpoxNet is proposed. Fast‐MpoxNet, with only 0.27 m parameters, can process input images at 68 frames per second (FPS) on the CPU. To detect subtle image differences and optimize model parameters better, Fast‐MpoxNet incorporates an attention‐based feature fusion module and a multiple auxiliary losses enhancement strategy. Experimental results indicate that Fast‐MpoxNet, utilizing transfer learning and data augmentation, produces 98.40% classification accuracy for four classes on the mpox dataset. Furthermore, its Recall for early‐stage mpox is 93.65%. Most importantly, an application system named Mpox‐AISM V2 is developed, suitable for both personal computers and smartphones. Mpox‐AISM V2 can rapidly and accurately diagnose mpox and can be easily deployed in various scenarios to offer the public real‐time mpox diagnosis services. This work has the potential to mitigate future mpox outbreaks and pave the way for developing real‐time diagnostic tools in the healthcare field. [ABSTRACT FROM AUTHOR]

Hand gesture recognition using a camera provides an intuitive and promising means of human-computer interaction and allows operators to execute commands and control machines with simple gestures. Research in hand gesture recognition-based control systems has garnered significant attention, yet the deploying of microcontrollers into this domain remains relatively insignificant. In this study, we propose a novel approach utilizing micro-hand gesture recognition built on micro-bottleneck Residual and micro-bottleneck Conv blocks. Our proposed model, comprises only 42K parameters, is optimized for size to facilitate seamless operation on resource-constrained hardware. Benchmarking conducted on STM32 microcontrollers showcases remarkable efficiency, with the model achieving an average prediction time of just 269ms, marking a 7× faster over the state-of-art model. Notably, despite its compact size and enhanced speed, our model maintains competitive performance result, achieving an accuracy of 99.6% on the ASL dataset and 92% on OUHANDS dataset. These findings underscore the potential for deploying advanced control methods on compact, costeffective devices, presenting promising avenues for future research and industrial applications. [ABSTRACT FROM AUTHOR]

One of the most challenging scenarios for drivers of open-pit mines is driving in dense fog. As a result, drivers cannot see the road, which causes significant vehicle colloidal accidents. To overcome the problem, an intelligent vision enhancement system has been developed by real-time processing and integrating data from the vehicle-to-everything (V2X) module, global navigation satellite system (GNSS), radar, and infrared cameras to enhance the visibility range of the operative road to the driver and mitigate the chances of accidents. Final images after image processing and object detection are displayed using a dashboard screen in front of the driver for adaptive control and safe operation. Performance analysis of the visual computing algorithm has been carried out in a mine based on image quality parameters. Field-tested images' average contrast value, peak signal-to-noise ratio, structural similarity index, visual information fidelity, and universal quality index were 0.87, 19.36, 0.39, 0.82, and 0.56, respectively, and the calculated accurateness of the convolution neural network (CNN)-based object detection model was 90%. These values indicate the output of the image processing algorithm provided better-enhanced images in the foggy condition. With the help of image processing algorithms, the camera view is able to provide a clear vision of more than 30 m in dense fog. Further, using V2X technology, the augmented visibility range has been increased up to 200 m. The 3D augmented reality (AR) view and 2D navigation view provide other surrounding vehicle locations. Untagged objects like rocks or bunds are also detected and displayed using radar. HIGHLIGHTS: The system fuses IR cameras, V2X, GNSS, radar, & visual computing for safe driving Image processing algorithm fuses CLAHE and DCP methods for clear image output CNN-based object detection algorithm integrated with image processing software Provides multi-stage safety using camera, 2D navigation, & 3D virtual reality views Intelligent system inbuilt with software and advanced analytics for fleet management [ABSTRACT FROM AUTHOR]

In coastal areas, due to their special geological conditions, soft soil foundation has become a common form of foundation in engineering construction. Soft soil foundation has low bearing capacity and large settlement deformation, posing a serious threat to surrounding building facilities and human life and property. Therefore, monitoring it is crucial. However, the inherent characteristics of soft soil foundation make it difficult to monitor, and traditional monitoring methods have shortcomings such as poor timeliness, low manual efficiency, and difficulty in sharing monitoring data. Therefore, in practical engineering, a combination of manual and automated methods is often used for monitoring. With the advancement of technology and the development of new technologies such as artificial intelligence, applying intelligent systems to soft soil foundation monitoring, real-time monitoring and early warning have become a new research direction. This article took a coastal soft soil foundation project as an example to introduce the application of intelligent systems in this project. [ABSTRACT FROM AUTHOR]

Infants are exceptionally fragile and physically vulnerable. Continuous monitoring of their vital characteristics and physiological parameters is vital for assessing their exact health status. The progressive achievements of bioelectrical, biophysical, biochemical and hybrid soft monitoring technologies in infant are discussed. We offer insights into the remaining challenges and translational hurdles of soft electronics for clinical-grade infant monitoring from the benchtop to the bedside. [Display omitted] Infants are exceptionally fragile and physically vulnerable who need special healthcare. Continuous monitoring of vital information of infants is essential for understanding their health status. Whereas modern infant health monitoring is carried out with dedicated facilities in controlled hospital settings, they are associated with several issues such as potential iatrogenic injuries, barriers for skin-to-skin parental bonding, and even complications during basic clinical tasks. Recently, soft electronics have become an accessory and mobile infant monitoring platform with remarkable advantages over existing technologies. This review comprehensively summarizes the research advances on soft electronics for advanced infant monitoring. We first introduce the physiological characteristics from anatomy, diseases, and treatments of infants. In accordance with these characteristics, we discuss the progressive achievements of soft bioelectrical, biophysical, biochemical, and hybrid monitoring technologies for infants. In addition, we offer insights into the remaining challenges and future directions of a soft electronics based intelligent system for clinical-grade infant monitoring from sensor design, data processing, power supply, and digital interactions in the era of Internet of Things. Finally, we highlight the translational challenges to be considered in bringing soft electronics for infant monitoring from the benchtop to the bedside. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) 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.)