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Industrial Control Systems (ICSs) have faced a significant increase in malware threats since their integration with the Internet. However, existing machine learning-based malware identification methods are not specifically optimized for ICS environments, resulting in suboptimal identification performance. In this work, we propose an innovative method explicitly tailored for ICSs to enhance the performance of malware classifiers within these systems. Our method integrates the opcode2vec method based on preprocessed features with a conditional variational autoencoder–generative adversarial network, enabling classifiers based on Convolutional Neural Networks to identify malware more effectively and with some degree of increased stability and robustness. Extensive experiments validate the efficacy of our method, demonstrating the improved performance of malware classifiers in ICSs. Our method achieved an accuracy of 97.30%, precision of 92.34%, recall of 97.44%, and F1-score of 94.82%, which are the highest reported values in the experiment.

With the rapid development of industry, the risks factories face are increasing. Therefore, the anomaly detection algorithms deployed in factories need to have high accuracy, and they need to be able to promptly discover and locate the specific equipment causing the anomaly to restore the regular operation of the abnormal equipment. However, the neural network models currently deployed in factories cannot effectively capture both temporal features within dimensions and relationship features between dimensions; some algorithms that consider both types of features lack interpretability. Therefore, we propose a high-precision, interpretable anomaly detection algorithm based on variational autoencoder (VAE). We use a multi-scale local weight-sharing convolutional neural network structure to fully extract the temporal features within each dimension of the multi-dimensional time series. Then, we model the features from various aspects through multiple attention heads, extracting the relationship features between dimensions. We map the attention output results to the latent space distribution of the VAE and propose an optimization method to improve the reconstruction performance of the VAE, detecting anomalies through reconstruction errors. Regarding anomaly interpretability, we utilize the VAE probability distribution characteristics, decompose the obtained joint probability density into conditional probabilities on each dimension, and calculate the anomaly score, which provides helpful value for technicians. Experimental results show that our algorithm performed best in terms of F1 score and AUC value. The AUC value for anomaly detection is 0.982, and the F1 score is 0.905, which is 4% higher than the best-performing baseline algorithm, Transformer with a Discriminator for Anomaly Detection (TDAD). It also provides accurate anomaly interpretation capability.

Human activity recognition widely exists in applications such as sports management and activity classification.The current human activity recognition applications are mainly divided into three types: camera-based, wearable device-based, and Wi-Fi awareness-based.Among them, the camera-based human activity recognition application has the risk of privacy leakage, and the wearable device-based human activity recognition application has problems such as short battery life and poor accuracy.Human activity recognition based on Wi-Fi sensing generally uses Wi-Fi network cards or software-defined radio devices to identify the rules of channel state information changes, so as to infer user activity.It does not have the problems of privacy leakage and short battery life.But Wi-Fi network cards need to rely on computers and software-defined radio platforms are expensive, which greatly limit the application scenarios of Wi-Fi sensing.Aiming at the above problems, a human activity recognition system based on the low-cost IoT chip ESP32 was proposed.Specifically, the Hampel filter and Gaussian filter were used to preprocess the channel state information obtained by ESP32.Then, the principal component analysis and discrete wavelet transform were utilized to reduce the dimension of the data.Finally, the K-nearest neighbor (KNN) algorithm was applied to classify data.The experimental results show that the system can achieve a recognition accuracy which close to the current mainstream Wi-Fi perception system (Intel 5300 network card) when only two ESP32 nodes are deployed, and the average accuracy rate for the six activities is 98.6%.

This paper aims to examine the effect of dividend regulation on cost stickiness (i.e. the asymmetric change in firm expense between sales increase and sales decrease) and explore the underlying mechanism. Based on the quasi-natural experiment of the Guideline for Dividend Policy of Listed Companies issued by the Shanghai Stock Exchange (SSE) in 2013, the authors employ a difference-in-difference model to investigate the impact of dividend regulation on cost stickiness. The authors find that the cost stickiness of treatment group firms has decreased significantly when compared with control group firms after the dividend regulation. Moreover, this effect is more pronounced among firms in lower marketization regions, in lower competition industries and those with less analyst coverage and lower cash flow levels. Further analyses show that dividend regulation reduces the cost stickiness of firms by mitigating agency problems. Finally, the conclusion holds after several robust tests, including controlling for firm fixed effect, propensity score matching (PSM), placebo test and reconstruction of expense variable. This paper confirms that dividend regulation serves an important role in corporate governance, which reduces firms' agency costs and thereby decreases cost stickiness. The conclusions shed light on the dividend policies of listed companies and capital market regulation in the future.

Abstract As images are attractive and contained in plenty of posts, some studies show that combining multimodal information helps to represent posts effectively, while these approaches (1) simply concatenated unimodal features without considering inter‐modality relations, which failed to improve the prediction performance and generalization ability; (2) lacked careful consideration of the social network data composition structure, assuming that social network data was composed of image‐text pairs, cannot process the data composed of multi‐images, and cannot operate when one of the modalities is missing. To address these issues, a novel framework named multitask multimodal rumor detection framework (MMRDF) is proposed, comprising three sub‐networks, which is available to generate joint multimodal representation through merging different levels of features in multi‐modalities and can deal with all types of tweets (pure text, pure image, image‐text pair and text with multi‐images) flexibly. Since the framework does not take the data that causes extra time delay as inputs, such as propagation structures and response content, it can be applied as soon as the tweets are posted up and decrease the time delays of rumor debunking. Experiments on two real‐world datasets demonstrate the framework significantly outperforms the state‐of‐the‐art by the accuracy of 6.5% and 7.6%.

With the gradual integration of internet technology and the industrial control field, industrial control systems (ICSs) have begun to access public networks on a large scale. Attackers use these public network interfaces to launch frequent invasions of industrial control systems, thus resulting in equipment failure and downtime, production data leakage, and other serious harm. To ensure security, ICSs urgently need a mature intrusion detection mechanism. Most of the existing research on intrusion detection in ICSs focuses on improving the accuracy of intrusion detection, thereby ignoring the problem of limited equipment resources in industrial control environments, which makes it difficult to apply excellent intrusion detection algorithms in practice. In this study, we first use the spectral residual (SR) algorithm to process the data; we then propose the improved lightweight variational autoencoder (LVA) with autoregression to reconstruct the data, and we finally perform anomaly determination based on the permutation entropy (PE) algorithm. We construct a lightweight unsupervised intrusion detection model named LVA-SP. The model as a whole adopts a lightweight design with a simpler network structure and fewer parameters, which achieves a balance between the detection accuracy and the system resource overhead. Experimental results on the ICSs dataset show that our proposed LVA-SP model achieved an F1-score of 84.81% and has advantages in terms of time and memory overhead.

The access control (AC) system in an IoT (Internet of Things) context ensures that only authorized entities have access to specific devices and that the authorization procedure is based on pre-established rules. Recently, blockchain-based AC systems have gained attention within research as a potential solution to the single point of failure issue that centralized architectures may bring. Moreover, zero-knowledge proof (ZKP) technology is included in blockchain-based AC systems to address the issue of sensitive data leaking. However, current solutions have two problems: (1) systems built by these works are not adaptive to high-traffic IoT environments because of low transactions per second (TPS) and high latency; (2) these works cannot fully guarantee that all user behaviors are honest. In this work, we propose a blockchain-based AC system with zero-knowledge rollups to address the aforementioned issues. Our proposed system implements zero-knowledge rollups (ZK-rollups) of access control, where different AC authorization requests can be grouped into the same batch to generate a uniform ZKP, which is designed specifically to guarantee that participants can be trusted. In low-traffic environments, sufficient experiments show that the proposed system has the least AC authorization time cost compared to existing works. In high-traffic environments, we further prove that based on the ZK-rollups optimization, the proposed system can reduce the authorization time overhead by 86%. Furthermore, the security analysis is presented to show the system’s ability to prevent malicious behaviors.

BackgroundIt is hard to distinguish cerebral aneurysms from overlapping vessels in 2D digital subtraction angiography (DSA) images due to these images’ lack of spatial information. ObjectiveThe aims of this study were to (1) construct a deep learning diagnostic system to improve the ability to detect posterior communicating artery aneurysms on 2D DSA images and (2) validate the efficiency of the deep learning diagnostic system in 2D DSA aneurysm detection. MethodsWe proposed a 2-stage detection system. First, we established the region localization stage to automatically locate specific detection regions of raw 2D DSA sequences. Second, in the intracranial aneurysm detection stage, we constructed a bi-input+RetinaNet+convolutional long short-term memory (C-LSTM) framework to compare its performance for aneurysm detection with that of 3 existing frameworks. Each of the frameworks had a 5-fold cross-validation scheme. The receiver operating characteristic curve, the area under the curve (AUC) value, mean average precision, sensitivity, specificity, and accuracy were used to assess the abilities of different frameworks. ResultsA total of 255 patients with posterior communicating artery aneurysms and 20 patients without aneurysms were included in this study. The best AUC values of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks were 0.95, 0.96, 0.92, and 0.97, respectively. The mean sensitivities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 89% (range 67.02%-98.43%), 88% (range 65.76%-98.06%), 87% (range 64.53%-97.66%), 89% (range 67.02%-98.43%), and 90% (range 68.30%-98.77%), respectively. The mean specificities of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 80% (range 56.34%-94.27%), 89% (range 67.02%-98.43%), 86% (range 63.31%-97.24%), 93% (range 72.30%-99.56%), and 90% (range 68.30%-98.77%), respectively. The mean accuracies of the RetinaNet, RetinaNet+C-LSTM, bi-input+RetinaNet, and bi-input+RetinaNet+C-LSTM frameworks and human experts were 84.50% (range 69.57%-93.97%), 88.50% (range 74.44%-96.39%), 86.50% (range 71.97%-95.22%), 91% (range 77.63%-97.72%), and 90% (range 76.34%-97.21%), respectively. ConclusionsAccording to our results, more spatial and temporal information can help improve the performance of the frameworks. Therefore, the bi-input+RetinaNet+C-LSTM framework had the best performance when compared to that of the other frameworks. Our study demonstrates that our system can assist physicians in detecting intracranial aneurysms on 2D DSA images.

As an essential requirement of pervasive smart devices, free hand gestural input considered as necessary for user interactions has attracted lots of research attention for nearly decades. Nevertheless, existing proposals heavily rely on either expensive pre-deployed equipment or user on-body sensors, thus confine their application scenarios. In this paper, we propose a novel hand gesture recognition system which purely relies on ubiquitous ambient light and low-cost photodiodes. The proposed system does not need any modification to existing lighting infrastructure. While without complex signal pre-processing for modulated light, very low-cost photodiodes and processors can capture and process the light variations caused by hand gesture. To produce accurate hand gesture recognition, we design efficient algorithms based on recurrent neural network to process sensing data collected by a photodiode array. We implement a prototype consisting of an array of 8 photodiodes and extensive experiments demonstrate that the proposed solution can achieve a very high overall recognition accuracy of 99.31%.

A three-party authenticated key agreement (3PAKA) protocol allows two protocol entities to authenticate and agree on a common session key over common communication channels with the help of a trust server. Due to the better robustness and high computation efficiency, Chebyshev chaotic maps technology has been widely used in the authenticated key agreement protocol. In recent years, many researches of the 3PAKA protocol based on Chebyshev chaotic maps have been well-documented. However, cryptanalysis of previous schemes showcased that existing 3PAKA protocols commonly could not resist known session specific temporary attack and could not guarantee users anonymity. In addition, they assumed that the communication channels were absolutely secure during the registration process, which is impracticable in major applications. To overcome the existing weaknesses, we propose a security-enhanced protocol. Finally yet importantly, the security of our proposed scheme has been validated with Strand Space technology and ProVerif tool. Contrary to the existing schemes, the proposed protocol showcased improved security with lower computation and communication cost.

Due to the great success of Vision Transformer (ViT) in image classification tasks, many pure Transformer architectures for human action recognition have been proposed. However, very few works have attempted to use Transformer to conduct bimodal action recognition, i.e., both skeleton and RGB modalities for action recognition. As proved in many previous works, RGB modality and skeleton modality are complementary to each other in human action recognition tasks. How to use both RGB and skeleton modalities for action recognition in a Transformer-based framework is a challenge. In this paper, we propose RGBSformer, a novel two-stream pure Transformer-based framework for human action recognition using both RGB and skeleton modalities. Using only RGB videos, we can acquire skeleton data and generate corresponding skeleton heatmaps. Then, we input skeleton heatmaps and RGB frames to Transformer at different temporal and spatial resolutions. Because the skeleton heatmaps are primary features compared to the original RGB frames, we use fewer attention layers in the skeleton stream. At the same time, two ways are proposed to fuse the information of two streams. Experiments demonstrate that the proposed framework achieves the state of the art on four benchmarks: three widely used datasets, Kinetics400, NTU RGB+D 60, and NTU RGB+D 120, and the fine-grained dataset FineGym99.

This study investigated the effects of phenyllactic acid (PL), lactic acid bacteria (LAB), and their mixture on fermentation characteristics and microbial community composition of timothy silage. Timothy silages were treated without (CK) or with PL [10 mg/kg fresh matter (FM) basis], LAB inoculant (IN; a mixture of Lactobacillus plantarum and L.buchneri, 105 cfu/g FM), and their mixture (PI) and stored at ambient temperature (5°C∼15°C) in a dark room for 60 days. Compared with CK, all treated silages showed lower (P < 0.05) levels of butyric acid and ammonia-N. Treatment with PL enhanced (P < 0.05) the crude protein preservation of silage by favoring the growth of L. curvatus and Saccharomyces cerevisiae and inhibition of lactic acid–assimilating yeast belonging to Issatchenkia during ensiling. In particular, treatment with PL advanced (P < 0.05) the productions of lactic acid and volatile fatty acid in IN-treated silage. Therefore, PL used as a new additive exhibited potential for improving silage fermentation when it is combined with LAB IN during ensiling.

Physical layer secret key generation (PLKG) is a promising technology for establishing effective secret keys. Current works for PLKG mostly study key generation schemes in ideal communication environments with little or even no signal interference. In terms of this issue, exploiting the reconfigurable intelligent reflecting surface (IRS) to assist PLKG has caused an increasing interest. Most IRS-assisted PLKG schemes focus on the single-input-single-output (SISO), which is limited in future communications with multi-input-multi-output (MIMO). However, MIMO could bring a serious overhead of channel reciprocity extraction. To fill the gap, this paper proposes a novel low-overhead IRS-assisted PLKG scheme with deep learning in the MIMO communications environments. We first combine the direct channel and the reflecting channel established by the IRS to construct the channel response function, and we propose a theoretically optimal interaction matrix to approach the optimal achievable rate. Then we design a channel reciprocity-learning neural network with an IRS introduced (IRS-CRNet), which is exploited to extract the channel reciprocity in time division duplexing (TDD) systems. Moreover, a PLKG scheme based on the IRS-CRNet is proposed. Final simulation results verify the performance of the PLKG scheme based on the IRS-CRNet in terms of key generation rate, key error rate and randomness.

In the task of image instance segmentation, semi-supervised instance segmentation algorithms have received constant research attention over recent years. Among these algorithms, algorithms based on transfer learning are better than algorithms based on pseudo-label generation in terms of segmentation performance, but they can not make full use of the relevant characteristics of source tasks. To improve the accuracy of these algorithms, this work proposes a semi-supervised instance segmentation model AFT-Mask (attention-based feature transfer Mask R-CNN) based on category attention. The AFT-Mask model takes the result of object-classification prediction as “attention” to improve the performance of the feature-transfer module. In detail, we designed a migration-optimization module for connecting feature migration and classification prediction to enhance segmentation-prediction accuracy. To verify the validity of the AFT-Mask model, experiments were conducted on two types of datasets. Experimental results show that the AFT-Mask model can achieve effective knowledge transfer and improve the performance of the benchmark model on semi-supervised instance segmentation.

Optical camera communication (OCC), enabled by light-emitting diodes (LEDs) and embedded cameras on smartphones, has drawn considerable attention thanks to the pervasive adoption of LED lighting and mobile devices. However, most existing studies do not consider the performance bottleneck of Region of Interest (RoI) extraction during decoding, making it challenging to improve communication capacity further. To this end, we propose a fast grid virtual division scheme based on pixel grayscale values, which extracts RoI quickly without sacrificing computational complexity, thereby reducing the decoding delay and improving the communication capacity of OCC. Essentially, the proposed scheme uses a grid division strategy to divide the received image into blocks and randomly sample several pixels within different blocks to quickly locate the RoI with high grayscale values in the original image. By implementing the lightweight RoI extraction algorithm, we experimentally verify its effectiveness in reducing decoding latency, demonstrating its superior performance in terms of communication capacity. The experimental results clearly show that the decoding delay of the proposed scheme is 70% lower than that provided by the Gaussian blur scheme for the iPhone receiver at a transmission frequency of 5 kHz.

Visible light positioning (VLP) has attracted intensive attention from both academic and industrial communities thanks to its high accuracy, immunity to electromagnetic interference, and low deployment cost. In general, the receiver in a VLP system determines its own position by exploring the received signal strength (RSS) from the transmitter according to a pre-built RSS attenuation model. In such model-based methods, the LED’s emission power and the receiver’s height are usually required known and constant parameters to obtain reasonable positioning accuracy. However, the LED’s emission power is normally time-varying due to the fact that the LED’s optical output power is prone to changing with the LED’s temperature, and the receiver’s height is random in a realistic application scenario. To this end, we propose a height-independent three-dimensional (3D) VLP scheme based on the RSS ratio (RSSR), rather than only using RSS. Unlike existing RSS-based VLP methods, our method is able to independently find the horizontal coordinate, i.e., two-dimensional (2D) position, without a priori height information of the receiver, and also avoids the negative effect caused by fluctuation of the LED’s emission power. Moreover, we can further infer the height of the receiver to achieve three-dimensional (3D) positioning by iterating the 2D results back into positioning equations. To quickly verify the proposed scheme, we conduct theoretical analysis with mathematical proof and experimental results with real data, which confirm that the proposed scheme can achieve high position accuracy without known information of the receiver’s height and LED’s emission power. We also implement a VLP prototype with five LED transmitters, and experimental results show that the proposed scheme can achieve very low average errors of 2.73 cm in 2D and 7.20 cm in 3D.

This study aimed to investigate the effects of delayed harvest and additives on the fermentation quality and bacterial community of corn stalk silage in South China. The corn stalks after ear harvest at the 0 day (D0), 7 days (D7), and 15 days (D15) were used to produce small-bale silages. The silages at each harvest time were treated without (control, CK) or with Lactobacillus plantarum (LP) and sodium benzoate (BF). The results showed that delayed harvest increased pH and acetic acid content and reduced lactic acid content in corn stalk silage (p < 0.05). Compared with CK, the additives decreased the contents of butyric acid and ammonia nitrogen (NH3-N; p < 0.05). The silage treated with LP increased the content of lactic acid and decreased pH (p < 0.05); the silage treated with BF decreased counts of coliform bacteria and yeasts and increased residual water soluble carbohydrates (WSC) content (p < 0.05). Single Molecule, Real-Time sequencing (SMRT) revealed that the abundance of L. plantarum increased, while the abundance of Lactobacillus brevis and Lactobacillus ginsenosidimutans decreased with the delayed harvest. Additives influenced the bacterial community structure of corn stalk silage, revealed by enhanced bacterial diversity on D0 and reduced on D7 (p < 0.05). Our research indicated that delayed harvest could exert a positive effect on acetic acid production, and additives could inhibit the butyric acid fermentation and protein degradation of corn stalk silage by shifting bacterial community composition.

Abstract Background An intracranial aneurysm is a cerebrovascular disorder that can result in various diseases. Clinically, diagnosis of an intracranial aneurysm utilizes digital subtraction angiography (DSA) modality as gold standard. The existing automatic computer-aided diagnosis (CAD) research studies with DSA modality were based on classical digital image processing (DIP) methods. However, the classical feature extraction methods were badly hampered by complex vascular distribution, and the sliding window methods were time-consuming during searching and feature extraction. Therefore, developing an accurate and efficient CAD method to detect intracranial aneurysms on DSA images is a meaningful task. Methods In this study, we proposed a two-stage convolutional neural network (CNN) architecture to automatically detect intracranial aneurysms on 2D-DSA images. In region localization stage (RLS), our detection system can locate a specific region to reduce the interference of the other regions. Then, in aneurysm detection stage (ADS), the detector could combine the information of frontal and lateral angiographic view to identify intracranial aneurysms, with a false-positive suppression algorithm. Results Our study was experimented on posterior communicating artery (PCoA) region of internal carotid artery (ICA). The data set contained 241 subjects for model training, and 40 prospectively collected subjects for testing. Compared with the classical DIP method which had an accuracy of 62.5% and an area under curve (AUC) of 0.69, the proposed architecture could achieve accuracy of 93.5% and the AUC of 0.942. In addition, the detection time cost of our method was about 0.569 s, which was one hundred times faster than the classical DIP method of 62.546 s. Conclusion The results illustrated that our proposed two-stage CNN-based architecture was more accurate and faster compared with the existing research studies of classical DIP methods. Overall, our study is a demonstration that it is feasible to assist physicians to detect intracranial aneurysm on DSA images using CNN.

As nodes' characteristics that they are self-governed and resource-limited, wireless sensor networks (WSNs) face potential threats due to various attacks, among which the most threatening attack is wormhole attack. Wormhole attack severely imperils WSNs and is difficult to be detected, for it causes incorrect routing by private tunnels and damages to WSNs in terms of data leakage, data dropping, and delayed delivery. However, the existing solutions are based on additional hardware, incur high communication overhead, or fail to give consideration to all types of wormholes. In this paper, we propose CREDND, a protocol for creating a Credible Neighbor Discovery against wormholes in WSN, which can detect not only external wormholes through the hop difference between the own exclusive neighbors but also internal wormholes through enabling the common neighbor nodes as witnesses to monitor whether the authentication packets are forwarded by malicious nodes. CREDND is a simple, localized protocol and needs no special hardware, localization, or synchronization, but it improves the ability of wormhole defense. The simulation results are provided, showing that CREDND outperforms in wormhole detection than other same types of solutions.

The civil aviation industry is undergoing rapid development. However, the on-time rate of airport flights and passenger service quality are not particularly satisfying. The cause of the above problems is the contradiction between the limited operational support capability and the continuous growth of passenger traffic volume. Therefore, the key to solving these problems is achieving situation awareness of airport operation. Many situation awareness algorithms, typically categorized into modeling and machine learning, have been proposed in the past years. However, existing models lack flexibility and their prediction accuracy is unstable. Machine learning's results cannot be timely and effective when external conditions are suddenly changed although some related algorithms have higher accuracy because of the retraining of artificial neural network (ANN). This paper proposes a situation awareness method based on Petri nets (PNs). This method introduces the queuing theory and perceptual parameters into the existing PN and constructs the perceptual PNs' model for general service systems so that it can quickly model different scene service systems. In combination with the ANN, this paper proposes a complete situation awareness algorithm to realize a sustained and accurate situation awareness prediction of the service system by solving point estimations of the macroscopic and microscopic situation in this model, which helps to address some challenges faced by current civil aviation airports. By experimenting on-ground support in civil aviation airports and the access of website as well as comparing the situation separately with Airport Collaborative Decision Making and stochastic PNs, the validity and accuracy of the algorithm proposed in this paper are well verified.

Anomaly detection is the foundation of intelligent operation and maintenance (O&M), and detection objects are evaluated by key performance indicators (KPIs). For almost all computer O&M systems, KPIs are usually the machine-level operating data. Moreover, these high-frequency KPIs show a non-Gaussian distribution and are hard to model, i.e., they are intricate KPI profiles. However, existing anomaly detection techniques are incapable of adapting to intricate KPI profiles. In order to enhance the performance under intricate KPI profiles, this study presents a seasonal adaptive KPI anomaly detection algorithm ASAD (Adaptive Seasonality Anomaly Detection). We also propose a new eBeats clustering algorithm and calendar-based correlation method to further reduce the detection time and error. Through experimental tests, our ASAD algorithm has the best overall performance compared to other KPI anomaly detection methods.

Lactic acid bacteria (LAB) have been proposed for the control of undesirable fermentation and, subsequently, aerobic deterioration due to their ability to produce antimicrobial metabolites in silage mass. To investigate the effect of specific LAB on the silage fermentation characteristics and bacterial community composition of oat in cold regions, silages were treated without (control) or with three LAB strains (LB, Lentilactobacillus buchneri; nLP, low temperature tolerant Lactiplantibacillus plantarum; pLP, phenyllactic acid-producing Lactiplantibacillus plantarum), and then stored at ambient temperature (−2.63 ± 5.47–14.29 ± 5.48 °C) for 30, 60, and 90 days. Compared with control, inoculation of LAB decreased the final pH value, butyric acid content, ammonia-N of total N and dry matter loss of silage. Treatments with nLP and pLP increased (p < 0.05) lactic acid content, whereas LB increased (p < 0.05) acetic acid content of silage. Lactiplantibacillus and Leuconostoc dominated in the silages with relative abundance of 68.29–96.63%. A prolonged storage period enhanced the growth of Leuconostoc in pLP-treated silage. In addition, pLP increased (p < 0.05) the aerobic stability of silage as compared with nLP. In conclusion, inoculation of LAB improved silage fermentation and/or delayed aerobic deterioration by shifting bacterial community composition during ensiling. Phenyllactic acid-producing Lactiplantibacillusplantarum as an inoculant exhibited potential for high quality silage production.

With the development of the Internet of Things for smart grid, the requirement for appliance monitoring has become an important topic. The first and most important step in appliance monitoring is to identify the type of appliance. Most of the existing appliance identification platforms are cloud based, thus they consume large computing resources and memory. Therefore, it is necessary to explore an edge identification platform with a low cost. In this work, a novel appliance identification edge platform for data gathering, capturing and labeling is proposed. Experiments show that this platform can achieve an average appliance identification accuracy of 98.5% and improve the accuracy of non-intrusive load disaggregation algorithms.

To effectively use local grass resources to cover the winter feed shortage on the Qinghai-Tibetan Plateau, the silage fermentation and in vitro digestibility of perennial oat (Helictotrichonvirescens Henr.) were investigated. Perennial oat was harvested at the heading/flowering stage, wilted under sunny conditions, chopped, vacuumed in small bag silos, and stored at ambient temperatures (5–15 °C) for 60 days. The silages were treated without (CK) or with local lactic acid bacteria (LAB) inoculant (IN1), commercial LAB inoculant (IN2), and sodium benzoate (BL). Control silages of perennial oat at early heading stage showed higher (p < 0.05) lactate and acetate contents and lower (p < 0.05) final pH, butyrate, and ammonia-N contents than those at the flowering stage. High levels of dry matter recovery (DMR) and crude protein (CP) were observed in IN1- and BL-treated silages, with high in vitro gas production and dry matter digestibility. Compared to CK, additives increased (p < 0.05) aerobic stability by inhibiting yeasts, aerobic bacteria, and coliform bacteria during ensiling. In particular, the local LAB inoculant increased (p < 0.05) concentrations of lactate, acetate and propionate, and decreased concentrations of butyrate and ammonia-N in silages. This study confirmed that local LAB inoculant could improve the silage quality of perennial oat, and this could be a potential winter feed for animals such as yaks on the Qinghai Tibetan Plateau.

This study aimed to investigate the effect of oat silage treated with a low-temperature-tolerant lactic acid bacteria (LAB) inoculant on milk yield and the quality of lactating yaks. Oat silages were prepared in big round bales, treated without (control) or with a low-temperature-tolerant LAB inoculant (a mixture of Lactobacillus plantarum BP18, Pediococcus pentosaceus HS1 and Lactobacillus buchneri LP22; the application rate of 105 cfu/g on a fresh matter basis). Eighteen lactating yaks were divided into nine pairs with a similar milk yield. Each pair of yaks was randomly allocated to the control or LAB-inoculated silage treatment. The inoculated silage increased the dry matter intake and the total volatile fatty acid (mainly acetate, propionate and butyrate) in rumen fluid compared with the control. The inoculated silage also enhanced the yield of yak milk with high contents of total N, fat and lactose. In addition, high levels of essential amino acids (Thr, Leu and Phe), polyunsaturated fatty acids and low saturated fatty acids were observed in milk when lactating yaks were fed with the inoculated silage. Therefore, inoculation with a low-temperature-tolerant LAB during ensiling could promote the milk yield of lactating yaks by enhancing dry matter intake and ruminal fermentation.

In an electrical safe microenvironment, all kinds of electrical appliances can be operated safely to ensure the safety of life and property. The significance of safety hazard factors detection is to detect safety hazards in advance, to remind the administrators to exclude risk, to reduce the unnecessary loss, and to ensure that the electrical operation is healthy and orderly before the occurrence of accidents. In this paper, batteries are selected as the primary research subject of safety detection because batteries are used more and more in the Internet of Things (IOT), and they often cause fire in the process of discharging and charging. The existing algorithms need to be embedded into the specialized sensor for each important electrical appliance. However, they are limited by the actual deployment, so it is extremely difficult to spread widely. According to the opinions above, an improved load disaggregation algorithm based on dictionary learning and sparse coding with optimal dictionary matrix period is proposed to detect potential safety hazards of battery loads. For safety-related electrical applications, doing so can increase interpretability. Through experiments, we test this algorithm on the REDD dataset, and compare it with the baseline algorithms (combinatorial optimization, factorial hidden Markov model, basic discriminative dictionary sparse coding algorithm) to achieve a degree of trust. The Mean Absolute Error (MAE) value is 8.26, which drops by 70%. The Root Mean Square Error (RMSE) value is 97.75, which is also better than those baseline algorithms.

Abstract Conduct disorder (CD) is a psychiatric disorder in children and adolescence. To investigate changes in the power distribution in brain networks between CD and typically developing (TD) groups, resting-state functional magnetic resonance imaging (rsfMRI) data of thirty-six subjects were first recorded, and then the data were preprocessed using DPARSF and SPM8. Meanwhile, the power of the blood oxygenation level-dependent (BOLD) signals of ninety brain regions was acquired using the integral of the Welch power spectral density (PSD). Additionally, the powers of the brain regions that reached significance (p

In the Industrial Internet, computing- and power-limited mobile devices (MDs) in the production process can hardly support the computation-intensive or time-sensitive applications. As a new computing paradigm, mobile edge computing (MEC) can almost meet the requirements of latency and calculation by handling tasks approximately close to MDs. However, the limited battery capacity of MDs causes unreliable task offloading in MEC, which will increase the system overhead and reduce the economic efficiency of manufacturing in actual production. To make the offloading scheme adaptive to that uncertain mobile environment, this paper considers the reliability of MDs, which is defined as residual energy after completing a computation task. In more detail, we first investigate the task offloading in MEC and also consider reliability as an important criterion. To optimize the system overhead caused by task offloading, we then construct the mathematical models for two different computing modes, namely, local computing and remote computing, and formulate task offloading as a mixed integer non-linear programming (MINLP) problem. To effectively solve the optimization problem, we further propose a heuristic algorithm based on greedy policy (HAGP). The algorithm achieves the optimal CPU cycle frequency for local computing and the optimal transmission power for remote computing by alternating optimization (AP) methods. It then makes the optimal offloading decision for each MD with a minimal system overhead in both of these two modes by the greedy policy under the limited wireless channels constraint. Finally, multiple experiments are simulated to verify the advantages of HAGP, and the results strongly confirm that the considered task offloading reliability of MDs can reduce the system overhead and further save energy consumption to prolong the life of the battery and support more computation tasks.

A safe charging algorithm in wireless rechargeable sensor network ensures the charging efficiency and the electromagnetic radiation below the threshold. Compared with the current charging algorithms, the safe charging algorithm is more complicated due to the radiation constraint and the mobility of the chargers. A safe charging algorithm based on multiple mobile chargers is proposed in this paper to charge the sensor nodes with mobile chargers, in order to ensure the premise of radiation safety, multiple mobile chargers can effectively complete the network charging task. Firstly, this algorithm narrows the possible location of the sensor nodes by utilizing the charging time and antenna waveform. Secondly, the performance of non-partition charging algorithm which algorithm allow chargers to charge different sensors sets in a different cycle is evaluated against the one of partition charging which does not allow for charging different ones. The moving distance of the charger node will be reduced by 18%. It not only improves the safety level which is inversely proportional to electromagnetic radiation but also expands the application scope of the wireless sensor nodes.

As an indispensable part of Internet of Things (IoT), wireless sensor networks (WSNs) are more and more widely used with the rapid development of IoT. The neighbor discovery protocols are the premise of communication between nodes and networking in energy-limited self-organizing wireless networks, and play an important role in WSNs. Because the node energy is limited, neighbor discovery must operate in an energy-efficient manner, that is, under the condition of a given energy budget, the neighbor discovery performance should be as good as possible, such that the discovery latency would be as small as possible and the discovered neighbor percentage as large as possible. The indirect neighbor discovery mainly uses the information of the neighbors that have been found by a pairwise discovery method to more efficiently make a re-planning of the discovery wake-up schedules of the original pairwise neighbor discovery, thereby improving the discovery energy efficiency. The current indirect neighbor discovery methods are mainly divided into two categories: one involves removing the inefficient active slots in the original discovery wake-up schedules, and the other involves adding some efficient active slots. However, the two categories of methods have their own limitations. The former does not consider that this removal operation destroys the integrity of the original discovery wake-up schedules and hence the possibility of discovering new neighbors is reduced, which adversely affects the discovered neighbor percentage. For the latter category, there are still inefficient active slots that were not removed in the re-planned wake-up schedules. The motivation of this paper is to combine the advantages of these two types of indirect neighbor discovery methods, that is, to combine the addition of efficient active slots and the removal of inefficient active slots. To achieve this goal, this paper proposes, for the first time, the concept of virtual nodes in neighbor discovery to maximize the integrity of the original wake-up schedules and achieve the goals of adding efficient active slots and removing inefficient active slots. Specifically, a virtual node is a collaborative group that is formed by nodes within a small range. The nodes in a collaborative group share responsibility for the activating task of one member node, and the combination of these nodes’ wake-up schedules forms the full wake-up schedule of a node that only uses a pairwise method. In addition, this paper proposes a set of efficient group management mechanisms, and the key steps affecting energy efficiency are analyzed theoretically to obtain the energy-optimal parameters. The extended simulation experiments in multiple scenarios show that, compared with other methods, our neighbor discovery protocol based on virtual nodes (VN-NDP) has a significant improvement in average discovery delay and discovered neighbor percentage performance at a given energy budget. Compared with the typical indirect neighbor discovery algorithm EQS, a neighbor discovery with extended quorum system, our proposed VN-NDP method reduces the average discovery delay by up to 10.03 % and increases the discovered neighbor percentage by up to 18.35 % .

With the quick development of Internet of Things (IoT), one of its important supporting technologies, i.e., wireless sensor networks (WSNs), gets much more attention. Neighbor discovery is an indispensable procedure in WSNs. The existing deterministic neighbor discovery algorithms in WSNs ensure that successful discovery can be obtained within a given period of time, but the average discovery delay is long. It is difficult to meet the need for rapid discovery in mobile low duty cycle environments. In addition, with the rapid development of IoT, the node densities of many WSNs greatly increase. In such scenarios, existing neighbor discovery methods fail to satisfy the requirement in terms of discovery latency under the condition of the same energy consumption. This paper proposes a group-based fast neighbor discovery algorithm (GBFA) to address the issues. By carrying neighbor information in beacon packet, the node knows in advance some potential neighbors. It selects more energy efficient potential neighbors and proactively makes nodes wake up to verify whether these potential neighbors are true neighbors, thereby speeding up neighbor discovery, improving energy utilization efficiency and decreasing network communication load. The evaluation results indicate that, compared with other methods, GBFA decreases the average discovery latency up to 10 . 58 % at the same energy budget.

Node localization is essential in wireless sensor networks (WSNs). However, due to limited node energy and overhead network, node localization faces many challenges in WSNs. In addition, it is difficult to meet the requirement of localization accuracy and energy consumption at the same time in existing algorithms. In this paper, we proposed a novel eliminating algorithm called NPLA which achieves high localization accuracy and reduces energy consumption effectively. It makes full use of multiple-hop neighbor nodes of unknown node to eliminate the possible location area (PLA) of the unknown node. Simulations show that the localization error of algorithm NPLA can be controlled within 20%.

This paper proposes a twice deployment node balance algorithm (TDNB) which guarantees successful detection of the moving target on road networks. Through dividing the deployment of the sensor nodes into two phases instead of deploying all the sensors at one time, TDNB has a better performance. In the former phase, some of the sensors are deployed on the road at random. In the latter phase, the rest of the sensors are deployed on demand to make the number on each path the same. Due to the equivalence of the node's number, the network will have a prolonged lifetime after inserting nodes into the segment according to this algorithm. TDNB extends the network's lifetime remarkably compared with the former algorithms. Furthermore, TDNB enables us to insert nodes to the segment required in the network instead of all the segments, which reduces the workload to a large extent. In short, without increasing the number of sensors in road network, TDNB has a better performance compared to VISA in terms of network lifetime, which meets the demand for persistent monitoring application.