Search

Geospatial data is essential for urban planning and environmental sustainability. Utilizing multiple robots, each equipped with 3-D LiDAR for remote sensing, to collaboratively construct environmental maps can significantly enhance the efficiency of geospatial data collection. However, efficiently identifying overlapping areas between robots and accurately merging the maps constructed by different robots remains a pressing challenge. This study proposes a multirobot collaborative simultaneous localization and mapping (SLAM) method based on a novel environmental feature descriptor to address this problem. In this method, a distributed multirobot collaborative SLAM system is first constructed. Then, an SLAM algorithm that integrates intensity features and ground constraint is proposed for the robots in the multirobot SLAM system. Additionally, a multilayer hybrid context descriptor is introduced to detect overlapping areas between different robots. To validate the effectiveness and advantages of our method, we conducted benchmark comparisons with other approaches. Our multirobot collaborative SLAM method demonstrated favorable experimental results.

The ongoing expansion of the Industrial Internet of Things (IIoT) is enabling the possibility of effective Industry 4.0, where massive sensing devices in heterogeneous environments are connected through dedicated communication protocols. This brings forth new methods and models to fuse the information yielded by the various industrial plant elements and generates emerging security challenges that we have to face, providing ad-hoc functions for scheduling and guaranteeing the network operations. Recently, the large development of Software-Defined Networking (SDN) and Artificial Intelligence (AI) technologies have made feasible the design and control of scalable and secure IIoT networks. This paper studies how AI and SDN technologies combined can be leveraged towards improving the security and functionality of these IIoT networks. After surveying the state-of-the-art research efforts in the subject, the paper introduces a candidate architecture for AI-enabled Software-Defined IIoT Network (AI-SDIN) that divides the traditional industrial networks into three functional layers. And with this aim in mind, key technologies (Blockchain-based Data Sharing, Intelligent Wireless Data Sensing, Edge Intelligence, Time-Sensitive Networks, Integrating SDN&TSN, Distributed AI) and improve applications based on AI-SDIN are also discussed. Further, the paper also highlights new opportunities and potential research challenges in control and automation of IIoT networks.

Abstract Background Rectal neuroendocrine neoplasms (NENs) are rare neoplasms with limited understanding of its genomic alterations and molecular typing. Methods The paraffin‐embedded tissue specimens of 38 patients with rectal NENs after surgery were subjected to whole gene sequencing (WGS), and mutation profilings were drawn to identify high‐frequency mutation genes, copy‐number variations (CNVs), tumor mutation burden (TMB), signal pathways, mutation signatures, DNA damage repair (DDR) genes, and molecular types. The differences of mutated genes and signaling pathways in different pathological grades and metastatic/non‐metastatic groups were compared. It helped to search for potential targets. Results C > T and T > C transitions are the most common base substitutions in rectal NENs. DNA mismatch repair deficiency, DNA base modifications, smoking and exposure to ultraviolet light might play a role in the occurrence of rectal NENs. DAXX, KMT2C, BCL2L1, LTK, MERTK, SPEN, PKN1, FAT3, and LRP2 mutations were found in only low‐grade rectal NETs, whereas APC, TP53, NF1, SOX9, and BRCA1 mutations were common in high‐grade rectal NECs/MiNENs. These genes helped in distinguishing poorly‐differentiated or well‐differentiated rectal NENs. Alterations in P53, Wnt and TGFβ signaling pathways were more pronounced in rectal NECs and MiNENs. Alterations in Wnt, MAPK and PI3K/AKT signaling pathways promoted metastases. Rectal NENs were classified into two molecular subtypes by cluster analysis based on the mutant genes and signaling pathways combined with clinicopathological features. Patients with mutations in the LRP2, DAXX, and PKN1 gene showed a trend of well‐differentiated and early‐stage tumors with less metastasis (p = 0.000). Conclusions This study evaluated risk factors for regional lymphatic and/or distant metastases, identified high‐frequency mutated genes, mutation signatures, altered signaling pathways through NGS. Rectal NENs were divided into two molecular types. This helps to evaluate the likelihood of metastasis, formulate follow‐up strategies for patients and provide a target for future research on precision treatment of rectal NENs. PARP inhibitors, MEK inhibitors, mTOR/AKT/PI3K and Wnt signaling pathway inhibitors may be effective drugs for the treatment of metastatic rectal NENs.

Background As a member of the immunoglobulin superfamily, hemolins play a vital role in insect development and defense against pathogens. However, the innate immune response of hemolin to baculovirus infection varies among different insects. Methods and results In this study, the hemolin-like gene from a Crambidae insect, Cnaphalocrocis medinalis, CmHem was cloned, and its role in insect development and baculovirus infection was analyzed. A 1,528 bp contig as potential hemolin-like gene of C. medinalis was reassembled from the transcriptome. Further, the complete hemolin sequence of C. medinalis (CmHem) was cloned and sequenced. The cDNA of CmHem was 1,515 bp in length and encoded 408 amino acids. The deduced amino acid of CmHem has relatively low identities (41.9–62.3%) to various insect hemolins. However, it contains four Ig domains similarity to other insect hemolins. The expression level of CmHem was the highest in eggs, followed by pupae and adults, and maintained a low expression level at larval stage. The synthesized siRNAs were injected into mature larvae, and the CmHem transcription decreased by 51.7%. Moreover, the abdominal somites of larvae became straightened, could not pupate normally, and then died. Infection with a baculovirus, C. medinalis granulovirus (CnmeGV), the expression levels of CmHem in the midgut and fat body of C. medinalis significantly increased at 12 and 24 h, respectively, and then soon returned to normal levels. Conclusions Our results suggested that hemolin may be related to the metamorphosis of C. medinalis. Exposure to baculovirus induced the phased expression of hemolin gene in the midgut and fat body of C. medinalis, indicated that hemolin involved in the immune recognition of Crambidae insects to baculovirus.

Cell wall hydrolases are ubiquitous among spore-form bacteria and essential for mother cell lysis. In this study, a novel cell wall hydrolase gene cwlE involved in mother cell lysis was characterized from Bacillus thuringiensis subsp. israelensis (Bti) strain Bt-59. cwlE was specifically expressed in Bti and located in the large plasmid carrying the insecticidal genes. The encoded CwlE protein consists of a MurNAc-LAA domain and two highly conserved catalytic residues (E26 and E151). The recombinant CwlE-His protein was able to digest the cell wall of Bti, indicating that CwlE is an N-acetylmuramoyl-L-alanine amidase. Transcriptional analysis indicated that cwlE began to express at the early stage of stationary phase and was controlled by SigE. Single mutation of cwlE gene delayed Bti mother cell lysis, while double mutation of cwlE and sigK completely blocked Bti mother cell lysis. After exposure to UV light to deactivate the crystal proteins, the level of decrease of insecticidal activity against mosquito larvae of Bt-59 (ΔcwlE-sigK) was less than that observed for Bt-59. This study elucidates the mechanism of Bti mother cell lysis and provides an effective strategy for mosquito control using Bt products with increased persistence.

The composition of microbiota in the digestive tract gut is essential for insect physiology, homeostasis, and pathogen infection. Little is known about the interactions between microbiota load and oral infection with baculoviruses. CnmeGV is an obligative baculovirus to Cnaphalocrocis medinalis. We investigated the impact of CnmeGV infection on the structure of intestinal microbes of C. medinalis during the initial infection stage. The results revealed that the gut microbiota profiles were dynamically driven by pathogen infection of CnmeGV. The numbers of all the OTU counts were relatively higher at the early and later stages, while the microbial diversity significantly increased early but dropped sharply following the infection. The compositional abundance of domain bacteria Firmicutes developed substantially higher. The significantly enriched and depleted species can be divided into four groups at the species level. Fifteen of these species were ultimately predicted as the biomarkers of CnmeGV infection. CnmeGV infection induces significant enrichment of alterations in functional genes related to metabolism and the immune system, encompassing processes such as carbohydrate, amino acid, cofactor, and vitamin metabolism. Finally, the study may provide an in-depth analysis of the relationship between host microbiota, baculovirus infection, and pest control of C. medinalis.

Abstract Link prediction is a fundamental research issue in complex network, which can reveal the potential relationships between users. Most of link prediction algorithms are heuristic and based on topology structure. Weisfeiler–Lehman Neural Machine (WLNM), regarded as a new-generation method, has shown promising performance and thus got attention in link prediction. WLNM extracts an enclosing subgraph of each target link and encodes the subgraph as an adjacency matrix. But it does not consider the relationship between other links of the enclosing subgraph and target links. Therefore, WLNM does not make full use of the topology information around the link, and the extracted enclosing subgraph can only partially represent the topological features around the target link. In this work, a novel approach is proposed, named weighted enclosing subgraph-based link prediction (WESLP). It incorporates the link weights in the enclosing subgraph to reflect their relationship with the target link, and the Katz index between nodes is used to measure the relationship between two links. The prediction models are trained by different classifiers based on these weighted enclosing subgraphs. Experiments show that our proposed method consistently performs well on different real-world datasets.

Traditional Internet of Things (IoT) networks have limited coverage and may experience failures due to natural disasters affecting critical IoT devices, making it difficult for them to provide communication services. Therefore, how to establish network communication service more efficiently in the presence of fault points is the problem we solve in this paper. To address this issue, this study constructs a hierarchical multi-domain data transmission architecture for an emergency network with unmanned aerial vehicles (UAVs) employed as core communication devices. This architecture expands the functionality of UAVs as key network devices and provides a theoretical basis for their feasibility as intelligent network controllers and switches. Firstly, the UAV controllers perceive the network status and learn the spatio-temporal characteristics of air-to-ground network links. Secondly, a routing algorithm within the domain based on federated reinforcement distillation (FedRDR) is developed, which enhances the generalization capability of the routing decision model by increasing the training data samples. Simulation experiments are conducted, and the results show that the average communication data size between each domain controller and the server is approximately 45.3 KB when using the FedRDR algorithm. Compared to the transmission of parameters through federated reinforcement learning algorithms, FedRDR reduces the transmitted parameter size by approximately 29%. Therefore, the FedRDR routing algorithm helps to facilitate knowledge transfer, accelerate the training process of intelligent agents within the domain, and reduce communication costs in resource-constrained scenarios for UAV networks and has practical value.

Confronting the challenge of identifying unknown fault types in rolling bearing fault diagnosis, this study introduces a multi-scale bearing fault diagnosis method based on transfer learning. Initially, a multi-scale feature extraction network, MBDCNet, is constructed. This network, by integrating the features of vibration signals at multiple scales, is dedicated to capturing key information within bearing vibration signals. Innovatively, this study replaces traditional convolution with dynamic convolution in MBDCNet, aiming to enhance the model’s flexibility and adaptability. Furthermore, the study implements pre-training and transfer learning strategies to maximally extract latent knowledge from source domain data. By optimizing the loss function and fine-tuning the learning rate, the robustness and generalization ability of the model in the target domain are significantly improved. The proposed method is validated on bearing datasets provided by Case Western Reserve University and Jiangnan University. The experimental results demonstrate high accuracy in most diagnostic tasks, achieving optimal average accuracy on both datasets, thus verifying the stability and robustness of our approach in various diagnostic tasks. This offers a reliable research direction in terms of enhancing the reliability of industrial equipment, especially in the field of bearing fault diagnosis.

In the field of deep-sea positioning, this paper aims to enhance accuracy and computational efficiency in positioning calculations. We propose an improved method based on layered clustering of sound velocity profiles, where the profiles are stratified according to maximum distance and maximum density. Subsequently, a secondary curve fitting is applied to the stratified data. Ultimately, the underwater positioning is conducted using the sound velocity profiles’ post-layered fitting. We compare our approach with traditional methods such as k-means clustering, layered clustering, and gradient-based stratification. Experimental results demonstrate that, in the application scenario of a USBL system with a transducer tilted at 30°, and under the premise of autonomously controlling the number of layers, our method significantly improves positioning accuracy.

Background: Programmed cell death protein-1 inhibitors combined with lenvatinib have become a popular treatment option for patients with unresectable hepatocellular carcinoma. Transarterial chemoembolization combined with programmed cell death protein-1 inhibitors and lenvatinib has also shown preliminary efficacy in the unresectable hepatocellular carcinoma. We conducted this observational, retrospective, cohort study to compare the clinical outcomes and safety of transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib versus programmed cell death protein-1 inhibitors plus lenvatinib in patients with unresectable hepatocellular carcinoma. Methods: Between November 2019 and November 2021, patients who were diagnosed with unresectable hepatocellular carcinoma and received transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib or programmed cell death protein-1 inhibitors plus lenvatinib treatment were reviewed for eligibility. The primary endpoints included objective response rate, overall survival, and progression-free survival. The secondary endpoint was the frequency of key adverse events. Results: In total, 105 patients were eligible for the present study, and they were divided into the transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib group (n = 46) and the programmed cell death protein-1 inhibitors plus lenvatinib group (n = 59). The patient cohort after a one-to-one propensity score matching (n = 86) was also analyzed. The transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib group had a higher objective response rate both in the patient cohort before propensity score matching (54.3% vs 25.4%, P = .002) and after propensity score matching (55.8% vs 30.2%, P = .017). The patients in the transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib group had prolonged overall survival (median, 20.5 vs 12.6 months, P = .015) and progression-free survival (median, 10.2 vs 7.4 months, P = .035). For patient cohort- propensity score matching, the overall survival (20.5 vs 12.8 months, P = .013) and progression-free survival (12.1 vs 7.8 months, P = .030) were also significantly better in the transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib group than in the programmed cell death protein-1 inhibitors plus lenvatinib group. There were no significant differences between the 2 groups concerning adverse reactions caused by immunotherapy and lenvatinib. The adverse reactions caused by transarterial chemoembolization were transient and were quickly reversed. Conclusions: Compared to programmed cell death protein-1 inhibitors plus lenvatinib, transarterial chemoembolization combined with programmed cell death protein-1 inhibitors plus lenvatinib may provide better treatment response and survival benefits for patients with unresectable hepatocellular carcinoma, and the adverse events were manageable.

The rapid development of the Internet of Things (IoT) has resulted in vast amounts of widely distributed data. Sharing these data can spur innovative advancements and enhance service quality. However, conventional data-sharing methods often involve third-party intermediaries, posing risks of single-point failures and privacy leaks. Moreover, these traditional sharing methods lack a secure transaction model to compensate for data sharing, which makes ensuring fair payment between data consumers and providers challenging. Blockchain, as a decentralized, secure, and trustworthy distributed ledger, offers a novel solution for data sharing. Nevertheless, since all nodes on the blockchain can access on-chain data, data privacy is inadequately protected, and traditional privacy-preserving methods like anonymization and generalization are ineffective against attackers with background knowledge. To address these issues, this paper proposes a decentralized, privacy-preserving, and fair data transaction model based on blockchain technology. We designed an adaptive local differential privacy algorithm, MDLDP, to protect the privacy of transaction data. Concurrently, verifiable encrypted signatures are employed to address the issue of fair payment during the data transaction process. This model proposes a committee structure to replace the individual arbitrator commonly seen in traditional verifiable encrypted signatures, thereby reducing potential collusion between dishonest traders and the arbitrator. The arbitration committee leverages threshold signature techniques to manage arbitration private keys. A full arbitration private key can only be collaboratively constructed by any arbitrary t members, ensuring the key’s security. Theoretical analyses and experimental results reveal that, in comparison to existing approaches, our model delivers enhanced transactional security. Moreover, while guaranteeing data availability, MDLDP affords elevated privacy protection.

In order to effectively estimate the depth of the source in the acoustic pressure field, this study investigated the relationship between the distribution of acoustic pressure fields in different adjacent mode groups and the depth of the source in shallow waveguides and proposed a method to estimate the depth of the source on the basis of the adjacent mode group acoustic pressure field. We first derived and calculated the adjacent mode group acoustic pressure field of a typical shallow waveguide, then verified the accuracy of this derivation process through simulations. In addition, combined with singular value decomposition mode extraction, the adjacent mode group acoustic pressure field of the SACLANT experimental data was obtained and used as a comparative parameter for the method presented in this paper. By using the depth of the source as the estimation variable, a simulated annealing algorithm and related parameters were designed, and the feasibility of this method was verified through simulation and experiments. The proposed method achieved a higher localization accuracy without the need for accurate modeling of underwater acoustic channels. Under the conditions of the simulation environment, the average estimation error rate of the method was 0.24%, and with increases in the temperature coefficient and Markov chain length, the average estimation error rate of the method decreased. In the experimental environment, the average estimation error rate of the method was 0.45%. This study provides a method to obtain the depth of source in a shallow waveguide via the adjacent mode group acoustic pressure field.

Underwater Wireless Sensor Networks (UWSNs) are widely used in many fields, such as regular marine monitoring and disaster warning. However, UWSNs are still subject to various limitations and challenges: ocean interferences and noises are high, bandwidths are narrow, and propagation delays are high. Sensor batteries have limited energy and are difficult to be replaced or recharged. Accordingly, the design of routing protocols is one of the solutions to these problems. Aiming at reducing and balancing network energy consumption and effectively extending the life cycle of UWSNs, this paper proposes a Hierarchical Adaptive Energy-efficient Clustering Routing (HAECR) strategy. First, this strategy divides hierarchical regions based on the depth of the sensor node in a three-dimensional (3D) space. Second, sensor nodes form different competition radii based on their own relevant attributes and remaining energy. Nodes in the same layer compete freely to form clusters of different sizes. Finally, the transmission path between clusters is determined according to comprehensive factors, such as link quality, and then the optimal route is planned. The simulation experiment is conducted in the monitoring range of the 3D space. The simulation results prove that the HAECR clustering strategy is superior to LEACH and UCUBB in terms of balancing and reducing energy consumption, extending the network lifetime, and increasing the number of data transmissions.

It is essential to accurately diagnose bearing faults to avoid property losses or casualties in the industry caused by motor failures. Recently, the methods of fault diagnosis for bearings using deep learning methods have improved the safety of motor operations in a reliable and intelligent way. However, most of the work is mainly suitable for situations where there is sufficient monitoring data of the bearings. In industrial systems, only a small amount of monitoring data can be collected by the bearing sensors due to the harsh monitoring conditions and the short time of the signals of some special motor bearings. To solve the issue above, this paper introduces a transfer learning strategy by focusing on the multi-local model bearing fault based on small sample fusion. The algorithm mainly includes the following steps: (1) constructing a parallel Bi-LSTM sub-network to extract features from bearing vibration and current signals of industrial motor bearings, serially fusing the extracted vibration and current signal features for fault classification, and using them as a source domain fault diagnosis model; (2) measuring the distribution difference between the source domain bearing data and the target bearing data using the maximum mean difference algorithm; (3) based on the distribution differences between the source domain and the target domain, transferring the network parameters of the source domain fault diagnosis model, fine-tuning the network structure of the source domain fault diagnosis model, and obtaining the target domain fault diagnosis model. A performance evaluation reveals that a higher fault diagnosis accuracy under small sample fusion can be maintained by the proposed method compared to other methods. In addition, the early training time of the fault diagnosis model can be reduced, and its generalization ability can be improved to a great extent. Specifically, the fault diagnosis accuracy can be improved to higher than 80% while the training time can be reduced to 15.3% by using the proposed method.

Abstract Background Short interspersed nuclear elements (SINEs) belong to non-long terminal repeat (non-LTR) retrotransposons, which can mobilize dependent on the help of counterpart long interspersed nuclear elements (LINEs). Although 234 SINEs have been identified so far, only 23 are from insect species (SINEbase: http://sines.eimb.ru/ ). Results Here, five SINEs were identified from the genome of Plutella xylostella, among which PxSE1, PxSE2 and PxSE3 were tRNA-derived SINEs, PxSE4 and PxSE5 were 5S RNA-derived SINEs. A total of 18 related SINEs were further identified in 13 lepidopteran insects and a baculovirus. The 3′-tail of PxSE5 shares highly identity with that of LINE retrotransposon, PxLINE1. The analysis of relative age distribution profiles revealed that PxSE1 is a relatively young retrotransposon in the genome of P. xylostella and was generated by recent explosive amplification. Integration pattern analysis showed that SINEs in P. xylostella prefer to insert into or accumulate in introns and regions 5 kb downstream of genes. In particular, the PxSE1-like element, SlNPVSE1, in Spodoptera litura nucleopolyhedrovirus II genome is highly identical to SfSE1 in Spodoptera frugiperda, SlittSE1 in Spodoptera littoralis, and SlituSE1 in Spodoptera litura, suggesting the occurrence of horizontal transfer. Conclusions Lepidopteran insect genomes harbor a diversity of SINEs. The retrotransposition activity and copy number of these SINEs varies considerably between host lineages and SINE lineages. Host-parasite interactions facilitate the horizontal transfer of SINE between baculovirus and its lepidopteran hosts.

Billions of low-end wireless devices, such as sensors, are permeating into almost every aspect of personal life, such as vehicles, washing machines, and air conditioners. These miniaturized and low-end devices are a promising solution to collect information and assist users in interacting with real-world objects. Frost and Sullivan reported that the global market of miniaturized devices is forecast to increase from 1.4 billion to 3.26 billion from 2014 to 2024. Unfortunately, the performance of miniaturized devices, which generally operate with limited battery power and transmit data over an unlicensed spectrum, is highly deteriorated due to resource scarcity issues in terms of energy and spectrum. The energy scarcity issue limits the longevity of devices and requires the operator to manually replace the depleted battery, which results in considerable maintenance costs. Even with sufficient energy supply, data transmission conflicts with other networks that coexist in the unlicensed spectrum band, leading to spectrum scarcity issues. To alleviate these energy and spectrum scarcity issues, numerous energy and spectrum harvesting technologies have emerged, such as mini solar panels, piezoelectric transducers, and cognitive radio. By embedding these modules, the devices can harvest energy from the ambient energy sources and explore the idle licensed spectrum for data transmission, leading to energy and spectrum harvesting-enabled devices.

Saline water covers approximately 360000000 km2, approximately 71% of Earth’s surface and 90% of Earth’s biosphere. The ocean contains 97% of Earth’s water, and oceanographers have stated that less than 5% of the World Ocean has been explored. The total volume is approximately 1.35 billion cubic kilometers with an average depth of nearly 3700 m. Our ocean and coasts provide jobs for millions of people in coastal communities across the world. Ocean industries such as commercial and recreational fisheries, tourism and recreation, and marine transportation generate thousands of billions of dollars every year. We must protect the ocean’s long-term health, not only for habitats and marine life that depend on it but also for the humans that have relied on its resources for generations. All of this requires maintaining a healthy ocean ecosystem, even as human demands and stresses to the ocean are increasing. It is more important than ever to understand how the ocean interacts with various offshore applications. To this end, underwater acoustic sensor networks (UASNs) play an important role in the ocean’s protection. However, ocean monitoring and research are not an easy task, since the ocean is big and most of the underwater environment is still unknown to us. In addition, due to the high pressures in deep water, it is not suitable for people to work a long time underwater.

Intestinal bacterial flora plays an important role in the nutrition, physiology, and behavior of herbivorous insects. The composition of gut microbiota may also be affected by the food consumed. Cnaphalocrocis medinalis is an oligophagous pest, feeds on rice leaves almost exclusively and causes serious damage to rice in Asian countries. Using antibiotic treatment and metagenome sequencing, we investigated the influence of the food sources (rice and maize seedlings) on the structure and functions of intestinal bacteria of C. medinalis. Firstly, food utilization indices, relative growth rate (RGR), relative consumption rate (RCR), efficiency of conversion of ingested food (ECI), and efficiency of conversion of digested food (ECD), were all significantly adversely affected in the antibiotic treatment eliminating gut bacteria, showing that the microbiota loading in the gut were essential for the larva growth and development of C. medinalis. Further, metagenome sequencing revealed that different diets caused a variation in gut microbiota composition of C. medinalis, indicating that the gut microbiota were in part driven by the diet provided. However, the larvae of C. medinalis hosted a core microbial community in the gut, which was independent from the diets changing. The dominant bacteria in the two feeding groups were highly consistent in the gut of C. medinalis larvae, with the gut bacterial community dominated by Firmicutes at the phylum level, Enterococcus at the genus level, Enterococcus sp. FDAARGOS-375, E. casseliflavus, E. gallinarum, and E. sp. CR-Ec1 accounted for more than 96% of the gut microbiota. Functional prediction analysis demonstrated that gut bacteria encoded a series of metabolism-related enzymes involved in carbohydrate metabolism and amino acid synthesis. Carbohydrate metabolism was the most enriched function in both groups and was more abundant in rice feeding group than in maize feeding group. The core dominant Enterococcus species possessed complete pathways of 14 carbohydrates metabolism, 11 amino acids biosynthesis, and two vitamins synthesize, implied to contribute an essential role to the nutrition intake and development of C. medinalis. Finally, the study may provide an in-depth analysis of the symbiont-host co-adaptation and new insights into the management of C. medinalis.

BackgroundSystemic therapies, including immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs), have challenged the use of conventional therapies for hepatocellular carcinoma (HCC). It is crucial to determine which patients could benefit most from combination therapy. This study aims to examine the associations of sarcopenia and systemic inflammation response index (SIRI) with the treatment responses and efficacies in patients with HCC treated with ICIs and tyrosine kinase inhibitors TKIs, as well as investigate the correlation between sarcopenia and inflammatory or immune states.MethodsWe reviewed 160 patients with HCC treated with TKIs and ICIs. The patients’ psoas muscle size was measured on axial computed tomography scans and normalized for the patients’ height squared. This value was referred to as the psoas muscle index (PMI). Sarcopenia was determined from PMI and their relationships with patients’ clinicopathological characteristics, inflammation indexes, peripheral blood T-cell subsets and survival were evaluated.ResultsSarcopenia and systemic inflammation response index (SIRI) were independent predictors for overall survival and progression-free survival. Patients with high PMI and low SIRI demonstrated significantly better median overall survival and progression-free survival (36.0 months and 9.6 months, respectively) than those with either low PMI or high SIRI (20.8 months and 6.0 months, respectively) and those with both high SIRI and low PMI (18.6 months and 3.0 months, respectively). Portal vein tumor thrombus (P=0.003), eastern cooperative oncology group performance status score of 1 (P=0.048), high alkaline phosphatase (P=0.037), high neutrophil-to-lymphocyte ratio (NLR) (P=0.012), low lymphocyte-to-monocyte ratio (LMR) (P=0.031), high platelet-to-lymphocyte ratio (PLR) (P=0.022) and high SIRI (P=0.012) were closely associated with an increased incidence of sarcopenia. PMI was negatively correlated with SIRI (r = -0.175, P=0.003), NLR (r = -0.169, P=0.036), and PLR (r = -0.328, P=0.000) and was significantly positively correlated with LMR (r = 0.232, P=0.004). The CD3+ and CD4+ T-cell counts of the high PMI group were significantly higher than those of the low PMI group.ConclusionSarcopenia and high SIRI were associated with reduced survival in patients with HCC treated with ICIs and TKIs. Sarcopenia could affect inflammatory states and the immune microenvironment.

Underwater vehicles are key carriers for underwater inspection and operation tasks, and the successful implementation of these tasks depends on the positioning and navigation equipment with corresponding accuracy. In practice, multiple positioning and navigation devices are often combined to integrate the advantages of each equipment. Currently, the most common method for integrated navigation is combination of the Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL). Various errors will occur when SINS and DVL are combined together, such as installation declination. In addition, DVL itself also has errors in the measurement of speed. These errors will affect the final accuracy of the combined positioning and navigation system. Therefore, error correction technology has great significance for underwater inspection and operation tasks. This paper takes the SINS/DVL integrated positioning and navigation system as the research object and deeply studies the DVL error correction technology in the integrated system.

In recent years, the industrial motor bearing fault diagnosis method based on deep learning and multi-source information fusion has made some research progress, and research results show that the uncertainty of noise interference and signal measurement error has been improved to a certain extent. However, the multi-source heterogeneous information of industrial motor bearings not only improves the stability and fault tolerance of the bearing fault diagnosis model but also brings conflicts in information fusion. If the conflicts caused by multi-source information cannot be reasonably resolved, it will be difficult to make further judgments on the bearing faults of industrial motors. Therefore, solving the multi-source information conflict effectively while fully using the complementarity of bearing multi-source heterogeneous information is an urgent problem to be solved in developing industrial motor-bearing fault diagnosis technology. This paper proposes an industrial motor bearing fault diagnosis algorithm based on multi-local model decision conflict resolution (MLMF-CR) to fully integrate multi-source heterogeneous information and reasonably resolve multi-source information conflicts. After the initial characteristic signal selection and cleaning of the vibration and current signals of industrial motor bearings, the algorithm deeply excavates the characteristic information of the bearing signals in each fault state through the local fault diagnosis model based on the bidirectional long short-term memory network (Bi-LSTM) and forms a local diagnosis. After the decision is made, evidence theory is used for fusion. In addition, the high conflict situation that may occur in the process of decision-making fusion is also considered. To this end, the trust degree distribution is introduced to reduce information conflict. Specifically, according to the difference in the sensitivity and reliability of bearing faults under different operating environments or specific conditions, the degree of difference in faults is refined into balanced sensitivity and unbalanced sensitivity. When the fault sensitivity is balanced, the trust of different information sources is quantified by support and uncertainty. When the sensitivity is unbalanced, gray relational analysis is used to assign trust degrees to different information sources. The algorithm can effectively resolve the high degree of conflict in the decision-making fusion process while considering the complementarity of multi-source heterogeneous information. Experiments evaluate the effectiveness of the proposed method.

Autonomous underwater vehicles (AUVs)-assisted mobile data collection in underwater wireless sensor networks (UWSNs) has received significant attention because of their mobility and flexibility. To satisfy the increasing demand of diverse application requirements for underwater data collection, such as time-sensitive data freshness, emergency event security as well as energy efficiency, in this paper, we propose a novel multi-modal AUV-assisted data collection scheme which integrates both acoustic and optical technologies and takes advantage of their complementary strengths in terms of communication distance and data rate. In this scheme, we consider the age of information (AoI) of the data packet, node transmission energy as well as energy consumption of the AUV movement, and we make a trade-off between them to retrieve data in a timely and reliable manner. To optimize these, we leverage a deep reinforcement learning (DRL) approach to find the optimal motion trajectory of AUV by selecting the suitable communication options. In addition to that, we also design an optimal angle steering algorithm for AUV navigation under different communication scenarios to reduce energy consumption further. We conduct extensive simulations to verify the effectiveness of the proposed scheme, and the results show that the proposed scheme can significantly reduce the weighted sum of AoI as well as energy consumption.

In modern transportation, traffic congestion has become an urgent problem in large and medium-sized cities. In smart transportation systems, it is an effective solution to design load balancing path planning algorithms that can dynamically adapt to traffic conditions in order to avoid congestion. In this work, a traffic path planning algorithm based on data prediction (TPPDP) is proposed to find the path with the shortest travel time, which is built on a predictive model based on historical traffic data and current traffic information. Furthermore, a path planning algorithm based on data prediction with load balancing (TPPDP-LB) is also proposed, which combines the predicted information and the number of concurrent requests to achieve the path with shortest travel time while maintaining global load balancing. A specific distributed computing framework for TPPDP-LB algorithm is designed to reduce the runtime of the algorithm. The simulation results proved that both TPPDP and TPPDP-LB algorithms have the advantage of shortest travel time, and TPPDP-LB algorithm achieves load balancing of computing. It is also proved that the distributed computing framework designed for TPPDP-LP algorithm can effectively reduce the runtime of system as well as keep the accuracy of algorithm.

Cloud computing is one of the most successful business models for providing a simple pay-as-you-go, and therefore is gaining much popularity in the industry. Customers and enterprises can maintain or scale-up a business easily while cutting down on their budget. However, energy consumption is one of the biggest problems in current cloud computing. It is both essential and urgent for governmental and industrial institutions to address this, to achieve rapid growth. The development of energy-efficient cloud computing has to be taken into consideration, which relies on the development of several key technologies: More energy-efficient mediums can be used in cloud computing at the platform level; energy-efficient scheduling algorithms, memory systems, storage systems, resource management policies, etc., can be adopted at the hypervisor level; energy-efficient scheduling, communications, and applications can be applied at the virtual machine level; and energy-efficient mobile cloud computing will be developed, involving green networking and wireless communications, cloud-based mobile applications, and limited resources management.

Serious challenges for to drive agricultural sustainability combined with climate crisis issues have induced an urgent need to decarbonise agriculture. In this paper, we briefly introduce a novel concept of the Photovoltaic Agricultural Internet of Things (PAIoT). This system approach fuses agricultural production with renewable power generation and control via an IoT platform. We discuss PAIoT applications and potential to realize the next generation of smart farming. In addition, we provide a review of key issues on the feasibility of PAIoT and further propose novel techniques to mitigate these key issues.

Abstract Background Helitrons play an important role in shaping eukaryotic genomes due to their ability to transfer horizontally between distantly related species and capture gene fragments during the transposition. However, the mechanisms of horizontal transfer (HT) and the process of gene fragment capturing of Helitrons still remain to be further clarified. Results Here, we characterized a novel Helitron family discontinuously distributed in 27 out of 256 insect genomes. The most prominent characteristic of Hel1 family is its high sequence similarity among species of different insect orders. Related elements were also identified in two spiders, representing the first report of spider Helitrons. All these elements were classified into 2 families, 9 subfamilies and 35 exemplars based on our new classification criteria. Autonomous partners of Helitron were reconstructed in the genomes of three insects and one spider. Integration pattern analysis showed that majority of Hel1A elements in Papilio xuthus and Pieris rapae inserted into introns. Consistent with filler DNA model, stepwise sequence acquisition was observed in Sfru_Hel1Aa, Sfru_Hel1Ab and Sfru_Hel1Ac in Spodoptera frugiperda. Remarkably, the evidence that Prap_Hel1Aa in a Lepdidoptera insect, Pieris rapae, was derived from Cves_Hel1Aa in a parasitoid wasp, Cotesia vestalis, suggested the role of nonregular host-parasite interactions in HT of Helitrons. Conclusions We proposed a modified classification criteria of Helitrons based on the important role of the 5′-end of Helitrons in transposition, and provided evidence for stepwise sequence acquisition and recurrent HT of a novel Helitron family. Our findings of the nonregular host-parasite interactions may be more conducive to the HT of transposons.

It is critical to detect malicious code for the security of the Internet of Things (IoT). Therefore, this work proposes a malicious code detection algorithm based on the novel feature fusion–malware image convolutional neural network (FF-MICNN). This method combines a feature fusion algorithm with deep learning. First, the malicious code is transformed into grayscale image features by image technology, after which the opcode sequence features of the malicious code are extracted by the n-gram technique, and the global and local features are fused by feature fusion technology. The fused features are input into FF-MICNN for training, and an appropriate classifier is selected for detection. The results of experiments show that the proposed algorithm exhibits improvements in its detection speed, the comprehensiveness of features, and accuracy as compared with other algorithms. The accuracy rate of the proposed algorithm is also 0.2% better than that of a detection algorithm based on a single feature.

In this paper, we study data transmission in the Terrestrial–Satellite Integrated Network (TSIN), where terrestrial networks and satellites are combined together to provide seamless global network services for ground users. However, efficiency of the data transmission is limited by the time-varying inter-satellite link connection and intermittent terrestrial–satellite link connection. Therefore, we propose a link-state aware hybrid routing algorithm, which selects the integrated data transmission path adaptively in this paper. First of all, a space–time topology model is constructed to represent the dynamic link connections in TSIN. Thus, the transmission delay can be analyzed accordingly, and the data transmission problem can then be formulated. To balance the effectiveness and accuracy of searching a hybrid path, we carefully discuss the optimization of space–time topology updating, and propose an inter-satellite link selection algorithm. For the terrestrial–satellite link in hybrid routing, the data transmission problem is transformed into a weighted bipartite graph matching problem and solved with a Kuhn–Munkres-based link selection algorithm. To verify the effectiveness of our proposed routing algorithm, extensive simulations are conducted based on a realistic Hongyun constellation project. Results show that the network performance is improved with respect to data transmission delay, packet loss rate, and throughput.

In cognitive radio networks (CRNs), if two unlicensed secondary users (SUs) want to communicate with each other, they need to rendezvous with each other on the same channel at the same time. Rendezvous is the first key step for SUs to be able to communicate with each other. Channel hopping (CH) is a representative technique to solve the rendezvous problem in CRNs. SUs equipped with multiple radios can significantly reduce the time-to-rendezvous (TTR) for several existing CH algorithms while the additional cost is low. However, several drawbacks exist in the existing CH algorithms based on multiple radios. One of the main drawbacks is that they cannot be well applied in heterogeneous CRNs. The reason is that the number of radios for different SUs is implicitly assumed same or must be more than one in the existing CH algorithms based on multiple radios, which is unrealistic for heterogeneous CRNs. In heterogeneous CRNs, SUs may be equipped with different numbers of radios including one radio. To mainly address the above issue, hybrid radios rendezvous (HRR) algorithm is proposed in this paper. Moreover, the upper bounds of maximum TTR (MTTR) for the HRR algorithm are derived by a theoretical analysis. Furthermore, extensive simulations are performed to evaluate the expected TTR (ETTR), the MTTR, and the channel qualities of the rendezvous channels for the HRR algorithm. Simulation results show that rendezvous can be guaranteed by the HRR algorithm in heterogenous CRNs. Besides, the qualities of the rendezvous channels can be improved by the HRR algorithm. In addition, our algorithms can achieve rendezvous faster than several existing algorithms.

Serviceability is the ability of a network to serve user equipments (UEs) within desired requirements (e.g., throughput, delay, and packet loss). High serviceability is considered as one of the key foundational criteria towards a successful fog radio access infrastructure satisfying the Internet of Things paradigm in the 5G era. In the article by Dao et al., "Adaptive resource balancing for serviceability maximization in fog radio access networks," the authors propose an adaptive resource balancing (ARB) scheme for serviceability maximization in fog radio access networks wherein the resource block (RB) utilization among remote radio heads (RRHs) is balanced using the backpressure algorithm with respect to a time-varying network topology issued by potential RRH motilities. The optimal UE selection for service migration from a high-RB-utilization RRH to its neighboring low RB-utilization RRHs is determined by the Hungarian method to minimize RB occupation after moving the service. Analytical results reveal that the proposed ARB scheme provides substantial gains compared to the standalone capacity-aware, max-rate, and cache-aware UE association approaches in terms of serviceability, availability, and throughput.

Big data processing technologies, e.g., multi-sensor data fusion and cloud computing are being widely used in research, development, manufacturing, health monitoring and maintenance of aero-engines, driven by the ever-rapid development of intelligent manufacturing and Industrial Internet of Things (IIoT). This has promoted rapid development of the aircraft engine industry, increasing the aircraft engine safety, reliability and intelligence. At present, the aero-engine data computing and processing platform used in the industrial Internet of things is not complete, and the numerical calculation and control of aero-engine are inseparable from the linear model, while the existing aero-engine model linearization method is not accurate enough to quickly calculate the dynamic process parameters of the engine. Therefore, in this paper, we propose a linear model of turbofan engine for intelligent analysis in IIoT, with the aim to provide a new perspective for the analysis of engine dynamics. The construction of the proposed model includes three steps: First, a nonlinear mathematical model of a turbofan engine is established by adopting the component modeling approach. Then, numerous parameters of the turbofan engine components and their operating data are obtained by simulating various working conditions. Finally, based on the simulated data for the engine under these conditions, the model at the points during the dynamic process is linearized, such that a dynamic real-time linearized model of turbofan engine is obtained. Simulation results show that the proposed model can accurately depict the dynamic process of the turbofan engine and provide a valuable reference for designing the aero-engine control system and supporting intelligent analysis in IIoT.

The fault prediction problem for modern industrial equipment is a hot topic in current research. So, this paper first proposes a dynamic cuckoo search algorithm. The algorithm improves the step size and discovery probability. Then, it introduces the change trend of fitness function value into the step size update formula to balance the search speed and accuracy. At the same time, the algorithm initial global search step is larger, while the step size of the local search is smaller in the latter part of the algorithm. In the process of discovering the global optimal solution, the probability of preserving the offspring with good fitness is increased, and the uncertainty of preference random walk is improved. As the search progresses, the probability of discovery is reduced, which makes it easy to produce new individuals in the later stage of evolution. Based on this, a BP neural network prediction model based on dynamic cuckoo search algorithm optimization is established. And the experimental results show that the proposed prediction model has faster convergence and higher accuracy.

It was observed that when the short vertical line array (SVLA) is located in the deeper part of the water column, where sound velocity is lower, a groove always exists in the receiving array response in the horizontal direction for distant sound sources in the shallower part of the water column, where the sound velocity is higher. Normal mode modeling is used to explain this result. According to the normal mode theory, the receiving array response of the SVLA to a distant sound source can be expressed in terms of modal beams weighted in accordance with the normal mode amplitude. This modal representation offers a physical interpretation of the receiving array response to a distant sound source in terms of normal modes. The environmental effects of the shape of the sound velocity profile and geo-acoustic properties of the seabed on the receiving array response are analyzed. Based on the results, three conditions for the existence of the groove in the receiving array response are obtained: 1) a gradient in the sound velocity profile, 2) an SVLA in a water column in which the sound velocity is lower and low-order normal modes are trapped, and 3) a distant sound source in a shallow water column in which the sound velocity is higher, and acoustic source couples weakly with low-order normal modes and strongly with high-order normal modes. Finally, the receiving array response of the SVLA to ocean noise and distant sound source are analyzed and discussed using the Mediterranean Sea data. It is shown that the receiving array response to ocean noise differs from that to a distant sound source. Utilizing this difference, the array can be steered carefully to improve the output signal-to-noise ratio and increase the passive detection range against a submerged target in shallow water.

This paper studies the consensus of multi-agent systems with piecewise continuous time-varying topology. The agents are assumed to have identical first-order linear dynamics, which their underlying communication topology is piecewise continuous time-varying. In the case of undirected time-varying communication topology, the consensus of the multi-agent system depends on the connectivity of its limit topology, and the states of all agents converge to the mean of their initial states. However, the consensus depends on the absolute integrability of the elements in the difference matrix between the Laplacian matrix and the limit matrix when the communication topology is directed and connected. Several simulation examples are presented to validate the proposed theories.

The Internet of Things (IoT) for seismicity in underground mine consists of a combination of sensor network hardware and software, which allows the collection of seismic data and data processing for basic event parameters. A seismic moment tensor is one of the essential parameters in evaluating the seismic hazard. The reliability of the resolved seismic moment tensor depends on numerous factors, and it is important to understand the influence of these disturbances. We focused on influencing factors, including the azimuthal coverage, the accuracy of source coordinate, the mismodeling of velocity structure, and the noise contamination. A set of moment tensor inversions using synthetic data simulated for a double couple source and a superimposed source from virtual IoT was carried out to test the effects of different factors. We concluded that the sensitivity of the moment tensor inversion to the azimuthal coverage is closely related to the type of the source. For the shear event, the inversion does not require a good azimuthal coverage, but for the non-pure shear event, at least 90° azimuthal coverage is required to get the reliable solution. The full moment tensor is not sensitive to the location accuracy when the source error is less than 60 m, regardless of the type of source. But the double couple deviation increases with the growing error of the location, especially for the fault-slip-related or the shear rupture-dominated event. The erroneous velocity models show nearly no influence on the full moment tensor inversion results. The fit of amplitude spectra does not require a precise alignment of the observed and the synthetic waveforms and is less dependent on the chosen velocity model. The superimposed source is more sensitive to noise than the pure shear source. Although the noise does not necessarily affect the fault plane solution of the event, it is capable to lead an inaccurate seismic moment tensor and mislead the interpretation of the source type.

In order to reduce power and energy costs, giant cloud providers now mix online and batch jobs on the same cluster. Although the co-allocation of such jobs improves machine utilization, it challenges the data center scheduler and workload assignment in terms of quality of service, fault tolerance, and failure recovery, especially for latency critical online services. In this paper, we explore various characteristics of co-allocated online services and batch jobs from a production cluster containing 1.3k servers in Alibaba Cloud. From the trace data, we find the following: 1) For batch jobs with multiple tasks and instances, 50.8% failed tasks wait and halted after a very long time interval when their first and the only one instance fails. This wastes much time and resources as the remaining instances are running for an impossible successful termination. 2) For online services jobs, they are clustered in 25 categories according to their requested CPU, memory, and disk resources. Such clustering can help the co-allocation of online services jobs with batch jobs. 3) Servers are clustered into seven groups by CPU utilization, memory utilization, and their correlations. Machines with a strong correlation between CPU and memory utilization provides an opportunity for job co-allocation and resource utilization estimation. 4) The MTBF (mean time between failures) of instances are in the interval [400, 800] seconds while the average completion time of the 99th percentile is 1003 seconds. We also compare the cumulative distribution functions of jobs and servers and explain the differences and opportunities for workload assignment between them. Our findings and insights presented in this paper can help the community and data center operators better understand the workload characteristics, improve resource utilization, and failure recovery design.

Today, a wide variety of applications with different requirements are rapidly developed in industrial wireless sensor networks, and providing the Quality of Service (QoS) for this kind of communication network is inevitable. It is difficult to solve the problems of poor adaptability and difficulty in the implementation of the QoS-based network configuration and management in traditional network architecture. We present a hierarchical software-defined network architecture for wireless sensor networks, which makes the complex network management possible and the system more adaptable. Furthermore, we propose a QoS-based routing protocol, called QSDN-WISE, which consists of a clustering algorithm, a routing algorithm, and local network maintenance. A double-cluster head-based uneven clustering algorithm, called DCHUC, avoids the energy hole phenomenon and reduces the workload of a single cluster head. The centralized QSDN-WISE routing algorithm constructs two heterogeneous forwarding paths for nodes, which meets the requirements for different data levels. Local network maintenance reduces the number of control messages in the network. The simulation results indicate that the QSDN-WISE can provide the QoS support for data with different requirements, balance the network energy consumption, and prolong the network's lifetime.

Owing to its high performance, small size, and low energy consumption, NAND flash memory has been extensively adopted in cyber-physical systems. However, the inherent characteristics of flash memory, including not-in-place update and asymmetric I/O latencies, present difficulties in the design of buffer management policies. In this paper, we propose an enhanced buffer management policy for the flash memory referred to as dynamic page weight least recently used (DPW-LRU), which considers temporal locality and simultaneously provides effective utilization of limited buffer resources. Page migration is further enhanced by identifying the page access mode and frequency while separating the buffer into two different regions. A novel eviction algorithm is also designed to reduce the write operations and maintain a high hit ratio of the buffer regions, combining dynamic temporal locality, real-time eviction cost, and recency of pages. The experimental results show that DPW-LRU improves the hit ratio by up to 8.3%, decreases the write operation by up to 22.6%, and reduces the overall latency by up to 18.8% relative to those of other state-of-the-art buffers management policies.

Ultrashort baseline positioning systems are used to localize manned deep-sea submersibles. A long baseline positioning system is a significant supplement to an ultrashort baseline positioning system and provides more precise positioning. The long baseline beacon array design applied is a primary factor affecting the accuracy of long baseline positioning. However, beacon array designs have mainly been studied in autonomous underwater vehicles (AUVs). Because the characteristics and dive tasks between AUVs and manned deep-sea submersibles are different, the simple beacon array formations used in AUVs cannot be directly used in manned deep-sea submersibles. To the best of our knowledge, this study is the first to present the optimal design of a beacon array for a long baseline positioning system used in manned deep-sea submersibles. In this paper, based on the characteristics of a manned deep-sea submersible, the seven basic principles used for the optimal design of a long baseline beacon array are presented. First, the dive tasks of a JIAOLONG manned deep-sea submersible are analyzed, including the relationship between the dive survey lines, and the depth and distance of each line. Second, we explore the minimum beacon number to cover the dive site and its vicinity. Third, we adjust the beacon position based on the seven optimal design principles. The beacon array is designed to satisfy manned deep-sea submersible dive requirements during the JIAOLONG Test Applications Voyage 2013. A sea trial demonstrates that the long baseline positioning results are reliable if at least three beacons are not blocked, and the accuracy of the long baseline positioning system is better than that of an ultrashort baseline.

In multi-function underwater wireless sensor networks (UWSNs), multiple applications share the same physical infrastructure to fully exploit network resources. Under such scenarios, diverse packets coexist in the same network which require differentiated delivery strategies to satisfy application demands, such as ocean monitoring packets and multimedia-based intrusion detection packets (e.g., video streams, voice or images) demand versatile support for data forwarding operation. However, the limited bandwidth and high propagation delay of acoustic channel pose great challenges to satisfy such demands. A feasible solution to solve such a problem is to exploit multimodal networks which integrate complementary acoustic and non-acoustic technologies to enhance transmission capability. Therefore, in this paper, we leverage both surface wireless radio frequency (RF) and underwater acoustic technology to fulfill different performance requirements of underwater sensor networks. We first propose two multimodal acoustic-RF adaptive routing schemes, and identify the major factors which influence the performance of these adaptive protocols. Then, we conduct extensive evaluations of the algorithms for both grid and random deployment scenarios. The simulation results confirm the feasibility of the proposed multimodal acoustic-RF routing protocols under diverse communication scenarios and channels.

Cloud computing is popular in nowadays for its convenient and cheap. Grid provides services of available everywhere, and shares everyone. Therefore, smart grid cloud is a good way to manage data for sharing with all power supply stations. Grid cloud task scheduling is one of the key technologies that affect resource allocation efficiency in cloud computing environment. The advantages and disadvantages of scheduling algorithms will directly affect the scheduling performance of both cloud computing and the stability of the entire system platform. Cloud task scheduling problem has been proved to be a NP-hard problem, The traditional task scheduling algorithm can no longer meet the actual needs of cloud task scheduling, but the Heuristic algorithm is an effective method to solve this problem. This paper studies and analyzes the application of heuristic algorithms in cloud task scheduling problems, and proposes a cloud task scheduling strategy to minimize the task completion time and execution cost (MCTE) for the smart grid cloud. Then, carry out mathematical modeling on the grid cloud task scheduling problem. The experimental results show MCTE is well for the smart grid cloud.

A novel frequency-dependent wall insertion loss model at 3–6 GHz is proposed in this paper. The frequency-dependence of the wall insertion loss is modeled by the Fourier triangular basis neural network. A method to determine the optimal weighted vector and the number of the neurons is introduced. In addition, the impact of the wider continuous spectrum on the wall insertion loss is analyzed and extensive measurements are performed to validate the proposed model. The results obtained with the proposed model match better with the measured results than other models. The proposed model can be used in future indoor Internet-of-Things applications such as service computing.

Time synchronization is a cooperative work's foundation among underwater sensor networks' nodes; it takes a crucial role in the application and the study of underwater sensor networks. Several time synchronization protocols have been presented for terrestrial radio networks, but they cannot be instantly utilized to real-time underwater sensor networks due to the slow propagation speed of the underwater acoustic signals, the mobility among sensor nodes, and the energy limitation in the underwater wireless sensor networks. In this paper, we propose an adaptive power-efficient time synchronization for mobile underwater sensor networks, called APE-Sync. The proposed scheme takes into account the time-varying clock skew and combines the Doppler-Enhanced synchronization protocol (DE-Sync) and the Kalman filter tracking the clock skew to achieve time synchronization. The proposed scheme also helps in reducing the energy consumption of the nodes by guaranteeing accurate time synchronization. The simulation results are presented, which show that APE-Sync outperforms existent time synchronization schemes in both power efficiency and precision.

Over recent years, traditional manufacturing factories have been accelerating their transformation and upgrade toward smart factories, which are an important concept within Industry 4.0. As a key communication technology in the industrial internet architecture, time-sensitive networks (TSNs) can break through communication barriers between subsystems within smart factories and form a common network for various network flows. Traditional routing algorithms are not applicable for this novel type of network, as they cause unnecessary congestion and latency. Therefore, this study examined the classification of TSN flows in smart factories, converted the routing problem into two graphical problems, and proposed two heuristic optimization algorithms, namely GATTRP and AACO, to find the optimal solution. The experiments showed that the algorithms proposed in this paper could provide a more reasonable routing arrangement for various TSN flows with different time sensitivities. The algorithms could effectively reduce the overall delay by up to 74% and 41%, respectively, with promising operating performances.

Abstract Industrial wireless sensor networks (IWSNs) have been widely used in many application scenarios, and data collection is an extremely significant part of IWSNs. Moreover, a mobile sink is widely used in industrial wireless sensor networks to collect sensory data and alleviate the “hot spot” problem effectively. However, usage of a mobile sink introduces some challenges, such as updating of a mobile sink’s location and planning of a mobile sink’s trajectory. Meanwhile, the impact of different distribution types of events on data collection has not been sufficiently valued in designing of data collection algorithm for IWSNs yet. To overcome these challenges, a virtual grid-based real-time data collection algorithm for applications with centrally distributed events (VGDCA-C) is proposed in this paper to gain a reliable data gathering for IWSNs. In the target application scenarios, the events are distributed centrally, so we mainly focus on how to shorten the routing paths and decrease the transmission delay. In our VGDCA-C, a mobile sink can adjust its movement dynamically according to the changes in event areas. The adjustment of a mobile sink movement strategy includes two aspects. The first one is the dynamic adjustment of a mobile sink’s parking time, and the second one denotes the moving toward event area of a mobile sink. Thus, a mobile sink adjusts its location such that it can get closer to the event area. Hence, the total length of routing is getting shorter so that source nodes can upload sensory data faster. Analysis and simulation results show that compared with the existing work, our VGDCA-C increases the network lifetime and decreases transmission delay.

With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.

Transformers have become popular in building end-to-end automatic speech recognition (ASR) systems. However, transformer ASR systems are usually trained to give output sequences in the left-to-right order, disregarding the right-to-left context. Currently, the existing transformer-based ASR systems that employ two decoders for bidirectional decoding are complex in terms of computation and optimization. The existing ASR transformer with a single decoder for bidirectional decoding requires extra methods (such as a self-mask) to resolve the problem of information leakage in the attention mechanism This paper explores different options for the development of a speech transformer that utilizes a single decoder equipped with bidirectional context embedding (BCE) for bidirectional decoding. The decoding direction, which is set up at the input level, enables the model to attend to different directional contexts without extra decoders and also alleviates any information leakage. The effectiveness of this method was verified with a bidirectional beam search method that generates bidirectional output sequences and determines the best hypothesis according to the output score. We achieved a word error rate (WER) of 7.65%/18.97% on the clean/other LibriSpeech test set, outperforming the left-to-right decoding style in our work by 3.17%/3.47%. The results are also close to, or better than, other state-of-the-art end-to-end models.

Information on the stage of liver fibrosis is essential for decisions on antiviral treatment for chronic hepatitis B virus (HBV). This paper aims to establish a non-invasive assessment model with serum markers using cloud computing and the Internet of Things for the evaluation of liver disease severity and its prognosis. Based on the Internet of Things, the multiple and key information system of liver fibrosis or cirrhosis are constructed using the serum markers data. In the cloud platform, the probability density functions of indexes are used to select the optimized indicators. The logistic regression is used to establish the non-invasive assessment model. The patients were selected with CHB and underwent liver biopsy in the Second Xiangya Hospital, Central South University. There are two inclusion criteria: first, the patient received a liver biopsy according to “Proclaim Prevention and Cure Guide For Chronic Hepatitis B”of Chinese Medical Association in 2015; second, the patient has a history of hepatitis B or HBV surface antigen (HBsAg) positive more than six months, and HBsAg and (or) HBV DNA is still positive. Results of clinical data applications show that the accuracy of the non-invasive assessment model reaches greater than 70% for the recognition of significant liver fibrosis. In addition, the discriminant accuracy can be improved by increasing the number of indicators. The established non-invasive assessment model can be used for auxiliary clinical diagnosis after the further validation.