Search

Abstract Secondary lung injury after SCI is a major cause of patient mortality, with apoptosis playing a key role. This study aimed to explore the impact of treadmill training and miR145-5p on the MAPK/Erk signaling pathway and apoptosis in rats with complete SCI. SD rats were used to establish T10 segmental complete SCI models and underwent treadmill training 3, 7, or 14 days postinjury. Various techniques including arterial blood gas analysis, lung wet/dry weight ratio, HE staining, immunofluorescence staining, immunohistochemical staining, qRT-PCR, and Western blotting were employed to assess alterations in lung function and the expression levels of crucial apoptosis-related factors. In order to elucidate the specific mechanism, the impact of miR145-5p on the MAPK/Erk pathway and its role in apoptosis in lung cells were confirmed through miR145-5p overexpression and knockdown experiments. Following spinal cord injury (SCI), an increase in apoptosis, activation of the MAPK/Erk pathway, and impairment of lung function were observed in SCI rats. Conversely, treadmill training resulted in a reduction in alveolar cell apoptosis, suppression of the MAPK/Erk pathway, and enhancement of lung function. The gene MAP3K3 was identified as a target of miR145-5p. The influence of miR145-5p on the MAPK/Erk pathway and its impact on apoptosis in alveolar cells were confirmed through the manipulation of miR145-5p expression levels. The upregulation of miR145-5p in spinal cord injury (SCI) rats led to a reduction in MAP3K3 protein expression within lung tissues, thereby inhibiting the MAPK/Erk signaling pathway and decreasing apoptosis. Contrarily, rats with miR145-5p knockdown undergoing treadmill training exhibited an increase in miR145-5p expression levels, resulting in the inhibition of MAP3K3 protein expression in lung tissues, suppression of the MAPK/Erk pathway, and mitigation of lung cell apoptosis. Ultimately, the findings suggest that treadmill training may attenuate apoptosis in lung cells post-spinal cord injury by modulating the MAP3K3 protein through miR145-5p to regulate the MAPK/Erk signaling pathway.

BackgroundSensory symptoms linked to tic disorder (TD) are challenging to quantify via self- or parent-reported measures. The current study aimed to develop a novel observer-rated semi-structured interview, namely, the Sensory Phenomenon Assessment Scale (SPAS), to aid clinical evaluation on symptoms of TD among children.MethodsTo test its psychometric properties, tic, premonitory urge (PU), and obsessive–compulsive symptoms (OCS) were also assessed in 223 children via the Yale Global Tic Severity Scale (YGTSS), the Premonitory Urge for Tic Scale (PUTS), and the Children’s Yale–Brown Obsessive–Compulsive Scale (CY-BOCS). Factor analysis and internal consistency test were carried out using data from TD-diagnosed individuals.ResultsGood internal consistency and test–retest reliability were observed. Criterion validity was established by significant correlations between the PUTS, the YGTSS, the CY-BOCS, and scores of the SPAS. Factor analyses supported a single-factor model of the SPAS, in which the five items each showed a factor loading above 0.6.ConclusionThis study demonstrated that the SPAS is reliable and valid and, thus, can serve as a good and concise measure of clinical symptoms among children and adolescents with TD.

Pulmonary fibrosis (PF) is a chronic and progressive disease characterized by fibrosis and interstitial pneumonia. It has similar clinical symptoms to “Fei Bi” and “Fei Wei” as described in the traditional Chinese medicine (TCM) classic Jingui Yaolue written by Zhang Zhongjing in the Han Dynasty. This study explored the potential of Maimendong Decoction (MMDD). MMDD consists of Ophiopogon japonicus (L.f) (ophiopogonis), Pinellia ternata (Thunb.) Breit. (pinellia), Panax ginseng C. A. Mey. (ginseng), Glycyrrhiza uralensis Fisch. (glycyrrhiza), Zizi phus jujuba Mill. (jujuba), and Oryza sativa L. (oryza sativa), with the function of nourishing the lung and stomach, and reducing the effect of reverse qi. It has been used clinically for over two thousand years to treat conditions like “Fei Bi” and “Fei Wei”. Previous research suggests that MMDD and its individual herbal extracts have anti-fibrotic effects. The main focus of MMDD in treating PF is to reduce inflammatory cytokines, inhibit pro-fibrotic factors and oxidative stress, promote differentiation and homing of bone marrow mesenchymal stem cells, and enhance cell autophagy activity. This review summarized the clinical applications, mechanisms, and pharmacological effects of MMDD in treating PF based on existing clinical applications and experimental research. It also discussed current issues and prospects, aiming to provide a reference for further research on the mechanism of PF, drug development, and clinical trials.

Collaborative machine learning is considered as the bedrock of the intelligent B5G networks, where distributed agents collaborate with each other to train learning models in a distributed fashion, without sharing data at a central entity. Despite its broad applicability, the main issue of collaborative learning is the need of local computing to build local learning models as well as iterative information exchange among agents, which may lead to high resource overhead unaffordable in many practical resource-limited systems such as unmanned aerial vehicles (UAVs) and Internet of Things (IoT). To alleviate this resource issue, it is essential to devise resource-efficient collaborative learning techniques, that can optimize the resource overhead in terms of communication, computing, and energy cost, and hence achieve satisfactory optimization/learning performance simultaneously.

We investigate a data quality-aware dynamic client selection problem for multiple federated learning (FL) services in a wireless network, where each client offers dynamic datasets for the simultaneous training of multiple FL services, and each FL service demander has to pay for the clients under constrained monetary budgets. The problem is formalized as a non-cooperative Markov game over the training rounds. A multi-agent hybrid deep reinforcement learning-based algorithm is proposed to optimize the joint client selection and payment actions, while avoiding action conflicts. Simulation results indicate that our proposed algorithm can significantly improve training performance.

Accurately localizing a target in three-dimensional (3D) space assisted with the fifth generation (5G) wireless systems in an indoor environment could enable a wide variety of new applications, including precise control for factory automation, self-maneuver of the vehicle and so on. However, controlling a target in 3D space relies on a rigid body modelling with six degrees of freedom, which dramatically increases the localization difficulty and complexity. Furthermore, for radio-based localization methods, the lack of line-of-sight (LOS) and the existence of reflection points in the environment will also influence the rigid body localization process. To improve the rigid body localization accuracy as well as unravel useful environmental information from the received signal, a novel rigid body joint active localization and environment sensing scheme is proposed in this article. Specifically, the multi-path effect of millimeter wave (mm-wave) signal with a single reflection can be exploited to enhance the rigid body localization accuracy, and it can also be utilized to locate the reflection points, which further enables a new way for environmental sensing. Hence, we first propose a two-step hierarchical compressive sensing algorithm to extract the angular and distance information of the LOS (if available) and single-bounce specular reflections. Then a particle swarm optimization (PSO) based method is derived to recover the posture of the rigid body and the location of reflection points. The Cramér-Rao lower bound (CRLB) on angle, rigid body posture and reflection points position uncertainty is also analyzed. The simulation results demonstrate that the proposed scheme can achieve high accuracy rigid body localization and locate the reflection points around the rigid body even under obstructed-line-of-sight (OLOS) conditions in an indoor scene.

BackgroundVocal features have been exploited to distinguish depression from healthy controls. While there have been some claims for success, the degree to which changes in vocal features are specific to depression has not been systematically studied. Hence, we examined the performances of vocal features in differentiating depression from bipolar disorder (BD), schizophrenia and healthy controls, as well as pairwise classifications for the three disorders.MethodsWe sampled 32 bipolar disorder patients, 106 depression patients, 114 healthy controls, and 20 schizophrenia patients. We extracted i-vectors from Mel-frequency cepstrum coefficients (MFCCs), and built logistic regression models with ridge regularization and 5-fold cross-validation on the training set, then applied models to the test set. There were seven classification tasks: any disorder versus healthy controls; depression versus healthy controls; BD versus healthy controls; schizophrenia versus healthy controls; depression versus BD; depression versus schizophrenia; BD versus schizophrenia.ResultsThe area under curve (AUC) score for classifying depression and bipolar disorder was 0.5 (F-score = 0.44). For other comparisons, the AUC scores ranged from 0.75 to 0.92, and the F-scores ranged from 0.73 to 0.91. The model performance (AUC) of classifying depression and bipolar disorder was significantly worse than that of classifying bipolar disorder and schizophrenia (corrected p

Named entity recognition (NER) plays a crucial role in information extraction but faces challenges in the Chinese context. Especially in Chinese paleontology popular science, NER encounters difficulties, such as low recognition performance for long and nested entities, as well as the complexity of handling mixed Chinese–English texts. This study aims to enhance the performance of NER in this domain. We propose an approach based on the multi-head self-attention mechanism for integrating Chinese lexicon-level features; by integrating Chinese lexicon boundary and domain term frequency weight features, this method enhances the model’s perception of entity boundaries, relative positions, and types. To address training prediction inconsistency, we introduce a novel data augmentation method, generating enhanced data based on the difference set between all and sample entity types. Experiments on four Chinese datasets, namely Resume, Youku, SubDuIE, and our PPOST, show that our approach outperforms baselines, achieving F1-score improvements of 0.03%, 0.16%, 1.27%, and 2.28%, respectively. This research confirms the effectiveness of integrating Chinese lexicon boundary and domain term frequency weight features in NER. Our work provides valuable insights for improving the applicability and performance of NER in other Chinese domain scenarios.

Device and network coordination is critical for efficient radio resource (RR) utilization while meeting Quality of Service (QoS) requirements in heavily congested future heterogeneous wireless networks featured with 3-Dimensional (3D) small cells (SCs). Device and network coordination assisted opportunistic and coordinated use of RRs in distinct bands could dramatically improve the spectrum utilization in these networks. In this study, overall communication performance enhancement through better utilization of opportunistically available spatially distributed RRs in a 3D SC is addressed considering two co-located networks operated in licensed band (LB) and unlicensed band (UB) while jointly accounting for several related factors like 3D spatial positions and QoS requirements of the devices. To confront this problem, a device and network coordination assisted solution is developed using Q-learning and Slotted-ALOHA principles. Then, to maintain performance standards, device and network coordination aided scheduling, power control and access prioritization schemes are discussed. Subsequently, regret based learning assisted algorithm is presented for the UB to optimally utilize RRs. In these solutions, both device-network and network-network interactions are considered. In results, approximately 75% better overall coordination efficiency over conventional methods is shown at the initial iterations for the scenarios with the highest device density demonstrating attractive performance.

Due to the greatly increased bandwidth of 5G networks compared with that of 4G networks, the power consumption brought by baseband signal processing of 5G networks is much higher, which inevitably raises the operation expenditures. Cloud Radio Access Network (CRAN) is widely adopted in 5G networks, which splits the traditional base stations into Remote Radio Heads (RRHs) and Baseband Units (BBUs), which are equipped with computing resource for baseband signal processing. The number of required BBUs varies due to the fluctuation of wireless traffic of RRHs. Hence, fixed computing resource allocation might waste power. This paper investigates energy-efficient dynamic computing resource allocation in CRAN by predicting the wireless traffic of RRHs and allocating computing resource based on the prediction results aiming at using fewest BBUs to minimize power consumption. For wireless traffic prediction, a novel method based on two-dimensional CNN LSTM model with temporal aggregation is proposed. By treating the wireless traffic data as images, this model could extract spatial correlation from these data to improve accuracy. Moreover, the problem of dynamic computing resource allocation in CRAN is formulated as an offline four-constraint bin packing problem, considering both uplink and downlink baseband signal processing capacities of BBUs and Common Public Radio Interface (CPRI) bandwidths. For solving this problem, a Multi-start Simulated Annealing (MSA) algorithm is proposed. Simulation results demonstrate that the proposed method for wireless traffic prediction could outperform the state-of-the-art deep learning models. In addition, the proposed MSA algorithm could achieve lower power consumption than the state-of-the-art heuristic algorithms.

Solving the stability region in the plane motion of vehicles has become a hot research topic in vehicle handling stability under extreme conditions, but there is still a lack of research on the stability region under steering and braking conditions. In this paper, a five-degree-of-freedom (5DOF) nonlinear dynamic model of a vehicle with braking torque introduced is established, and the model is transformed into an equivalent system by using the D’Alembert principle. Then, the equilibrium points of the equivalent system are solved by using an improved hybrid algorithm combining the genetic algorithm (GA) and sequential quadratic programming (SQP) method. According to the bifurcation characteristics of the equilibrium points, the boundary of the stability region at the given initial longitudinal velocity is determined, and the three-dimensional stability region is fitted. Finally, the stability region of the equivalent system and the original system are analyzed by the energy dissipation method, and the stability region determined by the equilibrium point bifurcation method is verified by simulation. The results show that as the braking torque increases, the number of equilibrium points increase to three from one, the equilibrium bifurcation method proposed in this paper can effectively solve the stability region of the equivalent system, and the solution results are consistent with the original system stability region. When the limited braking torque is 500 N·m and the initial longitudinal velocity increases from 30 m/s to 50 m/s, the absolute value of the front wheel steering angle at the boundary point changes from less than 0.02 rad to more than 0.02 rad.

BackgroundTourette syndrome (TS) is a developmental neuropsychiatric disorder. Behavior therapy, especially habit reversal training (HRT), has gradually become regarded as one of the core therapies for TS. Mindfulness approaches can improve psychological adjustment and reduce stress and anxiety, suggesting potential benefits when incorporated into behavior therapy. To improve the efficacy of HRT, we combined it with mindfulness, an approach named mindfulness-based habitual reversal training (MHRT). The aim of this protocol is to investigate the efficacy and neural mechanisms of MHRT for TS.Methods/designWe will perform a randomized control trial (RCT) to evaluate the efficacy and neural mechanisms of MHRT. The sample will include 160 participants (including 120 patients with TS and 40 healthy controls). The patient sample will be randomly divided into three groups exposed to three different types of training: MHRT, HRT, and psychoeducation and supportive therapy (PST). Participants will be assessed and undergo resting-state fMRI scans at baseline and at the end of the 12-week training. The Yale Global Tic Severity Scale (YGTSS) and Premonitory Urge for Tic Scale (PUTS) will be used to assess the severity of tic symptoms and premonitory urges. The primary outcomes are change scores on the YGTSS and other assessments from baseline and the end of the training. The secondary outcomes are the neural correlates of these trainings among these groups based on graph theory, which is used to characterize brain functional connectivity networks. The default mode network (DMN) and the salience network (SN) will be assessed (which have been associated with mindfulness as well as the generation of tic symptoms) by network parameters, including clustering coefficients and shortest path lengths. Changes in these network parameters will be regarded as the neural correlates of the behavioral training.DiscussionMHRT was newly developed for the treatment of TS. MHRT may lead to greater reductions in tic severity than traditional HRT. Changes in the network parameters of the DMN and SN may show associations with the efficacy of MHRT.Clinical trial registrationhttp://www.chictr.org.cn, ChiCTR2100053077, China.

Vehicle stability control strategies can improve driving safety effectively; however, there is still a lack of unified evaluation criteria for different control strategies. This paper proposes a vehicle control strategy evaluation method based on the driving stability region and is analyzed by using direct yaw moment control (DYC) and four-wheel steering (4WS) as examples. Firstly, the five-degree-of-freedom (5DOF) vehicle system models including DYC and 4WS are established, and the effectiveness of the control strategies is verified by nonlinear analysis methods; the dynamic characteristics of the system are also analyzed. Following this, a hybrid algorithm combining the Genetic Algorithm (GA) and Sequential Quadratic Programming (SQP) methods is used to solve the system equilibrium points, and the driving stability regions under different control strategies are obtained. Finally, the driving stability regions are tested based on the CarSim and Simulink simulations, and the control performance is evaluated. The results indicate that DYC and 4WS can improve vehicle stability and expand the range of driving stability regions. When the initial longitudinal velocity is below 30 m/s, the driving stability regions under DYC and 4WS expand to different extents compared to the original driving stability region. The expanded driving stability regions show that the stability region of the vehicle with DYC is larger than that of 4WS; thus, the control effect of DYC is better than that of 4WS. The proposed method can be used to evaluate the effective range of different control strategies.

A sensor for measuring the crankshaft angle of the main engine in ships is designed. Compared with the existing crankshaft angle encoder, this design’s advantage is that there is no need to add a gear system at the free end of the crankshaft, reducing machining complexity. The purpose of providing high angle resolution over a wide speed range is achieved. Inductive angular displacement sensors (IADSs) require an eddy current magnetic field as a medium to generate the induced voltage. The induced voltage also requires a complex linearization calculation to obtain a linear relationship between angle and voltage. Therefore, a model of the inductive angular displacement sensor based on magnetic focusing (IADSMF) is proposed. Magnetic focusing is introduced into the IADS to replace the eddy current magnetic field with a focusing magnetic field. The main disadvantage of traditional IADSs, which is that they cannot reduce the eddy current magnetic field, is mitigated. An approximate square–shaped focusing magnetic field (12.4 × 12.4 mm2) is formed using the magnetic field constraint of the magnetic conductor. When the receiving coil undergoes a position change relative to the square–shaped focusing magnetic field, the voltage generated via the receiving coil is measured using the electromagnetic induction principle to achieve angular displacement measurement. A mathematical model of the IADSMF is derived. Induced voltages at different frequencies and rotational speeds are simulated and analyzed via MATLAB. The results show that frequency is the main factor affecting the induced voltage amplitude. The sensitivity of the IADSMF is 0.2023 mV/°. The resolution and measurement of the IADSMF range from 0.06° and 0–360°. Compared with a conventional planar coil–based IADS, the eddy current loss is reduced from 2.1304 to 0.3625 W. Direct linearization of the angular displacement with the induced voltage is achieved through designing a square–shaped focusing field and receiving coil. After optimizing the sensor structure with the optimization algorithm, the linearity error is 0.6012%. Finally, this sensor provides a theoretical basis and research ideas for IADS development in ships and navigation.

The accumulation of pollutants on the surface of a Concentrating Solar Power (CSP) station reduces the power generation efficiency of the whole power plant, affects electricity output, and decreases sales income; therefore, it is particularly important to develop a reasonable and effective cleaning process. Surface pollutants which have a strong interaction with the cleaning process of power stations can determine the quality of cleaning to a certain extent. In this paper, the pollutants on the surface of a trough CSP station with different usage times were collected and characterized using a scanning electron microscope (SEM), a particle size analyzer (PSA), and a transmission electron microscope (TEM). It was found that most of the surface pollutants were of a fine size and included amorphous particles which mainly resulted from the sedimentation of particles suspended for a short time or particles in the process of atmospheric circulation for a long time. Considering the service life of the mirror and the scarcity of water resources in the area where the trough CSP power station is located, a brush-based dry cleaning process with different cleaning times was developed. By comparing the changes in the reflectivity and micro-morphology of the mirror surface before and after cleaning, the feasibility and superiority of the brush-based dry cleaning process were fully confirmed.

The fifth-generation (5G) wireless communication networks are expected to play an essential role in the transformation of vertical industries. Among many exciting applications to be enabled by 5G, logistics tasks in industry parks can be performed more efficiently via vehicle-to-everything (V2X) communications. In this paper, a multi-layer collaboration framework enabled by V2X is proposed for logistics management in industrial parks. The proposed framework includes three layers: a perception and execution layer, a logistics layer, and a configuration layer. In addition to the collaboration among these three layers, this study addresses the collaboration among devices, edge servers, and cloud services. For effective logistics in industrial parks, task collaboration is achieved through four functions: environmental perception and map construction, task allocation, path planning, and vehicle movement. To dynamically coordinate these functions, device–edge–cloud collaboration, which is supported by 5G slices and V2X communication technology, is applied. Then, the analytical target cascading method is adopted to configure and evaluate the collaboration schemes of industrial parks. Finally, a logistics analytical case study in industrial parks is employed to demonstrate the feasibility of the proposed collaboration framework.

To reveal the heat source and its formation mechanism of the northern Yadong-Gulu rift (YGR), we analyzed the helium isotope, carbon isotope (δ13CCO2), and CO2/3He and CH4/3He ratios of hot spring gases for tracing the source of volatiles and discussing their geological significance. The results show the following: helium is mainly derived from the crust, and the radioactive decay of the thicker crust and granites provided more 4He to the low helium isotopes; thermal decomposition of carbonate rocks is the main source of CO2; CH4 may be of organic origin. To sum up, the gas geochemical characteristics of hot springs in the northern YGR indicate that the volatiles are mainly derived from the crust. The crust/mantle heat flow ratios (qc/qm) calculated by helium isotopes cover a range of 0.84–1.48, suggesting that the heat is mainly contributed by the crust. The crustal origin gas and heat flow demonstrates that the heat source beneath the northern YGR is formed by the process of interior crust. Combined with geophysical data, we suggest that the stress heat caused by the collision of the Indo-Eurasian plate and the radiant heating of the crust lead to the heat source (partial melting) and provide heat for thermal activities.

Abstract The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14 and 25 years, with symptoms of anxiety and/or depression. This project includes 122 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using the Smart technology to enhance care. The technology uses mobile questionnaires (QnairesTM) to help promote self‐assessment and track changes to support the plan of care. The youth were provided a smartphone and talk/text/data plan, if needed. The majority of participants identified themselves as Caucasian (73.5%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (73.6% and 66.9% respectively). Findings from the qualitative summary established that both staff and youth found having a smartphone and data plan beneficial. Demographic variables such as age, sex, mental health and physical health did not predict which youth were more likely to use the application.

Active Queue Management (AQM) has been considered as a paradigm for the complicated network management task of mitigating congestion by controlling buffer of network link queues. However, finding the right parameters for an AQM scheme is very challenging due to the dynamics of the IP networks. In addition, this problem becomes even more complex in inter-domain scenarios where several organizations interconnect each other with the limitation of not sharing raw and private data. As a result, existing AQM schemes have not been widely employed despite their advantages. Therefore, we present a solution that tackles the challenges of tuning the AQM parameters for inter-domain congestion control scenarios where the network management goes beyond an organization's domain. We then introduce the Federated Intelligence for AQM (FIAQM) architecture, which enhances the existing AQM schemes by leveraging the Federated Learning approach. The proposed FIAQM framework is capable of dynamically adjusting the AQM parameters in a multi-domain setting, which is hard to achieve with the conventional AQM solutions working alone. To this end, FIAQM uses an artificial neural network, trained in a federated manner, to predict beyond-own-domain congestion and an intelligent AQM parameter tuner. The evaluation results show that FIAQM can effectively improve the performance of the inter-domain connections by reducing the congestion on their links while preserving the network data private within each participating domain.

While the unmanned aerial vehicles (UAVs) swarm travels under a dynamic environment, the cluster head (CH) switching is unavoidable due to the mitigation of mobility, quality of service, and energy consumption. If an attacker becomes the new CH, the entire swarm will be controlled and the sensitive data will be leaked. Unlike the other mobile networks with constant network connectivity, the authentication in the UAV swarm suffers from intermittent connection with the ground station under a hostile environment or spectrum constraint condition. Hence, this paper proposes a novel CH safeguarding mechanism enabled by edge intelligence utilizing a situational-aware authentication scheme. This low-latency mechanism provides extra security at the CH selection and switching without cloud server support. By adopting the unique cross-layer attributes, the system security is significantly improved based on the extracted multi-dimensional information. The Linear Discriminant Analysis (LDA) algorithm fuses the authentication decision accurately by projecting the high dimensional estimations into a low dimensional space for maximum separability by only keeping the necessary attributes. A situation-aware cross-layer attribute selection algorithm is developed to select a minimum number of attributes so that the time required for attribute estimation and computation overhead of authentication can be reduced. The simulation results demonstrate that our scheme performs better under a dynamic environment compared with the physical layer authentication scheme and some existing state-of-the-art authentication techniques.

Emerging vertical applications enabled by connected devices and smart infrastructures have created an ever-increasing demand for high data rates over 5th-Generation (5G) and beyond wireless networks. Deployment of dense small cells (SCs) and millimeter wave (mmWave) communication systems have become inevitable in future wireless networks. Consequently, it is more accurate to model such networks in the 3D space due to the spatially distributed nature of the SCs, locations of the devices, radio resources and propagation environment. Accurate estimation of location-specific path loss parameters is then essential for efficient utilization of radio resources and management of dynamic coverage in 3D SC networks. In the paper, a framework for location-specific path loss estimation is developed for efficient radio resource management, based on the principle of crowdsensing together with Linear Algebra (LA) and machine learning (ML) techniques considering 2.5 GHz and 28 GHz bands. The corresponding procedure for capturing dynamic coverage of a SC base station (BS) serving to an arbitrary cluster is proposed and examined based on its 3D propagation characteristics. Results show that the accuracy of 3D channel parameter estimation using gradient descent ML techniques is superior compared to LA technique and can achieve over 98% estimation accuracy. It is shown that using the proposed process, parameters can be extrapolated for the slightly extended 3D communication distances from the cluster boundary for the worst-case locations of devices based on already estimated propagation parameters with accuracy over 74% for certain distances. Although numerical results are presented for a single amorphous 3D cell of a wireless network, the framework given in the paper can be extended to any arbitrary 3D wireless cellular network.

Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service (QoS) in the 5th Generation (5G)-beyond and the 6th Generation (6G) networks. However, effective slicing of Radio Access Network (RAN) is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G networks. In this paper, we propose a self-sustained RAN slicing framework, which integrates the self-management of network resources with multiple granularities, the self-optimization of slicing control performance, and self-learning together to achieve an adaptive control strategy under unforeseen network conditions. The proposed RAN slicing framework is hierarchically structured, which decomposes the RAN slicing control into three levels, i.e., network-level slicing, next generation NodeB (gNodeB)-level slicing, and packet scheduling level slicing. At the network level, network resources are assigned to each gNodeB at a large timescale with coarse resource granularity. At the gNodeB-level, each gNodeB adjusts the configuration of each slice in the cell at the large timescale. At the packet scheduling level, each gNodeB allocates radio resource allocation among users in each network slice at a small timescale. Furthermore, we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing control. With the proposed RAN slicing framework, the QoS performance of emerging services is expected to be dramatically enhanced.

Abstract Very few materials show large magnetoresistance (MR) under a low magnetic field at room temperature, which causes the barrier to the development of magnetic field sensors for detecting low-level electromagnetic radiation in real- time. Here, a hybrid reduced graphene oxide (rGO)-based magnetic field sensor is produced by in situ deposition of FeCo nanoparticles (NPs) on reduced graphene oxide (rGO). Special quantum magnetoresistance (MR) of the hybrid rGO is observed, which unveils that Abrikosov’s quantum model for layered materials can occur in hybrid rGO; meanwhile, the MR value can be tunable by adjusting the particle density of FeCo NPs on rGO nanosheets. Very high MR value up to 21.02 ± 5.74% at 10 kOe at room temperature is achieved, and the average increasing rate of resistance per kOe is up to 0.9282 Ω kOe−1. In this paper, we demonstrate that the hybrid rGO-based magnetic field sensor can be embedded in a wireless system for real-time detection of low-level electromagnetic radiation caused by a working mobile phone. We believe that the two-dimensional nanomaterials with controllable MR can be integrated with a wireless system for the future connected society.

In this work, we propose a novel sidechain structure via an optimized two-way peg protocol for device authentication in the smart community in order to overcome the limitations of existing approaches. The proposed scheme uses private side blockchains to distribute and manage the local registration and authentication processes, in addition to a local mainchain block to circulate the information record with other smart systems. More importantly, we propose the optimized two-way peg protocol in the proposed sidechain system in order to prevent the worthless information injection attack during the authentication information sharing procedure between the main chain and side blockchains. The optimized two-way peg protocol supervises the availability of the required information by dynamically evaluating the trustworthiness of each smart device. The evaluation is based on numerous criteria, such as the authentication method, previous authentication information sharing history, and local authentication results. Consequently, the simulation results prove the superiority of the proposed scheme in terms of reducing authentication time, improving information management efficiency and decreasing storage consumption as compared to existing works, and the applicability and feasibility of the optimized two-way peg protocol have been approved. It is noteworthy that the proposed sidechain-based method shows its superiority in reducing the cost of authentication time compared with the blockchain-based method when using blockchain structure. The reflected savings are 33.33%, 34.29%, and 36.36% when in comparison to the conventional authentication process without applying any additional method, the authentication process using the proposed sidechain based method, and the authentication process using the blockchain based method, respectively.

In Unmanned Aerial Vehicle (UAV) networks, mobility of the UAV and the corresponding network dynamics cause frequent network adaptation. One key challenge caused by this in Flying Ad-hoc Network (FANET) is how to maintain the link stability such that both the packet loss rate and network latency can be reduced. Clustering of UAVs could effectively improve the performance of large-scale UAV swarm. However, the use of conventional clustering schemes in dynamic and high mobility FANET will lead to more link outages. Besides, frequent updates of cluster structure would cause the instability of network topology and the increase of control overhead and latency. To solve this problem, we propose a location-based $k$ -means UAV clustering algorithms by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network with limited resource. The objective of the proposed Mobility and Location-aware Stable Clustering (MLSC) mechanism is to enhance the stability and accuracy of the network by reducing unnecessary overheads and network latency through incorporating several design factors with minimum resource constraints. Furthermore, we derive the relationship between the maximum coverage probability of Cluster Head (CH) and cluster size to find the optimal cluster size to minimize the network overhead. Our simulation results show that the proposed MLSC scheme significantly reduces the network overheads, and also improves packet delivery ratio and network latency as compared to the conventional clustering methods.

Patent citations are significant components of patents, which play a vital role in the implementation of patent analysis. However, most of the existed models only focus on the text of patents and do not realize that citations can remedy missing information in the text. A method for citation modeling in patent analysis is proposed to generate patent citation trees in this paper. Correspondingly, a specific neural network is designed for extracting abstract features in patent citation trees. Then, on the basis of extracted features, a new citation-based vector space model (CVSM) combining citations with text of the patent database is constructed for the subsequent applications. An experiment is conducted based on real patents of USPTO. The experimental results show that the proposed CVSM has good performances in several applications, which demonstrate the effectiveness of the proposed CVSM.

Abstract Relay identification is necessary in many cooperative communication applications such as detecting the presence of malicious relays for communication security, selecting the intended relays for signal forwarding, and tracing a specific relay. However, this identification task becomes extremely challenging for amplify-and-forward (AF) relaying systems since AF relays usually have no capability of adopting traditional identification methods implemented above the physical layer. This paper proposes a physical-layer AF relay identification scheme based on the exploitation of the device-specific in-phase and quadrature-phase imbalance (IQI) feature. Given that IQI estimation is mandatory in most present receivers for compensation, it is cost-effective to make use of these estimation results for fingerprinting AF relays. A generalized likelihood ratio test-based fingerprint differentiation technique is adopted to detect the minor difference between two range-limited IQI fingerprints. Using this differentiation technique, a whitelist-based identification algorithm consisting of fingerprint registration, update, and identification is proposed. Furthermore, the optimal training signals that lead to the maximal detection probability are derived for the typical quadrature amplitude modulation and phase-shift keying modulation schemes. The simulation results validate our derivations and confirm that the proposed method can accurately identify AF relays.

We consider practical hardware implementation of polar decoders. To reduce latency due to the serial nature of successive cancellation, existing optimizations improve parallelism with two approaches, i.e., multi-bit decision or reduced path splitting. In this paper, we combine the two procedures into one with an error-pattern-based architecture. It simultaneously generates a set of candidate paths for multiple bits with pre-stored patterns. For rate-1 (R1) or single parity-check nodes, we prove that a small number of deterministic patterns are required to guarantee performance preservation. For general nodes, low-weight error patterns are indexed by syndrome in a look-up table and retrieved in $O(1)$ time. The proposed flip-syndrome-list decoder fully parallelizes all constituent code blocks without sacrificing performance, and thus is suitable for ultra-low-latency applications. Meanwhile, two code construction optimizations are presented to further reduce complexity and improve performance.

Dynamic spectrum access (DSA) emerging as an effective way of improving the utilization of the scarce spectrum has attracted great attention in the communication field. A key challenge in DSA is to perform an efficient spectrum sensing and sharing mechanism. In this paper, aiming at achieving a maximal spectrum utilization, we propose a privacy-aware crowdsourced spectrum sensing and multi-user sharing mechanism for DSA. Particularly, in the sensing stage, the advanced mobile crowd sensing is adapted to economically provide sufficient candidate sensing helpers for a sensing requestor. Considering the individual rationality and energy consumption, an incentive mechanism based on both monetary and social motivation is designed to motivate the finial participations of the sensing helpers. Moreover, with the increasing attention to individual privacy, a social network and location-based k-anonymity grouping algorithm are proposed to prevent each helper's privacy being attacked by the malicious requestor or mobile users. Then, for a sensing requestor, aiming at achieving a target detection performance with minimal payment, a truthful reverse auction-based winning group selection algorithm is designed. Furthermore, in the transmitting stage, a realistic scenario is considered where multiple transmitters may discover the same idle spectrum based on sensing helpers' detections and will transmit data simultaneously. Thus, we model this problem as a potential-game-based multi-user transmission mechanism where all the transmitters act as game players and will jointly adjust their transmission powers to maximize the global throughput. Accordingly, we also take advantage of an improved differential evolution algorithm for obtaining a better equilibrium solution in a decentralized way. Both the theoretical analysis and the simulation results prove the rationality and superiority of our proposed algorithms.

In this paper, to detect non-technical loss (NTL) of meter manipulating/malfunctioning and bypassing at the same time, we propose the intermediate monitor meter (IMM)-based power distribution network model with the concept of unit networks (UNs) dividing the network into smallest and independent networks to analyze the power flow in detail and effectively detect the NTL. We then propose an NTL detection algorithm to solve linear system of equations (LSE) constructed by energy balance analysis with IMMs and the collector, and describe the hardware architecture of IMMs. Simulation results show that the proposed NTL detection algorithm successively detects the honesty of energy consumers as well as the energy loss from bypassing. Furthermore, it is observed the proposed detection framework is time-efficient and detection accuracy is at least 95% when variability of meter reading value is 80%.

As an emerging paradigm enabling mobile devices to leverage additional computation resources from nearby MEC servers (MSs), mobile edge computing (MEC) has drawn great attention from academia to industry. Unlike the conventional cloud server, the MEC provides a medium-scale and portable computation ability at MSs without relying on the time-consuming and capacity-constrained backhaul. However, the MEC offloading process is still highly sensitive to the fluctuation of both radio and computing resources. In this paper, considering the independent variation of the wireless channel conditions and computing tasks, we propose a Mixed-timescale Joint Computational offloading and Wireless resource allocation (MJCW) algorithm for latency-critical applications, aiming at minimizing the total energy consumption. Through such a new approach, the original NP-hard problem is decoupled into a short-term stage problem seeking for the allocation of physical power and subcarrier and a long-term stage problem of task offloading and frequency scaling. The simulation results show that the proposed algorithm achieves excellent performance in energy saving in comparison with conventional schemes and realizes higher utilization of green energy by adjusting the energy price of MSs.

The methylammonium lead halide perovskites have shown excellent optoelectronic properties but the field-effect transistors are much less studied. Here Yu et al. synthesize micrometer-thin crystals of perovskites with low surface contamination and make ambipolar transistor devices with high mobilities.

The prism‐based surface plasmon resonance (SPR) sensing system provides a flexible way to modulate the resonance wavelength by changing the incident angle. Unfortunately, it is difficult to implement such a type of modulation for optical fiber SPR sensor. Herein, a novel and flexible SPR sensor based on a side‐polishing helical‐core fiber (HCF), which is fabricated by twisting an eccentric core fiber, is presented. For the first time to the best of knowledge, the sensing effect of such a sensor theoretically and experimentally is demonstrated. Two SPR excitation methods based on the cladding whispering gallery modes and the core mode are introduced into studying the sensor. The results show that the resonant wavelength of the HCF SPR sensor can be effectively adjusted by changing the twist pitch of HCF. An average sensitivity of 7400 nm/RIU is achieved for the refractive index sensing from 1.375 to 1.385 with a twist pitch of 1.9 mm. This sensitivity can be further improved by using a shorter pitch of HCF. Those characteristics enable the fabrication of sensor arrays in one helical single‐core or multicore fiber with several different twist pitches for multiparameter measurement and may also find wide application in chemo/biosensing.

The determination of changes in hydrogeological properties (e.g., permeability and specific storage) of aquifers disturbed by mining activity is significant to groundwater resource and ecological environment protection in coal mine areas. However, such parameters are difficult to continuously measure in situ using conventional hydrogeological methods, and their temporal changes associated with coal mining are not well understood. The response of well water level to Earth tides provides a unique probe to determine the in situ hydrogeological parameters and their variations. In this study, the tidal responses of well water level were employed to characterize the changes in hydrogeological parameters of the overburden aquifer induced by longwall mining in a coalfield, northwest China. Based on the long-term hourly recorded water level data, two analytical models were used to determine the temporal changes of permeability and specific storage of the overburden aquifer. The results showed that the hydrogeological parameters changed with the longwall coal face advance. When the longwall coal face approached the wells, the aquifer permeability increased several to dozens of times, and the response distance ranged from 80 m to 300 m. The specific storage decreased before the coal face reached wells and recovered after the coal face passed. The results of this study indicate that the hydrogeological parameter changes induced by coal mining are related to the location of the well relative to the coal face and the stress distribution in the overburden aquifer. This study revealed the changes in permeability and specific storage associated with the mining disturbance which could have great significance for quantitative assessment of the impact of mining on overburden aquifer.

Objective Clinical characteristics of patients with pyogenic liver abscess (PLA) of varying etiologies may be different. This study aimed to analyze the clinical characteristics, pathogenic bacteria, treatment, and prognosis of patients with PLA associated with diabetes and biliary disease. Methods Clinical, imaging, and laboratory data from 202 inpatients with PLA were retrospectively analyzed. Results Eighty-eight patients (43.6%) had a history of diabetes, 73 (36.1%) had a history of underlying biliary tract disease, and 24 (11.9%) had both the diseases. The level of C-reactive protein (CRP) increased in 99.2% (119/120) patients, and the level of procalcitonin (PCT) increased in 95.5% (148/155) patients. The main pathogen of PLA was Klebsiella pneumoniae . The incidence of bloodstream infection increased by 34.4% (22/64) in patients with PLA that was associated with diabetes mellitus, and that of K. pneumoniae infection was 88.6% (39/44). The readmission rate for patients with PLA with underlying biliary diseases was 10.2 to 12.5%. Conclusion The main pathogen of PLA is K. pneumoniae , which is sensitive to most antibiotics. Patients with PLA associated with diabetes were more likely to have bloodstream infections, and the recurrence rate of PLA with underlying biliary diseases was higher than without biliary duct disease.

With the capacity of achieving spectrum efficient wireless communications, cognitive radio enabled advanced metering infrastructure (CR-AMI) networks are expected to enhance the efficiency and practicability of future smart grids. CR-AMI networks which have been recognized as a fundamental component of the smart grid ecosystem, are practically utilized as a static multi-hop wireless mesh network. This paper focuses on the development of a novel routing protocol for low power and lossy networks based routing protocol for enhancing the energy efficiency in CR-AMI networks. For meeting the requirements of green communications in smart grids, the proposed routing protocol adopts the energy efficiency over virtual distance as the core of routing mechanism such that the energy-efficient route can be achieved. Furthermore, the protocol provides protection for primary users whilst meeting the utility requirements of secondary users. System-level evaluation indicates that the proposed protocol performs better than existing routing protocols for CR-AMI networks.

Nowadays, Internet of Things (IoT) devices are rapidly proliferating to support a vast number of end-to-end (E2E) services and applications, which require reliable device authentication for E2E data security. However, most low-cost IoT end devices with limited computing resources have difficulties in executing the increasingly complicated cryptographic security protocols, resulting in increased vulnerability of the virtual authentication credentials to malicious cryptanalysis. An attacker possessing compromised credentials could be deemed legitimate by the conventional cryptography-based authentication. Although inherently robust to upper-layer unauthorized cryptanalysis, the device-to-device physical-layer (PHY) authentication is practically difficult to be applied to the E2E IoT scenario and to be integrated with the existing, well-established cryptography primitives without any conflict. This paper proposes an enhanced E2E IoT device authentication that achieves seamless integration of PHY security into traditional asymmetric cryptography-based authentication schemes. Exploiting the collaboration of several intermediate nodes (e.g., edge gateway, access point, and full-function device), multiple radio-frequency features of an IoT device can be estimated, quantized, and used in the proposed PHY identity-based cryptography for key protection. A closed-form expression of the generated PHY entropy is derived for measuring the security enhancement. The evaluation results of our cross-layer authentication demonstrate an elevated resistance to various computation-based impersonation attacks. Furthermore, the proposed method does not impose any extra implementation overhead on resource-constrained IoT devices.

A novel random code-domain non-orthogonal multiple access (NOMA) framework, rateless multiple access (RMA), is studied in this paper. In RMA, instead of granting each user specific radio resources in a fixed and centralized manner, the access point simply assigns to it a random access control function, according to which the user independently chooses a pseudo-random number of symbols each time and transmits their weighted sum until receiving an acknowledgment that indicates the successful recovery of its information. In this way, the transmission process of each individual user as well as that of all the users as a whole, resembles a special linear superposition rateless encoder and, thus, information can be retrieved with the low-complexity belief propagation (BP) algorithm at the access point. In this paper, the asymptotic throughput of RMA with BP decoding is analyzed. Then, in order to bridge the performance gap between BP decoding and the optimal MAP (maximum aposteriori) decoding, we apply the most recently developed coding technique of spatial coupling to RMA and propose an enhanced version, SC-RMA, in which all the user codewords are properly spatially-coupled across different subsets of REs. We prove that SC-RMA asymptotically approaches the channel sum-rate capacity with high adaptability and low-signaling overhead, which makes it a viable candidate for massive access.

Sparsely spread code division multiple access (SCDMA) is a non-orthogonal superposition coding scheme that allows concurrent communications between a base station and multiple users over a common channel. However, the detection performance of an SCDMA system is mainly determined by its signature matrix, which should be sparse to facilitate the belief propagation (BP) detection. On the other hand, to guarantee good maximum likelihood (ML) detection performance, the minimum Euclidean distance for the equivalent signal constellation after multi-user superposition should be maximized. In this paper, a code distance analysis is proposed for SCDMA systems with a finite number of users and spreading lengths. Based on this analysis, good signature matrices whose factor graphs have very few short cycles and possess large superposed signal constellation distances are designed. The proposed signature matrices have both good BP and ML detection performances. Moreover, their BP detection performances exactly converge to their ML detection performances with few iterations. It is worth pointing out that the proposed signature matrix design could be directly applied to the 5G non-orthogonal multiple access systems.

Recently, big data analytics has received important attention in a variety of application domains including business, finance, space science, healthcare, telecommunication and Internet of Things (IoT). Among these areas, IoT is considered as an important platform in bringing people, processes, data and things/objects together in order to enhance the quality of our everyday lives. However, the key challenges are how to effectively extract useful features from the massive amount of heterogeneous data generated by resource-constrained IoT devices in order to provide real-time information and feedback to the end-users, and how to utilize this data-aware intelligence in enhancing the performance of wireless IoT networks. Although there are parallel advances in cloud computing and edge computing for addressing some issues in data analytics, they have their own benefits and limitations. The convergence of these two computing paradigms, i.e., massive virtually shared pool of computing and storage resources from the cloud and real-time data processing by edge computing, could effectively enable live data analytics in wireless IoT networks. In this regard, we propose a novel framework for coordinated processing between edge and cloud computing/processing by integrating advantages from both the platforms. The proposed framework can exploit the network-wide knowledge and historical information available at the cloud center to guide edge computing units towards satisfying various performance requirements of heterogeneous wireless IoT networks. Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. More importantly, we identify and describe the potential key enablers for the proposed edge-cloud collaborative framework, the associated key challenges and some interesting future research directions.

The automotive industry has recently shifted from developing advanced vehicles to concentrating on safety and comfort, which stimulates the development of new intelligent vehicles with advanced driver assistance systems and autonomous driving [item 1) in the Appendix]. However, the current technology is still far from meeting the practical requirements of autonomous driving. The existing control approaches are mainly based on expensive sensors and cameras equipped in vehicles. Thanks to the rapid development of advanced communication and computation techniques, vehicles easily communicate and cooperate to each other [item 2) in the Appendix]. Meanwhile, the control of vehicles become more reliable and cost-efficient with the communication between vehicles [item 3) in the Appendix]. Therefore, communication, computing and control (3C) will play a critical role in the development of next generation intelligent vehicular systems [item 4) in the Appendix].

With the rapid development and application of the mobile Internet, huge amounts of user data are generated and collected every day. How to take full advantages of these ubiquitous data is becoming the essential aspect of a recommender system. Collaborative filtering (CF) has been widely studied and utilized to predict the interests of mobile users and to make proper recommendations. In this paper, we first propose a framework of the CF recommender system based on various user data including user ratings and user behaviors. Key features of these two kinds of data are discussed. Moreover, several typical CF algorithms are classified as memory-based approaches and model-based approaches and compared. Two case studies are presented in an effort to validate the proposed framework.

With the rapid development of society and the economy, an increasing number of human activities have gradually destroyed the marine environment. Marine environment monitoring is a vital problem and has increasingly attracted a great deal of research and development attention. During the past decade, various marine environment monitoring systems have been developed. The traditional marine environment monitoring system using an oceanographic research vessel is expensive and time-consuming and has a low resolution both in time and space. Wireless Sensor Networks (WSNs) have recently been considered as potentially promising alternatives for monitoring marine environments since they have a number of advantages such as unmanned operation, easy deployment, real-time monitoring, and relatively low cost. This paper provides a comprehensive review of the state-of-the-art technologies in the field of marine environment monitoring using wireless sensor networks. It first describes application areas, a common architecture of WSN-based oceanographic monitoring systems, a general architecture of an oceanographic sensor node, sensing parameters and sensors, and wireless communication technologies. Then, it presents a detailed review of some related projects, systems, techniques, approaches and algorithms. It also discusses challenges and opportunities in the research, development, and deployment of wireless sensor networks for marine environment monitoring.

With the dramatic increase of connected devices, the Internet of things (IoT) paradigm has become an important solution in supporting dense scenarios such as smart cities. The concept of heterogeneous networks (HetNets) has emerged as a viable solution to improving the capacity of cellular networks in such scenarios. However, achieving optimal load balancing is not trivial due to the complexity and dynamics in HetNets. For this reason, we propose a load balancing scheme based on machine learning techniques that uses both unsupervised and supervised methods, as well as a Markov Decision Process (MDP). As a use case, we apply our scheme to enhance the capabilities of an urban IoT network operating under the LoRaWAN standard. The simulation results show that the packet delivery ratio (PDR) is increased when our scheme is utilized in an unbalanced network and, consequently, the energy cost of data delivery is reduced. Furthermore, we demonstrate that better outcomes are attained when some techniques are combined, achieving a PDR improvement of up to about 50% and reducing the energy cost by nearly 20% in a multicell scenario with 5000 devices requesting downlink traffic.