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Aiming at the difficulty of measuring various costs and time-consuming elements in multimodal transport, this paper constructs a green vehicle comprehensive multimodal transport model which incorporates transportation, transit, quality damage, fuel consumption, and carbon emission costs and proposes a hybrid embedded time window to calculate the time penalty cost in order to reflect the actual transport characteristics. Furthermore, in order to better solve the model, a hybrid sand cat swarm optimization (HSCSO) algorithm is proposed by introducing Logistic–Tent chaotic mapping and an adaptive lens opposition-based learning strategy to enhance the global search capability, and inspired by the swarm intelligence scheme, a momentum–bellicose strategy and an equilibrium crossover pool are introduced to improve the search efficiency and convergence ability. Through testing nine benchmark functions, the HSCSO algorithm exhibits superior convergence accuracy and speed in dealing with complex multi-dimensional problems. Based on the excellent global performance, the HSCSO algorithm was utilized for multimodal vehicle transportation in East China, and a path with a lower comprehensive cost was successfully planned, which proved the effectiveness of the HSCSO algorithm in green intermodal transport path planning.

Mitochondrial damage is an early and key marker of neuronal damage in prion diseases. As a process involved in mitochondrial quality control, mitochondrial biogenesis regulates mitochondrial homeostasis in neurons and promotes neuron health by increasing the number of effective mitochondria in the cytoplasm. Sirtuin 1 (SIRT1) is a NAD+-dependent deacetylase that regulates neuronal mitochondrial biogenesis and quality control in neurodegenerative diseases via deacetylation of a variety of substrates. In a cellular model of prion diseases, we found that both SIRT1 protein levels and deacetylase activity decreased, and SIRT1 overexpression and activation significantly ameliorated mitochondrial morphological damage and dysfunction caused by the neurotoxic peptide PrP106–126. Moreover, we found that mitochondrial biogenesis was impaired, and SIRT1 overexpression and activation alleviated PrP106–126-induced impairment of mitochondrial biogenesis in N2a cells. Further studies in PrP106–126-treated N2a cells revealed that SIRT1 regulates mitochondrial biogenesis through the PGC-1α-TFAM pathway. Finally, we showed that resveratrol resolved PrP106–126-induced mitochondrial dysfunction and cell apoptosis by promoting mitochondrial biogenesis through activation of the SIRT1-dependent PGC-1α/TFAM signaling pathway in N2a cells. Taken together, our findings further describe SIRT1 regulation of mitochondrial biogenesis and improve our understanding of mitochondria-related pathogenesis in prion diseases. Our findings support further investigation of SIRT1 as a potential target for therapeutic intervention of prion diseases.

Using first principles calculations based on the density functional theory and the structural, electronic, and optical properties of the substitution of K elements by Li+, Na+, Mg2+, and Ca2+ in KH2PO4 (KDP) crystals are investigated in depth. This study provides insight into the structural properties of crystals, with particular emphasis on internal chemical bonding effects. It further discusses the impact of H and O defects on the bandgap and optical properties of KDP crystals. Detailed analysis of structural characteristics and comprehensive examination of the effects of chemical bonds contribute to a deeper understanding of the effects of defects on the lattice and electronic structure, revealing potential alterations and their underlying mechanisms. Our investigation focuses on the regulatory role of different defects on the bandgap and optical properties. These comprehensive analyses provide a more thorough understanding of the impact of defects on the properties of KDP crystals, establishing an essential research foundation for further exploration of nonlinear optics crystal potential applications.

ObjectiveSubthreshold depression (StD)/subsyndromal depression refers to a threatening precursor to depression. Aerobic exercise is a promising self-supportive adjunctive intervention and an effective measure for StD. Our study utilizes regional homogeneity (ReHo) to investigate the impact of aerobic exercise on resting-state brain function.MethodsA total of 78 subjects, aged between 18 and 48 years, (StD group, n = 44; healthy control (HC) group, n = 34) engaged in moderate-intensity aerobic exercise 3–4 times per week for 8 weeks. Resting-state brain function and structural images were acquired before and after the exercise intervention. The ReHo method was employed to analyze abnormal changes in regional brain function, and a correlation analysis was performed using the Patient Health Questionnaire-9 (PHQ-9) and Self-Rating Anxiety Scale (SAS) scores.ResultsThe principal observation reveals synchronous abnormalities in the right anterior cingulate gyrus of the brain in StD subjects compared to HCs at baseline, with these differences dissipating after the implementation of aerobic exercise. After completing the aerobic exercise program, the StD group exhibited a difference in the right middle cingulate gyrus, while the left supplementary motor area (SMA) was altered in the HC group.ConclusionDisparities in neural synchronization are evident between HCs and StD subjects, and the implementation of aerobic exercise intervention can effectively mitigate these distinctions, leading to a significant reduction in depressive symptoms among StD subjects. The primary mechanism of StD symptoms may involve the inhibition of the anterior cingulate gyrus, while the effects of aerobic exercise may be related to the modulation of neural synchronization of emotional reflexes. The discovery of these fMRI evidence findings may offer novel strategies for early detection and intervention in cases of StD.

Low-frequency power oscillations in power systems have always been a serious risk for a stable power system. A fuzzy logic power system stabilizer (FLPSS) is proposed to mitigate system oscillation problems. Aimed at parameter configuration and rule setting of FLPSS, Grey Wolf Optimizer (GWO) based FLPSS is utilized to achieve quicker settling time and improve the damping oscillation effectiveness. Comparative studies between the proposed GWO-based FLPSS and conventional stabilizers are performed on a single-machine infinite bus system(SMIBS). The simulation results demonstrate the GWO-based FLPSS can better dampen out low-frequency oscillations and maintain the electricity network stability.

In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor–critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme’s comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively.

The accurate interpretation of trilobite moulting behaviour relies on a comprehensive understanding of their moult configurations, yet the focus has commonly been limited to a brief description of the exuviae, and how differences in moulting behaviour further impact the preservation of exuviae is often ignored. This study investigates the configuration, style, and process of moulting in Oryctocephalus indicus through analysis of 88 exuviae collected from the Kaili Formation (Cambrian, Wuliuan) in Guizhou Province, South China. The moult configurations of O. indicus are typically characterised by the lower cephalic unit (LCU), which comprises the librigenae and rostral-hypostomal plate connected as a whole, detached from the cephalon and positioned anterior to the thoracopygon, while the cranidium is mostly absent. From detailed observation and description of the available material, we believe that O. indicus completes its moult through an exuvial gape formed by disarticulation of the facial sutures, rostral sutures and/or sutures of the cephalothoracic joints. Although many exuviae exhibited an opening at the cephalothoracic joint—disjunction of which is usually accompanied by disarticulation of both the facial and rostral sutures—the Salter’s configuration produced by the ‘Salterian’ mode of moulting was not observed. Additionally, the structural characteristics of Henningsmoen’s configuration, Harrington’s configuration, and Somersault’s configuration are discussed based on the exuviae of O. indicus, and Henningsmoen’s configuration has been categorised into three types according to the different states of fossil preservation. In this article, apart from promoting further research on moulting behaviour in O. indicus, we also provide a supplement for moult configuration based on the exuviae, which offers new materials for studying moulting behaviour in oryctocephalid trilobites.

Aiming at the problem that the search efficiency of key vulnerable nodes in large-scale networks is not high and the consideration factors are not comprehensive enough, in order to improve the time and space efficiency of search and the accuracy of results, a key vulnerable node discovery method based on Bayesian attack subgraphs and improved fuzzy C-means clustering is proposed. Firstly, the attack graph is divided into Bayesian attack subgraphs, and the analysis results of the complete attack graph are quickly obtained by aggregating the information of the attack path analysis in the subgraph to improve the time and space efficiency. Then, the actual threat features of the vulnerability nodes are extracted from the analysis results, and the threat features of the vulnerability itself in the common vulnerability scoring standard are considered to form the clustering features together. Next, the optimal number of clusters is adaptively adjusted according to the variance idea, and fuzzy clustering is performed based on the extracted clustering features. Finally, the key vulnerable nodes are determined by setting the feature priority. Experiments show that the proposed method can optimize the time and space efficiency of analysis, and the fuzzy clustering considering multiple features can improve the accuracy of analysis results.

Proper mitochondrial performance is imperative for the maintenance of normal neuronal function to prevent the development of neurodegenerative diseases. Persistent accumulation of damaged mitochondria plays a role in prion disease pathogenesis, which involves a chain of events that culminate in the generation of reactive oxygen species and neuronal death. Our previous studies have demonstrated that PINK1/Parkin-mediated mitophagy induced by PrP106−126 is defective and leads to an accumulation of damaged mitochondria after PrP106−126 treatment. Externalized cardiolipin (CL), a mitochondria-specific phospholipid, has been reported to play a role in mitophagy by directly interacting with LC3II at the outer mitochondrial membrane. The involvement of CL externalization in PrP106−126-induced mitophagy and its significance in other physiological processes of N2a cells treated with PrP106−126 remain unknown. We demonstrate that the PrP106−126 peptide caused a temporal course of mitophagy in N2a cells, which gradually increased and subsequently decreased. A similar trend in CL externalization to the mitochondrial surface was seen, resulting in a gradual decrease in CL content at the cellular level. Inhibition of CL externalization by knockdown of CL synthase, responsible for de novo synthesis of CL, or phospholipid scramblase-3 and NDPK-D, responsible for CL translocation to the mitochondrial surface, significantly decreased PrP106−126-induced mitophagy in N2a cells. Meanwhile, the inhibition of CL redistribution significantly decreased PINK1 and DRP1 recruitment in PrP106−126 treatment but had no significant decrease in Parkin recruitment. Furthermore, the inhibition of CL externalization resulted in impaired oxidative phosphorylation and severe oxidative stress, which led to mitochondrial dysfunction. Our results indicate that CL externalization induced by PrP106−126 on N2a cells plays a positive role in the initiation of mitophagy, leading to the stabilization of mitochondrial function.

Vanadium-based phosphates are being extensively studied as an important family of sodium-ion battery (SIB) cathodes. Among many compositions, NaVOPO _4 is considered because of various polymorphs and the high redox potential of V ^4+/5+ . However, due to relatively poor intrinsic kinetics and electronic conductivity, approaches such as nanostructuring and carbon composites are commonly used to avoid fast performance degradation. Being different from mainstream approaches, this work utilizes the knowledge gained from potassium-ion batteries (PIBs) and applies layered KVOPO _4 , a PIB cathode material, as a SIB cathode material. The results demonstrate that KVOPO _4 experiences an electrochemical K ^+ -Na ^+ exchange during the initial cycle and a Na-dominated (de)intercalation process in the following cycles. The initial exchange results in a small amount of K ^+ (∼0.1 K per formula) remaining in the interlayer space and owing to the larger size of K ^+ than Na ^+ , the residual K ^+ effectively acts as ‘pillars’ to expand interlayer spacing and facilitates the Na (de)intercalation, leading to enhanced reversible Na storage and diffusion kinetics of KVOPO _4 compared to its Na counterpart NaVOPO _4 . KVOPO _4 delivers an initial discharge capacity of 120 mAh g ^−1 (90% of the theoretical capacity) at 10 mA g ^−1 and retains 88% capacity after 150 cycles. It also delivers 52 mAh g ^−1 at 1 A g ^−1 and 91% capacity retention after 1000 cycles at 100 mA g ^−1 , completely outperforming NaVOPO _4 .

Abstract A persistent accumulation of damaged mitochondria is part of prion disease pathogenesis. Normally, damaged mitochondria are cleared via a major pathway that involves the E3 ubiquitin ligase parkin and PTEN-induced kinase 1 (PINK1) that together initiate mitophagy, recognize and eliminate damaged mitochondria. However, the precise mechanisms underlying mitophagy in prion disease remain largely unknown. Using prion disease cell models, we observed PINK1-parkin-mediated mitophagy deficiency in which parkin depletion aggravated blocked mitochondrial colocalization with LC3-II-labeled autophagosomes, and significantly increased mitochondrial protein levels, which led to inhibited mitophagy. Parkin overexpression directly induced LC3-II colocalization with mitochondria and alleviated defective mitophagy. Moreover, parkin-mediated mitophagy was dependent on PINK1, since PINK1 depletion blocked mitochondrial Parkin recruitment and reduced optineurin and LC3-II proteins levels, thus inhibiting mitophagy. PINK1 overexpression induced parkin recruitment to the mitochondria, which then stimulated mitophagy. In addition, overexpressed parkin and PINK1 also protected neurons from apoptosis. Furthermore, we found that supplementation with two mitophagy-inducing agents, nicotinamide mononucleotide (NMN) and urolithin A (UA), significantly stimulated PINK1-parkin-mediated mitophagy. However, compared with NMN, UA could not alleviate prion-induced mitochondrial fragmentation and dysfunction, and neuronal apoptosis. These findings show that PINK1-parkin-mediated mitophagy defects lead to an accumulation of damaged mitochondria, thus suggesting that interventions that stimulate mitophagy may be potential therapeutic targets for prion diseases.

For Bluetooth low energy neighbor discovery protocol, it was usually difficult to assess the discovery latency performance.A latency performance model of neighbor discovery based on Chinese remainder theorem for Bluetooth 5.0 standard was proposed.A mathematical relationship between the discovery latency and key parameters was put forward, including advertising interval, scan interval and scan window.The effect of different parameter configurations on the neighbor discovery latency was verified.Experimental results show that the model can effectively predict the delay peak under the influence of parameter configuration, and provide parameter configuration verification and guidance for different application scenarios.

Abstract Background and purpose Previous studies have revealed abnormal regional homogeneity (ReHo) in individuals with autism spectrum disorder (ASD); however, there is little consistency across the findings within these studies, partly due to small sample size and great heterogeneity among participants between studies. Additionally, few studies have explored the association between ReHo aberrance and clinical symptoms in individuals with ASD. Methods Forty‐eight adolescents with high‐functioning ASD and 63 group‐matched typically developing (TD) controls received functional magnetic resonance imaging at rest. Group‐level analysis was performed to detect differences in ReHo between ASD and TD. Evaluation of symptom severity in individuals with ASD was based on the Autism Behavior Checklist (ABC). Voxel‐wise correlation analysis was undergone to examine the correlations between the symptom severity and ReHo map in individuals with ASD within brain areas with ReHo abnormalities. Results Compared with the TD controls, individuals with ASD exhibited increased ReHo in the bilateral anterior cingulate cortex, left caudate, right posterior cerebellum (cerebellar tonsil), and bilateral brainstem and decreased ReHo in the left precentral gyrus, left inferior parietal lobule, bilateral postcentral gyrus, and right anterior cerebellum (culmen). The correlation analysis indicated that the ReHo value in the brainstem was negatively associated with the ABC total scores and the scores of Relating factor, respectively. Conclusions Our findings indicated that widespread ReHo abnormalities occurred in ASD, shedding light on the underlying neurobiology of pathogenesis and symptomatology of ASD.

The application scenarios of IoT (Internet of Things) are complex and diverse. Failure of security defense in any part of IoT can lead to huge information leakage and incalculable losses. IoT security issues are affecting and limiting its application prospects, and have become one of the hotspots in the field of IoT. Identity resolution security of IoT has become a core issue in solving the security problem of IoT. The aim of this paper is to apply AHP, a well-known decision making method, to IoT identity resolution security. Selecting 6 indicators, several pairwise comparison matrices are constructed based on scores from experts and lab researchers. The AHP method is used to calculate malicious resolution value as a quantitative basis for judging the security performance of each resolution server. An experimental case is used to verify the validity and correctness of the AHP-based IoT identity resolution security evaluation model.

Blockchain is a disruptive technology for building consensus and trust in a peer-to-peer network without centralized control. It was first used in bitcoin, the very first cryptocurrency released at the beginning of 2009, to implement a secure ledger of transactions. This secure ledger ensures that once a transaction is placed in the ledger, it cannot be altered without being detected, which is a prerequisite for any digital currency implementation because it must guarantee that no one can double-spend one’s money. The interest in blockchain exploded in recent years. The number of publications indexed by Web of Science on blockchain increased from 2 in 2013 (the first year in which publications on blockchain started to appear), to 4 in 2014, to 21 in 2015, to 118 in 2016, to 521 in 2017, to 1954 in 2018, to 3667 in 2019, and to 3945 in 2020. As of June 29, 1569 articles have been published in 2021.

Trilobites achieved their maximum generic diversity in the Cambrian, but the peak of morphological disparity of their cranidia occurred in the Middle to Late Ordovician. Early to middle Cambrian trilobites with a specialized cephalon are rare, especially among the ptychoparioids, a group of libristomates featuring the so-called “generalized” bauplan. Here we describe an unusual ptychopariid trilobite Phantaspis auritus gen. et sp. nov. from the middle Cambrian (Miaolingian, Wuliuan) Mantou Formation in the Shandong Province, North China. This new taxon is characterized by a cephalon with an extended anterior area of double-lobate shape resembling a pair of rabbit ears in later ontogenetic stages; a unique type of cephalic specialization that has not been reported from other trilobites. Such a peculiar cephalon as in Phantaspis provides new insights into the variations of cephalic morphology in middle Cambrian trilobites, and may represent a heuristic example of ecological specialization to predation or an improved discoidal enrollment.

Emergency logistics is a special logistics activity that protects the need of personnel,materials and funds in the event of a major emergency.In the post transportation model of emergency supplies,how to quickly and accurately deliver a large amount of rescue materials to the place of need is a huge challenge to emergency logistics.The logistics cost and the logistics time in the entire logistics process could be minimized by the bi-level planning method while meeting the demand for emergency supplies at the demand point.The bi-level programming model was constructed with the minimum logistics cost of the upper layer and the shortest logistics time of the bottom layer as the goal,and a hybrid tabu search genetic algorithm (HTSGA) was designed to solve the model,which solved the problem of emergency logistics transportation route optimization after the disaster.Finally,the experimental result comparison verified the effectiveness of the model and algorithm.

Abstract In this paper, we consider a strong global search algorithm which exhibits strong exploration ability in unmanned aerial vehicle (UAV)-aided networks. UAVs in wireless communication have aroused great interest recently due to its low cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. Artificial bee colony algorithm is a powerful approach for such a scene. However, due to its one-dimensional and greedy search strategy, it still suffers slow convergence speed. In the traditional version, three types of bees, including employed bees, onlooker bees, and scouts, are employed and they cooperate with each other to find the best food source position. Though different roles, these three types of bees play, there is no difference of division within the internal of each type of bees. Considering this phenomenon, this paper proposes a modified artificial bee colony algorithm with intellective search and special division (ABCIS) to enhance its performance, where different employed bees and different onlooker bees use different search strategies to search for food sources. Besides, the greedy selection method is also abandoned and the food sources’ positions are updated at each iteration. Under this circumstance, the whole population’s experience is fully utilized to guide bee’s search. Finally, to testify the proposed algorithms’ competitiveness, a series of benchmarks are adopted, and the experimental results demonstrate its superior performance among other state-of-the-art algorithm in UAV-aided networks.

Blockchain is a point-to-point distributed ledger technology based on cryptographic algorithms. However, the open and transparent blockchain ledger supplemented by statistical methods such as sociological mining and data mining has caused users' privacy to face major threats. Therefore, privacy protection has become a focus of current blockchain technology research. Ring signature technology is a commonly used encryption technology in the field of privacy protection. Therefore, this paper constructs a blockchain privacy protection scheme based on ring signature. This solution built a privacy data storage protocol based on the ring signature on the elliptic curve, and used the complete anonymity of the ring signature to ensure the security of data and user identity privacy in blockchain applications. The correctness and safety proof analysis of the proposed scheme were also carried out.

In order to trace the logistics information and make use of the characteristics of the Consortium Blockchain, a logistics information traceability model based on the Consortium chain is proposed. The research focuses on two key technologies in the model, namely Multi-center Practical Byzantine Fault Tolerance (MCPBFT) consensus algorithm and Information Matching mechanism. In MCPBFT algorithm, many nodes involved in logistics information are divided into multiple consensus sets, and the consistency protocol is improved into two phases based on PBFT algorithm. In the information matching mechanism, the authenticity of the recorded on-chain information is ensured by matching the updated logistics information with the information stored on the chain in advance. By analyzing the two key technologies, the feasibility and superiority of these two technologies are explained. Among them, MCPBFT algorithm can effectively improve the efficiency of consensus and ensure that the logistics information can be updated in time; the information matching mechanism can be safely applied to the traceability model and improve the practicability of the traceability model.

SLM (Selective Laser Melting) is a unique additive manufacturing technology which plays an irreplaceable role in the modern industrial revolution. 3D printers can directly process metal powder quickly to obtain the necessary parts faster. Shortly, it will be possible to manufacture products at unparalleled speeds. Advanced manufacturing technology is used to produce durable and efficient parts with different metals that have good metal structure performance and excellent metal thermal performance, to lead the way for laser powder printing technology. Traditional creative ways are usually limited by time, and cannot respond to customers’ needs fast enough; for some parts with high precision and complexity, conventional manufacturing methods are inadequate. Contrary to this, SLM technology offers some advantages, such as requiring no molds this decreases production time and helps to reduce costs. In addition, SLM technology has strong comprehensive functions, which can reduce assembly time and improve material utilization. Parts with complex structures, such as cavities and three-dimensional grids, can be made without restricting the shape of products. Products or parts can be printed quickly without the use of expensive production equipment. The product quality is better, and the mechanical load performance is comparable to traditional production technologies (such as forging). This paper introduces in detail the process parameters that affect SLM technology and how they affect SLM, commonly used metal materials and non-metallic materials, and summarizes the current research. Finally, the problems faced by SLM are prospected.

A SDN (Software-Defined Network) separates the control layer from the data layer to realize centralized network control and improve the scalability and the programmability. SDN also faces a series of security threats. An intrusion detection system (IDS) is an effective means of protecting communication networks against traffic attacks. In this paper, a novel IDS model for SDN is proposed to collect and analyze the traffic which is generally at the control plane. Moreover, network congestion will occur when the amount of data transferred reaches the data processing capacity of the IDS. The suggested IDS model addresses this problem with a probability-based traffic sampling method in which the genetic algorithm (GA) is used to approach the sampling probability of each sampling point. According to the simulation results, the suggested IDS model based on GA is capable of enhancing the detection efficiency in SDNs.

IoT (Internet of Things) involves a wide range of fields, and its application scenarios are complex and diverse. Failure of security defense in any link of IoT may lead to huge information leakage and immeasurable losses. IoT security problem is affecting and restricting its application prospect, and has become one of the hotspots in the field of IoT. ONS (Object Naming Service) is responsible for mapping function from EPC code information to URI (Uniform Resource Identifier). The security mechanism of ONS has been extensively studied by more and more scholars in recent years. The purpose of this paper is to apply Rasch, a famous psychological model, to ONS resolution security technology. Through observing the past resolution result, the ability of ONS resolution and the difficulty of EPC code can be calculated. With the difference between the ability of ONS resolution and the difficulty of EPC code, this model can predict the probability of the ONS future resolution to achieve the purpose of privacy protection in IoT addressing. Through simulation and Ministep software, the feasibility of the model is verified.

Since the Internet of Things (IoT) applications today employ many different sensors to provide information, a large number of the Bluetooth low energy (BLE) devices will be developed as part of the IoT systems. The low-power and low-cost requirements of all BLE nodes are among the most challenging issues when supporting the neighbor discovery process (NDP) for such a large number of devices. Since the parameter settings are essential to achieve the required performance for the NDP, a parameter configuration method for neighbor discovery in BLE can be beneficial for determining some critical parameter metrics, such as the AdvInterval, ScanInterval, and ScanWindow. In this paper, we propose a parameter configuration scheme to balance the tradeoff between discovery latency and energy consumption. In the proposed scheme, the neighbor discovery latency and average energy consumption are expressed based on the Chinese Remainder Theory (CRT). With the expected primary performance, the parameters are configured accordingly using the parameter configuration algorithm. The experimental results show that the performance of the neighbor discovery varies with the parameter settings. Furthermore, two typical IoT applications are assessed in this paper. Compared with the simulation results, the proposed parameter configuration scheme can achieve high accuracy.

The rapid adoption of mobile devices has dramatically changed the access to various networking services and led to the explosion of mobile service traffic. Mobile service traffic classification has been a crucial task that attracts strong interest in mobile network management and security as well as machine learning communities for past decades. However, with more and more adoptions of encryption over mobile services, it brings a lot of challenges about mobile traffic classification. Although classical machine learning approaches can solve many issues that port and payload-based methods cannot solve, it still has some limitations, such as time-consuming, costly handcrafted features, and frequent features update. With the excellent ability of automatic feature learning, Deep Learning (DL) undoubtedly becomes a highly desirable approach for mobile services traffic classification, especially encrypted traffic. This survey paper looks at emerging research into the application of DL methods to encrypted traffic classification of mobile services and presents a general framework of DL-based mobile encrypted traffic classification. Moreover, we review most of the recent existing work according to dataset selection, model input design, and model architecture. Furthermore, we propose some noteworthy issues and challenges about DL-based mobile services traffic classification.

Mobile devices now serve to pay for goods and services by means of the transmission of data, a system known as mobile payments. Mobile payment is receiving growing attention globally, from consumers to merchants, as an alternative to using cash, check, or credit cards. Most encryption techniques applied in mobile payment are based on traditional public key infrastructure. However, the traditional public key encryption algorithm has higher requirements for hardware, which is not suitable for mobile terminals of limited computing resources. In addition, these public key encryption algorithms are vulnerable to quantum computing attacks and the availability of practical quantum computer is approaching faster than previously believed. Since mobile payment is facing more and more security issues, how to complete the payment process effectively and securely becomes a problem urgently to be solved. In this paper, we firstly analyze the problem of the signature scheme for mobile payment and propose an improved signature scheme based on the Number Theory Research Unit (NTRU). The results of the experiment show that the proposed signature scheme is secure and efficient which can raise the speed and probability of generating reasonable signature value. Second, we introduce a new public key infrastructure NTRU-WPKI and design a third-party mobile payment model based on NTRU-WPKI to increase the efficiency and security. Finally, by doing the overall simulation, we prove that our model is highly effective and secure.

Software Defined Networking (SDN) centrally manages the network data layer to improve the programmability and flexibility of networks by the controller. Because of centralized control, SDN is vulnerable to Distributed Denial of Service (DDoS) attacks. In order to protect the security of SDN, a method based on K-means++ and Fast K-Nearest Neighbors (K-FKNN) is proposed for DDoS detection in SDN, and the modular detection system is presented in the controller. The detailed experiments are conducted to evaluate the system performance. The results of the experiments show that K-FKNN improves the detection accuracy and efficiency of K-Nearest Neighbors (KNN), and has high precision and stability of DDoS detection in SDN.

In this paper, aiming at the payload residual vibration problem, a dynamic differential evolutionary algorithm-based sliding-mode controller (DDE-SMC) is designed for the under-actuated crane systems. According to the under-actuated crane systems mechanism of action, a fusion sliding function combined with position and angle sliding function is first given, and the corresponding control law by incorporating switching and equivalent control law is designed during the control process. Moreover, in order to configure the control parameter efficiently, the DDE algorithm is proposed and utilized for improving the anti-swing control performance. Through computer simulation and comparisons under different operation conditions, the proposed DDE-SMC demonstrates the effectiveness in damping the payload oscillations of the under-actuated crane system.

For a long time, it has been frequently reported that outdoor adventurers get lost and die. During the course of an outdoor adventure, it is a matter of life and death to locate the lost adventurer and start the emergency rescue after determining that someone was lost. In this paper, a new wireless sensor network localization algorithm for outdoor adventures is proposed. This algorithm is notably easy to implement, with rather low implementation overhead. In the proposed localization algorithm, first, the Fermat point model is used to divide the original triangular pyramid constructed by the neighbor anchor nodes of the unknown node into four smaller sub-triangular pyramids. Next, the available sub-triangular pyramid in which the unknown node is located can be further divided into a set of irregularly shaped subspaces by the mid-perpendicular plane model. Finally, the centroid of the available subspace can be computed and regarded as the location of the unknown node. Simulation results show that the proposed localization algorithm can remarkably improve the localization accuracy.

With the continuous innovation and development of agent technology, the application of smart technique is gradually changing to intelligent and human-centered technology. Also, intelligent technique is involved in many fields, and software defined network (SDN) is one of them. SDN is one of the most popular and most promising new technologies in the current network area, which has particular advantages over other traditional networks. However, most of the research work focus on the efficiency and function of SDN, while the access control and ciphertext search function in SDN have not been taken into full consideration. In this paper, we propose a new smart system based on SDN. It also equips with the functions of fine-grained access control and searchable encryption function, which combines a novel attribute-based searchable encryption scheme (ABSES) with SDN architecture. Our construction realizes inter-domain information sharing, fine-grained access control, and ciphertext searchable function. The proposed ABSES effectively ensures the security of ciphertext stored in data center, and that users cannot obtain unauthorized information or illegal network resources without any certification. It reduces network bandwidth and local resources, while improving the scalability and flexibility of SDN access control at the meantime. Finally, our ABSES is proved to be correct and secure under chosen-keyword attack. The comparison with other representative attribute-based searchable encryption schemes demonstrates that our ABSES has somewhat better performance. The proposed new smart system can be used in human life and add more convenience to our daily life.

Human-centered smart devices profiling with Wi-Fi networks has received much attention from both research and industry, especially those network operators and security agencies who aim to enhance user experience and security of home network as well as free Wi-Fi services. One type of such profiling is the extraction of mobile phone numbers. In traditional cellular networks, such as 3G and 4G, mobile phone number extraction can be achieved from the analysis of the authentication signaling. However, this method cannot be used in the broadband network environment, e.g., Wi-Fi. Operators and security agencies of Wi-Fi networks often apply manual statistics, telephone inquiries or user input for information. Unfortunately, those traditional methods are inefficient in practice. Moreover, authenticity cannot be guaranteed with the traditional methods. In this paper, we propose a smart method for mobile phone number extraction in smart home networks and systems. In particular, the proposed method is based on deep packet inspection of home broadband traffic. To improve the efficiency and accuracy of detection, we further propose a smart automated signature extraction method of mobile phone numbers from home network traffic. Our proposed method can achieve 86.2% accuracy in the real-life human-centered smart home network test.

As a new type of network structure, the Software Defined Network (SDN) provides a new solution for network flow management and optimization, which has made the accurate detection of anomaly SDN flows a hot research topic. This paper presents an SDN-based flow detection method, builds structures for detecting anomaly SDN flows and performs classification detection on the flows using the double P-value of transductive confidence machines for K-nearest neighbors algorithm. The experimental results show that the algorithm proposed achieves a lower false positive rate, higher precision, and better adaptation to the SDN environment than do other algorithms of the same type.

In the era of big data, with the increasing number of audit data features, human-centered smart intrusion detection system performance is decreasing in training time and classification accuracy, and many support vector machine (SVM)-based intrusion detection algorithms have been widely used to identify an intrusion quickly and accurately. This paper proposes the FWP-SVM-genetic algorithm (GA) (feature selection, weight, and parameter optimization of support vector machine based on the genetic algorithm) based on the characteristics of the GA and the SVM algorithm. The algorithm first optimizes the crossover probability and mutation probability of GA according to the population evolution algebra and fitness value; then, it subsequently uses a feature selection method based on the genetic algorithm with an innovation in the fitness function that decreases the SVM error rate and increases the true positive rate. Finally, according to the optimal feature subset, the feature weights and parameters of SVM are simultaneously optimized. The simulation results show that the algorithm accelerates the algorithm convergence, increases the true positive rate, decreases the error rate, and shortens the classification time. Compared with other SVM-based intrusion detection algorithms, the detection rate is higher and the false positive and false negative rates are lower.

Overhead crane is regarded as a kind of complex nonlinear dynamic system in which the underactuated characteristic makes the controller hardly control the space of load swing effectively. In addition, the system internal and external uncertain factors also give rise to the adverse effect for control performance. Therefore, aiming at these problems, a type-2 fuzzy theory-based sliding mode controller (type-2 FSMC) is designed for damping the oscillation of the payload in which the proposed type-2 FSMC inherits the benefits of type-2 fuzzy logic theory and sliding mode control theory for improving anti-swing control performance. Furthermore, in order to configure the type-2 FSMC parameter efficiently, an adaptive differential evolutionary algorithm-based parameter tuning mechanism is presented for acquiring the optimal control performance. Through computer simulation and comparisons under different operation conditions, the optimal type-2 FSMC demonstrates the effectiveness in eliminating the payload residual vibration.

Cognitive radio (CR) is promising for smart grids (SGs) in terms of overcoming the problem of spectrum inefficiency and interference mitigation. With the intensive research into full-duplex communication technologies, the licensed spectrum available for CR-based SGs (CR-SGs) may be more complex and difficult to use, as more primary traffic exists on the licensed spectrum. As a result, CR-SGs may experience more severe interference from primary users (PUs), and the corresponding transmission opportunities may be impaired. In this paper, based on distinctive polarization features of samples from different PUs, the signal polarization of each PU is extracted from the hybrid samples by studying the polarization similarity of the samples. Based on the signal polarization derived for each PU, the transmitting opportunities for CR-SGs can be derived. Both the interference from CR-SGs to PUs and the throughput of CR-SGs are examined. Numerical results show that the spectrum opportunities for CR-SGs can be guaranteed and that the interference inflicted on PUs is at a satisfactory level.

In human-centered smart systems, traditional cloud computing has been well adopted for several years for supporting IoT applications of smart systems, however, the requirements of the IoT applications vary due to different tasks in the smart systems. In order to offer location-aware, latency-sensitive monitoring, and intelligent control for IoT applications in the smart systems like smart home or smart grid, we introduce fog computing and discuss the combination of fog computing and data center hoping to improve the quality of experience of users in human-centered smart systems. In this paper, we propose a three-layer architecture of the data center by introducing the fog computing layer between the traditional centralized data center and terminal nodes. Performance related service latency of different cloud services is modeled and evaluated based on it. It is verified that the new architecture has a great improvement in terms of service latency and network load by simulation experiments.

Intelligent decision is the key technology of smart systems. Data mining technology has been playing an increasingly important role in decision-making activities. Frequent itemset mining (FIM), as an important step of association rule analysis, is becoming one of the most important research fields in data mining. Weighted FIM in uncertain databases should take both existential probability and importance of items into account in order to find frequent itemsets of great importance to users. However, the introduction of weight makes the weighted frequent itemsets not satisfy the downward closure property any longer. As a result, the search space of frequent itemsets cannot be narrowed according to downward closure property which leads to a poor time efficiency. In this paper, the weight judgment downward closure property for the weighted frequent itemsets and the existence property of weighted frequent subsets are introduced and proved first. Based on these two properties, the Weight judgment downward closure property-based FIM (WD-FIM) algorithm is proposed to narrow the searching space of the weighted frequent itemsets and improve the time efficiency. Moreover, the completeness and time efficiency of WD-FIM algorithm are analyzed theoretically. Finally, the performance of the proposed WD-FIM algorithm is verified on both synthetic and real-life data sets.

The diversity and complexity of network data bring great challenges to data classification technology. Feature selection has always been an important and difficult problem in classification technology. To improve the classification performance of the classifier, an improved feature selection algorithm, FACO, is proposed by combining the ant colony optimization algorithm and feature selection. A fitness function is designed, and the pheromone updating rule is optimized to effectively eliminate redundant features and prevent feature selection from falling into a local optimum. The experimental results show that the classification accuracy of the classifier can be significantly improved by selecting the data features using the FACO algorithm, which is of practical significance.

Male-male vocal competition in anuran species is critical for mating success; however, it is also energetically demanding and highly time-consuming. Thus, we hypothesized that males may change signal elaboration in response to competition in real time. Male serrate-legged small treefrogs (Kurixalus odontotarsus) produce compound calls that contain two kinds of notes, harmonic sounds called ‘A notes’ and short broadband sounds called ‘B notes’. Using male evoked vocal response experiments, we found that competition influences the temporal structure and complexity of vocal signals produced by males. Males produce calls with a higher ratio of notes:call, and more compound calls including more A notes but fewer B notes with contest escalation. In doing so, males minimize the energy costs and maximize the benefits of competition when the level of competition is high. This means that the evolution of sexual signal complexity in frogs may be susceptible to selection for plasticity related to adjusting performance to the pressures of competition, and supports the idea that more complex social contexts can lead to greater vocal complexity.

Ciphertext policy attribute-based encryption (CP-ABE) is a promising cryptographic technique for fine-grained access control of outsourced data in the cloud. However, some drawbacks of key management hinder the popularity of its application. One drawback in urgent need of solution is the key escrow problem. We indicate that front-end devices of clients like smart phones generally have limited privacy protection, so if private keys are entirely held by them, clients risk key exposure that is hardly noticed but inherently existed in previous research. Furthermore, enormous client decryption overhead limits the practical use of ABE. In this paper, we propose a collaborative key management protocol in CP-ABE. Our construction realizes distributed generation, issue and storage of private keys without adding any extra infrastructure. A fine-grained and immediate attribute revocation is provided for key update. The proposed collaborative mechanism effectively solves not only key escrow problem but also key exposure. Meanwhile, it helps markedly reduce client decryption overhead. A comparison with other representative CP-ABE schemes demonstrates that our scheme has somewhat better performance in terms of cloud-based outsourced data sharing on mobile devices. Finally, we provide proof of security for the proposed protocol.

The developments of Narrow Band Internet of Things have attracted much attention from both academia and industry nowadays. While Narrow Band Internet of Things is gradually designed for commercial applications, it also confronts with several security concerns such as identification and privacy. Blockchain technique is equipped with authentication, auditing, and accountability, and thus it can serve as a promising tool for providing secure data communication on the network. To enhance the security level and achieve reliable data authentication and accountability for Narrow Band Internet of Things system, in this article, we combine the merits of blockchain technique and propose a blockchain-based architecture for Narrow Band Internet of Things. The presented architecture consists of three layers which are sensing layer, transport layer, and transaction layer. We describe the interaction processes between each layer in detail and construct the algorithms which are used to conduct data authentication and blockchain generation. By evaluation, our scheme is demonstrated to obtain favorable performances in terms of privacy, authentication, accountability, and efficiency, which makes it an appropriate method to be applied for secure data communication in Narrow Band Internet of Things systems.

Visual browse and exploration in motion capture data take resource acquisition as a human–computer interaction problem, and it is an essential approach for target motion search. This paper presents a progressive schema which starts from pose browse, then locates the interesting region and then switches to online relevant motion exploration. It mainly addresses three core issues. First, to alleviate the contradiction between the limited visual space and ever-increasing size of real-world database, it applies affinity propagation to numerical similarity measure of pose to perform data abstraction and obtains representative poses of clusters. Second, to construct a meaningful neighborhood for user browsing, it further merges logical similarity measures of pose with the weight quartets and casts the isolated representative poses into a structure of phylogenetic tree. Third, to support online motion exploration including motion ranking and clustering, a biLSTM-based auto-encoder is proposed to encode the high-dimensional pose context into compact latent space. Experimental results on CMU’s motion capture data verify the effectiveness of the proposed method.

IPv6 over Bluetooth Low Energy (6LoBLE) can realize the seamless connection between Bluetooth Low Energy (BLE) devices and the Internet, solve the restrictions on Bluetooth applications as short distance communication solutions, and advance the development of applications of smart cities, such as smart buildings and smart city management. Since neighbor discovery is the first step for BLE devices to establish a connection with each other, this paper proposes a novel neighbor discovery protocol for IPv6 over BLE mesh networks, including the structure of IPv6 over BLE mesh networks, the networking solutions, and IPv6 address auto-configuration and update mechanism. Furthermore, we analyze and simulate the performance of the neighbor discovery protocol utilizing the Revierbed simulator, and the results show the effectiveness of the proposed neighbor discovery protocol even in a crowded environment.

IETF 6LoWPAN working group is engaged in the IPv6 protocol stack research work based on IEEE802.15.4 standard. In this working group, the routing protocol is one of the important research contents. In the 6LoWPAN, HiLow is a well-known layered routing protocol. This paper puts forward an improved hierarchical routing protocol GHiLow by improving HiLow parent node selection and path restoration strategy. GHiLow improves the parent node selection by increasing the choice of parameters. Simutaneously, it also improves path recovery by analysing different situations to recovery path. Therefore, GHiLow contributes to the ehancement of network performance and the decrease of network energy consumption.

Background The evolution of exaggerated vocal signals in anuran species is an important topic. Males and females have both evolved the ability to discriminate communication sounds. However, the nature of sexual dimorphism in cognition and sensory discrimination and in the evolution and limitation of sexual signal exaggeration remain relatively unexplored. Methods In the present study, we used male calls of varied complexity in the serrate-legged small treefrog, Kurixalus odontotarsus, as probes to investigate how both sexes respond to variations in call complexity and how sex differences in signal discrimination play a role in the evolution of sexual signal exaggeration. The compound calls of male K. odontotarsus consist of a series of one or more harmonic notes (A notes) which may be followed by one or more short broadband notes (B notes). Results Male playback experiments and female phonotaxis tests showed that increasing the number of A notes in stimulus calls elicits increased numbers of response calls by males and increases the attractiveness of the stimulus calls to females. The addition of B notes, however, reduces male calling responses. Moreover, call stimuli which contain only B notes suppress spontaneous male calling responses. Phonotaxis experiments show that females prefer calls with greater numbers of A notes and calls containing both A notes and B notes, but do not prefer calls with only B notes. Discussion Male-male competition and female choice appear to have played different roles in the evolution and limitation of signal complexity in K. odontotarsus. These results provide new insights into how exaggerated compound signals evolve and how signal complexity may be limited in anurans.

Power control technology is widely used in wireless sensor networks to improve network performance. The asymmetric transmission power strategy used in power control technology causes the re-emergence of hidden-node problems and more serious exposed-node problems. This article analyzes the reasons of these problems and proposes the concept of the pan-hidden node and pan-exposed node. To avoid pan-hidden-node problems and mitigate pan-exposed-node problems, a concurrent transmission mechanism is presented. The concurrent transmission mechanism contains a strict time schedule for different categories of frames and an interference degree criterion to ensure concurrent transmission. The rigorous time schedule can guarantee that different classes of frames do not interfere with one another. The interference among the frames of the same type is constrained by the interference degree criterion. Finally, the performance of the concurrent transmission mechanism is evaluated using simulation experiments.

Given that current Internet of Things (IoT) applications employ many different sensors to provide information, a large number of the Bluetooth low energy (BLE) devices will be developed for IoT systems. Developing low-power and low-cost BLE advertisers is one of most challenging tasks for supporting the neighbor discovery process (NDP) of such a large number of BLE devices. Since the parameter setting is essential to achieve the required performance for the NDP, an energy model of neighbor discovery in BLE networks can provide beneficial guidance when determining some significant parameter metrics, such as the advertising interval, scan interval, and scan window. In this paper, we propose a new analytical model to characterize the energy consumption using all possible parameter settings during the NDP in BLE networks. In this model, the energy consumption is derived based on the Chinese remainder theorem (CRT) for an advertising event and a scanning event during the BLE NDP. In addition, a real testbed is set up to measure the energy consumption. The measurement and experimental results reveal the relationship between the average energy consumption and the key parameters. On the basis of this model, beneficial guidelines for BLE network configuration are presented to help choose the proper parameters to optimize the power consumption for a given IoT application.

Most species are believed to evolve larger body sizes over evolutionary time. Previous studies have suggested that sexual selection, through male-male competition and female choice, favors larger males. However, there is little evidence of selection against large size. The female serrate-legged small treefrogs (Philautus odontotarsus) must carry passive males from leks to breeding grounds over relatively long distances after amplexus to find a suitable place to lay eggs. The costs of large male size may therefore decrease mating success due to reduced agility and/or higher energy requirements. Thus, we hypothesized that selection would not favor larger males in P. odontotarsus. Females can assess male body size on the basis of the dominant frequency of male calls in frogs. To assess female P. odontotarsus preferences for a potential mate's body size, male calls of high, average and low dominant frequency were played back to the females in phonotaxis experiments. Results showed that most females prefer the advertisement call with average dominant frequency. In addition, we compared the body mass distribution of amplectant males with that of single males in nature. The body masses of amplectant males are more narrowly distributed in the intermediate range than that of single males. The phonotaxis results and the data of actual female preferences in the field show that females strongly prefer potential mates of mean body sizes, consistent with the view that, in this species at least, larger males are not always perceived as better by females. In the present study, P. odontotarsus provides an example of an amphibian species in which large size does not have an advantage in mating success for males. Instead, our results provide evidences that stabilizing selection favors the optimal intermediate size of males.

In wireless sensor networks, cooperative communication can combat the effects of channel fading by exploiting diversity gain achieved via cooperation communication among the relay nodes. A cooperative automatic retransmission request (ARQ) protocol based on two-relay node selection was proposed in this paper. A novel discrete time Markov chain model in order to analyze the throughput and energy efficiency was built, and system throughput and energy efficiency performance of proposed protocol and traditional ARQ protocol were studied based on such model. The numerical results reveal that the throughput and energy efficiency of the proposed protocol could perform better when compared with the traditional ARQ protocol.