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Abstract Background Metastasis to the thyroid gland from lung adenocarcinoma is rare and challenging to diagnose due to similar histopathological features. This study aimed to analyze the clinicopathological characteristics of and treatment strategies for lung adenocarcinoma metastasis to the thyroid based on 11 years of institutional experience. Methods A retrospective study included patients with lung adenocarcinoma metastasis to the thyroid at our center from 2010 to 2023. Clinicopathological features and clinical outcomes were analyzed. Results Among 9714 lung adenocarcinoma patients, nine patients (five females, 55.6%) were diagnosed with thyroid metastasis, presenting primarily with cough symptoms. Most patients (88.9%) had synchronous tumors, whereas a minority (11.1%) had metachronous tumors. The median time from primary tumor diagnosis to metastasis was 4.8 months. Most patients developed bilateral thyroid metastases (88.9%). Diagnosis of thyroid metastasis was primarily through fine‐needle aspiration (FNA), with one case misdiagnosed as papillary thyroid carcinoma. Immunohistochemical staining revealed thyroid transcription factor‐1 (TTF‐1) and novel aspartic proteinase of pepsin family A (Napsin‐A) positivity and paired box 8 (PAX8) negativity. Genetic testing found epidermal growth factor receptor mutations in 71.4% of patients. The individualized comprehensive therapy included surgery, chemotherapy, immunotherapy, and targeted and supportive therapy. The median overall survival was 56.0 months, with a progression‐free survival of 12.7 months. Kaplan–Meier (K–M) analysis suggested improved survival with no advanced symptoms (p = 0.03) and targeted therapies (p = 0.05). Conclusions Lung adenocarcinoma metastasis to the thyroid is a rare disease, with an incidence of 0.1% among lung adenocarcinoma patients. Early treatment after symptom onset and personalized targeted therapies may improve prognosis. Despite rapid disease progression, favorable outcomes can be achieved with comprehensive treatment.

The unpredictable environmental changes cause large fluctuations in Wi-Fi signals, and it is difficult to quantify the static and behaviorally-aware dynamic components in the subcarriers, so it is not possible to accurately portray the fluctuating form of dynamic breathing characteristics.Based on this, a subcarrier mutual information breathing perception (SMIBP) system was proposed.Firstly, a form of inscribing dynamic component information (DCI) was proposed, and mutual information theory was utilized to extract the dynamic component information representing respiration in subcarriers.Then, analytic hierarchy process (AHP) was used to combine the subcarriers to maximize the dynamic characteristics of the respiratory signals to obtain the reconstructed DCI sensory base signals.Finally, the respiration rate was obtained by combining the wavelet transform and peak detection method, which revealed of a theoretical pathway representing the dynamic component of human respiration.Simulation results show that the proposed system can better characterize the dynamic breathing component in each subcarrier, and can significantly improve the sensing accuracy and range of Wi-Fi for small-scale actions in different scenarios.

Aiming at the problem of unreasonable resource allocation caused by the unloading decision in the multi-vehicle and multi-server IoV scenario, a two-stage heuristic IoV task offloading strategy was proposed.This strategy used the improved hybrid genetic algorithm (IHGA) and the improved artificial fish swarm algorithm (AFSA), combined with the system’s internal average overhead, delay and energy consumption requirements, the two improved algorithm was used for multiple iterations to achieve optimal resource allocation in the process of task unloading.The simulation results show that the proposed scheme can effectively reduce the system internal overhead and improve the task offloading efficiency compared with the benchmark scheme.

To solve the problems of the lack of longitudinal position information in indoor positioning, an indoor positioning algorithm based on effective AP selection and multi-class LDA was proposed.In the offline stage, the AP selection algorithm based on stability and difference was used to extract effective AP set in the fingerprint area, and the multi-class LDA algorithm was adopted to train the fingerprint data of effective AP set at different heights to obtain longitudinal position information recognition model.In the online stage, the AP information alike the offline effective AP subset was input into longitudinal position information recognition model to obtain recognition result, and then the online fingerprint plane positioning was completed by the positioning algorithm based on the fingerprint database partition.Simulation results demonstrate that the accuracy of the proposed algorithm for determining longitudinal floors can reach 98%.

In order to solve problems of high power consumption, spectrum shortage and low energy efficiency in the ultra-intensive 5G mobile communication scenario, a resource allocation algorithm based on the maximum energy efficiency for the two-layer heterogeneous cellular non-orthogonal multiple access network was proposed.The original NP-hard optimization problem on the downlink communication link of ultra-dense scene was divided into two subproblem, such as frequency resource allocation and power allocation, which became a deterministic constraint optimization problem.The frequency resource allocation scheme of different user groups was obtained by using base station clustering based on the improved k-means algorithm and users grouping based on spectral clustering algorithm.The fraction of energy efficiency optimization was transformed into a solvable continuous convex optimization problem and power distribution was realized by Dinkelbach method, and the Lagrange multiplier iterative algorithm, respectively.Jointly optimize system energy efficiency in terms of base station clustering, user grouping, resource block allocation and power allocation, which minimized the inter-cluster interference and intra-cluster interference of the base station efficiently.The simulation results show that the proposed algorithm is better on energy efficiency and computational efficiency compared with existing algorithms.

Based on reviews of the work regarding dense connection, the strategic technologies for dense connection in the upcoming future mobile communication were illuminated.The state of the art of dense connection, including network architecture and related technologies, the transmission technology of dense connection, and the access technology of dense connection were analyzed.A flexible access network structure with verity of connection forms coexisting was proposed.Aiming at the asymmetry of uplink and downlink coverage and considering the QoS requirements of users, a dense terminal access scheme based on the uplink-and-downlink-separated network architecture was proposed.The necessity of the three-dimensional network architecture and the integration of communication and computing to meet the needs of large-scale networking and dense connection were illuminated.Based on technologies such as terahertz and block chain, the future strategic exploratory trends such as the upward extension of frequency bands and the sharing of spectrum resources were discussed and prospected.

To solve the access selection problem of heterogeneous wireless network, an access selection algorithm based on dragonfly algorithm (DA) optimized fuzzy neural network (FNN) was proposed, considering the user’s business type and network state.In view of the low convergence speed of the fuzzy neural network, the dragonfly algorithm was used to optimize the membership function parameters of the second and fifth layers of the fuzzy neural network, so as to obtain the initial value of membership function parameters of the fuzzy neural network.The most suitable network was selected for the users according to their preference to the network and the output score of the network under different business types.The experimental results show that dragonfly algorithm optimization can improve the convergence speed of fuzzy neural network, improve system throughput, reduce blocking rate, and reduce switching times to some extent.

For the fluctuation of CFCC caused by environmental noise is the main reason for the low accuracy of keystroke detection,the spatial characteristics of adjacent between CFCC were studied,and the spatial gradient structure of CFCC based on points was established.On this basis,the effect of CFCC spatial gradient on keystroke content recognition and the selection of precise neighborhood points were studied on training and testing.Finally,a high-robustness keystroke recognition algorithm based on acoustic signals was constructed.Extensive experiments in different environments demonstrate that the proposed CFCC spatial gradient sound feature achieves great performance and the recognition accuracy is 96.15%.

In order to optimize the energy efficiency for relay-aided D2D communication underlaying cellular network,an algorithm was proposed while guaranteeing the quality of service of D2D users and cellular users.Firstly,the problem was formulated as a mixed integer nonlinear fractional programming problem,with the goal of maximizing the energy efficiency of all D2D users.Then,the maximizing energy efficiency problem was divided into three sub-problems,such as power allocation,relay selection and subcarrier assignment.Finally,the power allocation problem,the relay selection problem and the subcarrier assignment problem were addressed by Dinkelbach method and Lagrange multiplier method,Q-learning and Hungarian algorithm,respectively.The simulation results show that the proposed algorithm can improve the total energy efficiency of D2D users compared with existing algorithms.

The situation of high conflict between evidences and one ballot veto cannot be solved by classical D-S rule,and the results obtained from classical D-S rule are contrary to the facts.To solve this problem,a new standard to measure conflicts between evidences was proposed based on the combination of Pignistic function transformation and correlation coefficient,and also a novel kind of weighted combination method which was applied to measure conflicts between evidences was put forward according to the standard.After that,a support matrix was constructed based from which the credibility of evidence was obtained,and the weighted average method was used to revise the evidence.Finally,the combination was accomplished by using Dempster’s rule.The result of numerical examples shows that it’s effective to solve the combination of conflicting evidence.Compared with other methods,the proposed method has good astringency.

Research on IoT that merely aims at connecting and communicating is about to past. Thereafter, general objects should have the capability to learn, think, and understand both physical and social areas by themselves. Cognitive Internet of Things (CIoT) attempts to empower the current IoT with a “brain” for high-level intelligence, requiring networks to have the ability to bridge the physical and social worlds. This attempt means matching equipment and resources with people and their behavior. Therefore, accurate location information is crucial for equipment connecting to CIoT. This endeavor sets a higher requirement for the localization technology of wireless sensor networks in terms of accuracy, energy, and efficiency compared with that in the past. In this paper, we propose an efficient and accurate mobile anchor node assisted localization algorithm for WSNs based on diameter-varying spiral line (LDVSL), which broadcasts coordinates of the anchor node to assist localizing unknown sensor nodes. The proposed algorithm has two main innovations. First, we obtain the mobile anchor node position through a time and angle mechanism instead of GPS, given the unique characteristics of the diameter-varying spiral line. Second, the linear fitting method is adapted to select the key virtual node, which has the real maximum received signal strength indicator. Simulations indicate that the proposed LDVSL algorithm outperforms other similar algorithms in terms of average localization error and positionable node ratio. The simulations also show that the LDVSL is not affected by obstacles seriously and has good robustness. The LDVSL has a wide prospect of application in CIoT.

Video-based smoke detection is an effective method for fire alarm systems. Given the widespread use of high-definition cameras, a smoke detection method for high-definition video is needed. This paper proposes a smoke-detection framework for high-definition video, in which the main idea is to use the small smoke image blocks to match the image features of the motion area in the video and to use the support vector machine classifier for smoke recognition. The ViBe algorithm and other methods are used to effectively extract the areas for classification. This detection framework consists of spatial- and frequency-domain features. In the extraction of frequency domain features, we use local phase quantization (LPQ) features. In the local texture features of the spatial domain, we add the compensation of adjacent pixels and consider the gradient of the symmetrical pixels using the center-symmetric local binary pattern feature. To improve results, we also propose the trisection feature fusion scheme for features in the spatial and frequency domains. The experiments show that using the feature extraction and fusion schemes, our smoke-detection framework achieves the good performance in the detection of smoke in the video from different datasets.

Device-to-device (D2D) communications, as one of the promising technology for the future network, allow two devices in proximity to communicate directly with each other to alleviate the data traffic load for the base station (BS). In this paper, we analysis the joint relay selection and resource allocation scheme for relay-aided D2D communication networks. The objective is to maximize the total transmission data rates of the system while guaranteeing the minimum quality of service (QoS) requirements for both cellular users (CUs) and D2D users (DUs). We first propose a social-aware relay selection algorithm with low computational complexity to obtain the suitable relay nodes (RNs) for D2D links. Then the power control schemes are considered when a D2D link works in the D2D direct or relay mode to maximize the system transmission data rates. On basis of this, we propose a greedy-based mode selection and channel allocation algorithm, which can not only allocate the appropriate channel resource for D2D links, but also select the optimal communication mode for them. Numerical results demonstrate that the proposed scheme is capable of substantially improving the performance of the system compared with the other algorithms.

In order to solve the problem of interference caused by D2D users reuse cellular resources in cellular network,an algorithm of interference control and resource allocation of D2D communication in single cell cellular system was proposed.Firstly,an interference graph was constructed according to the interference between users in the system to find the reusable channel resource for D2D users.Then,the channel resource pre-allocation and the channel switching policy were performed for D2D users in sequence according to the preset D2D user priority.The simulation results show that the proposed algorithm can significantly improve the system throughput and the access rate of D2D users so that the system can achieve better performance in the presence of small or large number of D2D users.

In a vehicular ad hoc network (VANET), vehicles always move in high-speed which may cause the network topology changes frequently. This is challenging for routing protocols of VANET. Greedy Perimeter Stateless Routing (GPSR) is a representative routing protocol of VANET. However, when constructs routing path, GPSR selects the next hop node which is very easily out of the communication range in greedy forwarding, and builds the path with redundancy in perimeter forwarding. To solve the above-mentioned problems, we proposed Maxduration-Minangle GPSR (MM-GPSR) routing protocol in this paper. In greedy forwarding of MM-GPSR, by defining cumulative communication duration to represent the stability of neighbor nodes, the neighbor node with the maximum cumulative communication duration will be selected as the next hop node. In perimeter forwarding of MM-GPSR when greedy forwarding fails, the concept of minimum angle is introduced as the criterion of the optimal next hop node. By taking the position of neighbor nodes into account and calculating angles formed between neighbors and the destination node, the neighbor node with minimum angle will be selected as the next hop node. By using NS-2 and VanetMobiSim, simulations demonstrate that compared with GPSR, MM-GPSR has obvious improvements in reducing the packet loss rate, decreasing the end-to-end delay and increasing the throughput, and is more suitable for VANET.

ABSTRACT Highly transmissible influenza viruses (IV) must remain stable and infectious under a wide range of environmental conditions following release from the respiratory tract into the air. Understanding how expelled IV persist in the environment is critical to limiting the spread of these viruses. Little is known about how the stability of different IV in expelled aerosols is impacted by exposure to environmental stressors, such as relative humidity (RH). Given that not all IV are equally capable of efficient airborne transmission in people, we anticipated that not all IV would respond uniformly to ambient RH. Therefore, we have examined the stability of human-pathogenic seasonal and avian IV in suspended aerosols and stationary droplets under a range of RH conditions. H3N2 and influenza B virus (IBV) isolates are resistant to RH-dependent decay in aerosols in the presence of human airway surface liquid, but we observed strain-dependent variations in the longevities of H1N1, H3N2, and IBV in droplets. Surprisingly, low-pathogenicity avian influenza H6N1 and H9N2 viruses, which cause sporadic infections in humans but are unable to transmit person to person, demonstrated a trend toward increased sensitivity at midrange to high-range RH. Taken together, our observations suggest that the levels of vulnerability to decay at midrange RH differ with virus type and host origin. IMPORTANCE The rapid spread of influenza viruses (IV) from person to person during seasonal epidemics causes acute respiratory infections that can lead to hospitalizations and life-threatening illness. Atmospheric conditions such as relative humidity (RH) can impact the viability of IV released into the air. To understand how different IV are affected by their environment, we compared the levels of stability of human-pathogenic seasonal and avian IV under a range of RH conditions and found that highly transmissible seasonal IV were less sensitive to decay under midrange RH conditions in droplets. We observed that certain RH conditions can support the persistence of infectious viruses on surfaces and in the air for extended periods of time. Together, our findings will facilitate understanding of factors affecting the persistence and spread of IV in our environment.

In order to solve the problem of multi-tag anti-collision in radio frequency identification systems, a multi-tag anti-collision protocol based on 8-ary adaptive pruning query tree is proposed in this article. According to Manchester code, the highest collision bit can be detected. In the process of tag identification, the protocol only locks the target on the three consecutive bits which start from the highest collision bit. The protocol has two tag identification mechanisms, and which one is chosen is determined by the value of collision coefficient. Using the collision information characteristics of the three bits, idle timeslots are evitable, and some collision timeslots are eliminated at the same time. The 8-ary adaptive pruning query tree protocol fully takes into account several important performance indicators such as the number of query timeslots, communication complexity, transmission delay, and throughput. Both theoretical analysis and simulation results show that this protocol performs better than other tree-based anti-collision protocols. The throughput of 8-ary adaptive pruning query tree protocol remains approximately 0.625.

Radio frequency identification technology has been extensively used in various practical applications, such as inventory management and logistics control. When numerous tags respond to reader simultaneously, tags-to-tag collision occurs and causes the reader to identify tags unsuccessfully. Therefore, how to reduce tag collisions has already emerged as an urgent and crucial problem to be solved for speeding up the identification operation. This article designs a characteristic-value-based grouping rule and a collision bits rule to determine transmitted bit string combinations accurately and proposes a bit arbitration tree anti-collision protocol based on these two rules to decrease the time for collecting all tag IDs. Furthermore, we consider the case that received bit string less than three bits, which occurs during the tag identification operation. Both theory and simulation analyses show that the proposed protocol can reduce the number of idle slots and total number of slots and thereby improve system efficiency.

Existing hop-by-hop congestion control algorithms are mainly divided into two categories: those improving the sending rate and those suppressing the receiving rate. However, these congestion control algorithms have problems with validity and limitations. It is likely that the network will be paralyzed due to the unreasonable method of mitigating congestion. In this paper, we present a contention-based hop-by-hop bidirectional congestion control algorithm (HBCC). This algorithm uses the congestion detection method with queue length as a parameter. By detecting the queue length of the current node and the next hop node, the congestion conditions can be divided into the following four categories: 0−0, 0−1, 1−0, 1−1 (0 means no congestion, 1 means congestion). When at least one of the two nodes is congested, the HBCC algorithm adaptively adjusts the contention window of the current node, which can change the priority of the current node to access the channel. In this way, the buffer queue length of the congested node is reduced. When the congestion condition is 1−1, the hop-by-hop priority congestion control (HPCC) method proposed in this paper is used. This algorithm adaptively changes the adjustment degree of the current node competition window and improves the priority of congestion processing of the next hop node. The NS2 simulation shows that by using the HBCC algorithm, when compared with distributed coordination function (DCF) without congestion control, the proposed unidirectional congestion control algorithms hop-by-hop receiving-based congestion control (HRCC) and hop-by-hop sending-based congestion control (HSCC), and the existing congestion control algorithm congestion alleviation—MAC (CA-MAC), the average saturation throughput increased by approximately 90%, 62%, 12%, and 62%, respectively, and the buffer overflow loss ratio reduced by approximately 80%, 79%, 44%, and 79%.

The random placement of a large-scale sensor network in an outdoor environment often causes low coverage. In order to effectively improve the coverage of a wireless sensor network in the monitoring area, a coverage optimization algorithm for wireless sensor networks with a Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm is proposed. The simulation results show that the VFLGWO algorithm has a better optimization effect on the coverage rate, uniformity, and average moving distance of sensor nodes than a wireless sensor network coverage optimization algorithm using Lévy-embedded Grey Wolf Optimizer, Cuckoo Search algorithm, and Chaotic Particle Swarm Optimization. The VFLGWO algorithm has good adaptability with respect to changes of the number of sensor nodes and the size of the monitoring area.