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Studying the task assignment problem of multiple underwater robots has a broad effect on the field of underwater exploration and can be helpful in military, fishery, and energy. However, to the best of our knowledge, few studies have focused on multi-constrained underwater detection task assignment for heterogeneous autonomous underwater vehicle (AUV) clusters with autonomous decision-making capabilities, and the current popular heuristic methods have difficulty obtaining optimal cluster unit task assignment results. In this paper, a fast graph pointer network (FGPN) method, which is a hybrid of graph pointer network (GPN) and genetic algorithm, is proposed to solve the task assignment problem of detection/communication AUV clusters, and to improve the assignment efficiency on the basis of ensuring the accuracy of task assignment. A two-stage detection algorithm is used. First, the task nodes are clustered and pre-grouped according to the communication distance. Then, according to the clustering results, a neural network model based on graph pointer network is used to solve the local task assignment results. A large-scale cluster cooperative task assignment problem and a detection/communication cooperative work mode are proposed, which transform the cooperative cooperation problem of heterogeneous AUV clusters into a Multiple Traveling salesman problem (MTSP) for solving. We also conducted a large number of experiments to verify the effectiveness of the algorithm. The experimental results show that the solution efficiency of the method proposed in this paper is better than the traditional heuristic method on the scale of 300/500/750/1,000/1,500/2,000 task nodes, and the solution quality is similar to the result of the heuristic method. We hope that our ideas and methods for solving the large-scale cooperative task assignment problem can be used as a reference for large-scale task assignment problems and other related problems in other fields.

With the rapid development of artificial intelligence technology, many researchers have begun to focus on visual language navigation, which is one of the most important tasks in multi-modal machine learning. The focus of this multi-modal field is how to fuse multiple inputs, which is crucial for the integrated feedback of intrinsic information. However, the existing models are only implemented through simple data augmentation or expansion, and are obviously far from being able to tap the intrinsic relationship between modalities. In this paper, to overcome these challenges, a novel multi-modal matching feedback self-tuning model is proposed, which is a novel neural network called Vital Information Matching Feedback Self-tuning Network (VIM-Net). Our VIM-Net network is mainly composed of two matching feedback modules, a visual matching feedback module (V-mat) and a trajectory matching feedback module (T-mat). Specifically, V-mat matches the target information of visual recognition with the entity information extracted by the command; T-mat matches the serialized trajectory feature with the direction of movement of the command. Ablation experiments and comparative experiments are conducted on the proposed model using the Matterport3D simulator and the Room-to-Room (R2R) benchmark datasets, and the final navigation effect is shown in detail. The results prove that the model proposed in this paper is indeed effective on the task.

Sunshine-based trajectory analysis plays a significant impact on comprehensive scenarios, including driving safety and routing planning for mobile solar collection. To the best of our knowledge, however, a little work analyzes the impact of sunshine on mobile objects on different trajectories. This paper investigates the relationship between sunshine and the trajectories of mobile objects. We introduce a sunshine orientation model to integrate sunshine information with trajectories. A sunshine-based trajectory simplification model is proposed to remove redundant points in trajectories, which carry a little sunshine-related information. The sunshine-based trajectory simplification model is indeed versatile. We apply this model to help users efficiently query the sunshine information of their trips. Our design is evaluated with extensive simulations based on three-month general transit feed specification data from August to October in 2017 in five USA cities, including Washington, DC, USA, and Minneapolis. Furthermore, we develop a sunshine query system for mobile scenarios by embedding our sunshine-based trajectory simplification model and conduct a field study to show the validity and efficiency of this model. The evaluation results show that our design reduces 87.4% position points in trajectories while only sacrificing 9.6% of sunshine information in the field tests.

Trajectory analysis based on sunshine information shows that sunshine has a crucial effect on many conditions involving driving comfort and route planning. However, based on our best understanding, a little, if any, work takes into consideration the influence of sunshine on large numbers of trajectories of mobile objects such as vehicles. In this paper, we analyze, for the first time, the correlation between sunshine and large numbers of trajectories of mobile objects while presenting compression of the sunshine-based trajectories on a national scale. Within the research, a sunshine-oriented model is introduced to combine sunshine with real trajectories through the extraction of information centering around sunshine on arbitrary trajectories. Our trajectory analysis based on sunshine is versatile, and the model mentioned above is implemented to enhance the riding comfort of passengers. Our model is evaluated with an extensive simulation using general transit feed specification data over three months from August to October 2017 in five U.S. cities. We also conduct a field study in Minneapolis to show the validity and efficiency of our model. The evaluation results show that our design: 1) decreases trajectory computation by 88.7% with the trajectory compression and 2) achieves more than 90% accuracy for the trip sunshine information prediction in both the simulation and real-world tests.

Following the development of autonomous underwater vehicles (AUVs), multiple trajectory-based submarine target information collection constitutes one of the key technologies that significantly influence underwater information collection ability and deployment efficiency. In this paper, we propose an underwater information collection AUV, O-AUV, that can perceive the omnidirectional area and could detect a larger area than the traditional AUV. A 3D sensing model for the O-AUV is proposed to describe the complex underwater information collection spaces. Thereafter, a cube-based environment model involving candidate observation point calculation methods are suggested to adapt the O-AUV model. A voyage cost map is also built according to the multi-AUV path planning for a common submarine mission that must traverse numerous mission targets in complex environments through the R-Dijkstra algorithm. Specifically, the voyage planning problem is solved through a critical algorithm called ANSGA (accelerated NSGA-II algorithm), which in turn, is developed by modifying the non-dominated sorting genetic algorithm (NSGA-II) to accelerate the optimization rate for the Pareto solution. Experiments are carried out in MATLAB, and the results verify the validity of the proposed O-AUV+ANSGA algorithm framework.

There are a variety of complex constraints in the wide application of wireless sensor networks. Traditional coverage models and algorithms have been unable to meet the needs of multi-constrained sensor networks. At the same time, in a complex monitoring environment, a single type of sensor information cannot meet the requirements of reliable monitoring. Hence, in this paper, aiming at the multi-constraints in sensor networks, a compound event barrier coverage algorithm is proposed based on environment Pareto-dominated selection strategy, which can distribute the sensor resources reasonably and find out the coverage ratio efficiently in multi-constraints sensor networks. The proposed algorithm takes advantage of Pareto domination relationship, constraint violation degree, constraint boundary distance, and crowding degree to preserve the outstanding infeasible individuals in the evolutionary process and participate in the mutation operation; we also adopt the adaptive scaling factor and crossover probability strategy to avoid the algorithm into local optimal. The proposed algorithm is proved to be more highly efficient than the latest algorithm in the distribution of sensor resources by experimental results.

Indoor localization is a significant research area in wireless sensor networks (WSNs). Generally, the nodes of WSNs are deployed in the same plane, i.e., the floor, as the target to be positioned, which causes the sensing signal to be influenced or even blocked by unpredictable obstacles, like furniture. However, a 3D system, like Cricket, can reduce the negative impact of obstacles to the maximum extent and guarantee the sensing signal transmission by using the line of sight (LOS). However, most of the traditional localization methods are not available for the new deployment mode. In this paper, we propose the self-localization of beacons method based on the Cayley–Menger determinant, which can determine the positions of beacons stuck in the ceiling; and differential sensitivity analysis (DSA) is also applied to eliminate measurement errors in measurement data fusion. Then, the calibration of beacons scheme is proposed to further refine the locations of beacons by the mobile robot. According to the robot’s motion model based on dead reckoning, which is the process of determining one’s current position, we employ the H ∞ filter and the strong tracking filter (STF) to calibrate the rough locations, respectively. Lastly, the optimal node selection scheme based on geometric dilution precision (GDOP) is presented here, which is able to pick the group of beacons with the minimum GDOP from all of the beacons. Then, we propose the GDOP-based weighting estimation method (GWEM) to associate redundant information with the position of the target. To verify the proposed methods in the paper, we design and conduct a simulation and an experiment in an indoor setting. Compared to EKF and the H ∞ filter, the adopted STF method can more effectively calibrate the locations of beacons; GWEM can provide centimeter-level precision in 3D environments by using the combination of beacons that minimizes GDOP.

Localization as a technique to solve the complex and challenging problems besetting line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions has recently attracted considerable attention in the wireless sensor network field. This paper proposes a strategy for eliminating NLOS localization errors during calculation of the location of mobile terminals (MTs) in unfamiliar indoor environments. In order to improve the hidden Markov model (HMM), we propose two modified algorithms, namely, modified HMM (M-HMM) and replacement modified HMM (RM-HMM). Further, a hybrid localization algorithm that combines HMM with an interacting multiple model (IMM) is proposed to represent the velocity of mobile nodes. This velocity model is divided into a high-speed and a low-speed model, which means the nodes move at different speeds following the same mobility pattern. Each moving node continually switches its state based on its probability. Consequently, to improve precision, each moving node uses the IMM model to integrate the results from the HMM and its modified forms. Simulation experiments conducted show that our proposed algorithms perform well in both distance estimation and coordinate calculation, with increasing accuracy of localization of the proposed algorithms in the order M-HMM, RM-HMM, and HMM + IMM. The simulations also show that the three algorithms are accurate, stable, and robust.

The surveillance system, which is mainly used for detecting and tracking moving targets, is one of the most significant applications of wireless sensor networks. Up to present, received signal strength indicator is the most common measuring mean for estimating the distance in sensor networks. However, in the presence of noise, it is impossible to gain the accurate distance based on received signal strength indicator. In this article, we propose a new tracking scheme based on received signal strength difference, which is the difference value of received signal strength indicators between two neighboring sampling steps. Supposing the noise has a certain degree of correlation in a certain time interval, received signal strength difference can effectively reduce the negative impact from noise. The tracking algorithm based on received signal strength difference is built: The sensor nodes collectively estimate a possible zone of the target via the signs of received signal strength difference. Next, the possible zone is further immensely shrunk to the refined zone via the absolute values of received signal strength difference. Finally, we determine the target’s final location by choosing the reference dot with the minimum norm in the refined zone . The simulation results demonstrate that the proposed tracking method achieves higher localization accuracy than the typical received signal strength indicator–based scheme. The received signal strength difference–based method also has good generality and robustness with respect to the noises with different deviation values and the target following arbitrarily state model.

Following the development of wireless multimedia sensor networks (WMSN), the coverage of the sensors in the network constitutes one of the key technologies that have a significant influence on the monitoring ability, quality of service, and network lifetime. The application environment of WMSN is always a complex surface, such as a hilly surface, that would likely cause monitoring shadowing problems. In this study, a new coverage-enhancing algorithm is presented to achieve an optimal coverage ratio of WMSN based on three-dimensional (3D) complex surfaces. By aiming at the complex surface, the use of a 3D sensing model, including a sensor monitoring model and a surface map calculation algorithm, is proposed to calculate the WMSN coverage information in an accurate manner. The coverage base map allowed the efficient estimation of the degree of monitoring occlusion efficiently and improved the system’s accuracy. To meet the requests of complex 3D surface monitoring tasks for multiple sensors, we propose a modified cuckoo search algorithm that considers the features of the WMSN coverage problem and combines the survival of the fittest, dynamic discovery probability, and the self-adaptation strategy of rotation. The evaluation outcomes demonstrate that the proposed algorithm can describe the 3D covering field but also improve both the coverage quality and efficiency of the WMSN on a complex surface.

Barrier coverage, an important research area with respect to camera sensor networks, consists of a number of camera sensors to detect intruders that pass through the barrier area. Existing works on barrier coverage such as local face-view barrier coverage and full-view barrier coverage typically assume that each intruder is considered as a point. However, the crucial feature (e.g., size) of the intruder should be taken into account in the real-world applications. In this paper, we propose a realistic resolution criterion based on a three-dimensional (3D) sensing model of a camera sensor for capturing the intruder’s face. Based on the new resolution criterion, we study the barrier coverage of a feasible deployment strategy in camera sensor networks. Performance results demonstrate that our barrier coverage with more practical considerations is capable of providing a desirable surveillance level. Moreover, compared with local face-view barrier coverage and full-view barrier coverage, our barrier coverage is more reasonable and closer to reality. To the best of our knowledge, our work is the first to propose barrier coverage for 3D camera sensor networks.

This paper deals with the problem of localization of mobile robot in indoor environment with mixed line-of-sight/nonline-of-sight (LOS/NLOS) conditions. To reduce the NLOS errors, a prior knowledge-based correction strategy (PKCS) is proposed to locate the robot. This strategy consists of two steps: NLOS identification and mitigation. We propose an NLOS identification method by applying the statistical theory. Then we correct the NLOS errors by subtracting the expected NLOS errors. Finally, the residual weighting algorithm is employed to estimate the location of the robot. Simulation results show that the proposed strategy significantly improves the accuracy of localization in mixed LOS/NLOS indoor environment.

It is important to monitor compound event by barrier coverage issues in wireless sensor networks (WSNs). Compound event barrier coverage (CEBC) is a novel coverage problem. Unlike traditional ones, the data of compound event barrier coverage comes from different types of sensors. It will be subject to multiple constraints under complex conditions in real-world applications. The main objective of this paper is to design an efficient algorithm for complex conditions that can combine the compound event confidence. Moreover, a multiplier method based on an active-set strategy (ASMP) is proposed to optimize the multiple constraints in compound event barrier coverage. The algorithm can calculate the coverage ratio efficiently and allocate the sensor resources reasonably in compound event barrier coverage. The proposed algorithm can simplify complex problems to reduce the computational load of the network and improve the network efficiency. The simulation results demonstrate that the proposed algorithm is more effective and efficient than existing methods, especially in the allocation of sensor resources.

Abstract Extracellular adenosine is extensively involved in regulating the tumor microenvironment. Given the disappointing results of adenosine-targeted therapy trials, personalized treatment might be necessary, tailored to the microenvironment status of individual patients. Here, we introduce the adenosine signaling score (ADO-score) model using non-negative matrix fraction identified patient subtypes using publicly available melanoma dataset, which aimed to profile adenosine signaling-related genes and construct a model to predict prognosis. We analyzed 580 malignant melanoma samples and demonstrated its robust value for prognosis. Further investigation in immune checkpoint inhibitor dataset suggests its potential as a stratified factor of immune checkpoint inhibitor efficacy. We validated the power of the ADO-score at the protein level immunofluorescence in a melanoma cohort from Xiangya Hospital. More importantly, single-cell and spatial transcriptomic data highlighted the cell-specific expression patterns of adenosine signaling-related genes and the existence of adenosine signaling-mediated crosstalk between tumor cells and immune cells in melanoma. Our study reveals a robust connection between adenosine signaling and clinical benefits in melanoma patients and proposes a universally applicable adenosine signaling model, the ADO-score, in gene expression profiles and histological sections. This model enables us to more precisely and conveniently select patients who are likely to benefit from immunotherapy.

IntroductionThe Xinjiang Mongolians, located along the Silk Road, migrated westward from Northeast Asia in the 13th and 14th centuries. Despite its significance, genetic studies on Xinjiang Mongolians have been limited compared to other Mongolian populations.MethodsIn this study, we analyzed the non-recombining region of the Y-chromosome in 165 Xinjiang Mongolian males using 108 biallelic markers and 44 STRs.Results and discussionOur study identified prevalent haplogroups C2a1a3-F1918 (10%), C2a1a2-M48 (8%), N1a1a-M178 (5%), and R1a1a-M17 (10%) in the Xinjiang Mongolians. Additionally, our study suggested a genetic affinity between Xinjiang Mongolians and Inner Mongolia Mongolian populations, as well as other ethnic groups from northwest China, based on the PCA analysis. The Network analysis revealed distinct branching expansion patterns in haplogroups C2a1a3a-F3796, C2a1a2-M48, and N1a1a-M178, with estimated timeframes aligning with Genghis Khan's invasion of Xinjiang in the Yuan Dynasty. Notably, our analysis of the R1a1a-M17 Network highlighted the role of Xinjiang Mongolians in the expansion of Turkic-speaking populations in Xinjiang and surrounding regions. The integration of ancient DNA data suggested that the high frequency haplogroups C2a1a3a-F3796, C2a1a2-M48, and N1a1a-M178 could be traced back to their origin in Northeast Asia. Furthermore, the estimated TMRCA of haplogroup R1a1a-M17 implied cultural and genetic influences from Turkic populations during the Qagatay Khanate period. Overall, our study provided a genetic explanation for the ethnic origin of Xinjiang Mongolians, highlighting their migration from Northeast Asia and subsequent assimilation with the local populations in Xinjiang.

With over a million cases detected each year, skin disease is a global public health problem that diminishes the quality of life due to its difficulty to eradicate, propensity for recurrence, and potential for post-treatment scarring. Photodynamic therapy (PDT) is a treatment with minimal invasiveness or scarring and few side effects, making it well tolerated by patients. However, this treatment requires further research and development to improve its effective clinical use. Here, a piezoelectric-driven microneedle (PDMN) platform that achieves high efficiency, safety, and non-invasiveness for enhanced PDT is proposed. This platform induces deep tissue cavitation, increasing the level of protoporphyrin IX and significantly enhancing drug penetration. A clinical trial involving 25 patients with skin disease was conducted to investigate the timeliness and efficacy of PDMN-assisted PDT (PDMN-PDT). Our findings suggested that PDMN-PDT boosted treatment effectiveness and reduced the required incubation time and drug concentration by 25% and 50%, respectively, without any anesthesia compared to traditional PDT. These findings suggest that PDMN-PDT is a safe and minimally invasive approach for skin disease treatment, which may improve the therapeutic efficacy of topical medications and enable translation for future clinical applications.

Abstract Keloids are benign fibroproliferative tumors that severely diminish the quality of life due to discomfort, dysfunction, and disfigurement. Recently, ultrasound technology as a noninvasive adjuvant therapy is developed to optimize treatment protocols. However, the biophysical mechanisms have not yet been fully elucidated. Here, it is proposed that piezo‐type mechanosensitive ion channel component 1 (Piezo1) plays an important role in low‐frequency sonophoresis (LFS) induced mechanical transduction pathways that trigger downstream cellular signaling processes. It is demonstrated that patient‐derived primary keloid fibroblasts (PKF), NIH 3T3, and HFF‐1 cell migration are inhibited, and PKF apoptosis is significantly increased by LFS stimulation. And the effects of LFS is diminished by the application of GsMTx‐4, the selective inhibitor of Piezo1, and the knockdown of Piezo1. More importantly, the effects of LFS can be imitated by Yoda1, an agonist of Piezo1 channels. Establishing a patient‐derived xenograft keloid implantation mouse model further verified these results, as LFS significantly decreased the volume and weight of the keloids. Moreover, blocking the Piezo1 channel impaired the effectiveness of LFS treatment. These results suggest that LFS inhibits the malignant characteristics of keloids by activating the Piezo1 channel, thus providing a theoretical basis for improving the clinical treatment of keloids.