Global Multi-Source Information Fusion Management and Deep Learning Optimization for Tourism

The purpose is to solve the problems of sparse data information, low recommendation precision and recall rate and cold start of the current tourism personalized recommendation system. First, a context based personalized recommendation model (CPRM) is established by using the labeled-LDA (Labeled Latent Dirichlet Allocation) algorithm. The precision and recall of interest point recommendation are improved by mining the context information in unstructured text. Then, the interest point recommendation framework based on convolutional neural network (IPRC) is established. The semantic and emotional information in the comment text is extracted to identify user preferences, and the score of interest points in the target location is predicted combined with the influence factors of geographical location. Finally, real datasets are adopted to evaluate the recommendation precision and recall of the above two models and their performance of solving the cold start problem.

Physics-Informed Modeling and Control of Multi-Actuator Soft Catheter Robots

Catheter-based endovascular interventional procedures have become increasingly popular in recent years as they are less invasive and patients spend less time in the hospital with less recovery time and less pain. These advantages have led to a significant growth in the number of procedures that are performed annually. However, it is still challenging to position a catheter in a target vessel branch within the highly complicated and delicate vascular structure. In fact, vessel tortuosity and angulation, which cause difficulties in catheterization and reaching the target site, have been reported as the main causes of failure in endovascular procedures. Maneuverability of a catheter for intravascular navigation is a key to reaching the target area; ability of a catheter to move within the target vessel during trajectory tracking thus affects to a great extent the length and success of the procedure. To address this issue, this paper models soft catheter robots with multiple actuators and provides a time-dependent model for characterizing the dynamics of multi-actuator soft catheter robots. Built on this model, an efficient and scalable optimization-based framework is developed for guiding the catheter to pass through arteries and reach the target where an aneurysm is located. The proposed framework models the deflection of the multi-actuator soft catheter robot and develops a control strategy for movement of catheter along a desired trajectory. This provides a simulation-based framework for selection of catheters prior to endovascular catheterization procedures, assuring that given a fixed design, the catheter is able to reach the target location. The results demonstrate the benefits that can be achieved by design and control of catheters with multiple number of actuators for navigation into small vessels.

Dynamical Latent State Computation in the Posterior Parietal Cortex

Success in many real-world tasks depends on our ability to dynamically track hidden states of the world. To understand the underlying neural computations, we recorded brain activity in posterior parietal cortex (PPC) of monkeys navigating by optic flow to a hidden target location within a virtual environment, without explicit position cues. In addition to sequential neural dynamics and strong interneuronal interactions, we found that the hidden state -- monkey's displacement from the goal -- was encoded in single neurons, and could be dynamically decoded from population activity. The decoded estimates predicted navigation performance on individual trials. Task manipulations that perturbed the world model induced substantial changes in neural interactions, and modified the neural representation of the hidden state, while representations of sensory and motor variables remained stable. The findings were recapitulated by a task-optimized recurrent neural network model, suggesting that neural interactions in PPC embody the world model to consolidate information and track task-relevant hidden states.

An Embedded Model Estimator for Non-Stationary Random Functions Using Multiple Secondary Variables

An algorithm for non-stationary spatial modelling using multiple secondary variables is developed herein, which combines geostatistics with quantile random forests to provide a new interpolation and stochastic simulation. This paper introduces the method and shows that its results are consistent and similar in nature to those applying to geostatistical modelling and to quantile random forests. The method allows for embedding of simpler interpolation techniques, such as kriging, to further condition the model. The algorithm works by estimating a conditional distribution for the target variable at each target location. The family of such distributions is called the envelope of the target variable. From this, it is possible to obtain spatial estimates, quantiles and uncertainty. An algorithm is also developed to produce conditional simulations from the envelope. As they sample from the envelope, realizations are therefore locally influenced by relative changes of importance of secondary variables, trends and variability.

Dynamic path planning over CG-Space of 10DOF Rover with static and randomly moving obstacles using RRT* rewiring

Dynamic path planning is a core research content for intelligent robots. This paper presents a CG-Space-based dynamic path planning and obstacle avoidance algorithm for 10 DOF wheeled mobile robot (Rover) traversing over 3D uneven terrains. CG-Space is the locus of the center of gravity location of Rover while moving on a 3D terrain. A CG-Space-based modified RRT* samples a random space tree structure. Dynamic rewiring this tree can handle the randomly moving obstacles and target in real time. Simulations demonstrate that the Rover can obtain the target location in 3D uneven dynamic environments with fixed and randomly moving obstacles.

Amikacin potentiator activity of zinc complexed to a pyrithione derivative with enhanced solubility

Resistance to amikacin in Gram-negatives is usually mediated by the 6'-N-acetyltransferase type Ib [AAC(6')-Ib], which catalyzes the transfer of an acetyl group from acetyl CoA to the 6' position of the antibiotic molecule. A path to continue the effective use of amikacin against resistant infections is to combine it with inhibitors of the inactivating reaction. We have recently observed that addition of Zn2+ to in-vitro enzymatic reactions, obliterates acetylation of the acceptor antibiotic. Furthermore, when added to amikacin-containing culture medium in complex to ionophores such as pyrithione (ZnPT), it prevents the growth of resistant strains. An undesired property of ZnPT is its poor water-solubility, a problem that currently affects a large percentage of newly designed drugs. Water-solubility helps drugs to dissolve in body fluids and be transported to the target location. We tested a pyrithione derivative described previously (Magda et al. Cancer Res 68:5318–5325, 2008) that contains the amphoteric group di(ethyleneglycol)-methyl ether at position 5 (compound 5002), a modification that enhances the solubility. Compound 5002 in complex with zinc (Zn5002) was tested to assess growth inhibition of amikacin-resistant Acinetobacter baumannii and Klebsiella pneumoniae strains in the presence of the antibiotic. Zn5002 complexes in combination with amikacin at different concentrations completely inhibited growth of the tested strains. However, the concentrations needed to achieve growth inhibition were higher than those required to achieve the same results using ZnPT. Time-kill assays showed that the effect of the combination amikacin/Zn5002 was bactericidal. These results indicate that derivatives of pyrithione with enhanced water-solubility, a property that would make them drugs with better bioavailability and absorption, are a viable option for designing inhibitors of the resistance to amikacin mediated by AAC(6')-Ib, an enzyme commonly found in the clinics.

Support Vector Regression for Mobile Target Localization in Indoor Environments

Trilateration-based target localization using received signal strength (RSS) in a wireless sensor network (WSN) generally yields inaccurate location estimates due to high fluctuations in RSS measurements in indoor environments. Improving the localization accuracy in RSS-based systems has long been the focus of a substantial amount of research. This paper proposes two range-free algorithms based on RSS measurements, namely support vector regression (SVR) and SVR + Kalman filter (KF). Unlike trilateration, the proposed SVR-based localization scheme can directly estimate target locations using field measurements without relying on the computation of distances. Unlike other state-of-the-art localization and tracking (L&T) schemes such as the generalized regression neural network (GRNN), SVR localization architecture needs only three RSS measurements to locate a mobile target. Furthermore, the SVR based localization scheme was fused with a KF in order to gain further refinement in target location estimates. Rigorous simulations were carried out to test the localization efficacy of the proposed algorithms for noisy radio frequency (RF) channels and a dynamic target motion model. Benefiting from the good generalization ability of SVR, simulation results showed that the presented SVR-based localization algorithms demonstrate superior performance compared to trilateration- and GRNN-based localization schemes in terms of indoor localization performance.

An Efficient Maritime Target Joint Detection and Imaging Method with Airborne ISAR System

In this paper, a joint maritime moving target detection and imaging approach, referred to as the fast inverse synthetic aperture radar (ISAR) imaging approach, based on the multi-resolution space−time adaptive processing (STAP), is proposed to improve the target detection performance and the target imaging efficiency in an airborne radar system. In the target detection stage, the sub-band STAP is introduced to improve the robustness of clutter suppression and to enhance the target output power with the decreased range resolution, by which the coarse estimation of target range-Doppler (R-D) location is obtained as the prior knowledge. In the following target imaging stage, the ISAR imaging is applied in the localized R-D zone surrounding with the target location. However, it is difficult to directly apply ISAR imaging with the conventional R-D algorithm because the slow-moving maritime target cannot be separated from the clutter interference in the Doppler frequency dimension. In this regard, the full-band STAP is applied in the R-D two-dimensional frequency domain for the simultaneous clutter suppression and high-resolution ISAR imaging, in which the envelope alignment and phase compensation are achieved by adaptive match filtering with the target Doppler frequency coarse estimation. Moreover, the reduced-dimension STAP applied in the target-surrounded localized Doppler frequency zone gives facilities for alleviating the computation burden. Simulation results corroborate the effectiveness of the proposed method.

Monitoring Slope Creep Motion using Multi Temporal Interferometry Synthetic Aperture Radar in Semarang, Indonesia

Landslide is one type of slope movement, where the slope movement includes creep. Although creep movement does not have an impact on the risk of loss of life, this creep movement takes place constantly and invisible which has an impact on economic losses. In this study, a time-series monitoring was carried out from 2018 to 2020 to see the movement of the slopes in the study area using the Multi-Temporal Interferometry Synthetic Aperture Radar (MTInSAR). A time series method from Sentinel 1A/B data, which includes Trangkil Sejahtera Housing (PTS), Soegijapranata Catholic University (UNIKA), and 17 August 1945 University (UNTAG) in Semarang City, Indonesia. The results of data processing indicate that there are slope movement in the target location, namely Trangkil Sejahtera and Selorejo Housing (southwest of UNIKA). Based on BPBD 2021 data, landslides occurred in the Trangkil Baru Housing Center (to the north of PTS) and the Garang River landslide channel west of Selorejo. This shows that there is a link between crawling in 2018-2020 and landslides in 2021. Although the use of satellite data has some drawbacks, the results can be taken into consideration in building an early warning system and reducing losses due to landslides.

Visual Sequence Algorithm for Moving Object Tracking and Detection in Images

Objective. The effects of different algorithms on detecting and tracking moving objects in images based on computer vision technology are studied, and the best algorithm scheme is confirmed. Methods. An automatic moving target detection and tracking algorithm based on the improved frame difference method and mean-shift was proposed to test whether the improved algorithm has improved the detection and tracking effect of moving targets. The algorithm improves the traditional three-frame difference method and introduces a single Gaussian background model to participate in target detection. The improved frame difference method is used to detect the target, and the position window and center of the target are determined. Combined with the mean-shift algorithm, it is determined whether the template needs to be updated according to whether it exceeds the set threshold so that the algorithm can automatically track the moving target. Results. The position and size of the search window change as the target location and size change. The Bhattacharyya similarity measure ρ (y) exceeds the threshold r, and the target detection algorithm is successfully restarted. Conclusion. The algorithm for automatic detection and tracking of moving objects based on the improved frame difference method and mean-shift is fast and has high accuracy.