Accurate localization of the shearer is key to achieving efficient, automated, unmanned mining. Unfortunately, the existing positioning algorithms usually yields low-precision, insufficient applicability and unreliable final estimation due to the existence of non-line-of-sight (NLOS) error and because they ignore the position error of anchor nodes (ANs). To bridge this technology gap, we developed a novel localization strategy by incorporating calibration, variational Bayesian unscented Kalman filter (VBUKF), total least squares (TLS) and water cycle algorithm (WCA) to enhance the location estimation accuracy of Ultra-wideband (UWB) positioning system in complicated underground environment. Firstly, calibration was implemented based on the selection of appropriate reference nodes (RNs) in line-of-sight (LOS) scenarios to estimate the scale-factor error, bias, and the position errors for each AN; then the VBUKF smoothing with the consideration of time-variant measurement noise was used to reduce the interference of NLOS owing to the difficult separation of bias and NLOS error. Secondly, the TLS method was implemented to calculate the target node’s position based on the smoothed distances and calibrated positions of ANs. Lastly, the WCA embedded scheme was executed to further ameliorate estimation accuracy. The experimental results demonstrated that the proposed TLS-WCA approach, after the implementation of calibration and VBUKF, was best able to improve the localization accuracy remarkably, and outperformed the other compared methods, which can efficiently attain higher estimation accuracy, highlighting the outstanding localization performance.

To maintain the straightness of an unmanned longwall mining face, a track geometry surveying model of the scraper conveyor was constructed based on the position of the shearer. A surveying instrument was developed employing an inertial measurement unit and axial encoder. Surveying tests were conducted using a longwall mining face mock-up, and the 3-D accuracy of the surveying instrument based on the mean radial spherical error and the spherical error probable (SEP) radius was determined to be 20.78 mm and 16.57 mm, respectively. The accuracy of the surveying system satisfied the requirements of a longwall mining face.

Glioblastoma multiforme (GBM) has several distinctive characteristics linked to a poor early-stage prognosis. The crucial obstacle in the treatment of GBM is the inability of chemo drugs or other anticancer medicines to reach brain tumors due to the blood–brain tumor barrier (BBTB), leading to weak cytotoxic activity and drug resistance. Additionally, there is a limited number of clinically approved anticancer medicines for GBM because of the heterogeneity of this type of tumor. Presently, four FDA-approved drugs are available for the treatment of GBM, i.e., temozolomide, lomustine, carmustine, and bevacizumab. These drugs are primarily used to treat recurrent high-grade gliomas and their symptoms. Unfortunately, despite efforts to treat GBM over the last 60 years, no significant progress has been made in extending the overall survival (OS) of patients with this disease. Therefore, possible treatments and accessible drugs must be modified or advanced medicines developed to treat GBM. Several innovative strategies have been used to overcome these challenges, such as combining traditional therapies with emerging nanoscale-based biomaterials for multifunctional characteristics. These modified nanoscale biomaterials can cross the blood–brain barrier (BBB) and increase chemo-drug sensitivity through improved accumulation and efficiency. Herein, we review the recent developments in organic and inorganic biomaterial-based nanoparticles for GBM drug delivery. Firstly, we present a brief overview of the FDA-approved drugs and some additional chemo drugs for treating GBM, followed by a discussion on the drawbacks of the delivery of these drugs in GBM. Further, the current challenges in the field of GBM drug delivery, significant advancements in biomaterials research to overcome these obstacles, and subsequent considerations and opportunities for the application of biomaterials in the clinical treatment of GBM are highlighted.

Accurate positioning of the shearer remains a challenge for automation of the longwall coal mining process. In this paper, the popular Ultra-wideband (UWB) positioning system that has attracted considerable attention is adopted to obtain the target node location. Unfortunately, localization accuracy is still unsatisfactory and unreliable in mixed line of sight (LOS) and non-line of sight (NLOS) scenarios. To ameliorate localization accuracy of UWB for complicate underground environment where the positioning scenarios suffered from frequently switching among LOS, NLOS, and mixed LOS-NLOS condition, the novel positioning algorithm GMM-IMM-EKF was proposed. Gaussian mixed model (GMM) was employed to re-estimate the measurement distance, and two parallel variational Bayesian adaptive Kalman filters (VBAKFs) under the structure of interacting multiple model (IMM) was utilized to smoothen the result of GMM to eliminate the LOS and NLOS errors, respectively. Then, the position of the target node was determined by exploiting extended Kalman filter (EKF) based on the outcome of IMM-VBAKF. The proposed approach was assessed by exploiting UWB P440 modules. Comparative experimental verification demonstrated that GMM-IMM-EKF strategy outperformed other positioning approaches, which can effectively reduce the adverse effect of NLOS errors and achieve higher positioning accuracy in underground environment with LOS/NLOS/LOS-NLOS transition conditions.