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Based on previous studies, we found that Bacillus Calmette-Guerin(BCG) vaccination may have a role in preventing SARS-CoV-2 infection. Thus, we conducted this meta-analysis to investigate this protective effect. We searched in Embase, PubMed, Web of Science, Cochrane Library, BioRxiv, and MedRxiv databases for studies that evaluated the relationship between BCG vaccination and SARS-CoV-2 infection or COVID-19 disease. The quality of all included studies was assessed through the Risk of Bias in Non-randomized Studies - of Interventions and the Agency for Healthcare Research and Quality. Review Manager (Version 5.3) was used for conducting all data analyses. A total of 8 studies were ultimately included in our meta-analysis. Our primary analysis found significantly lower SARS-CoV-2 infection rate in the BCG vaccination group compared to the control group, with a odds ratio (OR) of 0.61, (95% confidence interval (CI) 0.39 to 0.95, P = 0.03; I2 = 31%, and P = 0.21 for heterogeneity). Our study indicated that BCG vaccination can protect against SARS-CoV-2 infection. However, there is insufficient evidence that BCG vaccination can reduce the severity of COVID-19.

Focusing on the accurate size, direction and position of dynamic meshing impact, an innovative semi-finite element method (semi-FEM) forecasting model is proposed for spiral bevel gear transmission under both high speed and heavy load conditions. To distinguish with the traditional rotor dynamic analysis focusing on vibration effect for global system, a new dynamic loaded tooth contact analysis (DLTCA) focusing on loaded contact effect for local contact area is proposed for the geometric boundary forecasting. Where, DLTCA shows a systematically time-varying characteristics including the dynamic loaded contact pattern, pressure, transmission error. Considering tooth flank flexural behaviors, data-driven tooth flank simulation and finite element modeling is developed by using universal motion concept (UMC) machine settings. At each finite element in a local geometric boundary, the dynamic compatibility and equilibrium equations are established as a semi-FEM dynamic meshing impact forecasting model. Moreover, for a dynamic transmission system, a basic judgement criterion is firstly developed. Then, the improved simplex method is applied to solve the established model for the dynamic meshing impact size evaluations including transmission error, torque, angular velocity, and acceleration. Finally, numerical and test instances are provided to verify the proposed method.

As the Ultra High Frequency (UHF) passive Radio Frequency IDentification (RFID) technology becomes increasingly deployed, it faces an array of new security attacks. In this paper, we consider a type of attack in which a malicious RFID reader could arbitrarily access the tags, e.g., retrieve or modify IDs or other data in the memory, via standard commands. To deal with this type of attack, we propose a physical-layer tag protection framework, namely Arbitrator2.0, that involves two operating mode, i.e., one is to passively listen on RF channels and identify unauthorized readers, the other is working as normal reader to access tag information but resilient to one-antenna eavesdropper. Our solution does not need to modify RFID tags or the underlying communication standards. In this study, we have implemented a prototype Arbitrator2.0 over the Universal Software Radio Peripheral (USRP) platform, and conducted extensive experiments to evaluate its performance. The results show that Arbitrator2.0 can effectively diminish the unauthorized access attacks and prevent eavesdropping.

This paper presents RF-Mehndi, a passive commercial RFID tag array formed identifier. The key RF-Mehndi novelty is that when the users fingertip touching on the tag array surface during the communication, the backscattered signals by the tag array become user-dependent and unique. Hence, if we enhance the communication modality of many personal cards nowadays by RF-Mehndi, in case that a card gets lost or stolen, it cannot be used illegally by the adversaries. To harvest such a benefit, we leverage two key observations in designing RF-Mehndi. The first one is when tags are nearby, their interrogated currents can change each others circuit characteristics, based on which unique phase features can be obtained from backscattered signals. The second observation is that when the users fingertip touches the tag array surface during communication, the phase feature can be further profiled by this user. Based on these observations, the card and its holder can be potentially authenticated at the same time. To transfer the RF-Mehndi idea to a practical system, we further address technical challenges. We implement a prototype system. Extensive evaluations show the effectiveness of RF-Mehndi, achieving excellent authentication performance.

This article proposes a standalone radio frequency identification (RFID)-based mobile robot navigation method, in which a mobile robot equipped with reader antennas can be continuously guided to a static object marked with a single passive UHF RFID tag. An observation model based on the RFID phase difference is built and integrated into a particle filter, by which the instantaneous relative position between the mobile robot and the tagged object can be detected in real time. Based on the position information extracted from the RFID system, the mobile robot adjusts its pose to move toward the RFID-tagged object. Compared with the existing RFID-based mobile robot navigation methods, the proposed method requires no external sensors other than the RFID and requires only a single passive tag. Experiments using commercial off-the-shelf (COTS) RFID devices are performed, and the results indicate that the mobile robot can satisfactorily realize navigation task with a distance accuracy of 4.04 cm and a bearing accuracy of 2.23°. The proposed method is well applicable for the navigation scenes in which the absolute position of the tagged target object is not known beforehand. Note to Practitioners —UHF radio frequency identification (RFID) has been widely applied as an asset management ID sensor in many fields. RFID-based mobile robot navigation technology can further increase its application value as a location sensor. This article proposes a standalone RFID-based mobile robot navigation method, in which the reference tag and the external sensors other than RFID are both not required. In the proposed method, only a single passive tag is attached to the static target object. Experimental results indicate that the proposed method can enable navigation task with good performance in situations in which the absolute navigation goal position is not known in advance.

Intelligent manufacturing (IM) talent education is becoming considerably relevant owing to the increasing versatility and multi-level compound requirements of academic and applied personnel. Serving as the core promoter of IM talent education, the actions and orientation of higher education are essential for IM society. However, there is no coherent framework in current engineering teaching programs, which can determine the key advancements of IM to facilitate theoretical learning and professional skills. Hence, this study presents a systematic reference training system in a highly student-centered interpretation for IM formation by establishing (1) innovation education platform, to bridge theoretical learning and hands-on practice and (2) enhanced curriculum development, which advocates broad training outcomes from multi-perspectives. First, the IM system architecture and the required attributes of IM talents are analyzed. Second, an innovation platform is constructed by incorporating interdisciplinary IM frontiers. Third, combining multimedia and interactive instruction, the IM training course clusters are reformed by covering overall cognition, module practice, and comprehensive application. In particular, a typical IM production line is depicted to increase students’ interests in participation, which can simultaneously develop disciplinary knowledge and practical skills. From the perspectives of students and instructors, evaluations and assessments demonstrate that this presented reference training system is beneficial for supporting modern IM talent education.

This paper presents an anti-spoofing design to verify whether a voice command is spoken by one live legal user, which supplements existing speech recognition systems and could enable new application potentials when many crucial voice commands need a higher-standard verification in applications. In the literature, verifying the liveness and legality of the command's speaker has been studied separately. However, to accept a voice command from a live legal user, prior solutions cannot be combined directly due to two reasons. First, previous methods have introduced various sensing channels for the liveness detection, while the safety of a sensing channel itself cannot be guaranteed. Second, a direct combination is also vulnerable when an attacker plays a recorded voice command from the legal user and mimics this user to speak the command simultaneously. In this paper, we introduce an anti-spoofing sensing channel to fulfill the design. More importantly, our design provides a generic interface to form the sensing channel, which is compatible to a variety of widely-used signals, including RFID, Wi-Fi and acoustic signals. This offers a flexibility to balance the system cost and verification requirement. We develop a prototype system with three versions by using these sensing signals. We conduct extensive experiments in six different real-world environments under a variety of settings to examine the effectiveness of our design.

Shuyuan Liu,1,2,&ast; Ling Xing,1,3,&ast; Qian Wang,4 Tianyu Xin,2 Handing Mao,1 Ye Tao,5 Jinbao Zhao,2 Xin Li,4 Cong Li,1 Qinghua Li,6 Yan Dou,7 Yixin Li,8 Wei Zhang,9 Bo Ning,10 Qing Song1 1Medical School of Chinese PLA, Beijing, People’s Republic of China; 2Emergency Department, Sixth Medical Center, Chinese PLA General Hospital, Beijing, People’s Republic of China; 3Department of Emergency, Beijing Tongren Hospital, Capital Medical University, Beijing, People’s Republic of China; 4Emergency Department, Third Medical Center, Chinese PLA General Hospital, Beijing, People’s Republic of China; 5Health Care Department, Beijing Huangsi Aesthetic Surgery Hospital, Beijing, People’s Republic of China; 6Department of Intensive Care Unit, The 990th Hospital of Chinese PLA, Zhumadian, People’s Republic of China; 7Department of Intensive Care Unit, The 909th Hospital of Chinese PLA, Zhangzhou, People’s Republic of China; 8Department of Intensive Care Unit, The 910th Hospital of Chinese PLA, Quanzhou, People’s Republic of China; 9Emergency Department, The 900th Hospital of Chinese PLA, Fuzhou, People’s Republic of China; 10Department of Intensive Care Unit, Air Force Medical Center of China, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bo NingDepartment of Intensive Care Unit, Air Force Medical Center of China, Beijing, 100142, People’s Republic of ChinaTel +86 15611161110Fax +86 1068410099Email boningbn@163.comQing SongMedical School of Chinese PLA, Beijing, 100853, People’s Republic of ChinaTel +86 13911374534Fax +86 1068182255Email songqing_sq87@163.comObjective: The purpose of this study is to evaluate the factors involved in the early stage of exertional heat stroke (EHS) that are associated with mortality.Methods: In this retrospective, case-control study, patients from 11 tertiary medical centers in China were enrolled from January 1, 2012, to December 31, 2019. Demographic information, underlying diseases, ambient temperature, and relative humidity, clinical manifestations, initial body temperature, time from onset to diagnosis of EHS (including suspected), and the duration of body temperature > 38°C of all enrolled patients were recorded. The occurrence of organ dysfunction within 72 h was evaluated, and in-hospital deaths were recorded. The patients were subsequently divided into a survival group and a non-survival group. The “case” refers to patients in the non-survival group, while the “control” refers to patients without death.Results: Of the 214 hospitalized patients with EHS, 183 survived and 31 died, and the overall mortality was 14.49% (31/214). A binary logistic regression showed that only the duration of body temperature > 38°C (OR 1.80, 95% CI 1.34– 2.42) and the number of organs damaged within 72 h of onset (OR 6.54, 95% CI 2.31– 18.56) were statistically significant in terms of risk of death in hospital (p < 0.05). A goodness of fit test produced a p-value of 0.76. According to receiver operating characteristic curve (ROC) analysis, the areas under the curve (AUC) were 0.989 (95% CI 0.978– 1.000; p < 0.05) and 0.936 (95% CI 0.896– 0.976; p < 0.05).Conclusion: Of the various factors involved in the early stage of the disease, the duration of high body temperature and the number of organs damaged within 72 h of onset were independent risk factors and predictors associated with death.Keywords: heat stroke, exertional, cooling, mortality

The accurate estimation of the end-effector’s pose in large operating spaces is the key for the mobile manipulator to realize efficient manufacturing of large and complex components. We propose a novel pose tracking method in large-range using visual fiducial markers, and further propose the layout optimization method for the encoded fiducial markers. A metric named orientational dilution of precision (ODOP) is proposed to evaluate the magnification of the pose estimation error compared with the measurement error of the coded fiducial markers. The distribution pattern of the coded markers is analyzed based on ODOP, and the square-shaped layout is determined to be a satisfactory distribution pattern for the minimum positioning unit of markers, and the side length of the square-shaped layout is further selected. The simulations and experiments prove the effectiveness of the ODOP index. Finally, the square-shaped layout and the designed distribution density for positioning coded markers are adopted to realize the high-precision measurement of large components by the mobile manipulator.

A novel radio frequency identification (RFID)-based mobile robot global localization method combining two kinds of RFID signal information, i.e., phase difference and readability, is proposed. Specifically, a phase difference model and a classification logic strategy based on readability are built and integrated into a particle filter localization algorithm. Compared with existing RFID localization methods, the proposed localization method can achieve competitive localization performance in an environment with a relatively sparse reference tag distribution and without the need for offline phase drift calibration. A series of real experimental tests were performed, and the results show that the proposed method can localize a mobile robot with centimeter-level position accuracy and satisfactory attitude angle accuracy when the distance between adjacent reference tags is approximately 60 cm, even if all RFID devices are commercial off-the-shelf (COTS). The proposed method provides a promising option for mobile robot localization applications, such as path tracking of mobile robots. Note to Practitioners —Mobile robot localization is a key technology for its location-based services. Considering that radio frequency identification (RFID) is entirely unaffected by light interference and has a globally unique ID, RFID has been regarded as a localization sensor with broad application prospects. This article proposes an RFID-based mobile robot global localization method combining phase difference and readability, by which the mobile robot can be accurately localized in an environment with a relatively sparse reference tag distribution and without the need for offline phase drift calibration. The experimental results indicate that the proposed method can localize the mobile robot with good performance, including centimeter-level position accuracy and satisfactory attitude angle accuracy. The proposed method can effectively contribute to many practical applications, such as the path tracking of a mobile robot.

Introduction/aims Exertional rhabdomyolysis (ER) often occurs during prolonged intense exercise in hot environments, posing a threat to the health of military personnel. In this study we aimed to investigate possible risk factors for ER and provide further empirical data for prevention and clinical treatment strategies. Methods A retrospective investigation of 116 concurrent ER cases was conducted. Conditional logistic regression analyses were performed to assess the association between each potential risk (or protective) factor and ER. The clinical characteristics of the 71 hospitalized patients were analyzed descriptively. Results After screening, the following variables significantly increased the risk of ER: shorter length of service (recruits; odds ratios [OR], 7.49; 95% confidence interval [CI], 2.58-21.75); higher body mass index (BMI; OR, 1.14, 95% CI, 1.03-1.26); lack of physical exercise in the last half year (less than once per month; OR, 3.20; 95% CI, 1.08-9.44); and previous heat injury (OR, 2.94; 95% CI, 1.26-6.89). Frequent fruit consumption (OR, 0.57; 95% CI, 0.33-0.99), active hydration habit (OR, 0.37; 95% CI, 0.20-0.67), water replenishment of more than 2 L on the training day (OR, 0.15; 95% CI, 0.05-0.45), and water replenishment of at least 500 mL within 1 hour before training (OR, 0.33; 95% CI, 0.12-0.88) significantly decreased the risk of ER. Of the 71 hospitalized patients, 41 (57.7%) were diagnosed with hypokalemia on admission. Discussion In military training, emphasis should be placed on incremental adaptation training before more intense training, and close attention should be given to overweight and previously sedentary recruits. Fluid replenishment before exercise, increased fruit intake, and proper potassium supplementation may help prevent ER.

It is controversial whether there is a different risk of recurrence between two histological subtypes in craniopharyngioma (CP) patients. Some reported that adamantinomatous craniopharyngioma (ACP) had a higher risk of recurrence than papillary craniopharyngioma (PCP), but others reported that there is no significant difference between them. So, we conducted this systematic review and meta-analysis to determine the association between the histological subtype of CP and the rate of recurrence. A comprehensive literature search was undertaken in PubMed, EMBASE, and Web of Science for all English articles published up to November 2020. Recurrence data stratified by ACP and PCP were extracted from studies meeting inclusion criteria. A pooled analysis of the association between the histological subtype of craniopharyngioma and rates of recurrence was performed. Thirteen articles containing 974 patients were included. When stratified by two pathological subtypes, the total recurrence rate of ACP was 26.0% and PCP was 14.1%, which showed ACP associated with a higher risk of tumor recurrence than PCP (odds ratio [OR] = 2.12, 95% confidence interval [CI] = 1.36, 3.30, P = 0.00). This is the first meta-analysis focusing on histological subtypes of CP. PCP associates with a lower risk of recurrence than ACP, indicating that ACP could act as one of recurrence risk factors for CP patients. Nevertheless, large sample size and well-designed multicenter studies in which the other clinical variables are controlled to determine the histological subtype of CP as an independent recurrence risk factor are needed.

As robots normally perform repetitive work, reinforcement learning (RL) appears to be a promising tool for designing robot control. However, the learning cycle of control strategy tends to be long, thereby limiting the applications of RL for a real robotic system. This article proposes model-based actor-critic learning for optimal tracking control of robotic systems to address this limitation. A preconstructed critic is defined in the framework of linear quadratic tracker, and a model-based actor update law is presented on the basis of deterministic policy gradient algorithm to improve learning efficiency. A low gain parameter is introduced in the critic to avoid input saturation. Compared with neural network-based RL, the proposed method including the preconstructed critic and actor, has rapid, steady, and reliable learning process, which is friendly for the physical hardware. The performance and effectiveness of the proposed method are validated using a dual-robot test rig. The experimental results show that the proposed learning algorithm can train multiple robots to learn their optimal tracking control laws within a training time of 200 s.

The bearing internal load distribution and fatigue life estimation are accomplished based on the bearing static model at present, ignoring the gear engagement. Besides, bearings are modeled as a linear spring in gear dynamic models, failing to analyze bearing internal load. This article aims to study the bearing internal load fluctuation and corresponding bearing life in gear systems. A nonlinear dynamic model of the gear-bearing system is built, considering the time-varying mesh stiffness, the static transmission error, backlash, bearing clearance, and the nonlinear bearing force. The dynamic model is capable of calculating the bearing internal load. Based on the internal load, fatigue life is acquired using the linear damage theory. The numerical results show that the gear engagement has a significant influence on the bearing internal load analysis, in which periodic fluctuation is related to the mesh frequency and shaft frequency, resulting in the bearing life affected by gear meshing. Compared with the fatigue life predicted under the bearing static model, evident deviations are observed, pointing to the significant role of the gear engagement in the analysis of fatigue life estimation.

Scanner-based robotic grinding has shown great potential for replacing the manual method to achieve efficient and automatic manufacturing. However, its application has been limited by the grinding quality, which is affected by the pose errors between the robot and other workcells (a scanner, a workpiece, and a tool). To improve the pose accuracy, this article proposes a novel estimation method for above three pose errors, where a cylinder is used as the calibration object. Using differential kinematics, the relationship between the hand-eye pose error and the reconstructed error is built. The hand-eye pose error is estimated by scanning and reconstructing a cylinder. An iterative method is presented to find the optimized orientation error vector, avoiding the uncertain error of multiple solutions using the Schmidt method. Based on the speed adjoint transformation, a mathematical model between the workpiece/tool pose errors and grinding error is built. Then, an experimental estimation approach for the workpiece/tool pose errors is presented by shape matching the measured points of the grinded cylinder with the design model. Unlike the traditional static construction method, this method uses large-scale measured points with high measurement accuracy to improve the estimation accuracy and stability and can estimate the pose error caused during the dynamic grinding process, such as vibration and force deformation. Finally, pose error estimation and compensation experiments are performed to verify the feasibility of the proposed method.

Utilizing wireless signals for gesture recognition and human identification is an emerging type of technology for touchless user interface, which allows the computer to automatically identify the user and interpret his/her gestures as commands. Such techniques extract features to profile the fluctuation of time series wireless signals to infer human gestures/identities. Among which, device-free approach becomes more attractive because it does not need human to carry or wear sensing devices. Existing device-free solutions, though yielding good performance, require heavy crafting on data preprocessing and feature extraction. In this paper, we propose RFnet, a multi-branch 1D-CNN based framework, that explores the possibility of directly utilizing time series RFID signal to recognize static/dynamic gestures as well as the identity of users, which can benefit a large number of applications such as smart homes where security is also a prior concern. We conduct extensive experiments in three different environments. The results demonstrate the superior effectiveness of the proposed RFnet framework.

The precise understanding of hypothalamic injury (HI) patterns and their relationship with different craniopharyngioma (CP) classifications remains poorly addressed. Here, four HI patterns after CP resection based on endoscopic observation were introduced. A total of 131 CP cases treated with endoscopic endonasal approach (EEA) were reviewed retrospectively and divided into four HI patterns: no‐HI, mild‐HI, unilateral‐HI and bilateral‐HI, according to intraoperative findings. The outcomes were evaluated and compared between groups in terms of weight gain, endocrine status, electrolyte disturbance and neuropsychological function before and after surgery. A systematic correlation was found between CP origin and subsequent HI patterns. The majority of intrasellar and suprasellar stalk origins lead to a no‐HI pattern, the central‐type CP mainly develops a mild or bilateral HI pattern, and the majority of tumors with hypothalamic stalk origins result in unilateral HI and sometimes bilateral HI patterns. The proportion of tumors with a maximum diameter >3 cm in the no‐HI group was higher than that in the mild‐HI group, BMI and quality of life in the no‐HI group showed better results than those in the other groups. The incidence of new‐onset diabetes insipidus in the bilateral‐HI group was significantly higher than that in the other groups. Memory difficulty was observed mainly in the unilateral‐HI and bilateral‐HI groups. However, the outcomes of electrolyte disturbance, sleep, and cognitive disorder in the unilateral‐HI group were significantly better than those in the bilateral‐HI group. This study suggests the possibility of using pre‐ and intraoperative observation of CP origin to predict four HI patterns and even subsequent outcomes after tumor removal., This study provided possibility to use the pre‐ and intraoperative observation of craniopharyngiomas origin to predict the four hypothalamic injury patterns and subsequent outcomes after tumor removal.

Machine setting modification has been an increasingly important access to the accurate flank manufacturing geometric accuracy control for spiral bevel and hypoid gears. More recently, machine setting driven integration of the theoretical design and the actual gear manufacturing is gaining more and more attention. In this paper, the traditional machine setting modification is extended to the case when higher-order component of the prescribed ease-off flank topography is investigated in form of high-order polynomial expression. Moreover, the actual gear manufacturing and general measurement are integrated into an adaptive data-driven high-order machine setting modification. In particular, this modification method is used to perform adaptive modular control for computer aided process planning (CAPP). Here, a data-driven operation and optimization is developed for adaptive high-order modification. It mainly includes: (i) Polynomial fitting and its optimization by using overall interpolation based on energy method, (ii) Data-driven ease-off flank parametrization based on the fastest descent Newton iteration method, (iii) adaptive control strategy by considering the sensitivity analysis, and (iv) Levenberg-Marquardt (L-M) based approximation for high-order machine setting modification. Given numerical test can verify the proposed method.

Visual tracking and grasping of moving object is a challenging task in the field of robotic manipulation, which also has great potential in applications such as human-robot collaboration. Based on the particle filtering framework and position-based visual servoing, this paper proposes a new method for visual tracking and grasping of randomly moving objects. A geometric particle filter tracker is established for visual tracking. In order to deal with the tracking efficiency issue for particle filter, edge detection and morphological dilation are employed to reduce the computation burden of geometric particle filtering. Meanwhile, the HSV image feature is employed instead of the grayscale feature to improve the tracking algorithm’s robustness to illumination change. A grasping strategy combining tracking and interception is adopted along with the position-based visual servoing (PBVS) method to achieve stable grasp of the target. Comprehensive comparisons on open source dataset and a large number of experiments on real robot system are conducted, which demonstrate the proposed method has competitive performance in random moving object tracking and grasping.

The classical meter recognition method is severely affected by light and noise interference in the recognition of meters, which limits its generalization ability. Recent methods based on convolutional neural networks cannot handle the diversity of meters via detection of the key points (center of circle, maximum and minimum range). In this study, we investigate the recognition of a mechanical meter. According to the manual reading paradigm, the scale and pointer are first positioned, and the reading value is then determined based on the relative position of the pointer and scale, which is compatible with different meters. To extract the tick marks of meters in a stable and precise scheme, Meter scan net (MSN) is designed based on the low-occupancy features of the meter’s tick area to predict the heatmap. Utilizing our proposed Detail-Attention module, MSN can greatly retain detailed features and focus on the tick area, which is helpful for the final segmentation. Post-processing includes using the output of MSN to perform circle fitting to obtain the scale arc, sampling along the normal phase direction of the scale arc, and locating the scale and pointer position. To handle cases of missing tick marks, a frequency-based analysis method is proposed to restore the missing tick marks, which enlarges the application scope of our method. Finally, extensive experiments are conducted that reveal that the proposed method has high accuracy and is compatible with different types of meters.

Researches on the modeling of machining difficult-to-cut metals are important for optimization of the processing parameters, in which the force modeling is essential due to its significant influence on the performance of tools and the quality of parts. A semi-analytical method for force prediction in orthogonal cutting with chamfered tools considering both edge and size effect is proposed in this paper. The plastic deformation in the shear band was investigated using a parallel shear zone model and unequal division shear zone model. The influence of size effect on cutting force was discussed and a simplified expression of improvement factor is introduced to describe the sharp increase of shear stress under the condition of low feed rate. Simulations of orthogonal cutting with different chamfer lengths are conducted to analyze the variation of cutting force with respect to chamfer length, which reveals that the influence of chamfer length on cutting force is determined by the ratio of chamfer length to uncut chip thickness. A modified function considering the trend of material flow condition is proposed, which treats the total cutting force as a combination of cutting forces caused by chamfered edge and rake face. The calibration of constants in the proposed method is achieved using particle swarm optimization (PSO), a meta-heuristic algorithm for complicated non-linear models. The experiments show that the method works well on both fitting and predicting modules in orthogonal cutting of AISI 304 using cemented carbide tools with 15° chamfer angle or 25° chamfer angle.

In industrial applications, nearly half the failures of motors are caused by the degradation of rolling element bearings (REBs). Therefore, accurately estimating the remaining useful life (RUL) for REBs is of crucial importance to ensure the reliability and safety of mechanical systems. To tackle this challenge, model-based approaches are limited by the complexity of mathematical modeling. Conventional data-driven approaches, on the other hand, require massive efforts to extract the degradation features and construct the health index. In this article, a novel data-driven framework is proposed to exploit the adoption of deep convolutional neural networks (CNNs) in predicting the RULs of bearings. More concretely, raw vibrations of training bearings are first processed using the Hilbert–Huang transform to construct a novel nonlinear degradation energy indicator which can be used as the training label. The CNN is then employed to identify the hidden pattern between the extracted degradation energy indicator and the raw vibrations of training bearings, which makes it possible to estimate the degradation of the test bearings automatically. Finally, testing bearings’ RULs are predicted through using an $\epsilon$ -support vector regression model. The superior performance of the proposed RUL estimation framework, compared with the state-of-the-art approaches, is demonstrated through the experimental results. The generality of the proposed CNN model is also validated by performance test on other bearings undergoing different operating conditions.

We provide the first solution to an important question, “how a physical-layer authentication method can defend against signal replay attacks”. It was believed that if an attacker can replay the exact same reply signal of a legitimate authentication object (such as an RFID tag), any physical-layer authentication method will fail. This paper presents Hu-Fu, the first physical layer RFID authentication protocol that is resilient to the major attacks including tag counterfeiting, signal replay, signal compensation, and brute-force feature reply. Hu-Fu is built on two fundamental ideas, namely inductive coupling of two tags and signal randomization. Hu-Fu does not require any hardware or protocol modification on COTS passive tags and can be implemented with COTS devices. We implement a prototype of Hu-Fu and demonstrate that it is accurate and robust to device diversity and environmental changes, including locations, distance, and temperature. Hu-Fu provides a new direction of battery-free/low-power device authentication that enables numerous IoT applications.

In order to improve the robustness and optimize trajectory of projective homography-based uncalibrated visual servoing (PHUVS) proposed in our previous work, an analytical expression of optimal trajectory for camera in projective homography space is proposed in this article, which is totally free of camera parameters and is corresponding to camera’s shortest path in the 3-D space with straight path in translation and minimal geodesic in rotation. The projective homography is computed without scale ambiguity in both planning and tracking stages. The PHUVS controller is modified correspondingly to track the planned trajectory in projective homography space while maintaining superior characteristic of PHUVS under uncalibrated scenario. The simulations and experiments’ results reveal the effectiveness and necessity of the proposed trajectory optimization method in the existence of large initial errors. Note to Practitioners —The state-of-the-art visual-guided robotic technology in industry usually requires system calibration, which is often costly, vulnerable, and challenging for ordinary workers. In our previous work, we offered an uncalibrated visual servo method based on projective homography named projective homography-based uncalibrated visual servoing (PHUVS), which is suitable for plug and play application for eye-in-hand robot visual servo tasks. However, PHUVS suffers from some defects, including undesirable 3-D space motion and local convergence. In this article, we proposed the trajectory planning method along with a modified PHUVS controller to improve the original one from the disadvantages mentioned earlier. This planning method is also calibration-free. With pure image information, a straight-line path in translational motion along with minimal geodesic in rotary motion can be achieved. This approach is capable of extending the range of applications for uncalibrated visual servo technology in robotic tasks, such as assembling, painting, and robotic machining.

Nonlinear static modeling is crucial for the design of stages with large travel ranges. However, few studies have investigated complex spatial compliant mechanisms. The present study proposes an optimization algorithm based on substructure constraint conditions to formulate the nonlinearity of the force–displacement characteristic of a tip-tilt-piston stage comprising leaf-spring flexure hinges. First, the nonlinear force–displacement characteristics of the compound basic parallelogram mechanism are derived using an optimization algorithm based on two constraint conditions (I and II). Second, the nonlinear static modeling of the tip-tilt-piston stage is conducted based on the modeling results of the compound basic parallelogram mechanism. The stage is divided into three parts, and force analyses are conducted for all three parts. The vertical displacement of the compound basic parallelogram mechanism in part 1 and the rotational angle of the rotational plate in part 2 are calculated. Subsequently, the force–displacement characteristics of the tip-tilt-piston stage are obtained based on a third constraint condition (III). A comparison of the finite element analysis results and the theoretical calculation indicates less than 4% errors. In the experimental tests conducted on the proposed stage and four compound basic parallelogram mechanisms, the displacements were evidently larger than those calculated using the finite element analysis. Therefore, a weight coefficient of the axial force w is introduced in the theoretical calculation to solve the problem of the large deviations between the experimental results and the finite element analysis results. When w is set to 1, the theoretical results are in good agreement with the finite element analysis results; when w is set to 0.05 for the tip-tilt-piston stage and 0.15 for compound basic parallelogram mechanisms, the theoretical results are consistent with the experimental results (less than 8.5% errors).

It is evident that the prevailing solution, myoelectric pattern recognition for prosthetic manipulation, constrains gesture-based interaction because of the lack of proportional control information such as exerted force. This paper reports an attempt, named simultaneous gesture recognition and muscle contraction force estimation, to realize proportional pattern recognition (PPR) control taking advantage of arm muscle deformation via wearable ultrasound sensing. We experiment with eight types of predefined hand motions, with a range of 0-60% maximum voluntary contraction (MVC) using a wearable multichannel A-mode ultrasound system. The experiment result demonstrates that above 93.7% of gestures are correctly recognized during dynamic muscle contraction forces (0-60% MVC), albeit only training at a slight force level (bf2, and normalized root-mean-square error (nRMSE) of 0.927 and 0.102, respectively. These research outcomes demonstrate the feasibility of ultrasound-based PPR control, paving the way for musculature-driven applications such as finer prosthetic control, remote manipulation, and rehabilitation treatment.

Heat stroke (HS) is a fatal disease caused by thermal damage in the body, and it has a very high mortality rate. In 2015, the People’s Liberation Army Professional Committee of Critical Care Medicine published the first expert consensus on HS in China, Expert consensus on standardized diagnosis and treatment for heat stroke. With an increased understanding of HS and new issues that emerged during the HS treatment in China in recent years, the 2015 consensus no longer meet the requirements for HS prevention and treatment. It is necessary to update the consensus to include the latest research evidence and establish a new consensus that has broader coverage, is more practical and is more in line with China’s national conditions. This new expert consensus includes new concept of HS, recommendations for laboratory tests and auxiliary examinations, new understanding of diagnosis and differential diagnosis, On-site emergency treatment and In-hospital treatment, translocation of HS patients and prevention of HS.

Roller burnishing process is being extensively used to enhance the fatigue life of the aircraft engine components by introducing compressive residual stress and work hardening. The temperature rise during the burnishing process could be neglected due to the low friction rolling contact between the roller and workpiece, thus simplifying it an elastoplastic deformation process. In this paper, the digital image correlation technique was adopted to obtain the subsurface deformation field during the roller burnishing process. A 3D finite element model was built up to simulate the burnishing process. The predicted surface integrity parameters including subsurface deformation and residual stress are compared with the experimental measurements.

Passive Radio Frequency Identification (RFID) tags have been widely applied in many applications, such as logistics, retailing, and warehousing. In many situations, the order of objects is more important than their absolute locations. However, state-of-art ordering methods need a continuing movement of tags and readers, which limit the application domain and scalability. In this paper, we propose a 2-dimension ordering approach for passive tags that requires no device movement. Instead, our method utilizes signal changes caused by arbitrary movement of human beings around tags, who carry no device for horizontal dimension ordering. Hence, our method is called Human Movement based Ordering (HMO). The basic idea of HMO is that when people pass between the reader antenna and tags, the received signal strength will change. By observing the time-series RSS changes of tags, HMO can obtain the order of tags along with a specific horizontal direction. For vertical dimension, we employ a linear programming method that is tolerant of tiny errors in practice. We implement HMO with commodity off-the-shelf RFID devices. The experimental results show that HMO can achieve up to 88.71 and 90.86 percent average accuracies in the signal- and multi-person cases, respectively.

ObjectiveThe surgical management of lesions involving the lateral area of the suprasellar region, including the lateral aspect of the planum sphenoidale and the tight junction region of the optic canal (OC), anterior clinoid process (ACP), and internal carotid artery (ICA) and its dural rings, is extremely challenging. Here, these regions, namely, the “parasuprasellar” area, are described from the endonasal perspective. Moreover, the authors introduce two novels endoscopic endonasal supraoptic (EESO) and endoscopic endonasal infraoptic (EEIO) approaches to access the parasuprasellar area.MethodsSurgical simulation of the EESO and EEIO approaches to the parasuprasellar area was conducted in 5 silicon-injected specimens. The same techniques were applied in 12 patients with lesions involving the parasuprasellar area.ResultsThe EESO approach provided excellent surgical access to the lateral region of the planum sphenoidale, which corresponds to the orbital gyrus of the frontal lobe. With stepwise bone (OC, optic strut and ACP) removal, dissociation of the ophthalmic artery (OA) and optic nerve (ON), the EEIO approach enables access to the lateral region of the supraclinoidal ICA. These approaches can be used independently or in combination, but are more often employed as a complement to the endoscopic endonasal midline and transcavernous approaches. In clinical application, the EESO and EEIO approaches were successfully performed in 12 patients harboring tumors as well as multiple aneurysms involving the parasuprasellar area. Gross total and subtotal tumor resection were achieved in 9 patients and 1 patient, respectively. For two patients with multiple aneurysms, the lesions were clipped selectively according to location and size. Visual acuity improved in 7 patients, remained stable in 4, and deteriorated in only 1. No postoperative intracranial infection or ICA injury occurred in this series.ConclusionsThe EESO and EEIO approaches offer original treatment options for well-selected lesions involving the parasuprasellar area. They can be combined with the endoscopic endonasal midline and transcavernous approaches to remove extensive pathologies involving the intrasellar, suprasellar, sphenoid, and cavernous sinuses and even the bifurcation of the ICA. This work for the first time pushes the boundary of the endoscopic endonasal approach lateral to the supraclinoidal ICA and ON.

Background and ObjectiveHepatocellular carcinoma (HCC) is one of the most common cancers worldwide, and the DNA polymerase delta (POLD) family is significantly related to cancer prognosis. This study aimed to explore the significance of the POLD family in HCC via the DNA damage repair (DDR) pathway.MethodsData mining was conducted using bioinformatics methods. RNA sequencing and clinicopathological data were collected from The Cancer Genome Atlas, GTEx database and the Gumz Renal cohort. Statistical analyses were also performed in cancer samples (n>12,000) and the Affiliated Hospital of Youjiang Medical University for Nationalities (AHYMUN, n=107) cohort.ResultsThe POLD family (POLD1–4) was identified as the most important functional component of the DDR pathway. Based on the analysis of independent cohorts, we found significantly elevated POLD expression in HCC compared with normal tissues. Second, we investigated the prognostic implication of elevated POLD1 expression in HCC and pan-cancers, revealing that increased POLD1 levels were correlated to worse prognoses for HCC patients. Additionally, we identified 11 hub proteins interacting closely with POLD proteins in base excision repair, protein-DNA complex and mismatch repair signaling pathways. Moreover, POLD1 mutation functioned as an independent biomarker to predict the benefit of targeted treatment. Importantly, POLD1 expression was associated with immune checkpoint molecules, including CD274, CD80, CD86, CTLA4, PDCD1 and TCGIT, and facilitated an immune-excluded tumor microenvironment. Additionally, we confirmed that elevated POLD1 expression was closely correlated with the aggressive progression and poor prognosis of HCC in the real-world AHYMUN cohort.ConclusionWe identified a significant association between elevated POLD1 expression and poor patient survival and immune-excluded tumor microenvironment of HCC. Together, these findings indicate that POLD1 provides a valuable biomarker to guide the molecular diagnosis and development of novel targeted therapeutic strategies for HCC patients.

Spoolable reinforced plastic line pipes (RTPs), exhibiting a series of advantages such as good flexibility, few joints, long single length, light weight, easy installation, etc., have been widely used in the onshore oil and gas industry such as oil and gas gathering and transportation, high pressure alcohol injection, water injection, sewage treatment, and other fields. However, due to the lack of clear standard specificationof the limit operating properties for RTPs, three typical failure modes, i.e., tensile, flexure, and torsion, frequently occur in terrain changes, construction operation, and subsequent application, which seriously affects the promotion and use of RTPs. In this paper, the stress distribution of a non-bonded polyester fiber reinforced high-density polyethylene (HDPE) pipe (DN 150, PN 2.5 MPa) was systematically studied by the finite element method (FEM), and then the limit operating values under the axial tensile, coiled bending, and torsion load were determined. The corresponding experiments were conducted to validate the reliability and accuracy of the FEM model. The FEM results showed that the critical strain for axial tensile was 3%, the minimum respooling bend radius was 1016.286 mm, and the limit torsion angle of this RTP was 58.77°, which are very close to the experimental results. These limit values will be useful to establish normative guidelines for field construction and failure prevention of onshore RTP.

Background Liver cancer remains the leading cause of cancer death globally, and the treatment strategies are distinct for each type of malignant hepatic tumors. However, the differential diagnosis before surgery is challenging and subjective. This study aims to build an automatic diagnostic model for differentiating malignant hepatic tumors based on patients’ multimodal medical data including multi-phase contrast-enhanced computed tomography and clinical features. Methods Our study consisted of 723 patients from two centers, who were pathologically diagnosed with HCC, ICC or metastatic liver cancer. The training set and the test set consisted of 499 and 113 patients from center 1, respectively. The external test set consisted of 111 patients from center 2. We proposed a deep learning model with the modular design of SpatialExtractor-TemporalEncoder-Integration-Classifier (STIC), which take the advantage of deep CNN and gated RNN to effectively extract and integrate the diagnosis-related radiological and clinical features of patients. The code is publicly available at https://github.com/ruitian-olivia/STIC-model. Results The STIC model achieved an accuracy of 86.2% and AUC of 0.893 for classifying HCC and ICC on the test set. When extended to differential diagnosis of malignant hepatic tumors, the STIC model achieved an accuracy of 72.6% on the test set, comparable with the diagnostic level of doctors’ consensus (70.8%). With the assistance of the STIC model, doctors achieved better performance than doctors’ consensus diagnosis, with an increase of 8.3% in accuracy and 26.9% in sensitivity for ICC diagnosis on average. On the external test set from center 2, the STIC model achieved an accuracy of 82.9%, which verify the model’s generalization ability. Conclusions We incorporated deep CNN and gated RNN in the STIC model design for differentiating malignant hepatic tumors based on multi-phase CECT and clinical features. Our model can assist doctors to achieve better diagnostic performance, which is expected to serve as an AI assistance system and promote the precise treatment of liver cancer.

With the development of advanced image correlation and high-speed filming techniques, the kinematic field during the cutting process can be experimentally determined including the velocity and strain rate fields. As known, the setting parameters for the digital image correlation (DIC) as well as the optical parameters of the given camera and lighting system have a great influence on the spatial resolution and accuracy of the DIC results. In this study, the speckle pattern in terms of speckle size and intensity distribution are analyzed when using two different surface preparation methods. Moreover, the influences of the subset sizes for the image correlation on the strain rate are numerically studied. Interlaboratory measurements of the kinematic field during the orthogonal cutting of AISI 4140 were conducted with two different in-situ imaging setups. The material flow near the cutting tool edge determined from the velocity field is compared with the numerical simulation. The stagnation zone which is commonly found in the numerical simulation of the cutting process using a chamfered cubic boron nitride (CBN) tool was not observed in the experiments. Furthermore, slip-line fields were constructed from the experimentally determined strain rate components, from which the boundary conditions along the chip-free and chip-tool interface were derived.

This paper proposes a Kullback-Leibler (K-L) divergence-based visual servoing scheme. K-L divergence, also known as relative entropy, is a measure of the difference between two probability distributions. By employing the K-L divergence as a new error metric to evaluate the similarity between the actual and desired images, and then formulating the visual servoing process as an optimization problem with the K-L divergence as the optimization objective, the servoing control law can be designed to achieve the positioning task of the robot. The proposed method does not require any feature detection, tracking and matching. In addition, compared with the histogram-based visual servoing method, the proposed method has a faster convergence rate. Numerical simulations carried out on the ViSP platform validate the effectiveness of proposed method.

Coronary artery disease (CAD) is one of the biggest threats to human life. Circulating microRNAs (miRNAs) have been reported to be linked to the pathogenesis of CAD, indicating the possible role in CAD diagnosis. The present study aimed to explore the expression profile of plasma miRNAs and estimate their value in diagnosis for CAD. 67 Non‐CAD control subjects and 88 CAD patients were enrolled. We conducted careful evaluation by RT‐PCR analysis, Spearman rank correlation coefficients analysis, Receiver Operating Characteristic (ROC) curves analysis and so on. The plasma levels of six miRNAs known to be related to CAD were measured and three of them showed obvious expression change. Circulating miR‐29a‐3p, miR‐574‐3p and miR‐574‐5p were all significantly increased. ROC analysis revealed the probability of the three miRNAs as biomarkers with AUCs (areas under the ROC curve) of 0.830, 0.792 and 0.789, respectively. They were significantly correlated with each other in CAD patients, suggesting the possibility of joint diagnosis. The combined AUC was 0.915, much higher than each single miRNA. Therefore, our study revealed three promising biomarkers for early diagnosis of CAD. The combination of these miRNAs may act more effectively than individual ones for CAD diagnosis.

A novel ultrahigh-frequency (UHF) radio frequency identification (RFID) localization method is proposed in this paper, by which the location of a static passive tag can be easily obtained using a mobile RFID antenna. An unwrapped phase-position model with three parameters is built, and the location of the tag can be pinpointed through an ordinary nonlinear least-squares algorithm. The main advantage of this method is that it is cheap in computation cost compared with the existing grid-based methods. The experimental tests confirm that the proposed method can localize the RFID tags with a competitive computational efficiency and accuracy performance, i.e., millisecond-level computing time and centimeter-level location accuracy. The proposed UHF RFID localization method is well suited to the pervasive location-aware applications, searching RFID-tagged item in the intelligent warehouse by the mobile robot with an onboard RFID system, for example. Note to Practitioners —Ultrahigh-frequency (UHF) radio frequency identification (RFID) has become an efficient booster for the warehouse logistics management due to its low cost, battery-free, and unique identification. The location-aware technology based on RFID is an enabling technology for intelligent warehouse logistics. This paper proposes a passive UHF RFID tag localization method, by which the tags can be easily localized by the use of a mobile RFID system. The experimental results show that the proposed method can localize the static tags with good performance, especially low computation cost. The proposed localization method contributes to the pervasive location-aware application, in which their computation ability is usually limited. For example, mobile robots with the RFID system autonomously seek the RFID-tagged stocks in a warehouse.

Soft robotics with new designs, fabrication technologies and control strategies inspired by nature have been totally changing our view on robotics. To fully exploit their potential in practical applications, untethered designs are preferred in implementation. However, hindered by the limited thermal/mechanical performance of soft materials, it has been always challenging for researchers to implement untethered solutions, which generally involve rigid forms of high energy-density power sources or high energy-density processes. A number of insects in nature, such as rove beetles, can gain a burst of kinetic energy from the induced surface-energy gradient on water to return to their familiar habitats, which is generally known as Marangoni propulsion. Inspired by such a behavior, we report the agile untethered mobility of a fully soft robot in liquid based on induced energy gradients and also develop corresponding fabrication and maneuvering strategies. The robot can reach a speed of 5.5 body lengths per second, which is 7-fold more than the best reported, 0.69 (body length per second), in the previous work on untethered soft robots in liquid by far. Further controlling the robots, we demonstrate a soft-robot swarm that can approach a target simultaneously to assure a hit with high accuracy. Without employing any high energy-density power sources or processes, our robot exhibits many attractive merits, such as quietness, no mechanical wear, no thermal fatigue, invisibility and ease of robot fabrication, which may potentially impact many fields in the future.

BACKGROUND The factors associated with osteoporosis are poorly understood in the Chinese population. This study aimed to examine the factors associated with osteoporosis and with fractures in a Chinese elderly population. MATERIAL AND METHODS This was a cross-sectional study of elderly people living in Tianjin between 2012 and 2014. Bone mineral density was measured by dual X-ray absorptiometry. The subjects completed a questionnaire about lifestyle habits, personal and family medical history, calcium intake, and exercising. Data were gathered on occurrence of fracture at 5 years or August 2018, whichever occurred first. RESULTS There were 298 individuals with osteoporosis (18.5% male, median age 67 years) and 397 without (46.3% male, median age 62 years). Male sex (OR=0.051, 95% CI: 0.021-0.126), age (OR=1.049, 95% CI: 1.099-1.202), being divorced/widowed (OR=2.445, 95% CI: 1.219-4.904), digestive ulcer history (OR=3.805, 95% CI: 1.539-9.405), family history of hunchback (OR=2.659, 95% CI: 1.145-6.175), family history of osteoarthropathy (OR=4.222, 95% CI: 2.128-8.375), fracture history (OR=2.138, 95% CI: 1.307-3.496), drinking green tea (OR=0.352, 95% CI: 0.217-0.574), and exercising (OR=0.303, 95% CI: 0.193-0.475) were independently associated with osteoporosis. Digestive ulcer history (OR=3.183, 95% CI: 1.178-8.5992), exercising (OR=0.354, 95% CI: 0.139-0.903), and taking calcium supplements during follow-up (OR=0.262, 95% CI: 0.112-0.611) were independently associated with fractures in patients with osteoporosis. CONCLUSIONS Female sex, age, marital status, history of digestive ulcer and fracture, and family history of hunchback and osteoarthropathy are associated with osteoporosis among elderly subjects, while drinking green tea and exercising are inversely associated. Among the patients with osteoporosis, a history of digestive ulcer is associated with fractures, while exercising and taking calcium supplements are inversely associated.

In spiral bevel gear transmission system, the target is to get lower noise and higher strength as not only the environmental noise requirements but also customer demands must be met. The load transmission error (LTE) is the primary source of noise, and the elastic deformation is the primary source of strength. This paper proposes a new approach to computing LTE in consideration of effect of the elastic deformation under different bearing supports. Where, elastic deformation evaluation mainly includes the tooth flank contact deformation, shear deformation, especially the shaft deformation. In full consideration of above elastic deformation factors, a spiral bevel gear transmission system having the gear, web, gear shaft and bearing support structures is used as a basic design model by providing the different bearing supports. A numerical formulation of LTE considering multiple elastic deformation evaluations under different bearing supports is proposed by applying the elastic contact mechanics theory. Then, the simulated loaded tooth contact analysis (SLTCA) is used to further verify the numerical results of the formulation analysis. Finally, the given numerical instance can verify the proposed approach.

Although 3D printing was invented in 1984, it was not until recent years that it captured the imagination of everyone from industry experts to at-home hobbyists. Three-dimensional printing, also known as additive manufacturing or rapid prototyping, constructs an object by accumulating materials layer by layer. In recent years, 3D printing technology has been dramatically developed with respect to materials, printer, and process, which laid a foundation for 4D printing. Four-dimensional printing is the targeted evolution of the 3D-printed structure, concerning shape, property, and functionality. The object is produced by 3D printing firstly. Then, the object can self-deform, self-assemble, self-disassemble, self-repair, and change property or functionality over time when the external stimuli are imposed on it. This review mainly introduces the stimulus, types of shape-shifting behaviors, mechanisms of deformation, and applications of 4D printing.

Smart packaging adds sensing abilities to traditional packages. This paper investigates the possibility of using RF signals to test the internal status of packages and detect abnormal internal changes. Towards this goal, we design and implement a nondestructive package testing and verification system using commodity passive RFID systems, called Echoscope. Echoscope extracts unique features from the backscatter signals penetrating the internal space of a package and compares them with the previously collected features during the check-in phase. The use of backscatter signals guarantees that there is no difference in RF sources and the features reflecting the internal status will not be affected. Compared to other nondestructive testing methods such as X-ray and ultrasound, Echoscope is much cheaper and provides ubiquitous usage. Our experiments in practical environments show that Echoscope can achieve very high accuracy and is very sensitive to various types abnormal changes.

In this paper, we propose a system called R# to estimate the number of human objects using passive RFID tags but without attaching anything to human objects. The idea is based on our observation that the more human objects are present, the higher the variation in the RSS values of the tag backscattered RF signals. Thus, based on the received RF signals, the reader can estimate the number of human objects. R# includes an RFID reader and some (say 20) passive tags, which are deployed in the region that we want to monitor the number of human objects, such as the region in front of a supermarket shelf. The RFID reader periodically emits RF signals to identify all tags and the tags simply respond with their IDs via EPCglobal Class 1 Generation 2 protocol. We implemented R# using commercial Impinj H47 passive RFID tags and Impinj reader model R420. We conducted experiments in a simulated picking aisle area of the supermarket environment. The experimental results show that R# can achieve high estimation accuracy (more than 90 percent with up to ten human objects).

Despite significant progressions in treatment modalities over the last decade, either cancer incidence or mortality is continuously on the rise throughout the world. Current anticancer agents display limited efficacy, accompanied by severe side effects. In order to improve therapeutic outcomes in patients with cancer, it is crucial to identify novel, highly efficacious pharmacological agents. Curcumin, a hydrophobic polyphenol extracted from turmeric, has gained increasing attention due to its powerful anticancer properties. Curcumin can inhibit the growth, invasion and metastasis of various cancers. The anticancer mechanisms of curcumin have been extensively studied. The anticancer effects of curcumin are mainly mediated through its regulation of multiple cellular signaling pathways, including Wnt/β-catenin, PI3K/Akt, JAK/STAT, MAPK, p53 and NF-ĸB signaling pathways. Moreover, curcumin also orchestrates the expression and activity of oncogenic and tumor-suppressive miRNAs. In this review, we summarized the regulation of these signaling pathways by curcumin in different cancers. We also discussed the modulatory function of curcumin in the downregulation of oncogenic miRNAs and the upregulation of tumor-suppressive miRNAs. An in-depth understanding of the anticancer mechanisms of curcumin will be helpful for developing this promising compound as a therapeutic agent in clinical management of cancer.

Large skin parts play an important role in the aerospace industry. The wall thickness of the machined pocket in the skin part needs to be strictly controlled to ensure the transport capacity and structural strength. The wall-thickness accuracy is generally decreased by various factors, such as the shaping error of the workpiece blank, fixing error, machine tool error, and deformation caused by cutting force or internal stress. These factors are usually inevitable and stochastic due to the extremely weak rigidity and easy-to-deflect characteristics of the large skin parts. To ensure the wall-thickness accuracy, a fuzzy v-support vector machine (Fv-SVM)-based wall-thickness error decomposition method is proposed. The wall-thickness errors, which are monitored in the cutting process, are decomposed into spatial-related errors and time-related errors. The Fv-SVM-based decomposition method with the principle of spatial statistical analysis is a data-driven approach for intelligent manufacturing. The data-driven method can consider all factors that affect the wall-thickness accuracy, while the model-driven method usually only considers one factor, such as the workpiece deformation or fixing error. After decomposition, the spatial-related wall-thickness error is offline compensated, and the time-related wall-thickness error is compensated by using a real-time strategy. The novel method can be applied to complex tool paths. The cutting experiment of rectangular pockets in a large skin panel was conducted to verify the effectiveness of the proposed method. The wall-thickness accuracy can be improved to 0.05 mm for the workpiece with only 2 mm thickness.

In order to meet demands of low noise and strong strength from their transmission of spiral bevel gears, loaded tooth contact analysis (LTCA) technique has become an increasingly access to determining the loaded tooth contact performances directly affecting the above demands. Moreover, the misalignment always is a main noise factor in the actual manufacturing and can directly affect the loaded tooth contact performances. Distinguish with the conventional simulated loaded tooth contact analysis (SLTCA) based on finite element simulation software package, a numerical loaded tooth contact analysis (NLTCA) method in consideration of the misalignments is presented for the spiral bevel gears. First, an improved computation model is established to perform the tooth contact analysis (TCA) considering the misalignments. Moreover, the principal curvature of contact points is also determined as the input for the proposed numerical LTCA. In the consideration of influences of the different misalignment, the loaded tooth contact performance items such as loaded contact force, loaded contact pattern, elastic contact deformation and loaded transmission error are calculated, respectively. Finally, the numerical instance including the results from SLTCA is given to verify the proposed method.

RFID tag authentication is challenging because most commodity tags cannot run cryptographic algorithms. Prior research demonstrates that physical layer information based authentication is a promising solution, which uses special features from the physical backscatter signals from tags as their fingerprints. However, our recent studies show that existing physical-layer authentication may fail if feature collection and authentication are conducted in different locations, due to location-dependent noises, environmental factors, or reader hardware differences. This paper presents a new physical layer authentication scheme, called Butterfly, which is resilient to environment and location changes. Butterfly utilizes a pair of adjacent tags as an identifier of each object. By using the difference between the RF signals of the two tags as their fingerprint, the environmental factors can be effectively canceled. Butterfly is fully compatible with commodity RFID systems and standards. We set up a prototype Butterfly using commodity readers, tags, and RF devices. Extensive experiments show that Butterfly achieves high authentication accuracy for substantially different environments and device changes.

In this paper, a novel flux switching permanent- magnet machine (FSPM) with consequent-pole magnets and flux bridges in the stator core is proposed to improve the torque capability of FSPMs with high pole ratios. The stator yoke and flux bridges adjacent to the magnets can provide magnetic paths for the main working harmonic field, thus the magnetic reluctance of the working harmonic field is reduced, and the flux modulation effect, winding back-electromotive force (EMF), and output torque of the proposed FSPM can be significantly improved compared to the regular FSPM with high pole ratios. The operation principles and stator–slot/rotor–slot combinations of the proposed FSPM are introduced. Then, the influences of pole ratio, split ratio, slot opening ratio, and PM width on the torque density of the proposed FSPM is investigated using finite element analysis. Moreover, after the investigations, a proposed FSPM is compared to a regular FSPM in terms of back-EMF, rated torque, power factor, etc. Finally, to verify the theoretical analysis, a 12-stator–slot/14-rotor–slot FSPM is built and tested.

A data-driven optimization model to collaborative manufacturing system considering geometric and physical performances is proposed to improve competitiveness of hypoid gear product development facing with economic globalization. Firstly, to deal with the vagueness or impreciseness of the voice of customer (VOC), the fuzzy quality function deployment (fuzzy-QFD) using fuzzy weighted average method in the fuzzy expected value operator is introduced into hypoid gear manufacturing. It can convert them into the critical to qualities (CTQs), and the technical geometric and physical performance requirements. And then, the multi-objective optimization (MOO) modification of machine settings is proposed to establish a basic data-driven model for collaborative system. Different with the conventional modification only considering geometric performance, it provides an improved modification also considering the physical performances. Finally, a double-curved shell model of hypoid gear finite element is used to perform the numerical loaded tooth contact analysis (NLTCA), as well as to establish the data-driven relationships between machine settings and physical performance evaluations. Immediately, whole development is divided into three sub-problems: i) optimal operations of the noise factors by measurement and numerical control (NC) compensation, ii) identification of the prescribed ease-off topography by multi-objective optimization using iterative reference point approach and iii) modification considering geometric performance by a trust region algorithm with step strategy. The numerical instance in practical applications is given to verify the proposed methodology.

Jiaxiao Shi,1,2,* Yulin Li,1,2,* Cong Xing,1,2,* Peng Peng,1,2 Hongyu Shi,1,2 Han Ding,1,2 Pengyuan Zheng,1,2 Guangzhi Ning,1,2 Shiqing Feng1,2 1Department of Orthopaedics, Tianjin Medical University General Hospital, Heping District, Tianjin 300052, PR China; 2Tianjin Neurological Institute, Key Laboratory of Post-Neuroinjury Neuro-repair and Regeneration in Central Nervous System, Ministry of Education and Tianjin City, Heping District, Tianjin 300052, PR China *These authors contributed equally to this work Objectives: The application of urapidil for treating hypertensive patients with acute heart failure in the emergency department remains controversial. Our objective was to organize the relevant articles and assess the clinical indexes between urapidil and nitroglycerin. Materials and methods: PubMed, EMBASE, the Cochrane Library and China National Knowledge Infrastructure were searched for randomized studies that compared urapidil treatment with nitroglycerin treatment for hypertensive patients with acute heart failure. The risk ratio, with 95% CI, was calculated by using a corresponding effects model, according to the value of I2. Results: Seven randomized controlled trials were identified, in order to compare the clinical indexes. On comparing the clinical indexes, the urapidil group was found to be better than the nitroglycerin group in regard to left ventricular ejection fraction, systolic blood pressure, N-terminal prohormone of brain natriuretic peptide, left ventricular end-diastolic volume, cardiac index, ALT, AST and health complications (P0.05). Conclusion: Based on the current evidence, urapidil treatment had better clinical safety features than the traditional pharmaceutical treatment with nitroglycerin. For those indicators with a small amount of data, a greater number of randomized, high-quality controlled trials should be conducted in order to further verify the findings, which could give researchers a more comprehensive evaluation of urapidil treatment. Keywords: urapidil, nitroglycerin, acute heart failure, meta-analysis