PkCOs: synchronisation of packet-coupled oscillators in blast wave monitoring networks

Blast waves with a large amount of energy, from the use of explosive weapons, is a major cause of traumatic brain injury in armed and security forces. The monitoring of blast waves is required for defence and civil applications. The utilisation of wireless sensing technology to monitor blast waves has shown great advantages such as easy deployment and flexibility. However, due to drifting embedded clock frequency, the establishment of a common timescale among distributed blast monitoring sensors has been a challenge, which may lead to a network failing to estimate the precise acoustic source location. This work adopts a Packet-Coupled Oscillators (PkCOs) protocol to synchronise drifting clocks in a wireless blast wave monitoring network. In order to address packet collisions caused by the concurrent transmission, an anti-phase synchronisation solution is utilised to maintain clock synchronisation, and the corresponding superframe structure is developed to allow the hybrid transmission of the Sync packet and the blast wave monitoring data. As a network scales up and the hop distance grows, the packet exchange lag increases during a superframe. This, along with the drifting clock frequency, leads to the degradation of synchronisation performance while the clock frequency is usually assumed to be zero and non-drifting. Thus, a compensation strategy is proposed to eliminate the joint impacts and to improve the synchronisation precision. The theoretical performance analysis of the PkCOs algorithm in the network is presented along with verification by simulation means. Finally, the performance of the PkCOs synchronisation protocol is evaluated on an IEEE 802.15.4 hardware testbed. The experimental results show that the PkCOs algorithm provides an alternative clock synchronisation solution for blast wave monitoring networks

Validation of the Chinese version of the Brief Pain Inventory in patients with knee osteoarthritis

Abstract Background Knee osteoarthritis (KOA) primarily presents with symptoms of pain and compromised functionality. Pain is a subjective manifestation that necessitates the employment of reliable evaluation tools for practical assessment, thereby enabling the formulation of appropriate interventional strategies. The Brief Pain Inventory (BPI) is a widely utilized questionnaire for evaluating the status of chronic pain. The purpose of the present study is to translate the short form of BPI into Chinese version (BPI-CV) and conduct cross-cultural adaptation to evaluate the psychometric characteristics of BPI-CV in KOA patients. Methods BPI-CV was translated and cross-culturally adapted according to internationally recognized guidelines. A cohort comprising 150 patients diagnosed with KOA successfully completed the demographic questionnaire, BPI-CV, Western Ontario and McMaster University Osteoarthritis Index (WOMAC), and the EuroQoL Group's five-dimension questionnaire (EQ-5D). Internal consistency and test–retest analysis were used to evaluate the reliability. The internal consistency of the scale items was evaluated by calculating the Cronbach's α value (> 0.7). We chose to employ two scales commonly used in the evaluation of KOA patients: the disease-specific WOMAC scale and the universal EQ-5D scale. Construct validity was determined through Pearson correlation analysis, comparing BPI scores with those obtained from the WOMAC and EQ-5D scales. Exploratory factor analysis was used to structural validity. Results The BPI-CV was well accepted with no ceiling or floor effect. Cronbach's α for assessing internal consistency was 0.894. Test–retest reliability was excellent with an ICC of 0.852 (95%CI 0.785–0.905). The BPI-CV showed moderate to strong correlations with the pain dimension (r = 0.496–0.860) and the functional interference dimension (r = 0.517–0.712) of the WOMAC and the EQ-5D (r = 0.527–0.743). Three factors resulted using exploratory factor analysis: pain severity, activity interference, and emotional interference, accounting for 79.0% of the total variance. Standard error of measurement was 0.539. Conclusion BPI-CV has good feasibility, reliability, and validity. It can be recommended for KOA patients in mainland China

Robust Synchronised Data Acquisition for Biometric Authentication

Owing to its unique, concealment and easy customisation by combining different wrist and hand gestures, High-Density surface electromyogram (HD-sEMG) is recognised as a potential solution to the next generation biometric authentication, which usually adopts a wireless Body Sensor Network (BSN) to acquire the multi-channel HD-sEMG biosignals from distributed electrode arrays. For more accurate and reliable classification, biometric authentication requires the distributed biosignals to be sampled simultaneously and be well-aligned, which means that the sampling jitters among the arrays need to be tiny. To synchronise data sampling clocks of a cluster of BSN nodes for biometric authentication, this paper modifies the Packet-Coupled Oscillators protocol by using a Dynamic controller (D-PkCOs). This protocol only involves one-way single packet exchange, which reduces the communication overhead significantly. For the purpose of maintaining precise sampling of these BSN nodes subject to drifting clock frequency and varying delays, the dynamic controller is designed via the H∞ robust method, and it is proved that all the BSN nodes’ sampling jitters are bounded. The experimental results demonstrate that the D-PkCOs protocol can keep the sampling jitters less than a microsecond in a 10-node IEEE 802.15.4 network. The application of D-PkCOs to the BSN shows that the HD-sEMG signal with a high signal-to-noise ratio is obtained, which leads to better gesture classification performance

Cationic Copolymerization of Isobutylene with 4-Vinylbenzenecyclobutylene: Characteristics and Mechanisms

A random copolymer of isobutylene (IB) and 4-vinylbenzenecyclobutylene (4-VBCB) was synthesized by cationic polymerization at −80 °C using 2-chloro-2,4,4-trimethylpentane (TMPCl) as initiator. The laws of copolymerization were investigated by changing the feed quantities of 4-VBCB. The molecular weight of the copolymer decreased, and its molecular weight distribution (MWD) increased with increasing 4-VBCB content. We proposed a possible copolymerization mechanism behind the increase in the chain transfer reaction to 4-VBCB with increasing of feed quantities of 4-VBCB. The thermal properties of the copolymers were studied by solid-phase heating and crosslinking. After crosslinking, the decomposition and glass transition temperatures (Tg) of the copolymer increased, the network structure that formed did not break when reheated, and the mechanical properties remarkably improved

Bibliometric and visual analysis of research on analgesia and total knee arthroplasty from 1990 to 2022

Background: In recent decades, there have been notable advancements in the field of analgesia and total knee arthroplasty (TKA). This study aims to employ bibliometric analysis to elucidate the prevailing research focal points and trends within analgesia and TKA from 1990 to 2022. Material and methods: Relevant publications were retrieved from the Web of Science Core Collection. CiteSpace, VOSviewer, and Scimago Graphica were used for visualization and bibliometric analysis of countries, institutions, authors, journals, references, and keywords. Results: A total of 2776 publications on analgesia and TKA were identified, with the United States having the highest number of publications. The University of Copenhagen was the most productive institution, and Kehlet, Henrik was the most prolific author. The Journal of Arthroplasty had the most publications and citations. The most common keywords were “TKA,” “pain management,” “postoperative pain,” “Total hip arthroplasty (THA),” and “postoperative management.” Keyword burst detection demonstrated that adductor canal block (ACB) was a recent research hotspot. Conclusion: Our study revealed a sharp increase in global publications on analgesia and TKA, and this trend is expected to continue. Further research is necessary to determine the optimal regimen for multimodal analgesia, the ideal location and volume of ACB, and their clinical significance

Stem-Leaf Segmentation and Phenotypic Trait Extraction of Individual Maize Using Terrestrial LiDAR Data

Accurate and high throughput extraction of crop phenotypic traits, as a crucial step of molecular breeding, is of great importance for yield increasing. However, automatic stem-leaf segmentation as a prerequisite of many precise phenotypic trait extractions is still a big challenge. Current works focus on the study of the 2-D image-based segmentation, which are sensitive to illumination and occlusion. Light detection and ranging (LiDAR) can obtain accurate 3-D information with its active laser scanning and strong penetration ability, which breaks through phenotyping from 2-D to 3-D. However, few researches have addressed the problem of the LiDAR-based stem-leaf segmentation. In this paper, we proposed a median normalized-vector growth (MNVG) algorithm, which can segment stem and leaf with four steps, i.e., preprocessing, stem growth, leaf growth, and postprocessing. The MNVG method was tested by 30 maize samples with different heights, compactness, leaf numbers, and densities from three growing stages. Moreover, phenotypic traits at leaf, stem, and individual levels were extracted with the truly segmented instances. The mean accuracy of segmentation at point level in terms of the recall, precision, F-score, and overall accuracy were 0.92, 0.93, 0.92, and 0.93, respectively. The accuracy of phenotypic trait extraction in leaf, stem, and individual levels ranged from 0.81 to 0.95, 0.64 to 0.97, and 0.96 to 1, respectively. To our knowledge, this paper proposed the first LiDAR-based stem-leaf segmentation and phenotypic trait extraction method in agriculture field, which may contribute to the study of LiDAR-based plant phonemics and precise agriculture