Decay of geometry for a class of cubic polynomials

In this paper we study a class of bimodal cubic polynomials satisfying two critical points belong to one invariant Cantor set. These maps have generalized Fibonacci combinatorics in terms of generalized renormalization on the twin principal nest. It is proved that such maps possess `decay of geometry' in the sense that the scaling factor of its twin principal nest decreases at least exponentially fast. As an application, we prove that they have no Cantor attractor

EEG-EMG Analysis Method in Hybrid Brain Computer Interface for Hand Rehabilitation Training

Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient’s brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient’s overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography (EEG-EMG). The framework utilizes a combination of techniques: decoding EEG by using Graph Convolutional LSTM Networks (GCN-LSTM) to recognize the subject’s motion intention, and decoding EMG by using a convolutional neural network (CNN) to accurately identify hand movements. In EEG decoding, the correlation between channels is calculated using Standardized Permutation Mutual Information (SPMI), and the decoding process is further explained by analyzing the correlation matrix. In EMG decoding, experiments are conducted on two task paradigms, both achieving promising results. The proposed framework is validated using the publicly available WAL-EEG-GAL (Wearable interfaces for hand function recovery Electroencephalography Grasp-And-Lift) dataset, where the average classification accuracies of EEG and EMG are 0.892 and 0.954, respectively. This research aims to establish an efficient and user-friendly EEG-EMG hybrid BCI, thereby facilitating the hand rehabilitation training of stroke patients

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI

IntroductionBrain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.MethodsThis paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.ResultsA classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.DiscussionThis approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery

arrayMap: A Reference Resource for Genomic Copy Number Imbalances in Human Malignancies

Background: The delineation of genomic copy number abnormalities (CNAs) from cancer samples has been instrumental for identification of tumor suppressor genes and oncogenes and proven useful for clinical marker detection. An increasing number of projects have mapped CNAs using high-resolution microarray based techniques. So far, no single resource does provide a global collection of readily accessible oncoge- nomic array data. Methodology/Principal Findings: We here present arrayMap, a curated reference database and bioinformatics resource targeting copy number profiling data in human cancer. The arrayMap database provides a platform for meta-analysis and systems level data integration of high-resolution oncogenomic CNA data. To date, the resource incorporates more than 40,000 arrays in 224 cancer types extracted from several resources, including the NCBI's Gene Expression Omnibus (GEO), EBIs ArrayExpress (AE), The Cancer Genome Atlas (TCGA), publication supplements and direct submissions. For the majority of the included datasets, probe level and integrated visualization facilitate gene level and genome wide data re- view. Results from multi-case selections can be connected to downstream data analysis and visualization tools. Conclusions/Significance: To our knowledge, currently no data source provides an extensive collection of high resolution oncogenomic CNA data which readily could be used for genomic feature mining, across a representative range of cancer entities. arrayMap represents our effort for providing a long term platform for oncogenomic CNA data independent of specific platform considerations or specific project dependence. The online database can be accessed at http://www.arraymap.org.Comment: 17 pages, 5 inline figures, 3 tables, supplementary figures/tables split into 4 PDF files; manuscript submitted to PLoS ON

Research on the Influence of the Key Stratum Position on the Support Working Resistance during Large Mining Height Top-Coal Caving Mining

In recent years, in order to increase the coal recovery rate, the large mining height fully mechanized top-coal caving mining has been widely used because it has the advantages of both fully mechanized mining method and large mining height mining method. When this mining technology is used to exploit thick coal seam under upper goaf, the movement characteristics of the overlying strata and the bearing structure formed by the broken rock are complicated, which results in the abnormal pressure during mining, such as severe coal slabs and hydraulic supports being crushed. The key to solve these problems is to study the movement law and the structural evolution characteristics of the overlying strata during large mining height fully mechanized top-coal caving mining, and the movement characteristics of the overlying strata are all determined by the layer-position of the key stratum. UDEC models with different layer-position of the key stratum are established to investigate the influence of the key stratum position on the support working resistance during large mining height top-coal caving mining. Through comprehensive research, the source of support resistance comes from under different geological conditions was analyzed, and the formula for estimating the maximum support working resistance was deduced. In addition, in order to release the severe pressure during large mining height fully mechanized top-coal caving mining, it is recommended to use hydraulic fracturing method to weaken the key stratum in situ

Online collision avoidance for human-robot collaborative interaction concerning safety and efficiency

With the development of robot technology and the arrival of industry 4.0 era, society pays more attention to collaboration and interaction between human and robots. However, safety is still main concern in the development of human-robot collaboration. In this paper, a novel real-time collision avoidance approach for manipulator is proposed by considering the motion status of the human, which includes the relative minimum distance and velocity (both magnitude and direction) between the robot and the human. The distance and velocity of the human hand are first estimated online using a vision sensor, and then defined as danger factors in the potential function of the potential field. The novel potential function proposed in this paper considers not only the safety problem, but also the efficient problem, i.e., the manipulator canmakesmartcontroldecisiontoavoidthecollisionaccording to the relative velocity in case of the cross over. To overcome the local minimum problem and choose a best motion direction, we propose a motion sampling mechanism for motion planning. For each sample, the robot calculates the potential function to evaluate the safety and efficiency, and chooses a direction which is best for avoidance. We finally demonstrate our idea on a real manipulator platform in a human co-existance environment

Effect of aquatic physical therapy on chronic low back pain: a systematic review and meta-analysis

Abstract Background Chronic low back pain is a common musculoskeletal disease. With the increasing number of patients, it has become a huge economic and social burden. It is urgent to relieve the burden of patients. There are many common rehabilitation methods, and aquatic physical therapy is one of them. The purpose of this systematic review and meta-analysis is to summarize the existing literature and analyze the impact of aquatic physical therapy on pain intensity, quality of life and disability of patients with chronic low back pain. Methods Through 8 databases, we searched randomized controlled trials on the effect of aquatic physical therapy on patients with chronic low back pain. These trials published results on pain intensity, quality of life, and disability. This review is guided by Cochrane Handbook for systematic reviews of interventions version 5.1.0. The level of evidence was assessed through GRADE. Results A total of 13 articles involving 597 patients were included. The results showed that compared with the control group, aquatic physical therapy alleviated the pain intensity (Visual Analogue Scale: SMD = -0.68, 95%CI:-0.91 to -0.46, Z = 5.92, P < 0.00001) and improved quality of life (physical components of 36-Item Short Form Health Survey or Short-Form 12: SMD = 0.63, 95%CI:0.36 to 0.90, Ζ = 4.57, P < 0.00001; mental components of 36-Item Short Form Health Survey or Short-Form 12: SMD = 0.59, 95%CI:0.10 to 1.08, Ζ = 2.35, P = 0.02), and reduced disability (Roland Morris Disability Questionnaire: SMD = -0.42, 95%CI:-0.66 to -0.17, Ζ = 3.34, P = 0.0008; Oswestry Disability Index or Oswestry Low Back Pain Disability Questionnaire: SMD = -0.54, 95%CI:-1.07 to -0.01, Ζ = 1.99, P = 0.05). However, aquatic physical therapy did not improve patients' pain at rest (Visual Analogue Scale at rest: SMD = -0.60, 95%CI:-1.42 to 0.23, Ζ = 1.41, P = 0.16). We found very low or low evidence of effects of aquatic physical therapy on pain intensity, quality of life, and disability in patients with chronic low back pain compared with no aquatic physical therapy. Conclusions Our systematic review showed that aquatic physical therapy could benefit patients with chronic low back pain. However, because the articles included in this systematic review have high bias risk or are unclear, more high-quality randomized controlled trials are needed to verify

Study on the Annual Reduction Rate of Vehicle Emission Factors for Carbon Monoxide: A Case Study of Urban Road Tunnels in Shenzhen, China

Environmental pollution and energy conservation in urban tunnels have become important issues that affect the scientific design and sustainable development of urban tunnels. The carbon monoxide (CO) concentration in urban road tunnels is regarded as a direct reflection and a useful tracer of the intensity of anthropogenic transportation activities. Previous studies in recent years have paid more attention to pollutant emission factors, but less to the calculation parameters of ventilation design for tunnels. This paper aims to study a reasonable annual reduction rate of CO base emission factors. Therefore, a detailed field measurement was carried out in the four typical urban road tunnels, Henglongshan Tunnel, Cejiexian Tunnel, Jiuweiling Tunnel, and Dameisha Tunnel in Shenzhen, China, from March 29 to September 16, 2014. Measurement results showed that the traffic flow of the four urban tunnels had been approaching the design value, or even beyond the limit. The average daily air velocities in the four tunnels were all within 5 m/s, whereas the maximum air velocity had exceeded the limit of 10 m/s. The CO concentrations in Henglongshan Tunnel, Cejiexian Tunnel, Jiuweiling Tunnel, and Dameisha Tunnel were 17 ppm, 7 ppm, 39 ppm, and 8 ppm, respectively. Moreover, it was found that the average CO emission factors of Henglongshan Tunnel, Cejiexian Tunnel, Jiuweiling Tunnel, and Dameisha Tunnel were 1.075 g/(km·veh), 1.245 g/(km·veh), 4.154 g/(km·veh), and 1.739 g/(km·veh), respectively. Based on the statistical data, the CO emission factors of mixed traffic and passenger cars decrease by an average of 16.4% and 33.3%, respectively, per year through the regression method and by an average of 17.4% and 29.0%, respectively, per year through the extremum method. Finally, when considering the safety factor of 20%, it is more reasonable for the CO base emission to adopt 4% as an annual reduction rate for ventilation design in urban tunnels

Suspended silicon waveguide with sub-wavelength grating cladding for optical mems in mid-infrared

10.3390/mi12111311Micromachines1211131

Association between depression and pain, functional disability, disease activity and health-related quality of life in patients with systemic lupus erythematosus: a meta-analysis

Objective The aim of this study was to explore the effect of depression on pain, disease activity, functional disability and health-related quality of life measured by Visual Analogue Scale, Systemic Lupus Erythematosus Disease Activity Index, Health Assessment Questionnaire and Short Form-36 in patients with systemic lupus erythematosus.Design Meta-analysis.Data sources The PubMed, EMBASE, Cochrane Library and Web of Science were searched for obtaining available studies from inception to 7 March 2023.Eligibility criteria for selecting studies Studies evaluating the impact of depression on pain, disease activity, functional disability and quality of life were included.Data extraction and synthesis Two authors independently screened publications and extracted data according to set inclusion and exclusion criteria. Statistical analyses were conducted with RevMan V.5.3.0. Data were pooled using a fixed-effects or random-effects model according to heterogeneity.Results A total of 13 identified studies met the inclusion criteria, reporting on a total of 1911 patients with systemic lupus erythematosus. The Visual Analogue Scale score was significantly higher in patients with depression than those without depression (standardised mean difference (SMD)=0.84 (95% CI 0.27 to 1.42), p=0.004). The Health Assessment Questionnaire score was significantly higher in patients with depression than those without depression (SMD=1.05 (95% CI 0.14 to 1.95), p&lt;0.05). The Systemic Lupus Erythematosus Disease Activity Index score was significantly higher in patients with depression than those without depression (SMD=0.46 (95% CI 0.27 to 0.64), p&lt;0.00001). Scores in most Short Form-36 dimensions (physical function, role physical function, emotional role function, vitality, mental health, social function, general health, physical component scale, mental component scale) were lower in patients with depression than those without depression.Conclusion This meta-analysis showed that depression was associated with increased in pain, functional disability and disease activity, and decline in health-related quality of life in patients with systemic lupus erythematosus. Awareness of the importance of the relationship between depression in systemic lupus erythematosus patients and pain, functional disability and the quality of life might assist rheumatology physicians and nurses in assessing and preventing these symptoms.PROSPERO registration number CRD42021265694