Free-Living Gait Cadence Measured by Wearable Accelerometers for Assessing Fall Risk

Accelerometers are widespread in research applications, yet whether they are superior to structured clinic-based assessments is unknown. Using negative binomial regression, we compared traditional in-clinic measures of mobility (6-minute gait cadence, speed, and distance, and 4-meter gait speed) with free-living gait cadence from wrist accelerometers (Actigraph GT9X) in predicting fall rates in 432 older adults (age 77.29±5.46 yrs, 59.1% men, 80.2% White) participating in the Study to Understand Fall Reduction and Vitamin D in You (STURDY). Accelerometry-based gait cadence was estimated with the Adaptive Empirical Pattern Transformation algorithm. Across all participants, every 10 steps/min higher cadence was associated with a 13.2% lower fall rate (p=0.036). Mobility measures were not related to falls (p>0.05). Among higher-functioning participants (cadence ≥100 steps/min), every 10 steps/min higher free-living cadence (p=0.01) was associated with a 27.7% lower fall rate. Data collected from accelerometers may provide a more sensitive indicator of fall risk than in-clinic tests.

Local Pattern Transformation Based Feature Extraction for Recognition of Parkinson’s Disease Based on Gait Signals

Parkinson’s disease (PD) is a neuro-degenerative disorder primarily triggered due to the deterioration of dopamine-producing neurons in the substantia nigra of the human brain. The early detection of Parkinson’s disease can assist in preventing deteriorating health. This paper analyzes human gait signals using Local Binary Pattern (LBP) techniques during feature extraction before classification. Supplementary to the LBP techniques, Local Gradient Pattern (LGP), Local Neighbour Descriptive Pattern (LNDP), and Local Neighbour Gradient Pattern (LNGP) were utilized to extract features from gait signals. The statistical features were derived and analyzed, and the statistical Kruskal–Wallis test was carried out for the selection of an optimal feature set. The classification was then carried out by an Artificial Neural Network (ANN) for the identified feature set. The proposed Symmetrically Weighted Local Neighbour Gradient Pattern (SWLNGP) method achieves a better performance, with 96.28% accuracy, 96.57% sensitivity, and 95.94% specificity. This study suggests that SWLNGP could be an effective feature extraction technique for the recognition of Parkinsonian gait.

Transformation of Rural Landscape Patterns in Southwest China's Mountain: A Case Study Based on the Three Gorges Reservoir Area

Since the 21st century, China's rural areas have experienced far-reaching social and economic transformation. Studying the evolution and transformation of rural land use landscape pattern under this background is of great significance to reveal the evolution of human-land relationship in China’s mountains and explore the rural revitalization path. Based on the study of land use change and transition, we put forward the theoretical framework of landscape pattern transition, and establish the rural landscape pattern transformation model of mountain area in southwest China by use of high resolution remote sensing images and field investigation. The results show that: (1) The focus of landscape pattern transformation in the case region is mainly embodied in the reduction of cultivated land, the expansion of orchard and the restoration of woodland. There are obvious differences in landscape pattern transition among different terrain areas in the case region. (2) From 2000 to 2018, the landscape types in the valleys of the study area were gradually diversified, there was a concentration of intensive land use types, and the landscape types in mountainous and hilly area were gradually simplified, and forest land was restored. (3) On the whole, the landscape pattern of the study area has changed from production-oriented landscape pattern to eco-economic landscape pattern and eco-regulatory landscape pattern. The results of case study show that the rural landscape pattern in the mountainous areas of Southwest China has undergone a transformation under the background of land use transformation, which has its own particularity, and verifies the theoretical hypothesis proposed in this paper, so it is necessary to conduct a systematic and in-depth study on this. The theories and methods of this paper are helpful to understand the process of landscape pattern transition in mountainous areas, and provide reference value for the rational use of land resources, ecological management and rural revitalization in similar mountainous areas.

The Effect of Void Arrangement on the Pattern Transformation of Porous Soft Solids under Biaxial Loading

Structural topology and loading condition have important influences on the mechanical behaviors of porous soft solids. The porous solids are usually set to be under uniaxial tension or compression. Only a few studies have considered the biaxial loads, especially the combined loads of tension and compression. In this study, porous soft solids with oblique and square lattices of circular voids under biaxial loadings were studied through integrated experiments and numerical simulations. For the soft solids with oblique lattices of circular voids, we found a new pattern transformation under biaxial compression, which has alternating elliptic voids with an inclined angle. This kind of pattern transformation is rarely reported under uniaxial compression. Introducing tensile deformation in one direction can hamper this kind of pattern transformation under biaxial loading. For the soft solids with square lattices of voids, the number of voids cannot change their deformation behaviors qualitatively, but quantitatively. In general, our present results demonstrate that void morphology and biaxial loading can be harnessed to tune the pattern transformations of porous soft solids under large deformation. This discovery offers a new avenue for designing the void morphology of soft solids for controlling their deformation patterns under a specific biaxial stress-state.

Research Progress of Sand Transport Mechanism and Critical Conditions in Pipelines

Sand deposition and transportation in pipelines has become one of the major concerns for flow assurance in petroleum industry. However, research in this field is still in its infancy. This study describes the current development of sand deposition and sand transport in pipeline. The mechanism of particles deposition is described. The effects of particle properties, fluid properties and pipeline structure on the carrying capacity of single-phase and multiphase flow carrying sand are introduced, with emphasis on factors such as particle size, liquid viscosity, flow regime and pipeline inclination. As for modeling studies, the sand transport models can be classified to three categories based on the approach used to develop them: empirical, mechanistic, and semi-mechanistic. The methods for developing and extending models are illustrated in this study. Based on the experimental data, the prediction accuracy of four multiphase models for critical velocity in stratified flow is verified. Further researches should focus on the mechanisms and the establishment of the accurate model for sand flow pattern transformation boundary.

Correction to: Value of endometrial echo pattern transformation after hCG trigger in predicting IVF pregnancy outcome: a prospective cohort study

An amendment to this paper has been published and can be accessed via the original article.