Approximate Sparse Regularized Hyperspectral Unmixing

Sparse regression based unmixing has been recently proposed to estimate the abundance of materials present in hyperspectral image pixel. In this paper, a novel sparse unmixing optimization model based on approximate sparsity, namely, approximate sparse unmixing (ASU), is firstly proposed to perform the unmixing task for hyperspectral remote sensing imagery. And then, a variable splitting and augmented Lagrangian algorithm is introduced to tackle the optimization problem. In ASU, approximate sparsity is used as a regularizer for sparse unmixing, which is sparser than l1 regularizer and much easier to be solved than l0 regularizer. Three simulated and one real hyperspectral images were used to evaluate the performance of the proposed algorithm in comparison to l1 regularizer. Experimental results demonstrate that the proposed algorithm is more effective and accurate for hyperspectral unmixing than state-of-the-art l1 regularizer

Preparation of washable, highly sensitive and durable strain sensor based conductive double rib knitted fabric

A strain sensor based nylon/spandex double rib elastic knitted fabric has been fabricated by coating graphene and adhesive. The morphology, conductivity and sensing property of treated fabric are investigated. The coated knit fabric exhibits a good conductivity of 15.65 S/m and the resulting strain sensors could detect the small strains of about 0.2% with gauge factor of 29.15. Within a strain range of 0-20%, the gauge factor is found as 28.64. It also shows excellent performance in terms of sensitivity, stability and durability over 5000 wash cycles, and could monitor small external deformations with a response time of 0.24s. Moreover, it has good washability.

Patch-level based vegetation change and environmental drivers in Tarim River drainage area of West China

Information on vegetation-related land cover change and the principle drivers is critical for environmental management and assessment of desertification processes in arid environments. In this study, we investigated patch-level based changes in vegetation and other major land cover types in lower Tarim River drainage area in Xinjiang, West China, and examined the impacts of environmental factors on those changes. Patterns of land cover change were analyzed for the time sequence of 1987-1999-2004 based on satellite-derived land classification maps, and their relationships with environmental factors were determined using Redundancy Analysis (RDA). Environmental variables used in the analysis included altitude, slope, aspect, patch shape index (fractal dimension), patch area, distance to water body, distance to settlements, and distance to main roads. We found that during the study period, 26% of the land experienced cover changes, much of which were the types from the natural riparian and upland vegetation to other land covers. The natural riparian and upland vegetation patches were transformed mostly to desert and some to farmlands, indicating expanding desertification processes of the region. A significant fraction of the natural riparian and upland vegetation experienced a phase of alkalinity before becoming desert, suggesting that drought is not the exclusive environmental driver of desertification in the study area. Overall, only a small proportion of the variance in vegetation-related land cover change is explainable by environmental variables included in this study, especially during 1987-1999, indicating that patch-level based vegetation change in this region is partly attributable to environmental perturbations. The apparent transformation from the natural riparian and upland vegetation to desert indicates an on-going process of desertification in the region

Discussion on the value and importance of “practical exercises” in classroom teaching of craft products —take handicraft wax as an example

In the classroom teaching of process products, the actual operation is the most effective way to ensure that students can fully grasp the contents of classroom teaching. As a craft product that needs to be finished by hand, students’ practical operation in the classroom teaching of handicraft wax will further improve the classroom teaching effect of craft products. This paper mainly discusses the importance and value of practical process operation in classroom teaching of process wax

Effect of a Hypoxia-Controlled Atmosphere Box on Egg Respiration Intensity and Quality

Egg preservation is an important factor during storage and transportation. Fresh eggs were stored in boxes in a controlled atmosphere with an O2 concentration of 0% O2 + 100% nitrogen (N2), 5% O2 + 95% N2, 10% O2 + 90% N2, 15% O2 + 85% N2, and 20% O2 + 80% N2, and the effects of these storage conditions on large quantities of eggs were studied. The respiratory intensity and quality of eggs during storage were measured. We chose the weight loss rate of eggs, Haugh unit, pH, and the egg white total plate count as the characteristic indices of egg quality. We compared the changes in egg quality during and after storage at different O2 concentrations versus that at 25 °C. The stages were evaluated using the TOPSIS method to sort egg quality, and the optimal O2 concentration was selected. FLUENT was used to simulate and control the atmospheric requirements. Our findings showed that eggs stored in an air-conditioning chamber with O2 concentration ≤10% exhibited weak respiratory intensity (0–1 mg/(kg·h)). The rates of decrease in loss of egg weight and Haugh units were smaller. There were significant differences in the pH of egg white stored in different O2 concentrations (p 2 concentration in the egg-storage environment reduced the number of colonies in eggs and had a positive effect on egg preservation. Simulations using FLUENT revealed that only 1200 s were required to achieve the low-oxygen environment in the controlled atmosphere box (1.5 m × 1 m × 1 m). The storage environment of 5% O2 + 95% N2 had the best preservation effect on eggs. This approach is associated with low costs in practical application and can potentially be used for egg storage and transport

Effect of a Hypoxia-Controlled Atmosphere Box on Egg Respiration Intensity and Quality

Egg preservation is an important factor during storage and transportation. Fresh eggs were stored in boxes in a controlled atmosphere with an O2 concentration of 0% O2 + 100% nitrogen (N2), 5% O2 + 95% N2, 10% O2 + 90% N2, 15% O2 + 85% N2, and 20% O2 + 80% N2, and the effects of these storage conditions on large quantities of eggs were studied. The respiratory intensity and quality of eggs during storage were measured. We chose the weight loss rate of eggs, Haugh unit, pH, and the egg white total plate count as the characteristic indices of egg quality. We compared the changes in egg quality during and after storage at different O2 concentrations versus that at 25 °C. The stages were evaluated using the TOPSIS method to sort egg quality, and the optimal O2 concentration was selected. FLUENT was used to simulate and control the atmospheric requirements. Our findings showed that eggs stored in an air-conditioning chamber with O2 concentration ≤10% exhibited weak respiratory intensity (0–1 mg/(kg·h)). The rates of decrease in loss of egg weight and Haugh units were smaller. There were significant differences in the pH of egg white stored in different O2 concentrations (p < 0.05). Reducing the O2 concentration in the egg-storage environment reduced the number of colonies in eggs and had a positive effect on egg preservation. Simulations using FLUENT revealed that only 1200 s were required to achieve the low-oxygen environment in the controlled atmosphere box (1.5 m × 1 m × 1 m). The storage environment of 5% O2 + 95% N2 had the best preservation effect on eggs. This approach is associated with low costs in practical application and can potentially be used for egg storage and transport

Adaptive Transmission Disequilibrium Test for Family Trio Design

The transmission disequilibrium test (TDT) is a standard method to detect association using family trio design. It is optimal for an additive genetic model. Other TDT-type tests optimal for recessive and dominant models have also been developed. Association tests using family data, including the TDT-type statistics, have been unified to a class of more comprehensive and flexable family-based association tests (FBAT). TDT-type tests have high efficiency when the genetic model is known or correctly specified, but may lose power if the model is mis-specified. Hence tests that are robust to genetic model mis-specification yet efficient are preferred. Constrained likelihood ratio test (CLRT) and MAX-type test have been shown to be efficiency robust. In this paper we propose a new efficiency robust procedure, referred to as adaptive TDT (aTDT). It uses the Hardy-Weinberg disequilibrium coefficient to identify the potential genetic model underlying the data and then applies the TDT-type test (or FBAT for general applications) corresponding to the selected model. Simulation demonstrates that aTDT is efficiency robust to model mis-specifications and generally outperforms the MAX test and CLRT in terms of power. We also show that aTDT has power close to, but much more robust, than the optimal TDT-type test based on a single genetic model. Applications to real and simulated data from Genetic Analysis Workshop (GAW) illustrate the use of our adaptive TDT.

A New Deep Learning Neural Network Model for the Identification of InSAR Anomalous Deformation Areas

The identification and early warning of potential landslides can effectively reduce the number of casualties and the amount of property loss. At present, interferometric synthetic aperture radar (InSAR) is considered one of the mainstream methods for the large-scale identification and detection of potential landslides, and it can obtain long-term time-series surface deformation data. However, the method of identifying anomalous deformation areas using InSAR data is still mainly manual delineation, which is time-consuming, labor-consuming, and has no generally accepted criterion. In this study, a two-stage detection deep learning network (InSARNet) is proposed and used to detect anomalous deformation areas in Maoxian County, Sichuan Province. Compared with the most commonly used detection models, it is demonstrated that the InSARNet has a better performance in the detection of anomalous deformation in mountainous areas, and all of the quantitative evaluation indexes are higher for InSARNet than for the other models. After the anomalous deformation areas are identified using the proposed model, the possible relationship between the anomalous deformation areas and potential landslides is investigated. Finally, the fact that the automatic and rapid identification of potential landslides is the inevitable trend of future development is discussed

How to Sustainably Use Water Resources—A Case Study for Decision Support on the Water Utilization of Xinjiang, China

Global warming has led to a serious crisis on regional water resources. Establishing a decision support system (DSS) on the sustainable utilization of water resources for arid areas is an increasingly critical problem. Selecting Xinjiang as a case study, this paper developed a system dynamics (SD) model. Through the simulation operation of the model, we achieved the decision on sustainable utilization of water resources. The extensive economic development is the main factor restricting the sustainable utilization of water resources in Xinjiang. We propose to adjust the planting structure and implement water-saving irrigation in Xinjiang, especially the Tarim Basin and Turpan-Hami Basin. This research provides the sustainable utilization plan of water resources for Xinjiang and its sub-regions in the next 30 years. By 2050, we recommend that the reuse rate of urban domestic water consumption and industrial sewage should reach 75%; the rural domestic water quota should be 70 L/(person·day); water consumption per industrial output value of ten thousand Yuan should be 28 m3; the irrigation water quota should be 5000 m3/hectare in Xinjiang. This research can provide references for the decision on sustainable utilization of water resources in arid regions around the world

Preparation of washable, highly sensitive and durable strain sensor based conductive double rib knitted fabric

296-301A strain sensor based nylon/spandex double rib elastic knitted fabric has been fabricated by coating graphene and adhesive. The morphology, conductivity and sensing property of treated fabric are investigated. The coated knit fabric exhibits a good conductivity of 15.65 S/m and the resulting strain sensors could detect the small strains of about 0.2% with gauge factor of 29.15. Within a strain range of 0-20%, the gauge factor is found as 28.64. It also shows excellent performance in terms of sensitivity, stability and durability over 5000 wash cycles, and could monitor small external deformations with a response time of 0.24s. Moreover, it has good washability