Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions

Design Challenges of Intra- and Inter- Chiplet Interconnection

In a chiplet-based many-core system, intra- and inter- chiplet interconnection is key to system performance and power consumption. There are a few challenges in intra- and inter- chiplet interconnection network: 1) Fast and accurate simulation is necessary to analyze the performance metrics. 2) Efficient network architecture for inter- and intra- chiplet is necessary, including topology, PHY design and deadlock free routing algorithms, etc. 3) Deep learning based AI systems are demanding more computation power, which calls for the need of efficient and low power chiplet-based systems. This paper proposes network designs to address these challenges and provides future research directions

Oxamniquine resistance alleles are widespread in Old World Schistosoma mansoni and predate drug deployment

Do mutations required for adaptation occur de novo, or are they segregating within populations as standing genetic variation? This question is key to understanding adaptive change in nature, and has important practical consequences for the evolution of drug resistance. We provide evidence that alleles conferring resistance to oxamniquine (OXA), an antischistosomal drug, are widespread in natural parasite populations under minimal drug pressure and predate OXA deployment. OXA has been used since the 1970s to treat Schistosoma mansoni infections in the New World where S. mansoni established during the slave trade. Recessive loss-of-function mutations within a parasite sulfotransferase (SmSULT-OR) underlie resistance, and several verified resistance mutations, including a deletion (p.E142del), have been identified in the New World. Here we investigate sequence variation in SmSULT-OR in S. mansoni from the Old World, where OXA has seen minimal usage. We sequenced exomes of 204 S. mansoni parasites from West Africa, East Africa and the Middle East, and scored variants in SmSULT-OR and flanking regions. We identified 39 non-synonymous SNPs, 4 deletions, 1 duplication and 1 premature stop codon in the SmSULT-OR coding sequence, including one confirmed resistance deletion (p.E142del). We expressed recombinant proteins and used an in vitro OXA activation assay to functionally validate the OXA-resistance phenotype for four predicted OXA-resistance mutations. Three aspects of the data are of particular interest: (i) segregating OXA-resistance alleles are widespread in Old World populations (4.29–14.91% frequency), despite minimal OXA usage, (ii) two OXA-resistance mutations (p.W120R, p.N171IfsX28) are particularly common (>5%) in East African and Middle-Eastern populations, (iii) the p.E142del allele has identical flanking SNPs in both West Africa and Puerto Rico, suggesting that parasites bearing this allele colonized the New World during the slave trade and therefore predate OXA deployment. We conclude that standing variation for OXA resistance is widespread in S. mansoni

A Novel Method of Adaptive Traffic Image Enhancement for Complex Environments

There exist two main drawbacks for traffic images in classic image enhancement methods. First is the performance degradation that occurs under frontlight, backlight, and extremely dark conditions. The second drawback is complicated manual settings, such as transform functions and multiple parameter selection mechanisms. Thus, this paper proposes an effective and adaptive parameter optimization enhancement algorithm based on adaptive brightness baseline drift (ABBD) for color traffic images under different luminance conditions. This method consists of two parts: brightness baseline model acquisition and adaptive color image compensation. The brightness baseline model can be attained by analyzing changes in light along a timeline. The adaptive color image compensation involves color space remapping and adaptive compensation specific color components. Our experiments were tested on various traffic images under frontlight, backlight, and during nighttime. The experimental results show that the proposed method achieved better effects compared with other available methods under different luminance conditions, which also effectively reduced the influence of the weather

Exploration of the Common Gene Characteristics and Molecular Mechanism of Parkinson’s Disease and Crohn’s Disease from Transcriptome Data

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, and the mechanism of its occurrence is still not fully elucidated. Accumulating evidence has suggested that the gut acts as a potential origin of PD pathogenesis. Recent studies have identified that inflammatory bowel disease acts as a risk factor for Parkinson’s disease, although the underlying mechanisms remain elusive. The aim of this study was to further explore the molecular mechanism between PD and Crohn’s disease (CD). The gene expression profiles of PD (GSE6613) and CD (GSE119600) were downloaded from the Gene Expression Omnibus (GEO) database and were identified as the common differentially expressed genes (DEGs) between the two diseases. Next, analyses were performed, including functional enrichment analysis, a protein–protein interaction network, core genes identification, and clinical correlation analysis. As a result, 178 common DEGs (113 upregulated genes and 65 downregulated genes) were found between PD and CD. The functional analysis found that they were enriched in regulated exocytosis, immune response, and lipid binding. Twelve essential hub genes including BUB1B, BUB3, DLGAP5, AURKC, CBL, PCNA, RAF1, LYN, RPL39L, MRPL13, RSL24D1, and MRPS11 were identified from the PPI network by using cytoHubba. In addition, inflammatory and metabolic pathways were jointly involved in these two diseases. After verifying expression levels in an independent dataset (GSE99039), a correlation analysis with clinical features showed that LYN and RAF1 genes were associated with the severity of PD. In conclusion, our study revealed the common pathogenesis of PD and CD. These common pathways and hub genes may provide novel insights for mechanism research

Preparation and characterization of a novel emphatically charged strengthened chitosan composite nanofiltration membrane

In this paper, chitosan was grafted with acrylic acid and positively charged elements, and a series of reinforced nanofiltration membranes were prepared by coating modified chitosan polymer with polysulfone ultrafiltration membrane as support. The structure of chitosan derivatives and polymers was characterized by infrared spectrum, and the membrane structure was characterized by scanning electron microscope and atomic force microscope. The performance of composite nanofiltration membrane was closely related to the structure of polymer and the electrical properties of positively charged elements. The rejection rate of 50% molar ratio composite nanofiltration membrane to CaCl2 reached 97.4%. The corresponding flux is 430.7 Lm-2h-1. The retention order was: CaCl2 > NaCl > Na2SO4. The tensile strength increased by 27.3%. Acid resistance (5%HCl)) was improved by 50.5%. Alkali resistance (5%NaOH) increased by 42.2%. The results showed that the positive charge enhanced chitosan composite nanofiltration membrane had excellent performance. The charge effect had no effect on NaCl and Na2SO4, but had significant effect on CaCl2. The membrane is a typical positive charged nanofiltration membrane suitable for separating high-valent cations from weak acids or weak bases

Research on Navigation and Positioning Technology of Intelligent Accompanying Tool Car in Dispatching Automation Computer Room

In view of the wide variety of equipment in the dispatch automation machine room and the dense arrangement of cabinets, a navigation technology suitable for the dispatch automation machine room is proposed on the intelligent companion tool cart. In this paper, three sensors of ultrasonic, infrared and lidar are designed to form an intelligent neural sensor, and the information received by the intelligent neural sensor is calculated to generate a vector map through a software algorithm. At the same time, a plane coordinate network is established. The grid coordinate unit accuracy is 0.1cm. At the same time, the concept of “virtual fence” was put forward to fix the workers at the working point, which increased the safety of work. Subsequently, an experimental test of positioning and navigation of the tool cart was carried out. The experimental results showed that the tool cart can accurately locate and generate a vector map with an accuracy error of less than 10cm. The navigation method has a good application effect and has a good promotion value