Design and experiments of an automatic pipe winding machine

To solve the time-consuming and laborious problem of manual winding and unwinding water pipes by field workers during irrigation or pesticide spraying in agricultural production, an automatic pipe winding machine for winding and unwinding water pipes was designed. The guiding mechanism, pipe winding mechanism, and pipe arrangement mechanism of the pipe winding machine are designed, and the automatic deviation correction control method of pipe arrangement based on PID and the constant tension control method of pipe winding and unwinding is put forward, and the control system of the automatic pipe winding machine is developed. By making a prototype of an automatic pipe winding machine, the effects of pipe winding and unwinding and the constant tension control of the automatic winding machine are tested and analyzed. The test results show that under the condition of 4.0 km/h speed, the maximum angle error of the automatic pipe winding machine is 3.32°, the average absolute error is 0.95°, and the water pipes are arranged neatly and tightly. The maximum relative error of the water pipe tension is 9.3%, and the maximum relative error under the 0~4.0 km/h velocity step variable condition is 16.3%. The speed of the pipe winding and unwinding can adapt to the change of the vehicle’s speed automatically, and the tension of the pipe is within a reasonable range. The performance of the pipe arrangement and pipe coiling of the automatic pipe winding machine can meet the operating requirements

The art of defense: letting networks fool the attacker

Some deep neural networks are invariant to some input transformations, such as Pointnet is permutation invariant to the input point cloud. In this paper, we demonstrated this property could be powerful in defense of gradient-based attacks. Specifically, we apply random input transformation which is invariant to the networks we want to defend. Extensive experiments demonstrate that the proposed scheme defeats various gradient-based attackers in the targeted attack setting, and breaking the attack accuracy into nearly zero. Our code is available at: {\footnotesize{\url{https://github.com/cuge1995/IT-Defense}}}

An inventory of invasive alien species in China

Invasive alien species (IAS) are a major global challenge requiring urgent action, and the Strategic Plan for Biodiversity (2011–2020) of the Convention on Biological Diversity (CBD) includes a target on the issue. Meeting the target requires an understanding of invasion patterns. However, national or regional analyses of invasions are limited to developed countries. We identified 488 IAS in China’s terrestrial habitats, inland waters and marine ecosystems based on available literature and field work, including 171 animals, 265 plants, 26 fungi, 3 protists, 11 procaryots, and 12 viruses. Terrestrial plants account for 51.6% of the total number of IAS, and terrestrial invertebrates (104 species) for 21.3%. Of the total numbers, 67.9% of plant IAS and 34.8% of animal IAS were introduced intentionally. All other taxa were introduced unintentionally despite very few animal and plant species that invaded naturally. In terms of habitats, 64.3% of IAS occur on farmlands, 13.9% in forests, 8.4% in marine ecosystems, 7.3% in inland waters, and 6.1% in residential areas. Half of all IAS (51.1%) originate from North and South America, 18.3% from Europe, 17.3% from Asia not including China, 7.2% from Africa, 1.8% from Oceania, and the origin of the remaining 4.3% IAS is unknown. The distribution of IAS can be divided into three zones. Most IAS are distributed in coastal provinces and the Yunnan province; provinces in Middle China have fewer IAS, and most provinces in West China have the least number of IAS. Sites where IAS were first detected are mainly distributed in the coastal region, the Yunnan Province and the Xinjiang Uyghur Autonomous Region. The number of newly emerged IAS has been increasing since 1850. The cumulative number of firstly detected IAS grew exponentially

Role of T2 mapping of magnetic resonance imaging in the differentiation of endometrial cancer and benign endometrial lesions

PURPOSEThe T2 mapping of magnetic resonance imaging (MRI) in endometrial cancer (EC), benign endometrial lesions (BELs), and normal endometrium (NE) has rarely been reported. This study aimed to determine the T2 values of MRI in EC, BELs, and NE to investigate whether the T2 values can differentiate them and to assess the aggressiveness of EC.METHODSIn total, 73 patients [EC, 51 (age, 57.4 ± 5.4 years); BELs, 22 (age, 57.8 ± 11.8 years)] and 23 normal volunteers (age, 56.1 ± 6.6 years) were included. The T2 values of MRI of the EC (type I and II), BEL, and NE groups were described and compared. The relationships between the T2 values of MRI in EC and the pathological characteristics [International Federation of Gynecology and Obstetrics (FIGO) stage and grade] were analyzed.RESULTSThe median T2 values of NE, BEL, and EC were 197.5 (142.9–324.0) ms, 131.1 (103.2–247.9) ms, and 103.0 (71.6–243.5) ms (P < 0.001), respectively. The median T2 values of type I and type II EC were 100.8 (71.62–130.44) ms and 125.7 (119.7–243.5) ms, respectively. There were significant differences in the T2 values among the NE, BEL, type I EC, and type II EC groups (P < 0.001) except for between the type II EC and BEL groups (P = 0.938). The T2 value of MRI in type I EC was significantly lower than that in type II EC (P = 0.001). There were no significant differences in patients with type I EC having different FIGO stages (P = 0.273) or tumor grades (P = 0.686).CONCLUSIONT2 mapping of MRI has the potential to quantitatively differentiate between EC, BELs, and NE as well as between type I and type II EC

Causal association of NAFLD with osteoporosis, fracture and falling risk: a bidirectional Mendelian randomization study

IntroductionThe causal association between non-alcoholic fatty liver disease (NAFLD) and osteoporosis remains controversial in previous epidemiological studies. We employed a bidirectional two-sample Mendelian analysis to explore the causal relationship between NAFLD and osteoporosis.MethodThe NAFLD instrumental variables (IVs) were obtained from a large Genome-wide association study (GWAS) meta-analysis dataset of European descent. Two-sample Mendelian randomization (MR) analyses were used to estimate the causal effect of NAFLD on osteoporosis, fracture, and fall. Reverse Mendelian randomization analysis was conducted to estimate the causal effect of osteoporosis on NAFLD. The inverse-variance weighted (IVW) method was the primary analysis in this analysis. We used the MR-Egger method to determine horizontal pleiotropic. The heterogeneity effect of IVs was detected by MR-Egger and IVW analyses.ResultsFive SNPs (rs2980854, rs429358, rs1040196, rs738409, and rs5764430) were chosen as IVs for NAFLD. In forward MR analysis, the IVW-random effect indicated the causal effect of NAFLD on osteoporosis (OR= 1.0021, 95% CI: 1.0006-1.0037, P= 0.007) but not on fracture (OR= 1.0016, 95% CI: 0.998-1.0053, P= 0.389) and fall (OR= 0.9912, 95% CI: 0.9412-1.0440, P= 0.740). Furthermore, the reverse Mendelian randomization did not support a causal effect of osteoporosis on NAFLD (OR= 1.0002, 95% CI: 0.9997-1.0007, P= 0.231). No horizontal pleiotropic was detected in all MR analyses.ConclusionsThe results of this study indicate a causal association between NAFLD and osteoporosis. NAFLD patients have a higher risk of osteoporosis but not fracture and falling risk. In addition, our results do not support a causal effect of osteoporosis on NAFLD