Perception enhancement system for automotive steering

Laboratory-based experiments were conducted to evaluate the effect of the frequency and scale of transient vibration events on the human detection of road surface type by means of steering wheel vibration. The study used steering wheel tangential direction acceleration time histories which had been measured in a mid-sized European automobile that was driven over three different types of road surface. The steering acceleration stimuli were manipulated by means of the mildly non-stationary mission synthesis (MNMS) algorithm in order to produce test stimuli which were selectively modified in terms of the number, and size, of transient vibration events they contained. Fifteen test participants were exposed to both unmanipulated and manipulated steering wheel rotational vibration stimuli, and were asked to indicate, by either “yes or no”, whether the test stimuli was from a target road surface which was displayed on a board. The findings suggested that transient vibration events play a key role in the human detection of road surface type in driving situations. Improvements of up to 20 percentage points in the rate of correct detection were achieved by means of selective manipulation of the steering vibration stimuli. The results also suggested, however, that no single setting of the MNMS algorithm proved optimal for all three road surface types that were investigated

Resummation and Shower Studies

The transverse momentum spectra of the Z and Higgs bosons are studied, as probes of the consequences of multiple parton emissions in hadronic events. Emphasis is put on constraints, present in showers, that go beyond conventional leading log. It is shown that, if such constraints are relaxed, better agreement can be obtained with experimental data and with resummation descriptions.Comment: 6 pages, LaTeX, 3 eps figures, submitted to the proceedings of the Workshop on Physics at TeV Colliders, Les Houches, France, 26 May -- 6 June 200

Encapsulation of Cs/Sr contaminated clinoptilolite in geopolymers produced from metakaolin

The encapsulation of caesium (Cs) and strontium (Sr) contaminated clinoptilolite in Na and K based metakaolin geopolymers is reported. When Cs or Sr loaded clinoptilolite is mixed with a metakaolin geopolymer paste, the high pH of the activating solution and the high concentration of ions in solution cause ion exchange reactions and dissolution of clinoptilolite with release of Cs and Sr into the geopolymer matrix. The leaching of Cs and Sr from metakaolin-based geopolymer has therefore been investigated. It was found that Na-based geopolymers reduce leaching of Cs compared to K-based geopolymers and the results are in agreement with the hard and soft acids and bases (HSAB) theory. Cs ions are weak Lewis acids and aluminates are a weak Lewis base. During the formation of the geopolymer matrix Cs ions are preferentially bound to aluminate phases and replace Na in the geopolymer structure. Sr uptake by Na-geopolymers is limited to 0.4 mol Sr per mole of Al and any additional Sr is immobilised by the high pH which causes precipitation of Sr as low solubility hydroxide and carbonate phases. There was no evidence of any other phases being formed when Sr or Cs are added to metakaolin geopolymers

Multiangle observations of Arctic clouds from FIRE ACE: June 3, 1998, case study

In May and June 1998 the Airborne Multiangle Imaging Spectroradiometer (AirMISR) participated in the FIRE Arctic Cloud Experiment (ACE). AirMISR is an airborne instrument for obtaining multiangle imagery similar to that of the satellite-borne MISR instrument. This paper presents a detailed analysis of the data collected on June 3, 1998. In particular, AirMISR radiance measurements are compared with measurements made by two other instruments, the Cloud Absorption Radiometer (CAR) and the MODIS airborne simulator (MAS), as well as to plane-parallel radiative transfer simulations. It is found that the AirMISR radiance measurements and albedo estimates compare favorably both with the other instruments and with the radiative transfer simulations. In addition to radiance and albedo, the multiangle AirMISR data can be used to obtain estimates of cloud top height using stereoimaging techniques. Comparison of AirMISR retrieved cloud top height (using the complete MISR-based stereoimaging approach) shows excellent agreement with the measurements from the airborne Cloud Lidar System (CLS) and ground-based millimeterwave cloud radar

Amphibious NDT Robots

Oil, petrochemical, and food processing industries worldwide store their raw materials and product in tens of thousands of storage tanks. The tanks are mostly constructed using welded steel plates and therefore subject to corrosion and weld cracking. Testing the structural integrity of these storage tanks with non-destructive testing (NDT) techniques is an expensive and time consuming activity. The walls of a large tank can usually be tested manually (for corrosion thinning and weld defects using ultrasonic techniques) from outside the tank. Access to most areas of a wall is obtained by constructing scaffolding or abseiling down from the top. However, erecting scaffolding is expensive and the inspection is tedious and slow. These costs can be reduced and the inspection speeded up by using climbing robots that deploy ultrasonic probes with scanning arms

Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images

Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning. We find out that the deep learning networks pretrained on ImageNet have better performance than the popular handcrafted features used for breast cancer histology images. The best feature extractor achieves an average accuracy of 79.30%. To improve the classification performance, a random forest dissimilarity based integration method is used to combine different feature groups together. When the five deep learning feature groups are combined, the average accuracy is improved to 82.90% (best accuracy 85.00%). When handcrafted features are combined with the five deep learning feature groups, the average accuracy is improved to 87.10% (best accuracy 93.00%)

Excellent diagnostic characteristics for ultrafast gene profiling of DEFA1-IL1B-LTF in detection of prosthetic joint infections

The timely and exact diagnosis of prosthetic joint infection (PJI) is crucial for surgical decision-making. Intraoperatively, delivery of the result within an hour is required. Alpha-defensin lateral immunoassay of joint fluid (JF) is precise for the intraoperative exclusion of PJI; however, for patients with a limited amount of JF and/or in cases where the JF is bloody, this test is unhelpful. Important information is hidden in periprosthetic tissues that may much better reflect the current status of implant pathology. We therefore investigated the utility of the gene expression patterns of 12 candidate genes (TLR1, -2, -4, -6, and 10, DEFA1, LTF, IL1B, BPI, CRP, IFNG, and DEFB4A) previously associated with infection for detection of PJI in periprosthetic tissues of patients with total joint arthroplasty (TJA) (n = 76) reoperated for PJI (n = 38) or aseptic failure (n = 38), using the ultrafast quantitative reverse transcription-PCR (RT-PCR) Xxpress system (BJS Biotechnologies Ltd.). Advanced data-mining algorithms were applied for data analysis. For PJI, we detected elevated mRNA expression levels of DEFA1 (P < 0.0001), IL1B (P < 0.0001), LTF (P < 0.0001), TLR1 (P = 0.02), and BPI (P = 0.01) in comparison to those in tissues from aseptic cases. A feature selection algorithm revealed that the DEFA1-IL1B-LTF pattern was the most appropriate for detection/exclusion of PJI, achieving 94.5% sensitivity and 95.7% specificity, with likelihood ratios (LRs) for positive and negative results of 16.3 and 0.06, respectively. Taken together, the results show that DEFA1-IL1B-LTF gene expression detection by use of ultrafast qRT-PCR linked to an electronic calculator allows detection of patients with a high probability of PJI within 45 min after sampling. Further testing on a larger cohort of patients is needed.Web of Science5592697268

Network-analysis-guided synthesis of weisaconitine D and liljestrandinine.

General strategies for the chemical synthesis of organic compounds, especially of architecturally complex natural products, are not easily identified. Here we present a method to establish a strategy for such syntheses, which uses network analysis. This approach has led to the identification of a versatile synthetic intermediate that facilitated syntheses of the diterpenoid alkaloids weisaconitine D and liljestrandinine, and the core of gomandonine. We also developed a web-based graphing program that allows network analysis to be easily performed on molecules with complex frameworks. The diterpenoid alkaloids comprise some of the most architecturally complex and functional-group-dense secondary metabolites isolated. Consequently, they present a substantial challenge for chemical synthesis. The synthesis approach described here is a notable departure from other single-target-focused strategies adopted for the syntheses of related structures. Specifically, it affords not only the targeted natural products, but also intermediates and derivatives in the three families of diterpenoid alkaloids (C-18, C-19 and C-20), and so provides a unified synthetic strategy for these natural products. This work validates the utility of network analysis as a starting point for identifying strategies for the syntheses of architecturally complex secondary metabolites