Effects of low temperature and drought on the physiological and growth changes in oil palm seedlings

Water deficiency and low temperature are two important ecological factors which affect the distribution and cultivation of oil palm. To find out how oil palm adapts to the environmental conditions, the dynamics of a series of important physiological components derived from the leaves of potted oil palm seedlings under drought stress (DS) (water with holding) and low temperature stress (LTS) (10°C) were studied. The results showed that low temperature and water stress inhibited the growth of oil palm seedlings. The relative conductivity, injury index, malondialdehyde (MDA) and proline content in the leaves increased to different degrees with the extension of low temperature and drought stress. Superoxide dismutase (SOD) and peroxidase (POD) activities increased and then decreased gradually with the duration of treatment time. The variations of the earlier mentioned parameters except proline content under low temperature stress were greater than that under drought stress. Thus, oil palm possibly showed different response mechanisms under low temperature and drought stress by mediations of these substances, in order to increase plant defense capability. These data provided the information that was utilized to initiate the breeding programme used to improve drought and cold tolerance in oil palm.Keywords: Oil palm, drought stress, low temperature stress, physiological characteristic

Recognizing driver braking intention with vehicle data using unsupervised learning methods

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. In this paper, a various unsupervised clustering methods will be used to build a driver braking intention predictor which can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different methods like K-means and Gaussian mixture model will be compared. Additionally, the evaluation of features from raw data, which are important to driving maneuvers clustering will be proposed. The experiment data are collected from one hybrid electric vehicle in real world. Final results show that the proposed method can detect driver’s braking intention in a very beginning moment with a high accuracy and the most important sets of feature for driving maneuver clustering will be discussed.Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. In this paper, a various unsupervised clustering methods will be used to build a driver braking intention predictor which can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different methods like K-means and Gaussian mixture model will be compared. Additionally, the evaluation of features from raw data, which are important to driving maneuvers clustering will be proposed. The experiment data are collected from one hybrid electric vehicle in real world. Final results show that the proposed method can detect driver’s braking intention in a very beginning moment with a high accuracy and the most important sets of feature for driving maneuver clustering will be discussed

Spectrum Comparative Study of Commutation Failure and Short-Circuit Fault in UHVDC Transmission System

When commutation failure occurs in UHVDC transmission system, the transient process of DC voltage and current are similar to grounding short-circuit fault. In order to differentiate them effectively, the paper introduces mathematical morphology methods to analysis the spectrum of transient current. Base on Yunnan-Guangzhou kV UHVDC transmission system, the paper simulates the commutation failure and DC line short-circuit fault under different fault conditions in PSCAD/EMTDC.  By modified morphology filter, the transient signal of DC () is decomposed into six scales, and morphological characteristics of aerial mode component of  is analyzed under different scales. The simulation results show that when DC line short-circuit faults occurs, wherever in the rectifier side, in the DC transmission line midpoint or in the inverter side, the aerial mode component of  have more high frequency weight in ~ and decays gradually; When commutation failures, which are caused by the inverter side AC system single-phase grounding fault, phase to phase fault, three phase grounding fault or the inverter side transformer ratio increased,  the aerial mode component of  have less frequency weight in

Targeted next-generation sequencing of dedifferentiated chondrosarcoma in the skull base reveals combined TP53 and PTEN mutations with increased proliferation index, an implication for pathogenesis

Dedifferentiated chondrosarcoma (DDCS) is a rare disease with a dismal prognosis. DDCS consists of two morphologically distinct components: the cartilaginous and noncartilaginous components. Whether the two components originate from the same progenitor cells has been controversial. Recurrent DDCS commonly displays increased proliferation compared with the primary tumor. However, there is no conclusive explanation for this mechanism. In this paper, we present two DDCSs in the sellar region. Patient 1 exclusively exhibited a noncartilaginous component with a TP53 frameshift mutation in the pathological specimens from the first surgery. The tumor recurred after radiation therapy with an exceedingly increased proliferation index. Targeted next-generation sequencing (NGS) revealed the presence of both a TP53 mutation and a PTEN deletion in the cartilaginous and the noncartilaginous components of the recurrent tumor. Fluorescence in situ hybridization and immunostaining confirmed reduced DNA copy number and protein levels of the PTEN gene as a result of the PTEN deletion. Patient 2 exhibited both cartilaginous and noncartilaginous components in the surgical specimens. Targeted NGS of cells from both components showed neither TP53 nor PTEN mutations, making Patient 2 a naïve TP53 and PTEN control for comparison. In conclusion, additional PTEN loss in the background of the TP53 mutation could be the cause of increased proliferation capacity in the recurrent tumor

Nearly quantized conductance plateau of vortex zero mode in an iron-based superconductor

Majorana zero-modes (MZMs) are spatially-localized zero-energy fractional quasiparticles with non-Abelian braiding statistics that hold a great promise for topological quantum computing. Due to its particle-antiparticle equivalence, an MZM exhibits robust resonant Andreev reflection and 2e2/h quantized conductance at low temperature. By utilizing variable-tunnel-coupled scanning tunneling spectroscopy, we study tunneling conductance of vortex bound states on FeTe0.55Se0.45 superconductors. We report observations of conductance plateaus as a function of tunnel coupling for zero-energy vortex bound states with values close to or even reaching the 2e2/h quantum conductance. In contrast, no such plateau behaviors were observed on either finite energy Caroli-de Genne-Matricon bound states or in the continuum of electronic states outside the superconducting gap. This unique behavior of the zero-mode conductance reaching a plateau strongly supports the existence of MZMs in this iron-based superconductor, which serves as a promising single-material platform for Majorana braiding at a relatively high temperature

Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed

Identification and Transformation Difficulty in Problem Solving: Electrophysiological Evidence from Chunk Decomposition

A wealth of studies have investigated how to overcome experience-based constraints in creative problem solving. One such experience-based constraint is the tendency for people to view tightly organized visual stimuli as single, unified percepts, even when decomposition of those stimuli into component parts (termed chunk decomposition) would facilitate problem solving. The current study investigates the neural underpinnings of chunk decomposition in creative problem solving by analyzing event-related potentials. In two experiments, participants decomposed Chinese characters into the character’s component elements and then used the base elements to form a new valid character. The action could require decomposing a “tight” chunk, meaning that the component elements intersected spatially, or a “loose” chunk, in which the component elements did not overlap in space. Behaviorally, individuals made more errors and responded slower to trials involving tight chunks relative to loose chunks. Analysis of the ERPs revealed that relative to loose chunks, the electrophysiological response to tight chunks contained an increased N2, an increased N400, and a decreased late positive complex. Taken together, these results suggest that chunk tightness is a principle determinant of the difficulty of chunk decomposition, and that chunk tightness provokes neural conflict and semantic violations, factors known to influence the N2 and N400 ERP components