112 research outputs found
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Electrochemical characteristics of lithium-ion cells
The authors describe below the electrochemical performance characteristics, including charge-discharge characteristics at different rates, of cylindrical 18650 (18 mm diameter, 65 mm high) and prismatic lithium ion cells at ambient and sub-ambient temperatures. Ragone plots of power and energy data for these cells are compared and indicate that at room temperature the prismatic lithium ion cells (approx. 500 mAh) exhibit higher specific power and power density than the 18650 cells (approx. 1,100 mAhr). The cell impedance was measured between 35 C and {minus}40 C at three open circuit voltages: 4.1 v (fully charged), 3.6 v (partially discharged), and 3.1 v (almost completely discharged). Over the temperature range from 35 C to {minus}20 C, the cell impedance is nearly constant for both cell types and increases by 2 to 3 times at {minus}40 C. The impedance doesn`t vary significantly with open circuit voltage (OCV). These cells show very little voltage drop at room temperature for current pulses up to 1 A. The charge-discharge characteristics of the cells are being studied at different rates as a function of temperature to compute the power, energy, and capacity outputs. This will not only broaden the database on lithium ion cells, but will also allow us to evaluate the suitability of the cells as power sources for low temperature applications. Other electrochemical characteristics of these cells including pulse response are being evaluated. Impedance measurements of the cells under load are planned to make meaningful correlations between the voltage drop and the current pulse amplitude
Detection and isolation of black hole attack in mobile ad hoc networks: A review
© 2020 SPIE. Mobile Ad hoc Network or MANET is a wireless network that allows communication between the nodes that are in range of each other and are self-configuring. The distributed administration and dynamic nature of MANET makes it vulnerable to many kind of security attacks. One such attack is Black hole attack which is a well known security threat. A node drops all packets which it should forward, by claiming that it has the shortest path to the destination. Intrusion Detection system identifies the unauthorized users in the system. An IDS collects and analyses audit data to detect unauthorized users of computer systems. This paper aims in identifying Black-Hole attack against AODV with Intrusion Detection System, to analyze the attack and find its countermeasure
How useful is Active Learning for Image-based Plant Phenotyping?
Deep learning models have been successfully deployed for a diverse array of
image-based plant phenotyping applications including disease detection and
classification. However, successful deployment of supervised deep learning
models requires large amount of labeled data, which is a significant challenge
in plant science (and most biological) domains due to the inherent complexity.
Specifically, data annotation is costly, laborious, time consuming and needs
domain expertise for phenotyping tasks, especially for diseases. To overcome
this challenge, active learning algorithms have been proposed that reduce the
amount of labeling needed by deep learning models to achieve good predictive
performance. Active learning methods adaptively select samples to annotate
using an acquisition function to achieve maximum (classification) performance
under a fixed labeling budget. We report the performance of four different
active learning methods, (1) Deep Bayesian Active Learning (DBAL), (2) Entropy,
(3) Least Confidence, and (4) Coreset, with conventional random sampling-based
annotation for two different image-based classification datasets. The first
image dataset consists of soybean [Glycine max L. (Merr.)] leaves belonging to
eight different soybean stresses and a healthy class, and the second consists
of nine different weed species from the field. For a fixed labeling budget, we
observed that the classification performance of deep learning models with
active learning-based acquisition strategies is better than random
sampling-based acquisition for both datasets. The integration of active
learning strategies for data annotation can help mitigate labelling challenges
in the plant sciences applications particularly where deep domain knowledge is
required
A pilot clinical study of Δ9-tetrahydrocannabinol in patients with recurrent glioblastoma multiforme
Δ9-Tetrahydrocannabinol (THC) and other cannabinoids inhibit tumour growth and angiogenesis in animal models, so their potential application as antitumoral drugs has been suggested. However, the antitumoral effect of cannabinoids has never been tested in humans. Here we report the first clinical study aimed at assessing cannabinoid antitumoral action, specifically a pilot phase I trial in which nine patients with recurrent glioblastoma multiforme were administered THC intratumoraly. The patients had previously failed standard therapy (surgery and radiotherapy) and had clear evidence of tumour progression. The primary end point of the study was to determine the safety of intracranial THC administration. We also evaluated THC action on the length of survival and various tumour-cell parameters. A dose escalation regimen for THC administration was assessed. Cannabinoid delivery was safe and could be achieved without overt psychoactive effects. Median survival of the cohort from the beginning of cannabinoid administration was 24 weeks (95% confidence interval: 15–33). Δ9-Tetrahydrocannabinol inhibited tumour-cell proliferation in vitro and decreased tumour-cell Ki67 immunostaining when administered to two patients. The fair safety profile of THC, together with its possible antiproliferative action on tumour cells reported here and in other studies, may set the basis for future trials aimed at evaluating the potential antitumoral activity of cannabinoids
Protein alterations associated with temozolomide resistance in subclones of human glioblastoma cell lines
Temozolomide (TMZ) is the standard chemotherapeutic agent for human malignant glioma, but intrinsic or acquired chemoresistance represents a major obstacle to successful treatment of this highly lethal group of tumours. Obtaining better understanding of the molecular mechanisms underlying TMZ resistance in malignant glioma is important for the development of better treatment strategies. We have successfully established a passage control line (D54-C10) and resistant variants (D54-P5 and D54-P10) from the parental TMZ-sensitive malignant glioma cell line D54-C0. The resistant sub-cell lines showed alterations in cell morphology, enhanced cell adhesion, increased migration capacities, and cell cycle arrests. Proteomic analysis identified a set of proteins that showed gradual changes in expression according to their 50% inhibitory concentration (IC50). Successful validation was provided by transcript profiling in another malignant glioma cell line U87-MG and its resistant counterparts. Moreover, three of the identified proteins (vimentin, cathepsin D and prolyl 4-hydroxylase, beta polypeptide) were confirmed to be upregulated in high-grade glioma. Our data suggest that acquired TMZ resistance in human malignant glioma is associated with promotion of malignant phenotypes, and our reported molecular candidates may serve not only as markers of chemoresistance but also as potential therapeutic targets in the treatment of TMZ-resistant human malignant glioma, providing a platform for future investigations
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