Experimental and Established Oximes as Pretreatment before Acute Exposure to Azinphos-Methyl

Poisoning with organophosphorus compounds (OPCs) represents an ongoing threat to civilians and rescue personal. We have previously shown that oximes, when administered prophylactically before exposure to the OPC paraoxon, are able to protect from its toxic effects. In the present study, we have assessed to what degree experimental (K-27; K-48; K-53; K-74; K-75) or established oximes (pralidoxime, obidoxime), when given as pretreatment at an equitoxic dosage of 25% of LD₀₁, are able to reduce mortality induced by the OPC azinphos-methyl. Their efficacy was compared with that of pyridostigmine, the only FDA-approved substance for such prophylaxis. Efficacy was quantified in rats by Cox analysis, calculating the relative risk of death (RR), with RR=1 for the reference group given only azinphos-methyl, but no prophylaxis. All tested compounds significantly (p ≤ 0.05) reduced azinphos-methyl-induced mortality. In addition, the efficacy of all tested experimental and established oximes except K-53 was significantly superior to the FDA-approved compound pyridostigmine. Best protection was observed for the oximes K-48 (RR = 0.20), K-27 (RR = 0.23), and obidoxime (RR = 0.21), which were significantly more efficacious than pralidoxime and pyridostigmine. The second-best group of prophylactic compounds consisted of K-74 (RR = 0.26), K-75 (RR = 0.35) and pralidoxime (RR = 0.37), which were significantly more efficacious than pyridostigmine. Pretreatment with K-53 (RR = 0.37) and pyridostigmine (RR = 0.52) was the least efficacious. Our present data, together with previous results on other OPCs, indicate that the experimental oximes K-27 and K-48 are very promising pretreatment compounds. When penetration into the brain is undesirable, obidoxime is the most efficacious prophylactic agent already approved for clinical use.     

A Novel DUF569 Gene Is a Positive Regulator of the Drought Stress Response in Arabidopsis

In the last two decades, global environmental change has increased abiotic stress on plants and severely affected crops. For example, drought stress is a serious abiotic stress that rapidly and substantially alters the morphological, physiological, and molecular responses of plants. In Arabidopsis, several drought-responsive genes have been identified; however, the underlying molecular mechanism of drought tolerance in plants remains largely unclear. Here, we report that the “domain of unknown function” novel gene DUF569 (AT1G69890) positively regulates drought stress in Arabidopsis. The Arabidopsis loss-of-function mutant atduf569 showed significant sensitivity to drought stress, i.e., severe wilting at the rosette-leaf stage after water was withheld for 3 days. Importantly, the mutant plant did not recover after rewatering, unlike wild-type (WT) plants. In addition, atduf569 plants showed significantly lower abscisic acid accumulation under optimal and drought-stress conditions, as well as significantly higher electrolyte leakage when compared with WT Col-0 plants. Spectrophotometric analyses also indicated a significantly lower accumulation of polyphenols, flavonoids, carotenoids, and chlorophylls in atduf569 mutant plants. Overall, our results suggest that novel DUF569 is a positive regulator of the response to drought in Arabidopsis.     

Identification of probe request attacks in WLANs using neural networks

Any sniffer can see the information sent through unprotected ‘probe request messages’ and ‘probe response messages’ in wireless local area networks (WLAN). A station (STA) can send probe requests to trigger probe responses by simply spoofing a genuine media access control (MAC) address to deceive access point (AP) controlled access list. Adversaries exploit these weaknesses to flood APs with probe requests, which can generate a denial of service (DoS) to genuine STAs. The research examines traffic of a WLAN using supervised feed-forward neural network classifier to identify genuine frames from rogue frames. The novel feature of this approach is to capture the genuine user and attacker training data separately and label them prior to training without network administrator’s intervention. The model’s performance is validated using self-consistency and fivefold cross-validation tests. The simulation is comprehensive and takes into account the real-world environment. The results show that this approach detects probe request attacks extremely well. This solution also detects an attack during an early stage of the communication, so that it can prevent any other attacks when an adversary contemplates to start breaking into the network.     

Corrosion protection of magnesium ZM 21 alloy with polyaniline–TiO2 composite containing coatings

A polyaniline–TiO₂ composite (PTC) was prepared by oxidative polymerization of aniline in phosphoric acid with ammonium persulphate, in the presence of TiO₂. The composite was characterized by spectroscopic methods. An acrylic paint containing the PTC was prepared and the coating formed on magnesium alloy was evaluated by electrochemical impedance spectroscopy after exposure to salt spray test for a period of 250 h. The coating containing the PTC was found to protect the magnesium alloy more efficiently than the coating containing polyaniline.     

Synchronization of Fractional Order Neutral Type Fuzzy Cellular Neural Networks with Discrete and Distributed Delays via State Feedback Control

Neural Processing Letters - The motivation behind this paper is to explore the issue of synchronization of fractional order neutral type fuzzy cellular neural networks with state feedback control....     

A scalable hybrid decision system (HDS) for Roman word recognition using ANN SVM: study case on Malay word recognition

An off-line handwriting recognition (OFHR) system is a computerized system that is capable of intelligently converting human handwritten data extracted from scanned paper documents into an equivalent text format. This paper studies a proposed OFHR for Malaysian bank cheques written in the Malay language. The proposed system comprised of three components, namely a character recognition system (CRS), a hybrid decision system and lexical word classification system. Two types of feature extraction techniques have been used in the system, namely statistical and geometrical. Experiments show that the statistical feature is reliable, accessible and offers results that are more accurate. The CRS in this system was implemented using two individual classifiers, namely an adaptive multilayer feed-forward back-propagation neural network and support vector machine. The results of this study are very promising and could generalize to the entire Malay lexical dictionary in future work toward scaled-up applications.     

An efficient and noise resistive selective image encryption scheme for gray images based on chaotic maps and DNA complementary rules

A novel image encryption algorithm in streaming mode is proposed which exhaustively employs an entire set of DNA complementary rules alongwith one dimensional chaotic maps. The proposed algorithm is highly efficient due to encrypting the subset of digital image which contains 92.125 % of information. DNA addition operation is carried out on this MSB part. The core idea of the proposed scheme is to scramble the whole image by means of piecewise linear chaotic map (PWLCM) followed by decomposition of image into most significant bits (MSB) and least significant bits (LSB). The logistic sequence is XORed with the decoded MSB and LSB parts separately and finally these two parts are combined to get the ciphered image. The parameters for PWLCM, logistic map and selection of different DNA rules for encoding and decoding of both parts of an image are derived from 128-bit MD5 hash of the plain image. Simulated experimental results in terms of quantitative and qualitative ways prove the encryption quality. Efficiency and robustness against different noises make the proposed cipher a good candidate for real time applications.     

直觉线性μ-演算中的合成推理

讨论了以基于前缀封闭集合的Heyting代数的直觉解释的线性μ-演算(IμTL)作为描述“假设-保证”的逻辑基础的问题,提出了一个基于IμTL的“假设-保证”规则.该规则比往常应用线性时序逻辑(LTL)作为规范语言的那些规则具有更好的表达能力,扩展了对形如“always ?”等安全性质的“假设-保证”的范围,具备更一般的“假设-保证”推理能力及对循环推理的支持.     

Power efficient scheme for self localization in wireless sensor networks

With the tremendous applications of the wireless sensor network, self-localization has become one of the challenging subject matter that has gained attention of many researchers in the field of wireless sensor network. Localization is the process of assigning or computing the location of the sensor nodes in a sensor network. As the sensor nodes are deployed randomly, we do not have any knowledge about their location in advance. As a result, this becomes very important that they localize themselves as manual deployment of sensor node is not feasible. Also, in WSN the main problem is the power as the sensor nodes have very limited power source. This paper provides a novel solution for localizing the sensor nodes using controlled power of the beacon nodes such that we will have longer life of the beacon nodes which plays a vital role in the process of localization as it is the only special nodes that has the information about its location when they are deployed such that the remaining ordinary nodes can localize themselves in accordance with these beacon node. We develop a novel model that first finds the distance of the sensor nodes then it finds the location of the unknown sensor nodes in power efficient manner. Our simulation results show the effectiveness of the proposed methodology in terms of controlled and reduced power.     

Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation

Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In₂O₃) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.