Molecular Clone of the Na\u3csup\u3e+\u3c/sup\u3e/H\u3csup\u3e+\u3c/sup\u3e Antiporter Gene \u3cem\u3eAtNHX1\u3c/em\u3e and Study of Transgenic Salt Tolerant Lucerne

Lucerne (Medicago sativa) with its good quality and ease of cultivation occupies an important position in animal feeding. Salinity is a major constraint of crop productivity, because it reduces yield and limits expansion of agriculture. Na+ /H+ antiporter catalyses the counter transport of Na+ and H+ across membranes. Vacuolar Na+ /H+ antiporter plays an important role in developing salt-tolerance of plants. Therefore, we could use the gene involved in this mechanism to modify salt tolerance of lucerne

Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension

ChatGPT has shown its great power in text processing, including its reasoning ability from text reading. However, there has not been any direct comparison between human readers and ChatGPT in reasoning ability related to text reading. This study was undertaken to investigate how ChatGPTs (i.e., ChatGPT and ChatGPT Plus) and Chinese senior school students as ESL learners exhibited their reasoning ability from English narrative texts. Additionally, we compared the two ChatGPTs in the reasoning performances when commands were updated elaborately. The whole study was composed of three reasoning tests: Test 1 for commonsense inference, Test 2 for emotional inference, and Test 3 for causal inference. The results showed that in Test 1, the students outdid the two ChatGPT versions in local-culture-related inferences but performed worse than the chatbots in daily-life inferences. In Test 2, ChatGPT Plus excelled whereas ChatGPT lagged behind in accuracy. In association with both accuracy and frequency of correct responses, the students were inferior to the two chatbots. Compared with ChatGPTs' better performance in positive emotions, the students showed their superiority in inferring negative emotions. In Test 3, the students demonstrated better logical analysis, outdoing both chatbots. In updating command condition, ChatGPT Plus displayed good causal reasoning ability while ChatGPT kept unchanged. Our study reveals that human readers and ChatGPTs have their respective advantages and disadvantages in drawing inferences from text reading comprehension, unlocking a complementary relationship in text-based reasoning

Quantitative Performance Evaluation of Uncertainty-Aware Hybrid AADL Designs Using Statistical Model Checking

International audience— Architecture Analysis and Design Language (AADL) is widely used for the architecture design and analysis of safety-critical real-time systems. Based on the Hybrid annex which supports continuous behavior modeling, Hybrid AADL enables seamless interactions between embedded control systems and continuous physical environments. Although Hybrid AADL is promising in dependability prediction through analyzable architecture development, the worst-case performance analysis of Hybrid AADL designs can easily lead to an overly pessimistic estimation. So far, Hybrid AADL cannot be used to accurately quantify and reason the overall performance of complex systems which interact with external uncertain environments intensively. To address this problem, this paper proposes a statistical model checking based framework that can perform quantitative evaluation of uncertainty-aware Hybrid AADL designs against various performance queries. Our approach extends Hybrid AADL to support the modeling of environment uncertainties. Furthermore, we propose a set of transformation rules that can automatically translate AADL designs together with designers' requirements into Networks of Priced Timed Automata (NPTA) and performance queries, respectively. Comprehensive experimental results on the Movement Authority (MA) scenario of Chinese Train Control System Level 3 (CTCS-3) demonstrate the effectiveness of our approach

Throughput-Conscious Energy Allocation and Reliability-Aware Task Assignment for Renewable Powered In-Situ Server Systems

In-situ (InS) server systems are typically deployed in special environments to handle in-situ workloads which are generated from environmentally sensitive areas or remote places lacking modern power supply infrastructure. This special operating environment of InS servers urges such systems to be powered by renewable energy. In addition, the InS systems are vulnerable to soft errors due to the harsh environments they deploy. This paper tackles the problem of allocating harvested energy to renewable powered servers and assigning the in-situ workloads to these servers for optimizing throughput of both the overall system and individual servers under energy and reliability constraints. We perform the energy allocation based on system state. In particular, for systems in low energy state, we propose a game theoretic approach that models the energy allocation as a cooperative game among multiple servers and derives a Nash bargaining solution. To meet the reliability constraint, we analyze the reliability optimality of assigning tasks to multiple servers and design a reliability-aware task assignment heuristic based on the analysis. Experimental results show that with a small time overhead, the proposed energy allocation approach achieves a high throughput from perspectives of both the overall system and individual servers, and the proposed task assignment approach ensures an increased system reliability.</p

Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT

© 2018 Elsevier B.V. More and more enterprises and communities choose cloud computing platforms to deploy their commercial or scientific workflow applications along with the increasing popularity of pay-as-you-go cloud services. A major task of cloud service providers is to minimize the monetary cost and makespan of executing workflows in the Infrastructure as a Service (IaaS) cloud. Most of the existing techniques for cost and makespan minimization are designed for traditional computing platforms which cannot be applied to the cloud computing platforms with unique service-based resource managing methods and pricing strategies. In this paper, we study the joint optimization of cost and makespan of scheduling workflows in IaaS clouds, and propose a novel workflow scheduling scheme. In this scheme, a fuzzy dominance sort based heterogeneous earliest-finish-time (FDHEFT) algorithm is developed which closely integrates the fuzzy dominance sort mechanism with the list scheduling heuristic HEFT. Extensive experiments using the real-world and synthetic workflows demonstrate the efficacy of our scheme. Our scheme can achieve significantly better cost-makespan tradeoff fronts with remarkably higher Hypervolume and can run up to hundreds of times faster than the state-of-the-art algorithms

Resource management for improving soft-error and lifetime reliability of real-time MPSoCs

Multiprocessor system-on-chip (MPSoC) has been widely used in many real-time embedded systems where both soft-error reliability (SER) and lifetime reliability (LTR) are key concerns. Many existing works have investigated them, but they focus either on handling one of the two reliability concerns or on improving one type of reliability under the constraint of the other. These techniques are thus not applicable to maximize SER and LTR simultaneously, which is highly desired in some real-world applications. In this paper, we study the joint optimization of SER and LTR for real-time MPSoCs. We propose a novel static task scheduling algorithm to simultaneously maximize SER and LTR for real-time homogeneous MPSoC systems under the constraints of deadline, energy budget, and task precedence. Specifically, we develop a new solution representation scheme and two evolutionary operators that are closely integrated with two popular multiobjective evolutionary optimization frameworks, namely NSGAII and SPEA2. Extensive experimental results on standard benchmarks and synthetic applications show the efficacy of our scheme. More specifically, our scheme can achieve significantly better solutions (i.e., LTR-SER tradeoff fronts) with remarkably higher hypervolume and can be dozens or even hundreds of times faster than the state-of-the-art algorithms. The results also demonstrate that our scheme can be applied to heterogeneous MPSoC systems and is effective in improving reliability for heterogeneous MPSoC systems

Chinese JSL/JFL learners' online perception of Japanese verb conjugations: Evidence from a behavioral study

Verb conjugation is essential in learning Japanese as a second or foreign language (JSL/JFL). Previous studies showed that Chinese JSL/JFL beginners behaved differently in acquiring Japanese verb conjugations, but the results were obtained from offline tests (e.g., writing examination without time limitation), hard to reflect the real perception. On this background, the current study adopted a time-controlled lexical decision task (real-time automatic processing) to explore how Chinese intermediate JSL/JFL learners processed four types of verb conjugations (i.e., masu/tai form, te/ta form, nai form and yoo form). Based on the error rates and RTs collected form 27 Chinese intermediate JSL/JFL learners, the results showed that the JSL/JFL learners processed better in masu/tai form and te/ta form, followed by nai form and yoo form. The discrepant processing of the four types of Japanese verb conjugations suggests that the JSL/JFL learners do have difficulties in Japanese acquisition. Finally, a general discussion is offered from the perspective of verb conjugations' frequency, JSL/JFL learners' learning strategy and Japanese teaching method

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network&rsquo;s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach