Investigating the Influencing Factors of Teachers’ Information and Communications Technology-Integrated Teaching Behaviors toward “Learner-Centered” Reform Using Structural Equation Modeling

In the context of information-driven Education transformation, this study investigates factors that influence the continuous transformation of teacher information and communications technology (ICT) teaching methods. Although some studies have found that teacher psychological cognition exerts different effects on different types of teacher ICT-integrated teaching behaviors, the current literature on influencing factors lacks the classification of behaviors. Based on the learner-centered transformation, this study divides teacher ICT-integrated teaching behaviors into teacher-centered teaching behavior and student-centered teaching behavior, and constructs a hypothesis model of influencing factors on teacher ICT-integrated teaching behavior. We collected questionnaire data from 795 primary and secondary school teachers, then validated and adjusted the model through structural equation modeling (SEM). The social environment exerted a significant indirect impact on teacher technology application behaviors via mediation of teacher efficacy and outcome expectations. The two types of self-efficacy directly affected the student-centered ICT application behavior more than the teacher-centered ICT application behavior. The student-centered ICT application behavior exerted a significant impact on the teacher-centered ICT application behavior. This study confirms the significance of classifying teacher ICT-integrated teaching behavior and supports the transformation of learner-centered ICT-integrated teaching by improving the social environment to realize equitable and sustainable Education development.

Models@Runtime: The Development and Re-Configuration Management of Python Applications Using Formal Methods

Models@runtime (models at runtime) are based on computation reflection. Runtime models can be regarded as a reflexive layer causally connected with the underlying system. Hence, every change in the runtime model involves a change in the reflected system, and vice versa. To the best of our knowledge, there are no runtime models for Python applications. Therefore, we propose a formal approach based on Petri Nets (PNs) to model, develop, and reconfigure Python applications at runtime. This framework is supported by a tool whose architecture consists of two modules connecting both the model and its execution. The proposed framework considers execution exceptions and allows users to monitor Python expressions at runtime. Additionally, the application behavior can be reconfigured by applying Graph Rewriting Rules (GRRs). A case study using Service-Level Agreement (SLA) violations is presented to illustrate our approach.

LCL filter design for grid-connected single-phase flyback microinverter: a step by step guide

<span lang="EN-US">Recently, <em>LCL</em> has become amongst the most attractive filter used for grid-connected flyback inverters. Nonetheless, the switching of power devices in the inverter configuration creates harmonics that affect the end application behavior and might shorten its lifetime. Furthermore, the resonance frequencies produced by the <em>LCL</em> network contribute to the system instability. This paper proposes a step-by-step guide to designing an <em>LCL</em> filter by considering several key aspects such as the resonance frequency and maximum current ripple. A single-phase grid-connected flyback microinverter with an <em>LCL</em> filter was designed then constructed in the MATLAB/Simulink environment. Several different parameter variations and damping solutions were used to analyze the performance of the circuit. The simulation result shows a promising total harmonic distortion (THD) value below 5% and harmonic suppression up to 14%.</span>

Demystifying asynchronous I/O Interference in HPC applications

With increasing complexity of HPC workflows, data management services need to perform expensive I/O operations asynchronously in the background, aiming to overlap the I/O with the application runtime. However, this may cause interference due to competition for resources: CPU, memory/network bandwidth. The advent of multi-core architectures has exacerbated this problem, as many I/O operations are issued concurrently, thereby competing not only with the application but also among themselves. Furthermore, the interference patterns can dynamically change as a response to variations in application behavior and I/O subsystems (e.g. multiple users sharing a parallel file system). Without a thorough understanding, I/O operations may perform suboptimally, potentially even worse than in the blocking case. To fill this gap, this paper investigates the causes and consequences of interference due to asynchronous I/O on HPC systems. Specifically, we focus on multi-core CPUs and memory bandwidth, isolating the interference due to each resource. Then, we perform an in-depth study to explain the interplay and contention in a variety of resource sharing scenarios such as varying priority and number of background I/O threads and different I/O strategies: sendfile, read/write, mmap/write underlining trade-offs. The insights from this study are important both to enable guided optimizations of existing background I/O, as well as to open new opportunities to design advanced asynchronous I/O strategies.

A Reusable Characterization of the Memory System Behavior of SPEC2017 and SPEC2006

The SPEC CPU Benchmarks are used extensively for evaluating and comparing improvements to computer systems. This ubiquity makes characterization critical for researchers to understand the bottlenecks the benchmarks do and do not expose and where new designs should and should not be expected to show impact. However, in characterization there is a tradeoff between accuracy and reusability: The more precisely we characterize a benchmark’s performance on a given system, the less usable it is across different micro-architectures and varying memory configurations. For SPEC, most existing characterizations include system-specific effects (e.g., via performance counters) and/or only look at aggregate behavior (e.g., averages over the full application execution). While such approaches simplify characterization, they make it difficult to separate the applications’ intrinsic behavior from the system-specific effects and/or lose the diverse phase-based behaviors. In this work we focus on characterizing the applications’ intrinsic memory behaviour by isolating them from micro-architectural configuration specifics. We do this by providing a simplified generic system model that evaluates the applications’ memory behavior across multiple cache sizes, with and without prefetching, and over time. The resulting characterization can be reused across a range of systems to understand application behavior and allow us to see how frequently different behaviors occur. We use this approach to compare the SPEC 2006 and 2017 suites, providing insight into their memory system behaviour beyond previous system-specific and/or aggregate results. We demonstrate the ability to use this characterization in different contexts by showing a portion of the SPEC 2017 benchmark suite that could benefit from giga-scale caches, despite aggregate results indicating otherwise.

Decision-Making Behavior of Fertilizer Application of Grain Growers in Heilongjiang Province from the Perspective of Risk Preference and Risk Perception

Heilongjiang Province, as a major grain-planting province in China, under the condition of limited production level and cognitive level, the food and agriculture industry often adopts the “high input-high output” production model to achieve grain yield and increase production. As one of the important material input elements in agricultural production, chemical fertilizer plays an irreplaceable role in increasing crop output and farmers’ income. The reduced application of chemical fertilizer can improve the soil and water source, improve the production environment from the internal agricultural production, and ensure the quality and safety of agricultural products from the source, which is beneficial to the sustainable development of agriculture in China. In this paper, Probit model is used to analyze the risk preference and risk perception of grain farmers in Heilongjiang Province. The results showed that high degree of risk preference had a negative effect on decision behavior of fertilizer application, while high natural risk perception had a positive effect on fertilizer application behavior of grain farmers. At the same time, the results showed that the cultivated land area owned by farmers, the total income of agricultural production, the training of fertilizer technology, and the cognition of the impact of fertilizer on the environment all had significant effects on the chemical fertilizer application behavior of grain farmers. Finally, according to the results of this study, some feasible suggestions are put forward.

Generic SDE and GA-based workload modeling for cloud systems

Workload models are typically built based on user and application behavior in a system, limiting them to specific domains. Undoubtedly, such a practice creates a dilemma in a cloud computing (cloud) environment, where a wide range of heterogeneous applications are running and many users have access to these resources. The workload model in such an infrastructure must adapt to the evolution of the system configuration parameters, such as job load fluctuation. The aim of this work is to propose an approach that generates generic workload models (1) which are independent of user behavior and the applications running in the system, and can fit any workload domain and type, (2) model sharp workload variations that are most likely to appear in cloud environments, and (3) with high degree of fidelity with respect to observed data, within a short execution time. We propose two approaches for workload estimation, the first being a Hull-White and Genetic Algorithm (GA) combination, while the second is a Support Vector Regression (SVR) and Kalman-filter combination. Thorough experiments are conducted on real CPU and throughput datasets from virtualized IP Multimedia Subsystem (IMS), Web and cloud environments to study the efficiency of both propositions. The results show a higher accuracy for the Hull-White-GA approach with marginal overhead over the SVR-Kalman-Filter combination.

Social network, environmental cognition and organic fertilizer application behavior of small farmers

Small farmers are the main organizational form of agricultural production and management in China, and it is very important to study their organic fertilizer application behavior to promote the development of green agriculture in China. Based on the survey data of 334 small farmers in Sichuan Province, this paper uses bivariate Probit model to analyze the influence of social network and environmental cognition on organic fertilizer application behavior. The research shows that:(1) The application of commercial organic fertilizer and farmyard manure by small farmers has a significant complementary effect.(2) Social network has a significant positive impact on organic fertilizer application behavior, and there are significant differences between kinship social network and friendship social network on organic fertilizer application behavior.(3) The level of environmental cognition has a significant positive effect on the application behavior of organic fertilizer. Therefore, it is necessary not only to make full use of and expand the social network of small farmers, but also to pay attention to improving their environmental awareness and promoting organic fertilizer application behavior.