A multi-method integrated simulation system for water resources allocation

Water resources allocation is an important technical tool to alleviate the conflict between water supply and demand, improve the water resources utilization efficiency, and achieve the control target of total water resources utilization. However, the current water resources allocation theory is immature, and there are few objective and quantitative allocation methods, which leads to the relatively backward allocation practice. Moreover, the amounts of allocable water resources change dynamically, which makes the static and single traditional allocation scheme difficult to adapt to changes. To address the above issues, this research comprehensively integrated multiple types of allocation models to build a multi-method integrated simulation system for water resources allocation. The results show that the system supports visually generated schemes and dynamically simulates water resources allocation. The application of the simulation system enhances the reliability of results. And the dynamic adaptability of allocation results supports allocation decisions.

Synergistically Exploiting CNN Pruning and HLS Versioning for Adaptive Inference on Multi-FPGAs at the Edge

FPGAs, because of their energy efficiency, reconfigurability, and easily tunable HLS designs, have been used to accelerate an increasing number of machine learning, especially CNN-based, applications. As a representative example, IoT Edge applications, which require low latency processing of resource-hungry CNNs, offload the inferences from resource-limited IoT end nodes to Edge servers featuring FPGAs. However, the ever-increasing number of end nodes pressures these FPGA-based servers with new performance and adaptability challenges. While some works have exploited CNN optimizations to alleviate inferences’ computation and memory burdens, others have exploited HLS to tune accelerators for statically defined optimization goals. However, these works have not tackled both CNN and HLS optimizations altogether; neither have they provided any adaptability at runtime, where the workload’s characteristics are unpredictable. In this context, we propose a hybrid two-step approach that, first, creates new optimization opportunities at design-time through the automatic training of CNN model variants (obtained via pruning) and the automatic generation of versions of convolutional accelerators (obtained during HLS synthesis); and, second, synergistically exploits these created CNN and HLS optimization opportunities to deliver a fully dynamic Multi-FPGA system that adapts its resources in a fully automatic or user-configurable manner. We implement this two-step approach as the AdaServ Framework and show, through a smart video surveillance Edge application as a case study, that it adapts to the always-changing Edge conditions: AdaServ processes at least 3.37× more inferences (using the automatic approach) and is at least 6.68× more energy-efficient (user-configurable approach) than original convolutional accelerators and CNN Models (VGG-16 and AlexNet). We also show that AdaServ achieves better results than solutions dynamically changing only the CNN model or HLS version, highlighting the importance of exploring both; and that it is always better than the best statically chosen CNN model and HLS version, showing the need for dynamic adaptability.

Computational Fluid Dynamics Using the Adaptive Wavelet-Collocation Method

Advancements to the adaptive wavelet-collocation method over the last decade have opened up a number of new possible areas for active research. Volume penalization techniques allow complex immersed boundary conditions to be used with high efficiency for both internal and external flows. Anisotropic methods make it possible to use body-fitted meshes while still taking advantage of the dynamic adaptability properties wavelet-based methods provide. The parallelization of the approach has made it possible to perform large high-resolution simulations of detonation initiation and fluid instabilities to uncover new physical insights that would otherwise be difficult to discover. Other developments include space-time adaptive methods and nonreflecting boundary conditions. This article summarizes the work performed using the adaptive wavelet-collocation method developed by Vasilyev and coworkers over the past decade.

Synergized Network Asset: A Driver for Indonesia’s Furniture Industries to Elevate Marketing Performance

The main focus of this study is developing synergized network asset as a mediator variable for improving marketing performance. The research model was tested using AMOS SEM on 280 local market-oriented furniture makers. The output of the statistical analysis confirms that two buffer variables, namely relational capability and market dynamic adaptability, sustain synergized network asset and marketing performance. However, synergized network asset does not play a strong mediation role in increasing marketing performance. The slow-growing furniture industry is in greater need of responsive rather than synergistic cooperation. The concept of synergized network asset, which is characterized by the sustainability of collaboration quality, contributes to the development of business network theory as one of the main factors in marketing relationships.

Radio program hosts’ self-identity mobilization in Chinese radio-mediated medical consultations

While previous studies highlight the dynamic nature of identity co-construction, how and especially why speakers construct and shift their own multiple identities still remains understudied. The present study argues that identity is part of speaker communicative resources as evidenced by radio program hosts’ strategic employment and shift among their different identities to facilitate their interactional purposes. Based on data drawn from radio medical consultations, this article attempts to reveal the dynamic adaptability of hosts’ identity construction. It is found that (1) in general, hosts of medical consultation programs construct three identities for themselves, namely an authoritative expert identity, a caring friend identity and a sales representative identity; (2) the three identities constructed are respectively adaptable to power relationships, solidarity and role relationships between hosts and callers in conversation; (3) the three identities shift in conversations to facilitate callers’ purchasing acts.

Open project-based learning for dynamic adaptability of IT education

The pace of technological change is accelerating, the duration of the trends is becoming shorter and the number of technological alternatives to solve IT problems grows quickly. Keeping IT higher education course curricula up-to-date becomes more and more challenging. This paper presents an empirical study that aims to assess whether an open-statement and open-solution methodology in an IT-related project-based learning (PBL) course led to the adaptation of the skills learned by students to the trends of the main IT technology domains. The study analyses data from more than 90 projects by students from eight academic years of the course” Project on Information Technologies”, carried out by students in the last year of the Bachelor of Science in Informatics Engineering at the Barcelona School of Informatics. Our results identify a high correlation between the technologies chosen in student projects and technological trends, which shows that the methodology encourages students to incorporate the latest technological innovations in their project work plan.

Design of Effective Robotic Gaze-Based Social Cueing for Users in Task-Oriented Situations: How to Overcome In-Attentional Blindness?

Robotic eye-gaze-based cueing has been studied and proved to be effective, in controlled environments, in achieving social functions as humans gaze. However, its dynamic adaptability in various real interactions has not been explored in-depth. This paper addresses a case where a simplistic robotic gaze fails to achieve effective social cueing in human–robot communication, primarily due to in-attentional blindness (IB), and presents a method that enables the robot to deliver gaze-based social signals adequately. To understand the implications of IB and figure out ways to overcome any limitations from IB, which frequently arise in task-oriented situations, we designed a set of 1-on-1 communication experiments consisting of a robotic tutor and human learners participating in multiple-choice quiz sessions (task-oriented situation). Here, multimedia contents were utilized alongside the robot as visual stimuli competing for the human’s attention. We hypothesized that quiz scores would increase when humans successfully recognize the robot’s gaze-based cue signals hinting answers. We compared the impacts of two different cueing methods: oblique cueing (OC), where cues were straightforwardly provided regardless of whether participants were potentially experiencing IB or not, and leading cueing (LC), where cueing procedures were led through achieving eye contact and securing the participants’ attention before signaling the cue. By comparing participants’ test scores achieved by the control group with no cueing (NC) and two experimental groups of OC and LC, respectively, we found that there was a significant increase in test scores only when the LC method was utilized. This experiment illustrates the importance of proactively guiding a user’s attention through sophisticated interaction design in effectively attaining a user’s attention and successfully delivering cues. In future studies, we aim to evaluate different methods by which a social robot can intentionally shift a human’s attention, such as incorporating stimuli from various multi-modal human communication channels.

Autonomous Dynamic Adaptability System to Maintain Irradiance Levels in a Steady-State Solar Simulator

This paper presents the design of an autonomous dynamic adaptability system (ADAS) for maintaining the irradiance levels of a steady-state xenon arc lamp solar simulator (SS). The solar simulator is used to carry out indoor testing and accelerated age tests on photovoltaic (PV) cells at the Fort Hare Institute of Technology (FHIT). The ADAS was designed primarily for two reasons: Firstly, to maintain a set irradiance level, irrespective of external effects which may cause unintended irradiance drift or fluctuations, while carrying out indoor tests. Secondly, to achieve the solar simulator set point quicker, thus reducing temperature build up on the target area. At a cold start, the SS runs at 20% of its rated current (145 A). At 20% of 145 A, the simulator gave an irradiance of 145.97 Wm−2 with a non-uniformity of 1.02%, and a cell surface temperature of 24.9 °C. At 50%, the simulator produced irradiance of 501.30 Wm−2, with a non-uniformity of 1.53% and a cell surface temperature of 25.0 °C. The irradiance of 1000 Wm−2, with a non-uniformity of 3.26% and a cell surface temperature of 25.9 °C, was achieved at 90% of the rated current. From the results obtained, the ADAS demonstrates that it can reliably operate the SS with very minimal human–machine interaction. Through the autonomous dynamic adaptability, set irradiance levels are maintained in a steady-state solar simulator once the user supplies operational set points via the supervisory control and data acquisition (SCADA) interface.