A novel low power hybrid cache using GC-EDRAM cells

In a typical embedded CPU, large on-chip storage is critical to meet high performance requirements. However, the fast increasing size of the on-chip storage based on traditional SRAM cells makes the area cost and energy consumption unsustainable for future embedded applications. Replacing SRAM with DRAM on the CPU’s chip is generally considered not worthwhile because DRAM is not compatible with the common CMOS logic and requires additional processing steps beyond what is required for CMOS. However a special DRAM technology, Gain-Cell embedded-DRAM (GC-eDRAM)  [1], [2], [3] is logic compatible and retains some of the good properties of DRAM (small and low power). In this paper we evaluate the performance of a novel hybrid cache memory where the data array, generally populated with SRAM cells, is replaced with GC-eDRAM cells while the tag array continues to use SRAM cells. Our evaluation of this cache demonstrates that, compared to the conventional SRAM-based designs, our novel architecture exhibits comparable performance with less energy consumption and smaller silicon area, enabling the sustainable on-chip storage scaling for future embedded CPUs.     

Techno-economic feasibility assessment of sorption enhanced gasification of municipal solid waste for hydrogen production

Waste-to-fuel coupled with carbon capture and storage is forecasted to be an effective way to mitigate the greenhouse gas emissions, reduce the waste sent to landfill and, simultaneously, reduce the dependence of fossil fuels. This study evaluated the techno-economic feasibility of sorption enhanced gasification, which involves in-situ CO₂ capture, and benchmarked it with the conventional steam gasification of municipal solid waste for H₂ production. The impact of a gate fee and tax levied on the fossil CO₂ emissions in economic feasibility was assessed. The results showed that the hydrogen production was enhanced in sorption enhanced gasification, that achieved an optimum H₂ production efficiency of 48.7% (T = 650 °C and SBR = 1.8). This was 1.0% points higher than that of the conventional steam gasification (T = 900 °C and SBR = 1.2). However, the total efficiency, which accounts for H₂ production and net power output, for sorption enhanced gasification was estimated to be 49.3% (T = 650 °C and SBR = 1.8). This was 4.4% points lower than the figure estimated for the conventional gasification (T = 900 °C and SBR = 1.2). The economic performance assessment showed that the sorption enhanced gasification will result in a significantly higher levelised cost of hydrogen (5.0 €/kg) compared to that estimated for conventional steam gasification (2.7 €/kg). The levelised cost of hydrogen can be reduced to 4.5 €/kg on an introduction of the gate fee of 40.0 €/t_MSW. The cost of CO₂ avoided was estimated to be 114.9 €/tCO₂ (no gate fee and tax levied). However, this value can be reduced to 90.1 €/tCO₂ with the introduction of an emission allowance price of 39.6 €/tCO₂. Despite better environmental performance, the capital cost of sorption enhanced gasification needs to be reduced for this technology to become competitive with mature gasification technologies.     

互联网时代下的学校图书管理信息化建设

如何在互联网时代打造一个信息化的管理模式,已经成为广大学校图书管理发展过程中亟待处理的问题。文章就互联网时代下的高职图书管理信息化建设进行了详细探讨,以期能够给广大同仁提供一些借鉴参考,共同为图书管理工作的现代化改革和发展贡献力量。     

Co2FeO4@rGO composite: Towards trifunctional water splitting in alkaline media

Development of efficient, low cost and multifunctional electrocatalysts for water splitting to harvest hydrogen fuels is a challenging task, but the combination of carbon materials with transition metal-based compounds is providing a unique and attractive strategy. Herein, composite systems based on cobalt ferrite oxide-reduced graphene oxide (Co₂FeO₄) @(rGO) using simultaneous hydrothermal and chemical reduction methods have been prepared. The proposed study eliminates one step associated with the conversion of GO into rGO as it uses direct GO during the synthesis of cobalt ferrite oxide, consequently rGO based hybrid system is achieved in-situ significantly, the optimized Co₂FeO₄@rGO composite has revealed an outstanding multifunctional applications related to both oxygen evolution reaction (OER) and hydrogen counterpart (HER). Various metal oxidation states and oxygen vacancies at the surface of Co₂FeO₄@rGO composites guided the multifunctional surface properties. The optimized Co₂FeO₄@rGO composite presents excellent multifunctional properties with onset potential of 0.60 V for ORR, an overpotential of 240 mV at a 20 mAcm^?2 for OER and 320 mV at a 10 mAcm^?2 for HER respectively. Results revealed that these multifunctional properties of the optimized Co₂FeO₄@ rGO composite are associated with high electrical conductivity, high density of active sites, crystal defects, oxygen vacancies, and favorable electronic structure arisinng from the substitution of Fe for Co atoms in binary spinel oxide phase. These surface features synergistically uplifted the electrocatalytic properties of Co₂FeO₄@rGO composites. The multifunctional properties of the Co₂FeO₄@ rGO composite could be of high interest for its use in a wide range of applications in sustainable and renewable energy fields.     

Investigation of liquid water heterogeneities in large area proton exchange membrane fuel cells using a Darcy two-phase flow model in a multiphysics code

Proper management of the liquid water and heat produced in proton exchange membrane (PEM) fuel cells remains crucial to increase both its performance and durability. In this study, a two-phase flow and multicomponent model, called two-fluid model, is developed in the commercial COMSOL Multiphysics® software to investigate the liquid water heterogeneities in large area PEM fuel cells, considering the real flow fields in the bipolar plate. A macroscopic pseudo-3D multi-layers approach has been chosen and generalized Darcy's relation is used both in the membrane-electrode assembly (MEA) and in the channel. The model considers two-phase flow and gas convection and diffusion coupled with electrochemistry and water transport through the membrane. The numerical results are compared to one-fluid model results and liquid water measurements obtained by neutron imaging for several operating conditions. Finally, according to the good agreement between the two-fluid and experimentation results, the numerical water distribution is examined in each component of the cell, exhibiting very heterogeneous water thickness over the cell surface.     

PID Control of Planar Nonlinear Uncertain Systems in the Presence of Actuator Saturation

This paper investigates PID control design for a class of planar nonlinear uncertain systems in the presence of actuator saturation. Based on the bounds on the growth rates of the nonlinear uncertain function in the system model, the system is placed in a linear differential inclusion. Each vertex system of the linear differential inclusion is a linear system subject to actuator saturation. By placing the saturated PID control into a convex hull formed by the PID controller and an auxiliary linear feedback law, we establish conditions under which an ellipsoid is contractively invariant and hence is an estimate of the domain of attraction of the equilibrium point of the closed-loop system. The equilibrium point corresponds to the desired set point for the system output. Thus, the location of the equilibrium point and the size of the domain of attraction determine, respectively, the set point that the output can achieve and the range of initial conditions from which this set point can be reached. Based on these conditions, the feasible set points can be determined and the design of the PID control law that stabilizes the nonlinear uncertain system at a feasible set point with a large domain of attraction can then be formulated and solved as a constrained optimization problem with constraints in the form of linear matrix inequalities (LMIs). Application of the proposed design to a magnetic suspension system illustrates the design process and the performance of the resulting PID control law.      

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney,Melbourne and Brisbane

In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.     

Use of a smartphone-based Student Response System in large active-learning Chemical Engineering Thermodynamics classrooms

Smartphones are a promising tool as student response systems (SRS) for interactive teaching due to their widespread diffusion. Here, the main purpose is to assess the efficacy of smartphone-based SRS in large classroom settings of undergraduate Thermodynamics, as representative of engineering courses requiring high-level cognitive skills for problem solving. Four sets of multiple-choice questions were presented during the course. Overall, the results refer to 1055 students between control and SRS classes, each corresponding to a3 years period.One of the main results of this work is the strong linear correlation between the average questionnaire score and the final exam grade (R² = 0.91). A similar correlation, although with a lower value of R², is already found in the first questionnaire, thus showing the SRS high predictive power of class performance. The results of this study provide guidance for a quantitative use of smartphone-based SRS in teaching basic disciplines. The SRS monitoring capability allows early detection of struggling students, thus paving the way to personalized tutoring and improved student engagement in active learning practices. This approach is especially important in emergency situations, such as the SARS-Cov-2 pandemic, when distance learning is widely adopted, and remote interactive tools are highly needed.     

Industry 4.0: a supply chain innovation perspective

The Fourth Industrial Revolution – also known as Industry 4.0 (i4.0) – comprises the digitalisation of the industrial sector. This paper uses the theoretical lens of supply chain innovation (SCI) to investigate the implications of i4.0 on supply chain management. For these purposes, the method of structured content analysis is applied to more than 200 use cases of i4.0-enabled SCI introduced by both established and startup companies. i4.0-enabled SCI manifests along three dimensions: process, technology, and business architecture. The key findings of this study can be summarised as follows: first, i4.0-enabled SCI extends the initial focus on productivity improvements in SC processes towards scalability and flexibility. Second, extant i4.0 solutions rely mostly on analytics and smart things while omitting smart people technology and the human-centric approach associated with the i4.0 paradigm. Third, established companies adopt i4.0 merely to sustain their existing business architectures while startup companies radically change their operating models, relying heavily on data analytics and the platform economy. Consequently, established companies pursue a problem-driven, engineering-based approach to SCI while startup companies follow an ‘asset-light’, business-driven approach. Lastly, there are two distinct approaches to digitalising operational SC processes: platform-based crowdsourcing of standard processes and on-demand provision of customised services.