A computational design decision support tool for material waste minimisation during architectural design

Construction activities and building materials contribute to around 40% of global carbon emissions. Existing research shows that architectural design decisions about geometry and materials can significantly influence a project's building material waste extent. However, architects currently have limited means to understand the waste implications of their formal and material decisions while they are designing. Computational design tools offer ways to integrate material waste estimates into architectural design processes to make such information more accessible to architects while they are designing and in the early stages of the design process. Accordingly, this research investigates and develops a computational design tool to assist architects with understanding the relationship between design decisions and material waste estimates to influence waste minimisation 'at the source' in early-stage design. To do this, the research has adopted a computational design research approach to create and prototype a material waste estimation and optimisation tool that can be integrated into a 3D modelling environment and used by architects in early-stage design. To inform the development of the waste tool, the research also collected qualitative data using interviews about how architects perceive the issue of material waste and current waste mitigation methods adopted in their building projects in an Australian context. The interview findings were used to inform the iterative workflow design of the waste tool. The waste tool prototype operates by connecting to an external material database, mapping material selections to a 3D model using a material layout algorithm, calculating material waste offcut quantities (residual waste), and generating optimisation outputs. The performance of the prototype has been evaluated using architectural documentation for a number of multi-unit residential buildings provided by an architectural organisation. The research demonstrates that the waste tool prototype can visualise the problem of waste in a 3D environment and in relation to architecture design models and generate optimisation suggestions. Enabling architects and designers to engage with a material waste optimisation tool in the early design stage aims to foster awareness of zero material waste targets by providing actionable opportunities for material reduction

Exploring High School IT Course Teaching Resources in the Context of Educational Digital Transformation

The Chinese Ministry of Education initiated the implementation of the “National Education Digitalization Strategy Action” in 2022. The digital transformation of education has become a significant strategy in China’s educational reform and development in the new era, which is of great significance for the construction of high school information technology course teaching resources. This paper first introduces the concept and current situation of digital transformation and discusses the classification and existing issues of current high school information technology teaching resources. Next, it analyzes the future development direction and focus of high school information technology course teaching resources, including the development and utilization of digital educational resources, the construction and application of virtual laboratories, the development of personalized educational resources, the optimization and popularization of online education platforms, and the promotion of educational resource sharing and open education. Finally, this paper proposes development strategies and measures for high school information technology teaching resource construction, including strengthening the construction of educational informatization infrastructure, actively exploring the development and application of personalized educational resources, promoting educational resource sharing, and strengthening educational teaching management

Corporate Application Integration: Challenges, Opportunities, and Implementation Strategies

In recent years, corporate applications such as enterprise resource planning (ERP) systems, supply chain management (SCM) systems, customer relationship management (CRM) systems, sales force automation (SFA), and other corporate-level information systems have received a great deal of attention from large business enterprises. These applications have been around for about a decade now, and in that time their producers have refined them and perfected them to the point where they can be considered developmentally mature. At the same time, vendors have continued to introduce new products that have moved corporate applications toward a higher level of integration, both technically and organizationally. However, these higher levels of integration have brought with them complex technical, organizational, cultural, political, and legal issues that have made the integration process a very challenging task. This paper reviews relevant current literature, discusses several perspectives of corporate application integration, and points out potential opportunities and cludlenges inherent in the integration process. Risk reduction strategies and opportunities provided by some newly developed technologies (e.g., software agents) are also discussed

Exponential stability of delayed recurrent neural networks with Markovian jumping parameters

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.In this Letter, the global exponential stability analysis problem is considered for a class of recurrent neural networks (RNNs) with time delays and Markovian jumping parameters. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. The purpose of the problem addressed is to derive some easy-to-test conditions such that the dynamics of the neural network is stochastically exponentially stable in the mean square, independent of the time delay. By employing a new Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish the desired sufficient conditions, and therefore the global exponential stability in the mean square for the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A numerical example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany

State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays

This is the post print version of the article. The official published version can be obtained from the link - Copyright 2008 Elsevier LtdIn this Letter, we investigate the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters as well as mode-dependent mixed time-delays. The parameters of the discrete-time neural networks are subject to the switching from one mode to another at different times according to a Markov chain, and the mixed time-delays consist of both discrete and distributed delays that are dependent on the Markovian jumping mode. New techniques are developed to deal with the mixed time-delays in the discrete-time setting, and a novel Lyapunov–Krasovskii functional is put forward to reflect the mode-dependent time-delays. Sufficient conditions are established in terms of linear matrix inequalities (LMIs) that guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A numerical example is exploited to show the usefulness of the derived LMI-based conditions.This work was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) of the UK under Grants BB/C506264/1 and 100/EGM17735, the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grants GR/S27658/01 and EP/C524586/1, an International Joint Project sponsored by the Royal Society of the UK, the Natural Science Foundation of Jiangsu Province of China under Grant BK2007075, the National Natural Science Foundation of China under Grant 60774073, and the Alexander von Humboldt Foundation of Germany

Design of exponential state estimators for neural networks with mixed time delays

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Elsevier Ltd.In this Letter, the state estimation problem is dealt with for a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. The activation functions are assumed to be neither monotonic, nor differentiable, nor bounded. We aim at designing a state estimator to estimate the neuron states, through available output measurements, such that the dynamics of the estimation error is globally exponentially stable in the presence of mixed time delays. By using the Laypunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions to guarantee the existence of the state estimators. We show that both the existence conditions and the explicit expression of the desired estimator can be characterized in terms of the solution to an LMI. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, the Alexander von Humboldt Foundation of Germany, the Natural Science Foundation of Jiangsu Education Committee of China under Grants 05KJB110154 and BK2006064, and the National Natural Science Foundation of China under Grants 10471119 and 10671172

HBV core promoter mutations and AKT upregulate S-phase kinase-associated protein 2 to promote postoperative hepatocellular carcinoma progression

Mutations in the hepatitis B virus (HBV) core promoter (CP) have been shown to be associated with hepatocellular carcinoma (HCC). The CP region overlaps HBV X gene, which activates AKT to regulate hepatocyte survival. However, the cooperation between these two cascades in HCC progression remains poorly understood. Here, we assayed virological factors and AKT expression in liver tissues from 56 HCC patients with better prognoses (BHCC, ≥5-year survival) and 58 with poor prognoses (PHCC, <5-year survival) after partial liver resection. Results showed double mutation A1762T/G1764A (TA) combined with other mutation(s) (TACO) in HBV genome and phosphorylated AKT (pAKT) were more common in PHCC than BHCC. TACO and pAKT levels correlated with proliferation and microvascularization but inversely correlated with apoptosis in HCC samples. These were more pronounced when TACO and pAKT co-expressed. Levels of p21 and p27 were decreased in TACO or pAKT overexpressing HCC due to SKP2 upregulation. Levels of E2F1 and both mRNA and protein of SKP2 were increased in TACO expressing HCC. Levels of 4EBP1/2 decreased and SKP2 mRNA level remained constant in pAKT-overexpressing HCC. Therefore, TACO and AKT are two independent predictors of postoperative survival in HCC. Their co-target, SKP2 may be a diagnostic or therapeutic marker

Global exponential stability of generalized recurrent neural networks with discrete and distributed delays

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2006 Elsevier Ltd.This paper is concerned with analysis problem for the global exponential stability of a class of recurrent neural networks (RNNs) with mixed discrete and distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, by employing a new Lyapunov–Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish sufficient conditions for the RNNs to be globally exponentially stable. Therefore, the global exponential stability of the delayed RNNs can be easily checked by utilizing the numerically efficient Matlab LMI toolbox, and no tuning of parameters is required. A simulation example is exploited to show the usefulness of the derived LMI-based stability conditions.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Nuffield Foundation of the UK under Grant NAL/00630/G, and the Alexander von Humboldt Foundation of Germany