Explore the vast range of titles available.

With the increasing demand for energy efficiency optimization in the building industry, this study explores the application of machine learning technology in building energy efficiency design and evaluation. By comprehensively analyzing energy consumption data, environmental factors, building characteristics, and user behavior patterns, this paper proposes a machine learning-based approach aimed at accurately predicting and improving the energy efficiency of buildings. The study collected and pre-processed a large amount of data, built and trained multiple models, including neural networks, which showed a high degree of predictive accuracy in cross-validation. The results show that the neural network has obvious advantages in the task of building energy efficiency prediction. In addition, the interpretability of the model in practical applications and future research directions, such as the introduction of real-time monitoring data and in-depth study of the interpretability of the model, are also discussed. This study not only provides a new perspective for building energy efficiency optimization, but also provides a practical tool for intelligent building design and operation.

This paper constructs an efficient, budget-balanced, Bayesian incentive-compatible mechanism for a general dynamic environment with quasilinear payoffs in which agents observe private information and decisions are made over countably many periods. First, under the assumption of \"private values\" (other agents' private information does not directly affect an agent's payoffs), we construct an efficient, ex post incentive-compatible mechanism, which is not budget-balanced. Second, under the assumption of \"independent types\" (the distribution of each agent's private information is not directly affected by other agents' private information), we show how the budget can be balanced without compromising agents' incentives. Finally, we show that the mechanism can be made self-enforcing when agents are sufficiently patient and the induced stochastic process over types is an ergodic finite Markov chain.

We focus on the design of an organization's set of boundary-spanning transactions-business model design-and ask how business model design affects the performance of entrepreneurial firms. By extending and integrating theoretical perspectives that inform the study of boundary-spanning organization design, we propose hypotheses about the impact of efficiency-centered and novelty-centered business model design on the performance of entrepreneurial firms. To test these hypotheses, we developed and analyzed a unique data set of 190 entrepreneurial firms that were publicly listed on U.S. and European stock exchanges. The empirical results show that novelty-centered business model design matters to the performance of entrepreneurial firms. Our analysis also shows that this positive relationship is remarkably stable across time, even under varying environmental regimes. Additionally, we find indications of potential diseconomies of scope in design; that is, entrepreneurs' attempts to incorporate both efficiency- and novelty-centered design elements into their business models may be counterproductive.

Computational design is a test of our understanding of enzyme catalysis and a means of engineering novel, tailor-made enzymes. While the de novo computational design of catalytically efficient enzymes remains a challenge, designed enzymes may comprise unique starting points for further optimization by directed evolution. Directed evolution of two computationally designed Kemp eliminases, KE07 and KE70, led to low to moderately efficient enzymes (k cₐₜ/ K ₘ values of ≤ 5 10 ⁴ M ⁻¹s ⁻¹). Here we describe the optimization of a third design, KE59. Although KE59 was the most catalytically efficient Kemp eliminase from this design series (by k cₐₜ/ K ₘ, and by catalyzing the elimination of nonactivated benzisoxazoles), its impaired stability prevented its evolutionary optimization. To boost KE59’s evolvability, stabilizing consensus mutations were included in the libraries throughout the directed evolution process. The libraries were also screened with less activated substrates. Sixteen rounds of mutation and selection led to > 2,000-fold increase in catalytic efficiency, mainly via higher k cₐₜ values. The best KE59 variants exhibited k cₐₜ/ K ₘ values up to 0.6 10 ⁶ M ⁻¹s ⁻¹, and k cₐₜ/ k ᵤₙcₐₜ values of ≤ 10 ⁷ almost regardless of substrate reactivity. Biochemical, structural, and molecular dynamics (MD) simulation studies provided insights regarding the optimization of KE59. Overall, the directed evolution of three different designed Kemp eliminases, KE07, KE70, and KE59, demonstrates that computational designs are highly evolvable and can be optimized to high catalytic efficiencies.

The design of dose–response experiments is an important part of toxicology research. Efficient design of these experiments requires choosing optimal doses and assigning the correct number of subjects to those doses under a given criterion. Optimal design theory provides the tools to find the most efficient experimental designs in terms of cost and statistical efficiency. However, the mathematical details can be distracting and make these designs inaccessible to many toxicologists. To facilitate use of these designs, we present an easy to use web-app for finding two types of optimal designs for models commonly used in toxicology. We include tools for checking the optimality of a given design and for assessing efficiency of any user-supplied design. Using state-of-the-art nature-inspired metaheuristic algorithms, the web-app allows the user to quickly find optimal designs for estimating model parameters or the benchmark dose.

Identifying optimal designs for generalized linear models with a binary response can be a challenging task, especially when there are both discrete and continuous independent factors in the model. Theoretical results rarely exist for such models, and for the handful that do, they usually come with restrictive assumptions. In this article, we propose the d-QPSO algorithm, a modified version of quantum-behaved particle swarm optimization, to find a variety of D-optimal approximate and exact designs for experiments with discrete and continuous factors and a binary response. We show that the d-QPSO algorithm can efficiently find locally D-optimal designs even for experiments with a large number of factors and robust pseudo-Bayesian designs when nominal values for the model parameters are not available. Additionally, we investigate robustness properties of the d-QPSO algorithm-generated designs to various model assumptions and provide real applications to design a bio-plastics odor removal experiment, an electronic static experiment, and a 10-factor car refueling experiment. Supplementary materials for the article are available online.

Stepwise uncertainty reduction (SUR) strategies aim at constructing a sequence of points for evaluating a function  f in such a way that the residual uncertainty about a quantity of interest progressively decreases to zero. Using such strategies in the framework of Gaussian process modeling has been shown to be efficient for estimating the volume of excursion of f above a fixed threshold. However, SUR strategies remain cumbersome to use in practice because of their high computational complexity, and the fact that they deliver a single point at each iteration. In this article we introduce several multipoint sampling criteria, allowing the selection of batches of points at which f can be evaluated in parallel. Such criteria are of particular interest when f is costly to evaluate and several CPUs are simultaneously available. We also manage to drastically reduce the computational cost of these strategies through the use of closed form formulas. We illustrate their performances in various numerical experiments, including a nuclear safety test case. Basic notions about kriging, auxiliary problems, complexity calculations, R code, and data are available online as supplementary materials.

Interior design requires designer creativity and significant workforce investments. Meanwhile, Artificial Intelligence (AI) is crucial for enhancing the creativity and efficiency of interior design. Therefore, this study proposes an innovative method to generate multistyle interior design and videos with AI. First, this study created a new indoor dataset to train an AI that can generate a specified design style. Subsequently, video generation and super-resolution modules are integrated to establish an end-to-end workflow that generates interior design videos from texture-free 3D models. The proposed method utilizes AI to produce diverse interior design videos directly, thus replacing the tedious tasks of texture selection, lighting arrangement, and video rendering in traditional design processes. The research results indicate that the proposed method can effectively provide diverse interior design videos, thereby enriching design presentation and improving design efficiency. Additionally, the proposed workflow is versatile and scalable, thus holding significant reference value for transforming traditional design toward intelligence.

This paper develops a parsimonious process-level theory that connects organizational structure to exploration and exploitation. Toward this end, it develops a mathematical model of organizational decision making that combines an information processing approach in the spirit of Sah and Stiglitz [Sah RK, Stiglitz JE (1986) The architecture of economic systems: Hierarchies and polyarchies. Amer. Econom. Rev. 76(4):716–727] with elements from signal detection theory. The model is first used to explore a “design space” of organizations and identify trade-offs and dominance relationships among alternative organization designs. The paper then studies open questions in the organization design literature, such as the extent to which exploration and exploitation can be produced by one organization and what is the effect of organization size on exploration. More broadly, this research speaks to calls for the introduction of more process-level explanations in the organizations literature. The paper concludes with testable hypotheses and managerially relevant insights.

Calculations are providing a molecular picture of hydrogen production on catalytic surfaces and within enzymes. This knowledge may guide the design of new, more efficient catalysts for the hydrogen economy.