This paper studies linear programming (LP) relaxations for solving polynomial programming problems. A polynomial programming problem can be equivalently formulated as a quadratically constrained quadratic program (QCQP) by introducing new variables that represent nonlinear monomials and substituting them within the original formulation. Whereas Reformulation-Linearization Technique (RLT)-based LP relaxations constructed for the original formulation are tight, the relaxations generated using the equivalent lower degree formulations are smaller and require less computational effort to optimize in comparison to the effort the former requires.
In this study, we analyze the strength and tractability of the standard RLT, the J-set, and recursive McCormick relaxations for polynomial programming problems, and identify the superior relaxation depending on the problem characteristics. Extensive computational results are provided to demonstrate the relative effectiveness of the standard RLT, J-set, and recursive McCormick algorithms using problems from the literature and randomly generated test instances.

Building Energy Management is identified as a key part of the move towards localised energy generation and control. The significant discrepancy between building energy use as designed and during actual operation shows a need to evaluate the relationship between building occupants and energy requirements.
The need to better account for the influence of occupants on building energy use has been established through post-occupancy studies, highlighting the characteristics needed for more successful building control systems. This paper provides an overview of current building systems technology and discusses existing academic research into more advanced occupant-centric .
The potential for application of various methods is compared. It is found that study into occupant-centred  systems covers a wide array of approaches, ranging from simple presence-based switching of lighting systems to full model predictive control. Studies suggest an optimum point balancing the complexity of a system against its potential for saving energy.

Does moral culture contribute to the evolution of cooperation? Here, we examine individuals’ and communities’ models of what it means to be good and bad and how they correspond to corollary behavior across a variety of socioecological contexts. Our sample includes over 600 people from eight different field sites that include foragers, horticulturalists, herders, and the fully market-reliant. We first examine the universals and particulars of explicit moral models. We then use these moral models to assess their role in the outcome of an economic experiment designed to detect systematic, dishonest rule-breaking favoritism. We show that individuals are slightly more inclined to play by the rules when their moral models include the task-relevant virtues of “honesty” and “dishonesty.” We also find that religious beliefs are better predictors of honest play than these virtues. The predictive power of these values’ and beliefs’ local prevalence, however, remains inconclusive. In summary, we find that religious beliefs and moral models may help promote honest behavior that may widen the breadth of human cooperation.

This paper addresses the equilibrium traffic assignment problem involving battery electric vehicles (BEVs) with flow-dependent electricity consumption. Due to the limited driving range and the costly/time-consuming recharging process required by current BEVs, as well as the scarce availability of battery charging/swapping stations, BEV drivers usually experience fear that their batteries may run out of power en route. Therefore, when choosing routes, BEV drivers not only try to minimize their travel costs, but also have to consider the feasibility of their routes. Moreover, considering the potential impact of traffic congestion on the electricity consumption of BEVs, the feasibility of routes may be determined endogenously rather than exogenously.
A set of user equilibrium (UE) conditions from the literature is first presented to describe the route choice behaviors of BEV drivers considering flow-dependent electricity consumption. The UE conditions are then formulated as a nonlinear complementarity model. The model is further formulated as a variational inequality (VI) model and is solved using an iterative solution procedure. Numerical examples are provided to demonstrate the proposed models and solution algorithms. Discussions of how to evaluate and improve the system performance with non-unique link flow distribution are offered. A robust congestion pricing model is formulated to obtain a pricing scheme that minimizes the system travel cost under the worst-case tolled flow distribution. Finally, a further extension of the mathematical formulation for the UE conditions is provided.

Column generation is a linear programming method that, when combined with appropriate integer programming techniques, has been successfully used for solving huge integer programs. The method alternates between a restricted master problem and a column generation subproblem. The latter step is founded on dual information from the former one; often an optimal dual solution to the linear programming relaxation of the restricted master problem is used.
We consider a zero–one linear programming problem that is approached by column generation and present a generic sufficient optimality condition for the restricted master problem to contain the columns required to find an integer optimal solution to the complete problem. The condition is based on dual information, but not necessarily on an optimal dual solution. It is however most natural to apply the condition in a situation when an optimal or near-optimal dual solution is at hand.
We relate our result to a few special cases from the literature, and make some suggestions regarding possible exploitation of the optimality condition in the construction of column generation methods for integer programs.

This study investigated the life cycle greenhouse gases (GHG) mitigation effect of railway infrastructure through the application of an eco-concrete, using ground granulated blast furnace (GGBF) slag and electric arc furnace (EAF) slag as alternative materials in railway sleeper, and a concrete surface protection agent in railway track. A simplified life cycle assessment method was applied to compare GHG emissions and an instrumental analysis as well as a physical performance test were carried out to identify the protection mechanism between the agent and concrete along with field tests. From this study, it was found that two different approaches might contribute substantially to mitigate emissions from railway infrastructure. The surface protection agent with an anti-deterioration function showed high possibility of increasing lifespan of the concrete structure and the use of alternative materials, such as furnace slag, reduced the concrete consumption by more than 20% (w/w). It was estimated that the potential GHG mitigation effects from the surface protection agent and eco-concrete technology applied to a railway concrete track were at least 27 ton CO₂ eq. per km a year and 11.1 kg of CO₂ eq. per 1 sleeper, respectively.

Corrosion-induced deterioration of reinforced concrete (RC) structures results in premature failure of the RC structures. In practice concrete crack width is one of the most important criteria for the assessment of the serviceability of RC structures. It is therefore desirable to predict the growth of the crack width over time so that better informed decisions can be made concerning the repairs due to concrete cracking. Literature review shows that little research has been undertaken on numerical prediction of concrete crack width. The intention of this study was to develop a numerical method to predict concrete crack width for corrosion-affected concrete structures. A cohesive crack model for concrete is implemented in the numerical formulation to simulate crack initiation and propagation in concrete. Choices for evaluating the parameters of cohesive elements are extensively discussed which is a key for developing a plausible model employing cohesive elements. The surface crack width is obtained as a function of service time. Accurate prediction of crack width can allow timely maintenance which prolongs the service life of the reinforced concrete structures.

This paper explores tax policy effects and revenue implications of joining the World Trade Organization (WTO) and its predecessor, the General Agreement on Tariffs and Trade (GATT). It documents that countries joining GATT/WTO after 1990 have implemented tariff-cum-tax reforms, lowering tariff rates and raising consumption tax rates, in particular through reform or introduction of a value added tax (VAT). Employing a panel of 97 developing and transitional countries, 31 of which joined GATT/WTO between 1990 and 2011, using robust difference-in-difference specifications as well as the synthetic control method, we find a statistically and economically significant decline in revenues from import duties. This finding supports concerns about revenue losses, but also corroborates the efficacy of the late Uruguay GATT and the WTO trade regimes in promoting free trade among new members. Regarding consumption taxes, we find robust evidence that revenue substitution was successful, since revenue losses from import duties were more than compensated for by enhanced revenues from consumption taxes. With regard to the timing of the revenue effects, our results show that revenue losses in import duties mostly take place at the time of membership or later. Changes in consumption taxation, however, exhibit pre-membership effects, as revenues are increased, and VAT is adopted, often a few years ahead of losses in import duties. No such effects are found before the start of the accession negotiations, indicating that consumption tax reforms are initiated once a country is on the road to GATT/WTO membership.

Artificial intelligence (AI) is proving to be an efficient alternative approach to classical modeling techniques. AI refers to the branch of computer science that develops machines and software with human-like intelligence. Compared to traditional methods, AI offers advantages to deal with problems associated with uncertainties and is an effective aid to solve such complex problems. In addition, AI-based solutions are good alternatives to determine engineering design parameters when testing is not possible, thus resulting in significant savings in terms of human time and effort spent in experiments. AI is also able to make the process of decision making faster, decrease error rates, and increase computational efficiency. Among the different AI techniques, machine learning (ML), pattern recognition (PR), and deep learning (DL) have recently acquired considerable attention and are establishing themselves as a new class of intelligent methods for use in structural engineering. The objective of this review paper is to summarize techniques concerning applications of the noted AI methods in structural engineering developed over the last decade. First, a general introduction to AI is presented and the importance of AI in structural engineering is described. Thereafter, a review of recent applications of ML, PR, and DL in the field is provided, and the capability of such methods to address the restrictions of conventional models are discussed. Further, the advantages of employing such algorithmic methods are discussed in detail. Finally, potential research avenues and emerging trends for employing ML, PR, and DL are presented, and their limitations are discussed.