Various factors are considered when designing a floorplan layout, including the plan’s outer boundary, room shape and size, adjacency, privacy, and circulation space, among others. While graph-theoretic approaches have proven effective for floorplan generation, existing algorithms generally focus on defining the boundary of the plan or different room shapes, lacking the investigation of designing circulation space within a floorplan. However, the circulation design in architectural planning is a crucial factor that affects the functionality and efficiency of areas within a building. This paper presents a graph-theoretic approach for integrating circulation within a floorplan. In this study, we use plane graphs to represent floorplans and develop graph algorithms to incorporate various types of circulation within a floorplan as follows:

1. i. The first phase generates a spanning circulation, that is, a corridor leading to each room using a circulation graph.

2. ii. Subsequently, using an approximation algorithm, the circulation space is minimized, that is, generation of minimum circulation space covering all the rooms, thereby enhancing space utilization in the floorplan.

3. iii. Furthermore, customized circulations are generated to cater to user preferences, distinguishing between public and private spaces within the floorplan.

In addition to the theoretical framework, we have implemented our algorithms in Python and developed a user-friendly graphical interface (GUI), enabling seamless integration of our algorithms into architectural design processes.

The generation of floor plan layouts has been extensively studied in recent years, driven by the need for efficient and functional architectural designs. Despite significant advancements, existing methods often face limitations when dealing with specific input adjacency graphs or room shapes and boundary layouts. When adjacency graphs contain separating triangles, the floor plan must include rectilinear rooms (non-rectangular rooms with concave corners). From a design perspective, minimizing corners or bends in rooms is crucial for functionality and aesthetics. In this article, we present a Python-based application called G-Drawer for automatically generating floor plans with a minimum number of bends. G-Drawer takes any plane triangulated graph as an input and outputs a floor plan layout with minimum bends. It prioritizes generating a rectangular floor plan (RFP); if an RFP is not feasible, it then generates an orthogonal floor plan or an irregular floor plan. G-Drawer modifies orthogonal drawing techniques based on flow networks and applies them on the dual graph of a given PTG to generate the required floor plans. The results of this article demonstrate the efficacy of G-Drawer in creating efficient floor plans. However, in future, we need to work on generating multiple dimensioned floor plans having non-rectangular rooms as well as non-rectangular boundary. These enhancements will address both mathematical and architectural challenges, advancing the automated generation of floor plans toward more practical and versatile applications.

We report the results of a field experiment designed to increase honest disclosure of claims at a U.S. state unemployment agency. Individuals filing claims were randomized to a message (‘nudge’) intervention, while an off-the-shelf machine learning algorithm calculated claimants’ risk for committing fraud (underreporting earnings). We study the causal effects of algorithmic targeting on the effectiveness of nudge messages: Without algorithmic targeting, the average treatment effect of the messages was insignificant; in contrast, the use of algorithmic targeting revealed significant heterogeneous treatment effects across claimants. Claimants predicted to behave unethically by the algorithm were more likely to disclose earnings when receiving a message relative to a control condition, with claimants predicted to most likely behave unethically being almost twice as likely to disclose earnings when shown a message. In addition to providing a potential blueprint for targeting more costly interventions, our study offers a novel perspective for the use and efficiency of data science in the public sector without violating citizens’ agency. However, we caution that, while algorithms can enable tailored policy, their ethical use must be ensured at all times.

The intraclass correlation, ρ, is a parameter featured in much psychological research. Two commonly used estimators of ρ, the maximum likelihood and least squares estimators, are known to be negatively biased. Olkin and Pratt (1958) derived the minimum variance unbiased estimator of the intraclass correlation, but use of this estimator has apparently been impeded by the lack of a closed form solution. This note briefly reviews the unbiased estimator and gives a FORTRAN 77 subroutine to calculate it.

An algorithm, using a short cut due to Feldman and Klingler, for the Fisher-Yates exact test is presented: the algorithm is quick, simple and accurate.

A general solution for weighted orthonormal Procrustes problem is offered in terms of the least squares criterion. For the two-demensional case. this solution always gives the global minimum; for the general case, an algorithm is proposed that must converge, although not necessarily to the global minimum. In general, the algorithm yields a solution for the problem of how to fit one matrix to another under the condition that the dimensions of the latter matrix first are allowed to be transformed orthonormally and then weighted differentially, which is the task encountered in fitting analogues of the IDIOSCAL and INDSCAL models to a set of configurations.

In educational and psychological measurement when short test forms are used, the asymptotic normality of the maximum likelihood estimator of the person parameter of item response models does not hold. As a result, hypothesis tests or confidence intervals of the person parameter based on the normal distribution are likely to be problematic. Inferences based on the exact distribution, on the other hand, do not suffer from this limitation. However, the computation involved for the exact distribution approach is often prohibitively expensive. In this paper, we propose a general framework for constructing hypothesis tests and confidence intervals for IRT models within the exponential family based on exact distribution. In addition, an efficient branch and bound algorithm for calculating the exact p value is introduced. The type-I error rate and statistical power of the proposed exact test as well as the coverage rate and the lengths of the associated confidence interval are examined through a simulation. We also demonstrate its practical use by analyzing three real data sets.

Dietary strategies for weight loss typically place an emphasis on achieving a prescribed energy intake. Depending on the approach taken, this may be achieved by restricting certain nutrients or food groups, which may lower overall diet quality. Various studies have shown that a higher quality diet is associated with better cardiovascular (CV) health outcomes1. This study aimed to evaluate the effect of an energy restricted diet on diet quality, and associated changes in cardiovascular risk factors. One hundred and forty adults (42 M:98 F, 47.5 ± 10.8 years, BMI 30.7 ± 2.3 kg/m2) underwent an energy restricted diet (30% reduction) with dietary counselling for 3 months, followed by 6 months of weight maintenance. Four-day weighed food diaries captured dietary data at baseline, 3 and 9 months and were analysed using a novel algorithm to score diet quality (based on the Dietary Guideline Index, DGI)2. Total DGI scores ranged from 0-120, with sub scores for consumption of core (0-70) and non-core foods (0-50). For all scores, a higher score or increase reflects better diet quality. The CV risk factors assessed included blood pressure (SBP and DBP) and fasting lipids (total (TC), high and low-density lipoprotein cholesterol (HDL-C, LDL-C) and triglycerides (TAG). Mixed model analyses were used to determine changes over time (reported as mean ± standard error), and Spearman rho (rs) evaluated associations between DGI score and CV risk factors. Dietary energy intake was significantly restricted at 3 months (−3222 ± 159 kJ, P<0.001, n = 114) and 9 months (−2410 ± 167 kJ, P<0.001, n = 100) resulting in significant weight loss (3 months −7.0 ± 0.4 kg, P<0.001; 9 months −8.2 ± 0.4 kg, P<0.001). Clinically meaningful weight loss (>5% body mass) was achieved by 81% of participants by 3 months. Diet quality scores were low at baseline (scoring 49.2 ± 1.5), but improved significantly by 3 months (74.7 ± 1.6, P<0.000) primarily due to reductions in the consumption of non-core i.e. discretionary foods (Core sub-score +4.0. ± 0.7, Non-core sub-score +21.3.1 ± 1.6, both P<0.001). These improvements were maintained at 9 months (Total score 71.6 ± 1.7, P<0.000; Core sub-score +4.4 ± 0.7 from baseline, P<0.000; Non-core sub-score +17.9 ± 1.6 from baseline, P<0.000). There were significant inverse relationships between changes in Total DGI score and changes in DBP (rs = −0.268, P = 0.009), TC (rs = −0.298, P = 0.004), LDL-C (rs = −0.224, P = 0.032) and HDL-C (rs = −0.299, P = 0.004) but not SBP and TG at 3 months. These data emphasise the importance of including diet quality as a key component when planning energy restricted diets. Automated approaches will enable researchers to evaluate subtle changes in diet quality and their effect on health outcomes.