Extensible Automated Constraint Modelling

In constraint solving, a critical bottleneck is the formulationof an effective constraint model of a given problem. The CONJURE system described in this paper, a substantial step forward over prototype versions of CONJURE previously reported, makes a valuable contribution to the automation of constraint modelling by automatically producing constraint models from their specifications in the abstract constraint specification language ESSENCE. A set of rules is used to refine an abstract specification into a concrete constraint model. We demonstrate that this set of rules is readily extensible to increase the space of possible constraint models CONJURE can produce. Our empirical results confirm that CONJURE can reproduce successfully the kernels of the constraint models of 32 benchmark problems found in the literature

Resilient cities : 3D geoscience for sustainable subsurface management

It is estimated that global population reached 7 billion in 2011. Almost half of that population live in towns or cities. Rapid urbanisation driven by population change, socio-economic and technological development has placed increased pressure on the natural capital provided by the natural environment to society. Land use change and increased competition for space above and below ground places ever growing demands on the ability of urban ecosystems to deliver the goods and services on which society depends. A 3D geoscience framework provides a means to assess the impacts of predicted future demographic and environmental change on the subsurface and the demands placed upon i

Exploiting short supports for improved encoding of arbitrary constraints into SAT

Encoding to SAT and applying a highly efficient modern SAT solver is an increasingly popular method of solving finite-domain constraint problems. In this paper we study encodings of arbitrary constraints where unit propagation on the encoding provides strong reasoning. Specifically, unit propagation on the encoding simulates generalised arc consistency on the original constraint. To create compact and efficient encodings we use the concept of short support. Short support has been successfully applied to create efficient propagation algorithms for arbitrary constraints. A short support of a constraint is similar to a satisfying tuple however a short support is not required to assign every variable in scope. Some variables are left free to take any value. In some cases a short support representation is smaller than the table of satisfying tuples by an exponential factor. We present two encodings based on short supports and evaluate them on a set of benchmark problems, demonstrating a substantial improvement over the state of the art

The promoter from SlREO, a highly-expressed, root-specific Solanum lycopersicum gene, directs expression to cortex of mature roots

Root-specific promoters are valuable tools for targeting transgene expression, but many of those already described have limitations to their general applicability. We present the expression characteristics of SlREO, a novel gene isolated from tomato (Solanum lycopersicum L.). This gene was highly expressed in roots but had a very low level of expression in aerial plant organs. A 2.4-kb region representing the SlREO promoter sequence was cloned upstream of the uidA GUS reporter gene and shown to direct expression in the root cortex. In mature, glasshouse-grown plants this strict root specificity was maintained. Furthermore, promoter activity was unaffected by dehydration or wounding stress but was somewhat suppressed by exposure to NaCl, salicylic acid and jasmonic acid. The predicted protein sequence of SlREO contains a domain found in enzymes of the 2-oxoglutarate and Fe(II)-dependent dioxygenase superfamily. The novel SlREO promoter has properties ideal for applications requiring strong and specific gene expression in the bulk of tomato root tissue growing in soil, and is also likely to be useful in other Solanaceous crop

The Evaluation of a Hip Hop and School Counselor Education Course

The training of future school counselors to integrate school-based mental health resources has never been as important within a climate where US schools replicate the racial violence and fear we see nationally, at the expense of wellbeing. Fostering multicultural counseling competencies in school counselors are critical for their tackling of the challenges that historically marginalized and culturally diverse individuals, groups, and communities face. The purpose of this study was to assess the impact of a Hip Hop-based school counselor education course on the development of graduate student’s multicultural competence. Results from qualitative analysis of course assignments indicated that graduate school counseling students learned culturally responsive clinical skills, such as the use of Hip Hop lyrics in session, the facilitation of Hip Hop groups, and the use of varied Hip Hop group activities through their participation in this course. Additionally, pre- and post-course surveys showed positive changes in student’s multicultural self-efficacy

Neural network approach to modelling transport system resilience for major cities:case studies of Lagos and Kano (Nigeria)

Congestion has become part of everyday urban life, and resilience is very crucial to traffic vulnerability and sustainable urban mobility. This research employed a neural network as an adaptive artificially-intelligent application to study the complex domains of traffic vulnerability and the resilience of the transport system in Nigerian cities (Kano and Lagos). The input criteria to train and check the models for the neural resilience network are the demographic variables, the geospatial data, traffic parameters, and infrastructure inventories. The training targets were set as congestion elements (traffic volume, saturation degree and congestion indices), which are in line with the relevant design standards obtained from the literature. A multi-layer feed-forward and back-propagation model involving input–output and curve fitting (nftool) in the MATLAB R2019b software wizard was used. Three algorithms—including Levenberg–Marquardt (LM), Bayesian Regularization (BR), and a Scaled Conjugate Gradient (SCG)—were selected for the simulation. LM converged easily with the Mean Squared Error (MSE) (2.675 × 10−3) and regression coefficient (R) (1.0) for the city of Lagos. Furthermore, the LM algorithm provided a better fit for the model training and for the overall validation of the Kano network analysis with MSE (4.424 × 10−1) and R (1.0). The model offers a modern method for the simulation of urban traffic and discrete congestion prediction