We give an adequate, concrete, categorical-based model for Lambda-${\mathcal S}$, which is a typed version of a linear-algebraic lambda calculus, extended with measurements. Lambda-${\mathcal S}$ is an extension to first-order lambda calculus unifying two approaches of non-cloning in quantum lambda-calculi: to forbid duplication of variables and to consider all lambda-terms as algebraic linear functions. The type system of Lambda-${\mathcal S}$ has a superposition constructor S such that a type A is considered as the base of a vector space, while SA is its span. Our model considers S as the composition of two functors in an adjunction relation between the category of sets and the category of vector spaces over $\mathbb C$. The right adjoint is a forgetful functor U, which is hidden in the language, and plays a central role in the computational reasoning.

A spread-out lattice animal is a finite connected set of edges in $\{\{x,y\}\subset \mathbb{Z}^d\;:\;0\lt \|x-y\|\le L\}$. A lattice tree is a lattice animal with no loops. The best estimate on the critical point $p_{\textrm{c}}$ so far was achieved by Penrose (J. Stat. Phys. 77, 3–15, 1994) : $p_{\textrm{c}}=1/e+O(L^{-2d/7}\log L)$ for both models for all $d\ge 1$. In this paper, we show that $p_{\textrm{c}}=1/e+CL^{-d}+O(L^{-d-1})$ for all $d\gt 8$, where the model-dependent constant $C$ has the random-walk representation

\begin{align*} C_{\textrm{LT}}=\sum _{n=2}^\infty \frac{n+1}{2e}U^{*n}(o),&& C_{\textrm{LA}}=C_{\textrm{LT}}-\frac 1{2e^2}\sum _{n=3}^\infty U^{*n}(o), \end{align*}

$U^{*n}$

$n$

$d$

$\{x\in{\mathbb R}^d\;: \|x\|\le 1\}$

$p$

This article traces the history of the use and reception of field recordings on radio, in France and Britain, outside the categories considered as art or music such as hörspiel or musique concrète. It shows that radio producers had diverse reactions to the use of sonic ambiences recorded in the field. There was an opposition between a ‘Pure Sound School’, which promoted the use of field recordings instead of voice to depict the environment where the reporter was, and a school that privileged voice. If the use of recordings of sonic ambiences was not new, their utilisation on radio as elements autonomous in themselves was. They were falling between categories: they were not reports (because of the absence of voice), they were not musique concrète (because sounds were not modified and were presented within their context, that is, not as sound objects), they were not sound effects (because they lasted several minutes and could be composed through editing), and they were not wildlife recordings (because wildlife could be absent). Sonic ambiences were new sonic objects that took time to digest. This time also represented a listening mutation, and this will be analysed through the beginnings of radio documentaries and the works of sound hunters.

Human gait trajectory prediction is a long-standing research topic in human–machine interaction. However, there are two shortcomings in the current gait trajectory prediction technology. The first shortcoming is that the neural network model of gait prediction only predicts dozens of future time frames of gait trajectory. The second shortcoming is that the gait prediction neural network model is uninterpretable. We propose the Interpretable-Concatenation former (IC-former) model, which can predict long-term gait trajectories and explain the prediction results by quantifying the importance of data at different positions in the input sequence. Experiments prove that the IC-former model we proposed not only makes a breakthrough in prediction accuracy but also successfully explains the data basis of the prediction.

Measuring and attributing greenhouse gas (GHG) emissions remains a challenging problem as the world strives toward meeting emissions reductions targets. As a significant portion of total global emissions, the road transportation sector represents an enormous challenge for estimating and tracking emissions at a global scale. To meet this challenge, we have developed a hybrid approach for estimating road transportation emissions that combines the strengths of machine learning and satellite imagery with localized emissions factors data to create an accurate, globally scalable, and easily configurable GHG monitoring framework.

Acouscapes is a software designed as a simple educational solution for the creation of soundscapes and their use in the composition of soundscape music in primary and secondary education. The software has slots in which the user must place the sounds that will make up the desired soundscape, allowing them to make different soundwalks by interacting with the graphic interface. Acouscapes allows the content of these soundscapes to be modified by means of sound and structural processing, and includes a recording function. This article aims to present the conceptual and educational foundations of Acouscapes, to describe the software technically and functionally, and to offer some applications of this software as a mediation artefact in educational processes.

In order to resolve redundancy and path planning of a high DOF mobile manipulator using conventional approaches like Jacobian and a pseudoinverse method, researchers face the limitation of computational load and delay in response. If such kind of mobile manipulator is traversing the rough terrain, then conventional methods become too costly to implement due to the handling of redundant joints, obstacles, and wheel-terrain interaction. A few optimization-based redundancy resolution approaches try incorporating wheel-terrain interaction but fail in real-time response. This paper describes a 14 DOF Rover Manipulator System’s end-effector path-tracking approach using CG-Space framework to incorporate wheel-terrain interaction. CG-Space means the center of gravity (CG) locus of the Rover. The Rover’s CG is calculated while traversing over 3D terrain using a multivariable optimization method and a 3D point cloud image of the actual terrain and stored as CG-Space over the given terrain. First of all, we decide which part of the system moves to track the path, that is, arm or Rover, depending upon the manipulator’s work volume and manipulability measure restrictions. The next task is to obtain the Rover pose according to the end-effector path using a simple arm’s inverse kinematic solution between the CG-Space and end-effector task space without resolving redundancy. Meanwhile, obstacles and non-traversable regions are avoided in CG-Space. On diverse 3D terrains, simulation and experimental results support the suggested CG-Space framework for end-effector tracking.

This article explores the ability of ChatGPT to function as a virtual colleague in helping to design materials for higher education design students. Using a self-study methodology, two university educators attempted to collaborate with ChatGPT to create course materials targeted at higher education design students, before reflecting on its strengths and weaknesses during the process. Contextualising ChatGPT as the latest acute example of digital disruptors that design practices and processes have faced, the authors evaluated its current and potential threats and opportunities for the creation of design-focused learning content. The authors found that ChatGPT was a competent partner with regard to saving time, structuring textual content and documentation, and as a brainstorming tool. However, ChatGPT’s weaknesses included content generation that was often generic, usually requiring much human prompting, cajoling, and manual editing to produce desirable outcomes. Overall, ChatGPT was found to excel at its stated functionality as a language model, with some potentially useful functionality for the creation of higher education design course materials and outlines, as well as limitations. The reflections discussed can be used to inform design educators who may want to work with ChatGPT when designing course materials. However, acknowledging limitations and potential ethical challenges, the authors’ caution that educators may have to evaluate for themselves whether ChatGPT’s potential advantages outweigh its disadvantages.

The coronavirus disease-2019 (COVID-19) pandemic and the mobility restrictions governments imposed to prevent its spread changed the cities’ ways of living. Transport systems suffered the consequences of the falling travel demand, and readjustments were made in many cities to prevent the complete shutdown of services. In Córdoba, the second largest city in Argentina, the Municipality dictated route cuts and reduced frequencies to sustain the buses and trolleys system. In 2022, Martinazzo and Falavigna assessed potential accessibility to hospitals before (2019) and during the pandemic (2021). Overall, the study indicated that average travel times increased by 20% and that the gap between less vulnerable and more vulnerable population quintiles reached almost 8 points. In this paper, potential accessibility to public hospitals in 2022 and 2023 is calculated using Martinazzo and Falavigna’s (2022) work as a baseline to compare, considering that neither cutting the services during the pandemic nor recovering the service after the pandemic the Municipality performed an accessibility assessment. The main results showed that, despite the system having almost recovered its extension by 2023, it maintained the regressive tendency between less vulnerable and more vulnerable population quintiles, as the difference in average travel time between these two groups reached up to 14 min, while the cumulative opportunities measure for the high-income groups was up to 68% higher than the most vulnerable households.