Universities face a critical crossroads, in need of swift, targeted, and efficient actions to address future challenges. This necessities a strategic approach to updating and assessing engineering design education. Efforts to improve teaching and learning require systematic change in many universities, yet research on structuring such change is scarce. Few studies have combined a systems perspective with a functional operational level. This research embeds design thinking to structure to isolated actions. Drawing from an extensive literature review of educational change frameworks and several illustrative cases, this article demonstrates the potential of design-driven change. It highlights how dynamic interrelations can facilitate educational transformations across diverse academic levels. By presenting an educational ecosystem as a framework for systematic educational change, design thinking functions as a catalyst for educational transformation. The article also presents case findings that strengthen supportive actions ingrained in existing change research frameworks connecting, them to a transparent approach for sustainable and careful decision-making.

During the 20th century, dealing with grief through an ongoing involvement with the deceased (such as speaking to their grave) was seen as pathological by Western authors such as Sigmund Freud. Nowadays, we are presented with the opportunity to continue interacting with digital representations of the deceased. As a result, the paper adopts an Ubuntu perspective, i.e., a sub-Saharan African philosophy focussed on community and relationship to provide a toolkit for using this emerging technology. I will argue that the Ubuntu framework I propose contributes to the use of griefbots in two ways. The first is that it shows that it is morally permissible to use griefbots to assuage our grief. The second is that it delineates how we can ethically use the technology. To do so, I split my analysis into four sections. In the first section, I show that meaningful relationships can occur between the bereaved and griefbots. This will be done by exploring the Western theory of continuing bonds proposed by Dennis Klass, Phyllis Silverman and Steven Nickman. In my second, I flesh out my Ubuntu framework according to Thaddeus Metz’s accounts on Ubuntu as a modal-relational theory. In my third section, I apply my Ubuntu framework to the case of Roman Mazurenko. Furthermore, I consider some counterarguments to the Ubuntu framework regarding privacy, commercialisation and people replacement. Finally, I conclude that, despite these limitations, the Ubuntu framework positively contributes to determining whether we should communicate with the dead through griefbots to assuage our grief.

Quantum computing has been studied over the past four decades based on two computational models of quantum circuits and quantum Turing machines. To capture quantum polynomial-time computability, a new recursion-theoretic approach was taken lately by Yamakami [J. Symb. Logic 80, pp. 1546–1587, 2020] by way of recursion schematic definition, which constitutes six initial quantum functions and three construction schemes of composition, branching, and multi-qubit quantum recursion. By taking a similar approach, we look into quantum polylogarithmic-time computability and further explore the expressing power of elementary schemes designed for such quantum computation. In particular, we introduce an elementary form of the quantum recursion, called the fast quantum recursion, and formulate $EQS$ (elementary quantum schemes) of “elementary” quantum functions. This class $EQS$ captures exactly quantum polylogarithmic-time computability, which forms the complexity class BQPOLYLOGTIME. We also demonstrate the separation of BQPOLYLOGTIME from NLOGTIME and PPOLYLOGTIME. As a natural extension of $EQS$, we further consider an algorithmic procedural scheme that implements the well-known divide-and-conquer strategy. This divide-and-conquer scheme helps compute the parity function, but the scheme cannot be realized within our system $EQS$.

Static analysis of logic programs by abstract interpretation requires designing abstract operators which mimic the concrete ones, such as unification, renaming, and projection. In the case of goal-driven analysis, where goal-dependent semantics are used, we also need a backward-unification operator, typically implemented through matching. In this paper, we study the problem of deriving optimal abstract matching operators for sharing and linearity properties. We provide an optimal operator for matching in the domain $\mathtt{ShLin}^{\omega }$, which can be easily instantiated to derive optimal operators for the domains $\mathtt{ShLin}^2$ by Andy King and the reduced product $\mathtt{Sharing} \times \mathtt{Lin}$.

Due to the F2 ionospheric layer’s ability to reflect radio waves, the foF2 critical frequency is essential since sudden irregularities can disrupt communication and navigation systems, affecting the weather forecast’s accuracy. This paper aims to develop accurate foF2 critical frequency prediction up to 24 hours ahead, focusing on mid and high latitudes, using the long short-term memory (LSTM) model covering the 24th solar cycle from 2008 to 2019. To evaluate the effectiveness of the proposed model, a comparative analysis is conducted with commonly referenced machine learning techniques, including linear regression, decision tree algorithms, and multilayer perceptron (MLP) using the Taylor diagram and error plots. The study involved five monitoring stations, different years with minimum and maximum solar activity, and prediction timeframes. Through extensive experimentation, a comprehensive set of outcomes is evaluated across diverse metrics. The findings conclusively established that the LSTM model has demonstrated superior performance compared to the other models across all stations and years. On average, LSTM is 1.2 times better than the second-best model (DT), 1.6 times as effective as the multilayer perceptron MLP, and three times more accurate than linear regression. The results of this research hold promise for increasing the precision of foF2-prediction, with potential implications for enhancing communication systems and weather forecasting capabilities.

Online reviews have a significant impact on the purchasing decisions of potential consumers. Positive reviews often sway buyers, even when faced with higher prices. This phenomenon has given rise to a deceptive industry dedicated to crafting counterfeit reviews. Companies frequently indulge in procuring bulk fake reviews, employing them to tarnish their rivals’ reputations or artificially bolster their credibility. These spurious reviews materialize through automated systems or compensated individuals. Thus, detecting fake reviews is becoming increasingly important due to their deceptive nature, as they are extremely difficult for humans to identify. To address this issue, current work has focused on machine learning and deep learning techniques to identify fake reviews. However, they have several limitations, including a lack of sufficient training data, inconsistency in providing accurate solutions across different datasets, concept drift, and inability to address new methods that evolved to create fake reviews over time. The objective of this review paper is to find the gaps in the existing research in the field of fake review detection and provide future directions. This paper provides the latest, comprehensive overview and analysis of research efforts focusing on various techniques employed so far, distinguishing characteristics utilized, and the existing datasets used.