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We investigated bandgap engineering of spinel-structured Mg B 2O4 (B = Al, Ga, In) alloys. The trend of bandgap change was tunable from approximated 7.8–3.6 eV by substituting group III cation atoms in Mg B 2O4. To evaluate the doping possibility, we have calculated the natural band alignment and doping pinning energy of the normal and inverse spinel structures of Mg B 2O4 alloys. The calculated doping pinning energies show that spinel-structured Mg B 2O4 alloys can potentially control the electron concentration, but hole doping is difficult. Our computational results are expected to provide new insights into the doping control of ultra-wide bandgap semiconductors. [ABSTRACT FROM AUTHOR]

Flexible conductive hydrogels (FCHs) have attracted widespread interest as versatile monoliths that can be intricately integrated with various ingredients boasting multiple functionalities. The chemicophysical properties of FCHs cover a wide range, which significantly vary in their building blocks. However, achieving both favorable mechanical strength and high conductivity simultaneously through a facile approach remains a challenge. Herein, polyvinyl alcohol, dialdehyde cellulose nanofibrils, silver nanoparticles, borax, and tannic acid are readily "one-pot" incorporated into FCHs with great tensile stress (499 kPa), tensile strain (4591 %), and compressive stress (269 kPa) due to abundant hydrogen bonding, dynamic borate-diol bonding, and intermolecular acetalization. They also exhibit desired self-healing, generalized-adhesive, and antibacterial performances. Taking advantage of these, FCHs are further employed to support an epidermal sensor, on which remarkable strain sensitivity (gauge factor = 8.22), high-pressure sensitivity (≥ 0.258 kPa−1), and fast response (≤ 190 ms) are recorded. Its highly adaptive mechano-electric transformability and functions can be well maintained in serving as an array unit and touch screen pen. The results well addressed in this work are anticipated to pave the universal way of engineering FCHs. [Display omitted] [ABSTRACT FROM AUTHOR]

Nature- and society-inspired metaheuristic algorithms have recently become the most promising technological model. To solve more complex optimization problems and complicated engineering applications, this paper proposes a new people duality psychological tendency and feedback mechanism-based Inherited Optimization Algorithm(IOA), which is inspired by people showing positive-negative duality cognitive tendency and adaptive feedback behavior when selecting information resources with different identity attributes. The IOA algorithm contains the symmetric two exploration phases. The exploitation phase adaptively regulates the dualistic psychological balance of people in inheriting the information resources with better existence value through a feedback regulation mechanism controlled by the profitability awareness to increase population diversity. This paper qualitatively and quantitatively evaluates the optimization performance of IOA on 84 benchmarks, including swarm convergence behavior, effectiveness, convergence, robustness, and significance. The scalability of the IOA is investigated using the CEC2017 suites. The algorithm performance in solving constrained optimization is verified on 8 engineering problems. All statistical results of the IOA are compared with the most promising 12 metaheuristics, which shows that the absolute computational efficiency of IOA on four types of functions is 95%, 96.67%, 80.95%, and 76.92%, respectively, the average rank (rank sum ratio) of IOA is 1.08 (1.19%) among the 13 algorithms, ranking first. The Wilcoxon signed rank test results on the CEC2017 suites show that IOA contains 1437 significance indicators out of 1440 comparisons, with the proportion of significant differences 99.79%, which suggests the proposed IOA maintains efficient search efficiency. [ABSTRACT FROM AUTHOR]

Multivariate chaotic time series prediction is a challenging task, especially when multiple variables are predicted simultaneously. For multiple related prediction tasks typically require multiple models, however, multiple models are difficult to keep synchronization, making immediate communication between predicted values challenging. Although multi-task learning can be applied to this problem, the principles of allocation and layout options between shared and specific representations are ambiguous. To address this issue, a novel simplified multi-task learning method was proposed for the precise implementation of simultaneous multiple chaotic time series prediction tasks. The scheme proposed consists of a cross-convolution operator designed to capture variable correlations and sequence correlations, and an attention module proposed to capture the information embedded in the sequence structure. In the attention module, a non-linear transformation was implemented with convolution, and its local receptive field and the global dependency of the attention mechanism achieve complementarity. In addition, an attention weight calculation was devised that takes into account not only the synergy of time and frequency domain features, but also the fusion of series and channel information. Notably the scheme proposed a purely simplified design principle of multi-task learning by reducing the specific network to single neuron. The precision of the proposed solution and its potential for engineering applications were verified with the Lorenz system and power consumption. The mean absolute error of the proposed method was reduced by an average of 82.9% in the Lorenz system and 19.83% in power consumption compared to the Gated Recurrent Unit., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Semantic segmentation is one of the directions in image research. It aims to obtain the contours of objects of interest, facilitating subsequent engineering tasks such as measurement and feature selection. However, existing segmentation methods still lack precision in class edge, particularly in multi-class mixed region. To this end, we present the Feature Enhancement Network (FE-Net), a novel approach that leverages edge label and pixel-wise weights to enhance segmentation performance in complex backgrounds. Firstly, we propose a Smart Edge Head (SE-Head) to process shallow-level information from the backbone network. It is combined with the FCN-Head and SepASPP-Head, located at deeper layers, to form a transitional structure where the loss weights gradually transition from edge labels to semantic labels and a mixed loss is also designed to support this structure. Additionally, we propose a pixel-wise weight evaluation method, a pixel-wise weight block, and a feature enhancement loss to improve training effectiveness in multi-class regions. FE-Net achieves significant performance improvements over baselines on publicly datasets Pascal VOC2012, SBD, and ATR, with best mIoU enhancements of 15.19%, 1.42% and 3.51%, respectively. Furthermore, experiments conducted on Pole&Hole match dataset from our laboratory environment demonstrate the superior effectiveness of FE-Net in segmenting defined key pixels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In a 1987 article, Peter R. Rossi promulgated "The Iron Law of Evaluation and Other Metallic Rules." The Metallic Laws were meant as an informal (and humorous) overstatement of the weakness of contemporary evaluations of social programs. Rossi' s underlying worry was not so much about the state of evaluation technology in the abstract, but, rather, in its inability to advance our broad understanding of social problems and what to do about them---in other words, to make evaluation policy relevant. Rossi attributed the continuing failure to develop successful "large-scale social programs" to the failure to build a strong knowledge base for this kind of "social engineering." The qualities of studies that enable such accumulated learning are variously labeled "external validity," "generalizability," "applicability," or "transferability." This Special Issue includes five papers that seek to explore and apply this understanding., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

In this paper, a quantitative, descriptive, and correlational research is carried out on the perceptions of the digital competence of Latin American engineering professors, their assessment of the didactic use of Information and Communication Technologies (ICT) in different didactic areas (interaction, communication, distribution, and administration and evaluation), and on the growth or decrease in the frequency of their use after the COVID-19 pandemic. In particular, the Latin American engineering professors' perceptions of digital competence is studied. Also, the existence of gender gaps (both in general and differentiating between private and public universities, and between professors with a more or less teaching experience) has been explored in the assessment of the perceptions of digital competence and the didactic use of ICT by engineering professors, as well as in the influence that the pandemic has had on the habits of ICT use. The results reveal that the use of ICT by engineering professors has achieved increases of over 40% in some educational activities due to the pandemic, and it has had a homogenizing effect on the frequency of use of ICT tools in the different activities. Female engineering professors have increased their use of ICT more than males, and engineering professors in private universities more than those in public universities. Consequently, it is suggested to the universities to reinforce the continuous training of engineering professors, considering the pedagogical specificities of engineering education and the necessary integration of ICT in the teaching activity of female engineering professors. [ABSTRACT FROM AUTHOR]

In this paper, the Ru/RuS 2 nanoparticles as dual co-catalysts were self-assembled on the surface of g-C 3 N 4 nanotubes (Ru/RuS 2 /g-C 3 N 4 NTs) for photocatalytic H 2 production. The Ru/RuS 2 /g-C 3 N 4 NTs showed greatly enhanced photocatalytic H 2 production activity (1409 μmol·g−1·h−1), 1.16 times of RuS 2 /g-C 3 N 4 NTs (1212 μmol·h−1·g−1), 10.51 times of Ru/g-C 3 N 4 NTs (134 μmol·g−1·h−1), and 82.88 times of the pure g-C 3 N 4 NTs (17 μmol·g−1·h−1). Besides, the apparent quantum yield value (AQE) of Ru/RuS 2 /g-C 3 N 4 NTs is 3.92% at 370 nm. Ru/RuS 2 as dual co-catalysts are self-assembled on the surface of g-C 3 N 4 NTs and show strong electronic synergistic interaction between the interfaces, reduce Δ G H* of RuS 2 and desorption energy of Ru, and promote the selectivity and activity of HER kinetically and thermodynamically respectively, which exhibits higher photogenerated carrier concentration and lower charge migration resistance than RuS 2 and Ru, showing the synergistic effect to facilitate the generation and migration of carriers and provides active sites for photocatalysis. [Display omitted] • Ru/RuS 2 nanoparticles as dual co-catalysts are self-assembled on the surface of g-C 3 N 4 nanotubes. • Ru/RuS 2 /g-C 3 N 4 NTs show greatly enhanced photocatalytic H 2 production activity (1409 μmol·g−1·h−1). • Ru/RuS 2 show strong electronic synergistic interaction between the interfaces. • Ru/RuS 2 promote the selectivity and activity of HER kinetically and thermodynamically respectively. [ABSTRACT FROM AUTHOR]