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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]

In this paper, we consider the evolution of a new probability distribution called the exponential flexible Weibull distribution. The proposed exponential flexible Weibull distribution is obtained by combining the flexible Weibull extension with the exponential T- X strategy. For the exponential flexible Weibull, the estimators of the model parameters are derived mathematically. The appraisal of these parameters is accomplished through a simulation study. The applicability and virtuoso of the exponential flexible Weibull distribution are exemplified via two data sets. Furthermore, we implement the state-of-the-art deep learning algorithms, specifically Artificial Neural Networks and Extreme Gradient Boosting, which are widely employed in real-world applications. A comprehensive comparative analysis is conducted to assess the relative performance of these methodologies. [ABSTRACT FROM AUTHOR]

In this paper, we consider the evolution of a new probability distribution called the exponential flexible Weibull distribution. The proposed exponential flexible Weibull distribution is obtained by combining the flexible Weibull extension with the exponential T-X strategy. For the exponential flexible Weibull, the estimators of the model parameters are derived mathematically. The appraisal of these parameters is accomplished through a simulation study. The applicability and virtuoso of the exponential flexible Weibull distribution are exemplified via two data sets. Furthermore, we implement the state-of-the-art deep learning algorithms, specifically Artificial Neural Networks and Extreme Gradient Boosting, which are widely employed in real-world applications. A comprehensive comparative analysis is conducted to assess the relative performance of these methodologies.

With complex, visual concepts prevailing in science and mathematics curricula, these subjects are often inaccessible to students with visual impairments (VI), leading to their underrepresentation in science, technology, engineering and mathematics (STEM) careers. While researchers have identified strategies that can facilitate students with VI's learning within special schools, less is known about how students with VI access science and mathematics within a mainstream context, even though this is where the majority are educated. This seems important to address given the additional barrier students with VI face within mainstream schools, including negative attitudes from mainstream teachers. Consequently, the current study was conducted to explore how students with VI's access to and learning of science and mathematics can be improved within the mainstream context. Two interviews were conducted with qualified teachers of children and young people with vision impairment (QTVIs), revealing the importance of classroom adaptations, such as hands-on experience and increased lesson time, to facilitate students with VI's learning of science and mathematics. These findings are discussed in relation to policy and practice, suggesting mainstream teachers should be trained to make small but effective adaptations in their teaching, and that students with VI are given the opportunity to learn skills needed to become independent learners. [ABSTRACT FROM AUTHOR]

Microbial electrosynthesis (MES) is a promising technology that mainly utilizes microbial cells to convert CO2 into value-added chemicals using electrons provided by the cathode. However, the low electron transfer rate is a solid bottleneck hindering the further application of MES. Thus, as an effective strategy, genetic tools play a key role in MES for enhancing the electron transfer rate and diversity of production. We describe a set of genetic strategies based on fundamental characteristics and current successes and discuss their functional mechanisms in driving microbial electrocatalytic reactions to fully comprehend the roles and uses of genetic tools in MES. This paper also analyzes the process of nanomaterial application in extracellular electron transfer (EET). It provides a technique that combines nanomaterials and genetic tools to increase MES efficiency, because nanoparticles have a role in the production of functional genes in EET although genetic tools can subvert MES, it still has issues with difficult transformation and low expression levels. Genetic tools remain one of the most promising future strategies for advancing the MES process despite these challenges. [ABSTRACT FROM AUTHOR]

The main goal of the article is to develop a toolkit algorithm for the application of computer modelling in the analysis of hazardous areas during flooding using ArcGIS software for representatives of administrative-territorial authorities. The created toolkit algorithm can be utilized at the regional and local self-government levels for analysing potential negative consequences of flooding, followed by decision-making regarding the implementation of appropriate protective measures. The authors have developed a toolkit algorithm for the application of computer modelling to analyse hazardous areas during the flooding of territories using ArcGIS software, specifically designed for representatives of administrative-territorial authorities. This algorithm involves modelling the watershed basins of the area and identifying hazardous areas that may pose additional dangers or lead to a "domino effect" during the flooding of the studied territory. The authors have identified a list of hazardous areas that pose additional risks to the population's livelihood in the territory that may be affected by flooding. Additionally, the practical application of the proposed computer modelling algorithm has been examined using the example of the Drohobych district in the Lviv region, where frequent flooding has been observed in the past. The Drohobych district includes the territorial communities of Medenychi, Drohobych, Truskavets, Skhidnytsia, and Boryslav. In the region, there are two solid household waste landfills near the villages of Bronytsia and the city of Boryslav, which can be considered hazardous areas in the event of flooding. The greatest danger of consecutive contaminations due to flooding occurs in the area of the cities of Borislav and Truskavets. Water covers four cemeteries and comes dangerously close to the landfill as a result of flooded territories. The city of Drohobych is in a relatively safe zone; however, the water sources supplying the city are within the boundaries of the city of Truskavets, which also causes an additional danger to these and adjacent settlements. Subsequently, the developed toolkit algorithm can be utilized at the regional and community levels for analysing potential negative consequences of flooding, followed by decision-making regarding the implementation of appropriate engineering protection measures in specific areas. [ABSTRACT FROM AUTHOR]

Purpose: The current COVID-19 pandemic is influencing our life in every aspect, including working and living environments. Millions of people were forced to isolate themselves in their homes, which has posed significant pressure on buildings and shown us that our dwellings are not designed for such purposes. This is partly due to the fact that homes are designed and built for occasional use rather than isolated and long-term occupation. The legislative system of a country plays an important role in defining and shaping the conditions of people living there. Hence, the aim of the study is to evaluate the readiness of Kazakhstani and the EU construction-related legislation for pandemics. Design/methodology/approach: Previously developed pandemic-resilient indicators were used for the evaluation of construction legislation. Both legislative systems were reviewed, and the quality of responses was evaluated by assigning response scores. Findings: The results based on response scores indicate that the environmental resource consumption sub-category was better covered by EU legislation. At the same time, the buildings' health, safety and comfort are better taken into account in Kazakhstani legislation. Seven pandemic-resilient indicators were not responded to by any legislative system indicating a gap between current legislation and requirements for new living conditions. Originality/value: No study has analyzed how COVID-19 can transform construction legislation. The study reveals the limitation of current construction legislation in Kazakhstan (KZ) and the EU, indicating the need for transformation to meet the requirements of the pandemic era. [ABSTRACT FROM AUTHOR]

This article discusses the importance of reproducibility in academic research, specifically in the field of bridge engineering. The authors argue that reproducibility is challenging due to factors such as paper documentation, technology constraints, and proprietary issues. They propose implementing a policy that encourages researchers to make their data and models available to others, although they acknowledge that full reproducibility may not always be practical. To incentivize this practice, the Journal of Bridge Engineering plans to introduce a visible recognition badge for papers that provide open data, models, and codes. The authors also outline the review process for papers seeking the "Data Verified Paper" designation. [Extracted from the article]

This paper examines the seismic response of buckling restrained braced frames (BRBFs) under near-field (NF) ground motions to obtain the interrelationships among different engineering demand parameters (EDPs). Several case study BRBFs with varying span lengths and heights are designed based on relevant US design standards. These study frames are numerically developed in the OpenSees platform, incorporating the applicable nonlinear characteristics of the pertinent structural components. Nonlinear time response analyses are conducted on the adopted frames under a suite of NF ground motion records, and the results are post-processed to obtain structural responses in the form of EDPs such as inter-storey drift ratio (IDR), residual drift ratio (RDR), maximum ductility demand (μ max), and cumulative ductility demand (μ cum). Regression relationships among EDPs are also explored. Subsequently, probabilistic seismic demand models are developed, and the resulting fragility curves are generated. The results from the study can display the approximate regression relationships among the adopted EDPs under NF records. Identifying RDR as the critical EDP that governs the BRBF response under NF seismic excitation, distinct damage state (DS) values are also proposed for the IDR, μ max, and μ cum to facilitate fragility curve development. The study also shows that the increase in span length makes the BRBFs more vulnerable to permanent damage under NF disturbances. • Examines near-field seismic response of buckling-restrained braced frames factoring in bay span length & frame height. • For the accounted scenario, the inter-relation among the different engineering demand parameters (EDPs) of BRBFs, namely, inter-storey drift ratio (IDR), residual drift ratio (RDR), maximum buckling-restrained brace (BRB) ductility (μ max)and cumulative BRB ductility (μ cum) are derived. • Additionally, the associated median estimate of capacity (Sc) of IDR, μ max and μ cum are obtained based on the code given RDR median capacity value and the nonlinear time response analyses results. • Finally, the resulting fragility curves for RDR, IDR μ max and μ cum are generated and discussed. [ABSTRACT FROM AUTHOR]

There is a pressing requirement to design a simple battery thermal model that balances simulation accuracy regarding battery thermal characteristics and engineering application feasibility. The paper carries out the experimental study and model characterization of thermoelectric coupling characteristics of ternary lithium batteries with different shapes under ambient temperature conditions. The paper designs a characterization method of the battery thermoelectric coupling model. It conducts experiments to analyze the battery's charging and discharging characteristics and extracts essential modeling parameters. It reveals the differences in the modeling parameters of ternary lithium batteries with different shapes and constructs the thermoelectric coupling models of the square and 21700 cylindrical ternary lithium battery respectively. At the same time, the paper applies the model to carry out the simulation and experimental verification of the battery thermal behavior. The experimental data monitored by the infrared thermal imager and temperature sensors are compared with the simulation results. The errors are less than 5 %. It can provide a reference for related researches and engineering applications of battery thermal behavior and thermal management. • Designing a model characterization method for batteries with different shapes. • Revealing differences in modeling parameters of batteries with different shapes. • Constructing a simpler battery thermal model with low calculation volume. • Balancing the accuracy of simulation with the feasibility of applications. [ABSTRACT FROM AUTHOR]

The traditional approach to formulating building codes often is slow and labor-intensive, and may struggle to keep pace with the rapid evolution of technology and domain findings. Overcoming such challenges necessitates a methodology that streamlines the modernization of codal provisions. This paper proposes a machine learning (ML) approach to append a variety of codal provisions, including those of empirical, statistical, and theoretical natures. In this approach, a codal provision (i.e., equation) is analyzed to trace its properties (e.g., engineering intuition and causal logic). Then a ML model is tailored to preserve the same properties and satisfy a collection of similarity and performance measures until declared equivalent to the provision at hand. The resulting ML model harnesses the predictive capabilities of ML while arriving at predictions similar to the codal provision used to train the ML model, and hence it becomes possible to use in lieu of the codal expression. This approach was examined successfully for seven structural engineering phenomena contained within various building codes, including those in North America and Australia. The findings suggest that the proposed approach could lay the groundwork for implementing ML in the development of future building codes. [ABSTRACT FROM AUTHOR]

Strain engineering, as a cutting-edge method for modulating the electronic structure of catalysts, plays a crucial role in regulating the interaction between the catalytic surface and the adsorbed molecules. The electrocatalytic performance is influenced by the electronic structure, which can be achieved by introducing the external forces or stresses to adjust interatomic spacing between surface atoms. The challenges in strain engineering research lie in accurately understanding the mechanical impact of strain on performance. This paper first introduces the basic strategy for generating the strain, summarizes the different strain generation forms and their advantages and disadvantages. The progress in researching the characterization means for the lattice strains and their applications in the field of electrocatalysis is also emphasized. Finally, the challenges of strain engineering are introduced, and an outlook on the future research directions is provided. [ABSTRACT FROM AUTHOR]

The article discusses the skills that marketers need to successfully deploy generative AI tools. It emphasizes the importance of problem formulation, which involves defining the problem's focus, scope, and parameters before crafting the perfect query. The article also highlights the need for skills such as exploration, experimentation, and critical evaluation to effectively leverage generative AI. It suggests that continuous adaptation, learning, and development are crucial for marketers to stay ahead in the evolving landscape of generative AI. [Extracted from the article]

• A novel framework (MCMD) to classify days based on traffic state similarities measured across multiple traffic datasets. • MCMD outputs scale-invariant and scale-variant similarity-based classification labels and outliers. • A novel multi-view feature engineering algorithm is proposed for joint dimensionality reduction of traffic datasets. • Missing data is effectively handled by leveraging shared information across datasets. A common task in traffic data analysis and management is categorizing different days based on similarities in their network-wide traffic states. Given the multifaceted nature of traffic, it is essential to consider multiple attributes for a comprehensive quantification. However, challenges arise when combining these attributes to achieve consistent day-to-day classification across datasets. While various data-driven classification algorithms have been proposed in traffic literature, challenges persist. These include a) applicability limited to univariate datasets, b) incompatibility with datasets containing missing values, c) distance concentration problem in high-dimensional clustering, d) inability to classify outliers, and e) computationally expensive hyperparameter optimization. This research introduces the MCMD (Multi-view Classification based on Consensus Matrix Decomposition) framework, a novel approach for the joint classification of multi-view traffic data. MCMD treats multiple traffic datasets with varying geographical coverage as complementary views of the entire network's traffic state. It then extracts shared hidden features across these datasets and assigns each day classification labels that are consistent across views. MCMD consists of three key modules: the novel Multi-view Uni-orthogonal Non-negative Matrix Factorization (MUNMF) algorithm, an outlier removal module, and the Ordering Points to Identify the Clustering Structure (OPTICS) algorithm. A logical integration of the above-stated modules enables MCMD to a) output scale-invariant (SI) and scale-variant (SV) classifications and b) identify outlier days based on the shape and scale of multi-view traffic-state profiles. Compared to existing clustering methods, the design of the MCMD algorithm offers greater versatility in handling both single- and multi-view datasets for SI and SV clustering, computational robustness to missing data, and resilience to the "distance concentration problem" associated with the curse of dimensionality. These advantages stem from its ability to extract relevant cross-dataset features, reduce dimensionality, and eliminate redundancy. Although the primary motivation of the framework is derived from the need to develop a traffic pattern repository to support reliable prior Origin-Destination (OD) selection for online dynamic OD demand adjustment, the paper, through extensive experiments on real-world and synthetic traffic datasets, demonstrates the effectiveness of MCMD from several generic standpoints. These include a) demonstration of the meaningfulness of SI and SV labels, b) assessment of the robustness toward missing information, c) evaluation of its effectiveness in classifying days with special events, d) benchmarking properties against alternative joint day-to-day clustering algorithms, and e) demonstrating the efficacy of the proposed hyperparameter selection method for efficient joint classification of multiple large-scale traffic datasets. [ABSTRACT FROM AUTHOR]

[Display omitted] • Main principles and methods for measuring acoustic cavitation are discussed. • Acoustic wave and system parameters affecting US process efficiency are discussed. • US for agri-food applications improved product quality and extraction efficiency. • US can be combined with conventional agri-food processes to improve operations. • Innovative designs for large-scale US assisted food processing are described. Acoustic cavitation, an intriguing phenomenon resulting from the interaction of sound waves with a liquid medium, has emerged as a promising avenue in agri-food processing, offering opportunities to enhance established processes improving primary production of ingredients and further food processing. This comprehensive review provides an in-depth analysis of the mechanisms, design considerations, challenges and scale-up strategies associated with acoustic cavitation for agri-food applications. The paper starts by elucidating the fundamental principles of acoustic cavitation and its measurement, delving then into the diverse effects of different parameters associated with, the acoustic wave, mechanical design and operation of the ultrasonic system, along with those related to the food matrix. The technological advancements achieved in the design and set-up of ultrasonic reactors addressing limitations during scale up are also discussed. The design, engineering and mathematical modelling of ultrasonic equipment tailored for agri-food applications are explored, along with strategies to maximize cavitation intensity and efficiency in the application of brining, freezing, drying, emulsification, filtration and extraction. Advanced US equipment, such as multi-transducers (tubular resonator, FLOW:WAVE®) and larger processing surface areas through innovative designing (Barbell horn, CascatrodesTM), are one of the most promising strategies to ensure consistency of US operations at industrial scale. This review paper aims to provide valuable insights into harnessing acoustic cavitation's potential for up-scaling applications in food processing via critical examination of current research and advancements, while identifying future directions and opportunities for further research and innovation. [ABSTRACT FROM AUTHOR]

Acoustic cavitation, an intriguing phenomenon resulting from the interaction of sound waves with a liquid medium, has emerged as a promising avenue in agri-food processing, offering opportunities to enhance established processes improving primary production of ingredients and further food processing. This comprehensive review provides an in-depth analysis of the mechanisms, design considerations, challenges and scale-up strategies associated with acoustic cavitation for agri-food applications. The paper starts by elucidating the fundamental principles of acoustic cavitation and its measurement, delving then into the diverse effects of different parameters associated with, the acoustic wave, mechanical design and operation of the ultrasonic system, along with those related to the food matrix. The technological advancements achieved in the design and set-up of ultrasonic reactors addressing limitations during scale up are also discussed. The design, engineering and mathematical modelling of ultrasonic equipment tailored for agri-food applications are explored, along with strategies to maximize cavitation intensity and efficiency in the application of brining, freezing, drying, emulsification, filtration and extraction. Advanced US equipment, such as multi-transducers (tubular resonator, FLOW:WAVE®) and larger processing surface areas through innovative designing (Barbell horn, CascatrodesTM), are one of the most promising strategies to ensure consistency of US operations at industrial scale. This review paper aims to provide valuable insights into harnessing acoustic cavitation's potential for up-scaling applications in food processing via critical examination of current research and advancements, while identifying future directions and opportunities for further research and innovation.

Introduction: This exploratory study delves into the relationships between noncognitive skills and academic achievement within the unique context of STEM schools in Kazakhstan. Methods: Survey data were collected from 109 teachers and 395 students at a STEM secondary school in Kazakhstan. Correlational, regression and path analyses were conducted to explore the relationship between non-cognitive skills and academic performance in Mathematics, Computer Science, Physics, and Chemistry classes. Results: The results showed that out of the 26 skills, eight had a direct impact, 12 had an indirect impact, and six had no impact on academic achievement of students in the four STEM subjects. Discussion: This study is the first to explicitly examine the impact of one group of non-cognitive skills on academic achievement of students in STEM subjects mediated by another group of these skills. Teachers are encouraged to integrate non-cognitive skill development into curricula, tailored to subject-specific needs. Policymakers can use findings to inform equitable skill development policies. [ABSTRACT FROM AUTHOR]

Scientists have designed a liquid that behaves as both a solid and a fluid owing to the presence of tiny gas-filled capsules. An unusual relationship between pressure and volume enables this material to grasp fragile objects. Capsule-filled liquid straddles solid and fluid phases. [ABSTRACT FROM AUTHOR]

Communication with hand gestures plays a significant role in human‐computer interaction by providing an intuitive and natural way for humans to communicate with machines. Ultrasound‐based devices have shown promising results in contactless hand gesture recognition without requiring physical contact. However, it is challenging to fabricate a densely packed wearable ultrasound array. Here, a stretchable ultrasound array is demonstrated with closely packed transducer elements fabricated using surface charge engineering between pre‐charged 1–3 Lead Zirconate Titanate (PZT) composite and thin polyimide film without using a microscope. The array exhibits excellent ultrasound properties with a wide bandwidth (≈57.1%) and high electromechanical coefficient (≈0.75). The ultrasound array can decipher gestures up to 10 cm in distance by using a contactless triboelectric module and identify materials from the time constant of the exponentially decaying impedance based on their triboelectric properties by utilizing the electrostatic induction phase. The newly proposed metric of the areal‐time constant is material‐specific and decreases monotonically from a highly positive human body (1.13 m2 s) to negatively charged polydimethylsiloxane (PDMS) (0.02 m2 s) in the triboelectric series. The capability of the closely packed ultrasound array to detect material along with hand gesture interpretation provides an additional dimension in the next‐generation human‐robot interaction. [ABSTRACT FROM AUTHOR]

The statistical study of droplets in spray evaporation tower has guiding significance for understanding spray evaporation and designing drying tower. The key to spray evaporation simulation is establishing a suitable single droplet evaporation model through experiment. In this paper, a visualized single droplet evaporation experiment platform was established. The mass, diameter, and temperature during the evaporation of 10 % NaCl solution droplets were obtained. The Reaction Engineering Approach model was developed and validated to describe the single droplet evolution process in saline water spray. A comparison calculation between the spray evaporation of pure and saline water was executed, and the distributions of residence time of droplets were obtained. It reveals that droplets with initial diameters less than 117 μm can fully evaporate, and for larger droplets, pure water droplets represent a higher evaporation ratio. About 50 % of the fully evaporated saline droplets stay in the backflow area. Contrast numerical experiments of saline water spray evaporation were performed, concluded that similar performance can be attained with the same gas-liquid ratio, and excessive inlet air will reduce the residence time of droplets. The influence of other operating parameters on the performance was also discussed, providing a comprehensive understanding of the regularity of spray drying. • A visualized single droplet evaporation experiment platform was established. • Effect of flow-induced vibration of cantilever was mitigated by low pass filtering. • REA model for NaCl solution droplets was first established and verified. • Salt particles trapped in circulation was observed in spray drying tower. [ABSTRACT FROM AUTHOR]