Search

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]

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.

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]

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]

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]

[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.

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]

Beer has over 600 flavor compounds and creates a positive tasting experience with acceptable sensory properties, which are essential for the best consumer experience. Spontaneous and mixed-culture fermentation beers, generally classified as sour beers, are gaining popularity compared to typical lager or ale styles, which have dominated in the USA for the last few decades. Unique and acceptable flavor compounds characterize sour beers, but some unfavorable aspects appear in conjunction. One such unfavorable flavor is called "mousy". This description is usually labeled as an unpleasant odor, identifying spoilage of fermented food and beverages. It is related as having the odor of mouse urine, cereal, corn tortilla chips, or freshly baked sour bread. The main compounds responsible for it are N-heterocyclic compounds: 2-acetyltetrahydropyridine, 2-acetyl-1-pyrroline, and 2-ethyltetrahydropyridine. The most common beverages associated with mousy off-flavor are identified in wines, sour beers, other grain-based beverages, and kombucha, which may contain heterofermentative lactic acid bacteria, acetic acid bacteria, and/or yeast/fungus cultures. In particular, the fungal species Brettanomyces bruxellensis are associated with mousy-off flavor occurrence in fermented beverages matrices. However, many factors for N-heterocycle formation are not well-understood. Currently, the research and development of mixed-cultured beer and non/low alcohol beverages (NABLAB) has increased to obtain the highest quality, sensory, functionality, and most notably safety standards, and also to meet consumers' demand for a balanced sourness in these beverages. This paper introduces mousy off-flavor expression in beers and beverages, which occurs in spontaneous or mixed-culture fermentations, with a focus on sour beers due to common inconsistency aspects in fermentation. We discuss and suggest possible pathways of mousy off-flavor development in the beer matrix, which also apply to other fermented beverages, including non/low alcohol drinks, e.g., kombucha and low/nonalcohol beers. Some precautions and modifications may prevent the occurrence of these off-flavor compounds in the beverage matrix: improving raw material quality, adjusting brewing processes, and using specific strains of yeast and bacteria that are less likely to produce the off-flavor. Conceivably, it is clear that spontaneous and mixed culture fermentation is gaining popularity in industrial, craft, and home brewing. The review discusses important elements to identify and understand metabolic pathways, following the prevention of spoilage targeted to off-flavor compounds development in beers and NABLABs.

Navigating the complex pavement system to the circular economy is a multi-dimensional and time-consuming endeavour. Obstructed by lock-in mechanisms of the prevailing "take-make-dispose" linear mode, the current circular economy studies in pavement infrastructure often focus only on one certain type of practice, featured by fragmentation from a system perspective. This study presents the first systematic literature review on the historical development trend of the circular economy concept in the pavement sector based on the collected 59 studies from 2006 to 2022. This review also identifies the soft (regulatory, social/environmental) and hard (technical, market/economic) drivers of- and barriers to the paradigm shift, aiming to coordinate the existing fragmented findings and facilitate the future systemic implementation of the circular economy. The synthesis of the results indicates that the underdeveloped technical guidance and legal system are inhibiting the paradigm shift to the circular economy. Furthermore, the authors argue that the importance of transparent and standardized data along the whole supply chain should be highlighted. Ultimately, this paper suggests that a welcoming environment set by the road authorities is crucial for innovation and entrepreneurship, as well as for constructing a system where stakeholders can confidently take responsibility and initiative. • First systematic review of circular economy transition in pavement engineering. • Identify the soft and hard drivers of- and barriers to the sustainable transition. • Guide towards the future adoption of material passports in the pavement sector. • Call for decentralization and multi-stakeholder engagement in the decision-making process. [ABSTRACT FROM AUTHOR]

Highlights: The boosted heterogeneous interfaces in titanium nitride (TiN) nanotube/polydimethylsiloxane (PDMS) composite contributed to strong polarization loss relaxation ability. The TiN nanotubes/PDMS composite possessed both good impedance matching behavior and strong dielectric loss ability in wide temperature spectrum. The TiN nanotubes/PDMS composite exhibited excellent EMWA performances (effective absorption bandwidth value of 3.23 GHz and minimum reflection loss value of − 44.15 dB) at the varied temperature from 298 to 573 K. Currently, the microwave absorbers usually suffer dreadful electromagnetic wave absorption (EMWA) performance damping at elevated temperature due to impedance mismatching induced by increased conduction loss. Consequently, the development of high-performance EMWA materials with good impedance matching and strong loss ability in wide temperature spectrum has emerged as a top priority. Herein, due to the high melting point, good electrical conductivity, excellent environmental stability, EM coupling effect, and abundant interfaces of titanium nitride (TiN) nanotubes, they were designed based on the controlling kinetic diffusion procedure and Ostwald ripening process. Benefiting from boosted heterogeneous interfaces between TiN nanotubes and polydimethylsiloxane (PDMS), enhanced polarization loss relaxations were created, which could not only improve the depletion efficiency of EMWA, but also contribute to the optimized impedance matching at elevated temperature. Therefore, the TiN nanotubes/PDMS composite showed excellent EMWA performances at varied temperature (298–573 K), while achieved an effective absorption bandwidth (EAB) value of 3.23 GHz and a minimum reflection loss (RLmin) value of − 44.15 dB at 423 K. This study not only clarifies the relationship between dielectric loss capacity (conduction loss and polarization loss) and temperature, but also breaks new ground for EM absorbers in wide temperature spectrum based on interface engineering. [ABSTRACT FROM AUTHOR]

This study shows the link between computer science and applied mathematics. It conducts a dynamics investigation of new root solvers using computer tools and develops a new family of single-step simple root-finding methods. The convergence order of the proposed family of iterative methods is two, according to the convergence analysis carried out using symbolic computation in the computer algebra system CAS-Maple 18. Without further evaluations of a given nonlinear function and its derivatives, a very rapid convergence rate is achieved, demonstrating the remarkable computing efficiency of the novel technique. To determine the simple roots of nonlinear equations, this paper discusses the dynamic analysis of one-parameter families using symbolic computation, computer animation, and multi-precision arithmetic. To choose the best parametric value used in iterative schemes, it implements the parametric and dynamical plane technique using CAS-MATLAB@R2011b. The dynamic evaluation of the methods is also presented utilizing basins of attraction to analyze their convergence behavior. Aside from visualizing iterative processes, this method illustrates not only iterative processes but also gives useful information regarding the convergence of the numerical scheme based on initial guessed values. Some nonlinear problems that arise in science and engineering are used to demonstrate the performance and efficiency of the newly developed method compared to the existing method in the literature. [ABSTRACT FROM AUTHOR]

A newly invented structure called Multi-Fin-based FinFET (M-FinFET) device is a promising candidate for future improvisation of the semiconductor industry. In this article, Multi-channel FinFET (Mch-FinFET) is proposed. A comparative investigation of various DC, analog/linearity attributes is studied for gate length variation and oxide thickness through a Sentaurus TCAD tool. The simulation study concluded that the increased number of channels (= 3no.) has enhanced ION by 409.71% compared to single-channel FinFET. The decreased value of Fin width and Fin height has shown an impressive improvement of sub-threshold swing (SS) and leakage current, which helps achieve a better switching ratio. Mch-FinFET device with lower oxide thickness (Tox=1 nm) enhances the transconductance (Gm), drain conductance (Gd), intrinsic gain (Av), and transconductance gain factor (TGF) by 52.42%, 41.17%, 85.03%, respectively. Various linearity parameters like higher-order harmonics (Gm2 and Gm3), voltage intercepts points (VIP2 and VIP3), and 1-dB compression point has improved by 32.32%, 110.71% 77%, 60.09%, 418.86%, 411.5% respectively gate length of 10 nm. Besides that, a symmetric dual spacer material is introduced to the proposed structure to analyze the importance of spacer engineering. The simulation study reveals that the Mch-FinFET device with HfO2 spacer has improved driving current by 21.42%. The optimization of various short channel effects (SCEs) such as threshold voltage roll-off, sub-threshold swing (SS), and leakage current is reflected in introducing HfO2 spacer material. This detailed study is expected to design low-power RF circuits that would benefit future CMOS technology. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a multidisciplinary analysis of the Fukushima Dai-ichi Nuclear Power Plant accident. Along with the latest observations and simulation studies, we synthesize the time-series and event progressions during the accident across multiple disciplines, including in-plant physics and engineering systems, operators’ actions, emergency responses, meteorology, radionuclide release and transport, land contamination, and health impacts. We identify three key factors that exacerbated the consequences of the accident: (1) the failure of Unit 2 containment venting, (2) the insufficient integration of radiation measurements and meteorology data in the evacuation strategy, and (3) the limited risk assessment and emergency preparedness. We conclude with new research and development directions to improve the resilience of nuclear energy systems and communities, including (1) meteorology-informed proactive venting, (2) machine learning-enabled adaptive evacuation zones, and (3) comprehensive risk-informed emergency planning while leveraging the experience from responses to other disasters.

Summary: Marine activities typically face various risk factors such as marine animal attacks or unexpected collisions. In this paper, we develop underwater smart glasses (USGs) based on visual-tactile fusion for underwater hazard detection in real-time, ensuring operational safety. The proposed USG is composed of the vision module by artificial intelligence (AI)-enabled optical sensing and the tactile module by triboelectric metamaterials-enabled mechanical sensing. The vision module is obtained based on the underwater target detection algorithm you only look once-underwater hazard (YOLO-UH) developed by the dataset to detect toxic marine organisms in the visual field. The tactile module is designed with the kirigami tribo-materials (KTMs) to sensitively detect and warn of collisions outside the visual field. Through numerical simulations, laboratory tests, and real-world experiments, we validated the performance of both modules. The reported USG with its visual-tactile fusion concept enables near-far all-around hazard detection and reduces the danger for divers working underwater.

Existing simplified procedures for evaluating soil liquefaction potential or for estimating excess pore pressures during earthquakes are typically based on undrained cyclic tests performed on saturated soil samples under controlled loading and boundary conditions. Under such conditions, the effect of excess pore pressure (ue) dissipation and redistribution to neighboring soil layers cannot be accounted for. Existing simplified procedures treat liquefiable layers as isolated soil layers without any boundary conditions even if dense and loose layers are very thin, permeable, and adjacent to each other. However, redistribution is likely to increase and decrease ue in the neighboring dense and loose layers respectively. Until now, no procedure short of fully coupled numerical analysis is available to estimate the importance of redistribution. This paper presents an approximate analytical procedure for assessing the effects of ue redistribution in (1) soil layers that would have liquefied if they were undrained, and (2) soil layers that would have not liquefied even if undrained. It is found that a layer that is initially assumed liquefied under undrained conditions might not even liquefy accounting for the ue redistribution to neighboring layers. On the other hand, a layer initially assumed to not liquefy can develop significant ue and can even liquefy due to pore pressure migration from the neighboring layers. Thus, accounting for redistributed ue is important for liquefaction consequence assessment quantification, particularly in systems that span the depth of these effects like deep foundations. Migration of u toward the tip of a pile can reduce its capacity, even if the tip is embedded in a dense sand layer. On the other hand, if redistribution can result in the reduction of ue in initially assumed liquefied layers, risks associated with liquefaction might be avoided. A criterion is also developed to evaluate the thicknesses of a layer below which redistribution could prevent liquefaction even if the layer is deemed liquefied according to the existing liquefaction-triggering procedures. Finally, the proposed procedure is illustrated by application to selected shaking events of centrifuge tests involving liquefaction of layered soil profiles. The predictions from the procedure matched the centrifuge test results reasonably.

This document is a summary of a special issue of the journal "Materials" focused on the topic of surface engineering for wear, corrosion, and erosion prevention in metallic and ceramic materials. The special issue includes twelve scientific papers covering various aspects of the operational performance and properties of metallic and ceramic materials. The papers discuss topics such as the effects of peening treatments, corrosion resistance, tribological behavior, and wear improvement through microstructural modification and the deposition of wear-resistant coatings. The special issue received significant interest from authors and readers, leading to the announcement of a second volume on the same topic. The summary acknowledges the need for experimental data in surface engineering to understand the wear and performance of engineering materials. [Extracted from the article]

An enhanced dung beetle optimization algorithm (EDBO) is proposed for nonlinear optimization problems with multiple constraints in manufacturing. Firstly, the dung beetle rolling phase is improved by removing the worst value interference and coupling the current solution with the optimal solution to each other, while retaining the advantages of the original formulation. Subsequently, to address the problem that the dung beetle dancing phase focuses only on the information of the current solution, which leads to the overly stochastic and inefficient exploration of the problem space, the globally optimal solution is introduced to steer the dung beetle, and a stochastic factor is added to the optimal solution. Finally, the dung beetle foraging phase introduces the Jacobi curve to further enhance the algorithm's ability to jump out of the local optimum and avoid the phenomenon of premature convergence. The performance of EDBO in optimization is tested using the CEC2017 function set, and the significance of the algorithm is verified by the Wilcoxon rank-sum test and the Friedman test. The experimental results show that EDBO has strong optimization-seeking accuracy and optimization-seeking stability. By solving four engineering optimization problems of varying degrees, EDBO has proven to have good adaptability and robustness. [ABSTRACT FROM AUTHOR]

Rockfalls are an important factor affecting underground engineering safety. However, there has been limited progress in understanding and predicting these disasters in the past few years. Therefore, a large-scale three-dimensional experimental simulation apparatus to study failure mechanisms of rockfalls occurring during underground engineering was developed. This apparatus, measuring 4 m × 4 m × 3.3 m in size, can achieve vertical and horizontal symmetric loading. It not only simulates the structure and stress environment of a rock mass but also simulates the stepwise excavation processes involved in underground engineering. A complete simulation experiment of rockfalls in an underground engineering context was performed using this apparatus. Dynamic evolution characteristics of block displacement, temperature, natural vibration frequency, and acoustic emissions occurring during rockfalls were studied during the simulation. These data indicate there are several indicators that could be used to predict rockfalls in underground engineering contexts, leading to better prevention and control. [ABSTRACT FROM AUTHOR]

Anonymous networks, which aim primarily to protect user identities, have gained prominence as tools for enhancing network security and anonymity. Nonetheless, these networks have become a platform for adversarial affairs and sources of suspicious attack traffic. To defend against unpredictable adversaries on the Internet, detecting anonymous network traffic has emerged as a necessity. Many supervised approaches to identify anonymous traffic have harnessed machine learning strategies. However, many require access to engineered datasets and complex architectures to extract the desired information. Due to the resistance of anonymous network traffic to traffic analysis and the scarcity of publicly available datasets, those approaches may need to improve their training efficiency and achieve a higher performance when it comes to anonymous traffic detection. This study utilizes feature engineering techniques to extract pattern information and rank the feature importance of the static traces of anonymous traffic. To leverage these pattern attributes effectively, we developed a reinforcement learning framework that encompasses four key components: states, actions, rewards, and state transitions. A lightweight system is devised to classify anonymous and non-anonymous network traffic. Subsequently, two fine-tuned thresholds are proposed to substitute the traditional labels in a binary classification system. The system will identify anonymous network traffic without reliance on labeled data. The experimental results underscore that the system can identify anonymous traffic with an accuracy rate exceeding 80% (when based on pattern information). [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the state of engineering and design research for sustainable construction. It aims to report the current status and future trends within this dynamic field, combining econometric and content analysis using the Bibliometrix R encapsulation tool. This study reviewed academic journals using bibliometric analysis. We selected articles by searching the Scopus database. Primarily relevant articles were identified and screened. The dataset comprised a substantial compilation of 731 journal articles from 278 sources published between January 2000 and June 2023, which formed the basis of the in-depth analysis. The importance of sustainable construction is gradually gaining more attention, and engineering and design are the significant phases of construction. This research identifies that sustainable construction is nothing in isolation; instead, it warrants the holistic integration of multiple factors, as illustrated in the Sankey diagram. Recycling, durability, life cycle assessment, innovative materials, and energy efficiency have emerged as central themes, reflecting global concern to enhance sustainability, reduce environmental impacts, and optimize resource utilization. This study is a valuable resource for researchers, practitioners, and policymakers, offering guidelines for collaborative efforts towards sustainable development. This paper paves the way for interdisciplinary cooperation and strategic alignment among diverse stakeholders, promoting innovative approaches to sustainable construction. [ABSTRACT FROM AUTHOR]

Diamond layers can help to dissipate the heat generated by high-power semiconductor devices. This effect has now been enhanced by adding layers of materials and engineering their crystal-lattice vibrations to be compatible at the interfaces. Layers engineered to maximize heat flow in semiconductor devices. [ABSTRACT FROM AUTHOR]

An array of robots has been set up so that pushes between them produce movements that do not conform to the usual laws of motion. Fascinating behaviour emerges from these interactions: wave phenomena known as solitons.Wave phenomena in an array of robots. [ABSTRACT FROM AUTHOR]

Proton resonance frequency shift (PRFS) MR thermometry is the most common method used in clinical thermal treatments because of its fast acquisition and high sensitivity to temperature. However, motion is the biggest obstacle in PRFS MR thermometry for monitoring thermal treatment in moving organs. This challenge arises because of the introduction of phase errors into the PRFS calculation through multiple methods, such as image misregistration, susceptibility changes in the magnetic field, and intraframe motion during MRI acquisition. Various approaches for motion correction have been developed for real-time, motion-robust, and volumetric MR thermometry. However, current technologies have inherent trade-offs among volume coverage, processing time, and temperature accuracy. These tradeoffs should be considered and chosen according to the thermal treatment application. In hyperthermia treatment, precise temperature measurements are of increased importance rather than the requirement for exceedingly high temporal resolution. In contrast, ablation procedures require robust temporal resolution to accurately capture a rapid temperature rise. This paper presents a comprehensive review of current cutting-edge MRI techniques for motion-robust MR thermometry, and recommends which techniques are better suited for each thermal treatment. We expect that this study will help discern the selection of motion-robust MR thermometry strategies and inspire the development of motion-robust volumetric MR thermometry for practical use in clinics.

Lithium-ion batteries age continuously during usage due to their characteristics and the influence of various external factors, but as degradation deepens, it can lead to an apparent decrease in battery safety and reliability. Therefore, predicting remaining useful life from the current to end state and preventing possible dangerous incidents are essential for battery health management. A novel method from the perspective of providing aging features reference and balance prognostics speed and precision is proposed. Firstly, the used dataset is preprocessed in many ways. Then, through feature engineering, 79 features are extracted in the dataset from three perspectives: direct, evolution, statistics, and most representative 16 features are filtered to form the final feature set based on the method of combining discretization and importance. Feature engineering is established to find crucial information within the dataset that correlates highly with remaining life and represents most battery recession paths. Finally, the sparse autoencoder and Transformer integrated approach is proposed to build the life prediction model, and it can learn the temporal relationship between the feature set and remaining cycles quickly and accurately. According to the forecasting results of 62 batteries with different aging conditions, the error can reach 7.43% by only using the feature sequence under 30 cycle lengths. For early life prediction, the error of using the first 100 cycles data is at most 2.6%. • A novel aging feature filtering approach is proposed based on feature engineering. • A prediction model is established by using a sparse autoencoder and Transformer. • More accurate RUL and early life of a battery can be predicted by using fewer cycle data. • After the feature engineering presented in this paper, the final aging feature set selected has strong applicability. [ABSTRACT FROM AUTHOR]