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My comments concern the significant errors in the crystallographic part of the commented paper. The diffraction patterns are erroneously analysed. The studied sample contain two phases what can influence on the final results of experiments. [ABSTRACT FROM AUTHOR]

Stereolithography 3D printing and digital masks in sequence are used herein to manufacture 3D µPAD in a monolithic layer of paper within 1 s. Multiple experiments were systematically conducted to further understand the fundamentals and limitation of this manufacturing process, and a novel 3D mixer and a fluorescent chemosensor assay used to determine dopamine concentration at high-alkaline pH conditions were successfully demonstrated. Experiment results showed: (1) the 3D mixer is superior than conventional 2D mixer regarding mixing performance and repeatability, (2) dopamine detection and pH indicator can be realized with fewer operation steps and shorter processing time, (3) a truly 3D µPAD, including multilayer microchannels within a monolithic paper substrate, can be created with fewer reagent loss and higher transportation efficiency, (4) a smallest microchannel, 100 µm wide microchannel, can be successfully created on µPAD. These results clearly proved that this proposed 3D µPAD is not only a novel format of µPAD, but it can also simplify the operation steps for an analytical assay while improving the mixing phenomenon via 3D mixer and using minimum sample volume. [Display omitted] • Using SLA 3D printing and multiple digital masks to manufacture monolithic 3D µPAD. • 3D mixer is superior than 2D mixer regarding mixing performance and repeatability. • A truly 3D µPAD can result fewer reagent loss and higher transportation efficiency. • Dopamine detection and pH indicator can be realized with fewer operation steps. • A smallest microchannel, 100 µm wide microchannel, was successfully created. [ABSTRACT FROM AUTHOR]

Applications of electrochemical detection methods in microfluidic paper-based analytical devices (μPADs) has revolutionized the area of point-of-care (POC) testing towards highly sensitive and selective quantification of various (bio)chemical analytes in a miniaturized, low-coat, rapid, and user-friendly manner. Shortly after the initiation, these relatively new modulations of μPADs, named as electrochemical paper-based analytical devices (ePADs), gained widespread popularity within the POC research community thanks to the inherent advantages of both electrochemical sensing and usage of paper as a suitable substrate for POC testing platforms. Even though general aspects of ePADs such as applications and fabrication techniques, have already been reviewed multiple times in the literature, herein, we intend to provide a critical engineering insight into the area of ePADs by focusing particularly on the practical strategies utilized to enhance their analytical performance (i.e. sensitivity), while maintaining the desired simplicity and efficiency intact. Basically, the discussed strategies are driven by considering the parameters potentially affecting the generated electrochemical signal in the ePADs. Some of these parameters include the type of filter paper, electrode fabrication methods, electrode materials, fluid flow patterns, etc. Besides, the limitations and challenges associated with the development of ePADs are discussed, and further insights and directions for future research in this field are proposed. • Parameters contributing to the performance of electrochemical paper-based analytical devices (ePADs) are outlined. • Strategies on paper substrate, electrode fabrication, fluid flow manipulation to enhance ePADs performance are discussed. • Strategies on device design, hyphenation of analytical procedures, and signal amplification are reviewed. • Perspectives focusing on development of new methodologies to enhance ePADs performance are proposed. [ABSTRACT FROM AUTHOR]

Recently, chalcogenides have attracted much attention as electrocatalysts in hydrogen evolution reaction (HER). However, few studies have been conducted on the electrocatalytic properties of gallium oxides and chalcogenides. In this paper, a Ga 2 XY (X ≠ Y, X, Y=O, S, Se, Te) defect structure doped by non-metal B, C, N, P, Si, and As have been designed. According to the study, the doping of non-metal atoms can significantly enhance their HER properties, the Ga 2 OSe-As Xi -Xi structure and Ga 2 SeTe-Si Xi -NM structure possess intensely excellent HER properties in this study with the Gibbs free energy of 0.01 eV and 0.00 eV, respectively. It is found that the Ga 2 SeTe structure has a more concentrated electron transfer range compared to the Ga 2 OSe structure, leading to a superior HER performance. This work provides a new idea for the study of HER electrocatalytic performance of the Ga 2 XY system, and it is expected to be applied to HER catalysts affordably and efficiently. • Screening of non-metal doped Ga 2 XY defective structures for HER electrocatalytic performance. • The introduction of Ga vacancies significantly enhances the HER performance of Ga 2 XY structures. • The Ga 2 SeTe defective doped structure has a more concentrated electron transfer range compared to the Ga 2 OSe structure. • The As-doped Ga 2 OSe structure and the Si-doped Ga 2 SeTe structure containing Ga defect have a near-zero ΔG H. [ABSTRACT FROM AUTHOR]

Significant efforts are being made to produce biofuels to replace fossil fuels and address the issues caused by global climate change. Due to its potential better conversion efficiency to useable power, decreased emission of pollutants, and high energy density, H 2 is one of the prospective options that are seen as a desirable future clean energy carrier. Although there are numerous technologies available for producing H 2 , this review concentrates on fermentative H 2 production techniques, their drawbacks, and current developments. While being a promising method, fermentative strategies still have several drawbacks, including low H 2 production yields. Many approaches have been used to address these issues; among them, the field of metabolic pathway engineering has made enormous strides. To improve H 2 generation, this paper reviewed and discussed several metabolic pathways and modified strains. As well as the challenges involved in H 2 scale-up from a laboratory setting to a commercial scale. • Biohydrogen production via fermentative methods is reviewed and elaborated. • Metabolic engineering methods are proposed for scaling up fermentative biohydrogen. • Challenges of fermentative biohydrogen production and potential solutions. • Bionanotechnology and modified strains enhanced biohydrogen production. [ABSTRACT FROM AUTHOR]

[Display omitted] • Ru nanoparticles (NPs) anchored on heterointerface of 1 T-MoS 2 and carbon paper (Ru@1T-MoS 2 /CFP) is designed. • The Ru 2 @MoS 2 -85 %/CFP presents superb HER performance with overpotential of 19 mV and a low Tafel slope of 29 mV dec–1. • The strong interaction between 1 T-MoS 2 and OH* makes OH* migrate from Ru to MoS 2 , alleviating the poisoning of Ru sites. • Ru NPs close to the interface of 1 T-MoS2/CFP can achieve an optimal hydrogen adsorption energy for high HER performance. Ru is considered as highly efficient Pt-free catalyst in hydrogen evolution reaction (HER) due to its fast water dissociation kinetics. However, its strong affinity to OH* and H* greatly limits its HER performance. Herein, Ru nanoparticles (NPs) anchored on the mixed substrates of MoS 2 (different 1T contents) and carbon fiber paper (Ru@MoS 2 -1T%/CFP) is designed and synthesized with three kinds of interfaces (MoS 2 /CFP, Ru/CFP, and Ru/MoS 2) as excellent electrocatalyst for HER. Particularly, the Ru 2 @MoS 2 -85%/CFP presents superb HER performance with overpotential of 19 mV at 10 mA cm−2, a low Tafel slope of 29 mV dec–1 and stability of up to 10 days, outperforming reported Ru-based catalysts and Pt/C. The experimental and DFT studies disclose that the hydroxyl migration-assisted dissociation of water and interface-engineering desorption of H 2 process, occurring at the mutually hybridized interface sites of Ru/MoS 2 /CFP, simultaneously speed Volmer/Tafel processes. Compared to Ru, the stronger interaction of 1T-MoS 2 to OH* makes OH* migrate from Ru to MoS 2 , alleviating the poisoning of Ru sites. Also, only Ru NPs close to the interface of 1T-MoS 2 /CFP can achieve an optimal hydrogen adsorption energy for high HER performance. This designed strategy provides a novel idea for preparing supported Ru catalysts for excellent HER performance. [ABSTRACT FROM AUTHOR]

• A light-weight UAV formation control algorithm based on inner and outer loop control is proposed. • An improving ADRC based on prescribed-time extended state observer and broad learning is proposed. • The broad learning is used to dynamically optimize the weights according to the different wind disturbances. Considering that the formation composed of light-weight UAVs is highly susceptible to the interference, which may from the changes in the external environment and the uncertainty of system, a formation control method based on the inner and outer loops is proposed. In the inner loop, the single UAV stable flight can be achieved via controlling the state variables of UAV angles. In the outer loop, we can achieve multi-UAVs cooperative flight through the leader-follower strategy. In this paper, we mainly focus on the stability control of each member UAV, which is the basic composition of formation control. For this purpose, an optimized active disturbance rejection control (ADRC) is proposed which combined an improved prescribed-time extended state observer (PTESO) and weight optimization module based on broad learning. In this way, the single UAV can dynamically adjust the control weights according to different wind degrees, so that the influence of environmental changes on the control system is reduced. Then, the effectiveness and superiority of the proposed formation control methods are verified by simulations. The stability enhancement control method proposed in this paper provides a new and effective theoretical support for the actual control of the light-weight UAV formation, which has a well engineering application prospect. [ABSTRACT FROM AUTHOR]

Paper based lateral flow assays (LFAs) have achieved grand success in point-of-care diagnostics; nevertheless, they are still facing challenges. This review outlines current major challenges in LFAs addressing particularly sensitivity, specificity, selectivity, reproducibility and multiplex capability. Potential solutions to meet these challenges are proposed in the areas of chemistry, nanobiotechnology, and engineering. Specifically, approaches for enhancement of sensitivity through introducing alternative nanomaterials and new signal amplification strategies, improvement of specificity through seeking for more efficient recognition molecules, optimization of selectivity by treatment of the paper surface to achieve anti-fouling sensing interface, and development of improved controls on environment, raw materials and device engineering for better reproducibility, are reviewed and discussed. The success in these perspectives will help to pave the way for the development of more efficient LFAs for future research and applications in clinical settings, food safety and quality, environment monitoring, biosecurity, etc. • Paper lateral flow assays (LFAs) with different formats and signal readout. • Address challenges in sensitivity, specificity, selectivity, reproducibility, multiplexing. • Sensitivity enhancement by nanotechnology and biotechnology. • Chemical and engineering strategies to improve the analytical performance of LFAs. [ABSTRACT FROM AUTHOR]

The USA is confronted with three epic-size problems: (1) the need for production of energy on a scale that meets the current and future needs of the nation, (2) the need to confront the climate crisis head-on by only producing renewable, green energy, that is 100% emission-free, and (3) the need to forever forestall the eruption of the Yellowstone Supervolcano. This paper offers both a provable practical, novel solution, and a thought experiment, to simultaneously solve all of the above stated problems. Through a new copper-based engineering approach on an unprecedented scale, this paper proposes a safe means to draw up the mighty energy reserve of the Yellowstone Supervolcano from within the Earth, to superheat steam for spinning turbines at sufficient speed and on a sufficient scale, in order to power the entire USA. The proposed, single, multi-redundant facility utilizes the star topology in a grid array pattern to accomplish this. Over time, bleed-off of sufficient energy could potentially forestall this Supervolcano from ever erupting again. With obvious importance to our planet and the research community alike, COMSOL simulation demonstrates and proves the solution proposed herein, to bring vast amounts of green, emission-free energy to the planet's surface for utilization. Well over 11 Quadrillion Watt hours of electrical energy generated over the course of one full year, to meet the current and future needs of the USA is shown to be practical. Going beyond other current and past research efforts, this methodology offers tremendous benefits, potentially on a planetary scale. • New and revolutionary engineering approach to clean energy production is proposed. • Scalable energy is produced using copper cylinders and computer science techniques. • Energy production from the Yellowstone Supervolcano prevents its future eruption. [ABSTRACT FROM AUTHOR]

Abstract Practicing engineers and undergraduate engineering and technology students seek and use information differently within the research and design process. This paper presents the results of a survey conducted by librarians at Purdue University and information specialists at Caterpillar Inc. to analyze self-reported information habits and challenges of both user populations. The authors created surveys containing similar questions for each user group, using a framework that asked participants to think about their information needs and use during a recent engineering project. The survey questions discerned users' confidence in their information abilities, and their preferences and barriers for finding and using information. The results of this study reveal differences between students and engineers and are informative for both academic and corporate librarians. Key findings affirm previous research that novices are more confident in their abilities than experts. Additionally, the findings suggest undergraduates prefer quick, easy to digest content like online videos and news, while engineers are more likely to learn by consulting a colleague or other subject expert, and through reading journals and trade literature. While students rated themselves as more confident information users, engineers reported working in a more complex information landscape, which includes internal document management systems and numerous places to look for technical information. Findings within this paper can inform the development of information literacy curricula that better parallels the corporate environment, and can give corporate librarians insight into how recent graduates may expect to interact with information in a new work environment. [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]

[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]

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]

Accelerating the charge transfer (CT) capability of photocatalysts is an efficient way to improve the overall photocatalytic performance, yet the precise regulation of CT in photocatalyst systems is still lacking. In this paper, a series of hybrid photocatalysts composed of graphitic carbon nitride (CN) and various viologens (V) were prepared for the photocatalytic hydrogen evolution (PHE) from water splitting under visible-light irradiation. Considering the fixed energy structure of CN , the different electron-withdrawing substituents were introduced to engineer the band structure of V delicately and modulate the CT process between CN and V. It was shown that all the hybrid photocatalysts CN-x%V y exhibited higher photocatalytic performance, of which CN–1%V 3 , possessing the strongest electron withdrawing group (-NO 2), demonstrated the best PHE performance (3572.3 μmol g−1 h−1), exceeding 29 times over the unmodified CN. It was proposed that the introduction of V can optimize the interfacial photogenerated electron transfer (CN → V →Pt) of the whole photocatalytic system effectively. We highlighted the V as an efficient chemical segment to modify semiconductors toward enhanced activity due to the following unique characteristics: (i) the unique redox ability, (ii) the easy synthetic methods for controlling the band structures precisely, and (iii) the inherent positively charged feature. This work provides a deep understanding of CT for the rational design of high-performance photocatalysts through band engineering. [Display omitted] • Hybrid photocatalysts CN-x%V y composed of g -C 3 N 4 (CN) and viologens (V) were prepared. • The photocatalytic performance of CN-x%V y catalysts was dependent on the band engineering of V. • Charge transfer of CN-x%V y catalysts was precisely regulated. • CN–1%V 3 exhibited a 29 times higher photocatalytic H 2 evolution rate than the unmodified CN. [ABSTRACT FROM AUTHOR]

For engineering applications, an innovative design can be developed by reusing and modifying existing models with similar features and manufacturing properties, and accurately searching CAD models has become a valuable knowledge acquisition technique in product design. Although there are many retrieval technologies, most methods focus on the global shape of models. In this paper, a CAD model retrieval framework based on local feature segmentation is proposed, offering users a free-form way to choose any subpart as a query from a given model. First, a novel model segmentation method based on the vertex-neighbor extension is proposed to divide a target CAD model into overlapping local features. The chosen subpart with arbitrary boundaries is also successfully described for retrieval by combining local features. Then, a composite descriptor considering both shapes and attribute characteristics is established, which is proved effective for distinguishing local features. Finally, the problem of matching free-form queries to CAD models in the dataset is transformed into descriptor set measurement and is implemented using the bag-of-word algorithm. The proposed model retrieval approach outperforms many existing approaches in matching performance and provides a user-friendly query mode. [Display omitted] • Solve the partial CAD models retrieval (B-rep) to support engineering reuse. • Introduce a innovative model segmentation method to generate local features. • Apply the bag-of-word algorithm to measure the similarity. • Allow arbitrary queries acquiring from free-form interaction with the CAD system. [ABSTRACT FROM AUTHOR]

Grouting lifting is the effective approach for treating differential settlement of existing tunnels, but it easily deteriorates the tunnel structure again and seriously threatens the operational safety of urban rail transit. Aiming to solve the above problem, this paper considers the interaction between the tunnel and the foundation, and establishes a series of analytical solutions of the stress, strain, axial force, and bending moment of the tunnel under single point grouting. Based on the characteristics of shield tunnel correction engineering and the analytical solution under single point grouting, the equation of the internal force and deformation of tunnel structure under multi-point grouting considering different grouting angles and different strata is obtained. The grouting correction engineering case for Shenzhen Metro Line 1 shows that the analytical solution to the displacement of the tunnel structure is well matched with the measured displacement of the tunnel structure is well matched with the measured data of the case and the difference is less than 15 %. The parameter analysis results indicate that the main loaded area of the tunnel under single point grouting is related to the stratum, and the main loaded area of the soft soil stratum is - 42. 4 ° + 42. 4 ° around the grouting point. Convergence deformation of the tunnel under multi-point grouting reaches the maximum value at the diameter passing through the center of the grouting zone and the minimum value at the diameter which is perpendicular to the former. Under the condition of limiting horizontal convergence deformation, the reasonable grouting angle range of the multi-point grouting zone is about 120 ° 150 °. This paper provides a theoretical basis that can immediately evaluate the mechanical structure performance and to determine whether to impose greater pressure when the tunnel is continuously lifted. [ABSTRACT FROM AUTHOR]

In this paper, we present a detailed study of the effect of WO 3 and Ni films in the presence of BP and BlueP-MDC nanomaterial layers on the sensitivity of a surface plasmon resonance (SPR) following the change in refractive index of the detection medium. Our nanostructure is based on the Kretschmann multilayer configuration composed of a BK7 prism, an Ag metallic film deposited on ZnO and a hybrid multilayer composed of WO 3 /Ni/2D-nanomaterials. In the first part, the study aims to optimize the effect of the thicknesses of the layers of the nanomaterials of the structure on the surface plasmon resonance. We obtain an angular sensitivity of the order of 480 deg/RIU for a figure of merit of 177.11 RIU−1 at the wavelength of 633 nm. These values are respectively 300% and 342% higher than those of a conventional SPR sensor (BK7/Ag/SM). After modeling the SPR biosensor studied in this paper, we showed how the plasmon resonance and sensitivity of the SPR sensor are affected by the refractive index of liquid biochemical elements. The proposed SPR structure can be used in highly sensitive urinary glucose detection and medical analysis applications. [Display omitted] • Engineering and optimization of the SPR device highly sensitive for biomedical processing and detection. • The optimized nanostructure shows the highest sensitivity 480 deg/RIU. • Highest sensitivity enhancement 300% for SPR biosensor. • This study demonstrated high FoM of 177.11 RIU-1 with an enhancement of 342%. • Detection of glucose concentration in human urine samples to test the glucose levels of people with diabetes. [ABSTRACT FROM AUTHOR]

Resilient performance is a crucial characteristic of complex socio-technical systems, enabling them to sustain essential functionality during changing or stressful conditions. Resilience Engineering (RE), a sub-field of safety research, focuses on this perspective of resilience. RE emphasises its "cornerstone model", presenting the RE system goals of "anticipating, monitoring, responding and learning". The cornerstone of learning remains fragmented and undertheorized in the existing literature. This paper aims to enrich RE research and its practical implications by developing a nuanced and comprehensive understanding of the role of learning from resilient performance. To achieve this aim, a scoping literature review was conducted to assess how learning is conceptualised in the RE literature and the theoretical foundations on which previous work rest. The main findings show that RE researchers view learning as the process of understanding the system, sharing knowledge, and re-designing system properties. The application of established learning theories is limited. This paper contributes to research by proposing an organisational process for the RE cornerstone of learning, paving the way for deeper discussions in future studies about learning from resilient performance within complex socio-technical systems. • Organisational learning is a core requirement for the safety and optimal functionality of complex socio-technical systems. • Conceptualizations of organisational learning from resilient performance are under-developed. • The literature review shows that learning from resilient performance is fragmented and undertheorized. • The research lays groundwork for future discussions on learning from resilient performance • A refined organisational process for learning within RE, grounded in established learning theory, is proposed. [ABSTRACT FROM AUTHOR]

We report on our success stories in developing and using Model-Driven Engineering (MDE) tools for information systems on real-world projects within different application domains. It is necessary that we ensure the extensibility and adaptability of code generators if we want to reuse them for different domains. Up to now, research on reusing software has been mainly conducted in the software product line community but rarely discussed in the context of code generators. This paper introduces the generation framework MontiGem and shows how it has been used and evolved within five different research and industry projects in the domains of financial management, IoT, energy management, privacy policy, and wind turbine engineering. We have developed the code generator within the first project and further refined it with each of the following projects. This paper describes the projects, shows how MDE helped us in the software engineering process, and discusses the lessons we learned. These examples show how MDE techniques can be successfully applied to the development of information systems in practice, although further requirements have been met over time. [ABSTRACT FROM AUTHOR]

Chatbots are becoming a common component of many types of software systems. But they are typically developed as a side feature using ad-hoc tools and custom integrations. Moreover, current frameworks are efficient only when designing simple chatbot applications while they still require advanced technical knowledge to define complex interactions and are difficult to evolve along with the company needs. In addition, the deployment of a chatbot application usually requires a deep understanding of the targeted platforms, especially back-end connections, increasing the development and maintenance costs. In this paper, we discuss our experiences building, evolving and distributing the Xatkit framework. Xatkit is a model-based framework built around a Domain-Specific Language to define chatbots (and voicebots and bots in general) in a platform-independent way. Xatkit also comes with a runtime engine that automatically deploys the chatbot application and manages the defined conversation logic over the platforms of choice. Xatkit has significantly evolved since its initial release. This paper focuses on describing the evolution and the reasons (technical and non-technical) that triggered them. We believe our lessons learned can be useful to any other initiative trying to build a successful industrial-level chatbot platform, and in general, any type of model-based solution. • Model-driven engineering can be successfully applied to new domains such as AI-based software. • Model-driven tools have limitations when it comes to developing industrial-strength solutions. • Commercial success of model-driven based approaches depends on numerous factors beyond technical ones. [ABSTRACT FROM AUTHOR]

Single-atom catalysts have attracted extensive attention due to their excellent performance and high atom utilization. This paper provides a full review of examples based on defect engineering in the preparation of SACs. Furthermore, this paper presents a unique perspective on the synergistic mechanism of single atoms and defects during biomass conversion reactions. [Display omitted] • Defect engineering is beneficial for the preparation of single-atom catalysts. • The synergistic mechanism of defects and single atoms is summarized. • Combination of single atoms and defects facilitates biomass conversion reactions. • Single-atom catalysts in biomass conversion reactions is still challenging. Single-atom catalysts (SACs) have attracted intensive attention due to their highest atom utilization, especially for expensive noble metal catalysts. This article provides a comprehensive review of defect engineering in the preparation of SACs. A unique view of the synergy of single atoms and defects during biomass conversion reactions is presented. The role of defects on SACs in biomass conversion reactions is discussed. The adsorption activation mechanism, local promotion mechanism, active site renewal mechanism, and selective recognition mechanism of defects are proposed and explained. Through the classification and in-depth analysis of typical biomass conversion reactions using SACs, the promising potential of defect engineering in broader biomass upgrading is illustrated. It is hoped that this paper provides guidance for the rational design of SACs and their application in a wide range of systematic biomass conversions. [ABSTRACT FROM AUTHOR]

This review paper presents the status of piezoelectric-based micro-blowers/pumps used in various applications. Many researchers have proposed different models, changes, and advanced technologies of micro-blowers/pumps for improved system efficiency. These devices are used for fulfilling the requirement of controlled and accurate transfer of fluids (air, water, oil, etc.). Various technologies have been developed and implemented to reduce micro-blower size, power consumption, and system efficiency. A few studies have focused on piezoelectric-based micro-blowers/pumps in various applications. The present paper gives a detailed review of the development of piezoelectric based micro-blowers/pumps, their design configurations, and their applications such as electronic cooling, fuel cell, biomedical, chemistry, precision controlling, drug delivery and chemistry applications. The paper also focused on key futures of micro-blowers/pumps such as working principles, construction, and performance parameters. Significant findings and observations have been reported. This review assists researchers and industries who work on piezoelectric-based systems by providing huge information within an article so that they can make systems that are more efficient. [Display omitted] • Micro-blowers/pumps are extensively reviewed for different engineering applications. • Their design configurations, sizes, components, categories and materials are assessed. • Working concept, construction, input and performance parameters of micro-blowers/pumps are discussed. • Various applications such as electronic cooling, fuel cell, biomedical, chemistry, drug delivery were reviewed. • Piezoelectric pump/blower could deliver the fluid with a flow rate ranging from 5.8 × 10−5 to 30.8 cm3/s. [ABSTRACT FROM AUTHOR]

Engineering failure investigations seek to reconstruct the actual causes of major engineering failures. The investigators need to establish the existence of certain past events and the actual causal relationships that these events bear to the failures in question. In this paper, I examine one method for reconstructing the actual causes of failure events, which I call "feature dependence". The basic idea of feature dependence is that some features of an event are informative about the features of its causes; therefore, the investigators can use the features of a known failure event to reconstruct details of its causes. I make explicit the structure of feature dependence and the evidential basis of its key premises, and show how feature dependence works in the investigation of the American Airlines Flight 191 accident. [ABSTRACT FROM AUTHOR]

Silicone resins, filled with phosphates and other oxide fillers, have been recently proposed as feedstock for the manufacturing of scaffolds with a composition resembling that of commercial Biosilicate® glass-ceramics. Silicones and engineered fillers enable the preparation of novel carbon-containing Biosilicate-based composites and, fundamentally, the easy application of additive manufacturing technologies. After successful demonstration of the applicability of direct ink writing of silicone-based pastes, the present paper is dedicated to preparation of highly porous scaffolds obtained by masked stereolithography, starting from a simple blend of silicone resin with commercial photocurable acrylates. Deviations in the desired phase assemblage were corrected by calibration of the silicone/fillers ratio. The more advanced printing technology, combined with ceramic transformation, allowed fabrication of scaffolds with a complex geometry and a distinctive control of overall porosity. [ABSTRACT FROM AUTHOR]

• Data-centric water engineering is emerging as a new paradigm for water research and practice. • The data pipeline is powered by AI for knowledge and insight extraction from data. • The new paradigm embraces three principles – data, integration and decision making. Artificial intelligence (AI) is expected to transform many scientific disciplines, with the potential to significantly accelerate scientific discovery. This perspective calls for the development of data-centric water engineering to tackle water challenges in a changing world. Building on the historical evolution of water engineering from empirical and theoretical paradigms to the current computational paradigm, we argue that a fourth paradigm, i.e., data-centric water engineering, is emerging driven by recent AI advances. Here we define a new framework for data-centric water engineering in which data are transformed into knowledge and insight through a data pipeline powered by AI technologies. It is proposed that data-centric water engineering embraces three principles – data-first, integration and decision making. We envision that the development of data-centric water engineering needs an interdisciplinary research community, a shift in mindset and culture in the academia and water industry, and an ethical and risk framework to guide the development and application of AI. We hope this paper could inspire research and development that will accelerate the paradigm shift towards data-centric water engineering in the water sector and fundamentally transform the planning and management of water infrastructure. [Display omitted] [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]

• By controlling the concentration of Li-ions, the phase composition of high entropy oxide (HEO) can be regulated. • A high concentration of point defects (i.e., oxygen vacancies) and extended defects (such as dislocations and stacking faults) can be introduced into the spinel phase. • Successful control over defect engineering in spinel-rocksalt dual-phase HEO has been achieved through phase-selective regulation. • The incorporation of an intergrown interface improves the conductivity and structural stability of electrode materials. This paper presents the effectiveness of phase-selective regulation in the Mg-Co-Ni-Mn-Zn-O-based spinel-rocksalt dual-phase high entropy oxide (HEO) for achieving novel electrochemical properties. The key factor distinguishing spinel from rocksalt in response to Li-ion incorporation is the separation of ions at tetrahedral sites, which offers greater structural adjustability and enables incoordinate defect regulation of individual phases. The optimized Li incorporation facilitates a high concentration of point defects (specifically oxygen vacancies) and appropriately extended defects (such as dislocations and stacking fault) in the spinel phase, accompanied by a spinel-rocksalt intergrown interface. These exquisite adjustments improve the conductivity, structural stability, and specific capacity of the HEO electrode material. Nevertheless, the ever-increasing Li content triggers the transformation from a spinel-rocksalt dual-phase HEO to a single-phase rocksalt HEO, resulting in serious lattice distortion and excessive defects that hinder the efficient transport of Li-ion, ultimately leading to unsatisfactory electrochemical performance. Thus, phase-selective engineering facilitates the determination of appropriate defects that synergistically interact with the intergrown interface and the dual-phase structure. The optimized HEO20 exhibits outstanding electrochemical performance, with a high initial discharge capacity of 2049.8 mAh g−1 at 0.1C and a high-rate capability of 502.9 mAh g−1 at 2C. Moreover, it demonstrates remarkable battery capacity retention of 99.5% after 1300 cycles at 0.5C and a stable cycle performance of 717.3 mAh g−1. [ABSTRACT FROM AUTHOR]

• 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]

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]

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]

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]

In this paper, a polarization-independent chain Huygens' metasurface is proposed. It is polarization multiplexing of two chain structures. Owing to the polarization-independent property, it can be conveniently applied to design double-layer dual-polarized transmitarray antennas (TAAs) with low profile. The measured results show that, for the designed low-profile dual-polarized TAA, its peak gain is 29.7 dBi at 28 GHz with aperture efficiency of 48.9 %, and the 3-dB gain bandwidths is 8.8 %. With the F/D ratio of only 0.36, the proposed low-profile dual-polarized TAA based on the chain Huygens' metasurface has critical applications in antenna engineering. [ABSTRACT FROM AUTHOR]

• ENEN creation: historical context. • The nuclear engineering academic offer today. • Education in nuclear in an international perspective. • ENEN contributions to nuclear education (international mobility and EMSNE). • Nuclear applications spectrum and related ENEN prospects. The European Nuclear Engineering Network Association (ENEN), later named European Nuclear Education Network, was created 20 years ago after concerns had been expressed by the nuclear industrial sector about the recruitment of qualified workforce. Networking between European universities and research centers appeared as a tool to attract more students in nuclear engineering by offering them various harmonized curricula and enhanced learning opportunities in terms of knowledge, skills and attitudes (KSA). This was achieved by developing new programs in areas such as radiochemistry, radiation protection, nuclear safety engineering, geological disposal and radioactive waste management, and by offering important mobility opportunities for engineering education and training. Through its successive Framework Programs, the European Commission supplied an important financial support to ENEN projects: besides the mobility actions, around 40 thematic projects enabled to develop new products such as well-coordinated programs, learning platforms, and distance learning courses. The paper concludes with suggestions for the future. A suggested area for new inter-university co-operations is the strengthening of the academic programs in the field of the global energy approach for the benefit of decarbonization. This would involve courses on technical, economic and environmental aspects of the energy mix. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning (TLS) has gained prominence as a deformation monitoring technology in various fields such as bridges and tunnels. This paper offers a comprehensive overview of TLS-based deformation monitoring, covering measurement principles, stochastic models, registration, change detection, and practical applications. We systematically classify, detail, and compare methods for change detection, registration, and georeferencing, shedding light on their strengths, weaknesses, and applicability. The paper also provides an extensive comparison of deformation detection techniques for structures like bridges and tunnels, as well as ground deformation in landslides and unstable rock masses. Challenges related to achieving high-precision, reliable, and efficient deformation monitoring are discussed, and avenues for future research are outlined. Emphasis is placed on the importance of measurement planning to ensure data accuracy and support informed decision-making, particularly for large or spatially constrained structures. Future research should explore leveraging geometric constraints for more accurate change detection and enhancing deformation analysis reliability. • TLS-based deformation monitoring shows commonalities and specific characteristics across scenarios. • Multi-epoch point cloud comparison is essential for all types of deformation monitoring applications. • Measurement planning is crucial for accurate and reliable monitoring in complex structures. • Future research should leverage geometric constraints for precise analysis of deformation. • Research is needed to accurately quantify deformations in monitoring irregular ground surfaces. [ABSTRACT FROM AUTHOR]

The secure operation of offshore wind turbines is at risk due to local scour. To mitigate this risk, effective countermeasures can be implemented to either halt or slow the progression of scour. In this paper, a thorough review of over one hundred and fifty references has been conducted to summarize the latest advancements in preventing scouring for offshore wind turbine monopile foundations. The scour countermeasures have been categorized into three groups based on the mechanisms that influence scouring: armouring protection, flow-altering protection, and comprehensive protection. Additionally, a comprehensive overview of more than twenty scour countermeasures has been provided, including commonly used measures such as riprap, experimental measures like collars that have demonstrated effectiveness, and measures with potential for future development such as biological protection. Furthermore, a comprehensive comparison and evaluation of these scour countermeasures has been conducted, taking into account factors such as effectiveness of protection, adaptability, cost, environmental impact, and additional effects. The strengths and weaknesses of these scour countermeasures have been thoroughly analyzed. The research presented in this paper serves as a valuable reference for selecting appropriate scour countermeasures in practical engineering applications. [ABSTRACT FROM AUTHOR]

With the scale and dimension of engineering optimization and real-world problems increasing, it will be difficult to find the optimum solutions. This paper proposes a novel people identity attributes-based heuristic technology, named the People Identity Attributes-based Information-learning Search Optimization (ISO), inspired by people's psychological assessment and learning behaviors for information resources based on the social status-based identity attribute and the self-survival demands-based identity attribute. For the former, a four-level information delivery mechanism is constructed through leader, manager, executor, and freelancer, emphasizing global optimization. For the latter, the staged transforming model based on random accumulation and directed induction behaviors are constructed according to self-survival demands, emphasizing global optimization and exploration. The dual identity attributes-based search strategy emphasizes psychological assessment and selection for the information loss, including the learning behavior for two kinds of information resources with different identity attributes, and the recovering behavior for the information loss by constructing the information resilience equation, which emphasizes local optimization and exploitation. This paper qualitatively analyzes the swarm behavior, search history, and the exploration and exploitation capabilities of ISO. The optimization performances are quantitatively analyzed for ISO and 9 competitive algorithms on 39 benchmark tests, including the convergence, solution accuracy, robustness, sensitivity, significance, statistical investigation-based Wilcoxon test and Friedman test. The scalability of ISO is investigated on CEC2017 (30Dim, 50Dim, 100Dim) and the latest CEC2022 (10Dim, 20Dim) suites. The results reveal that compared to other competitive algorithms, ISO possesses best computing performance with ranking the first in all competitors. In addition, the proposed ISO and 12 competitors consider 10 constraint engineering optimization problems and the real application of path planning with multiple obstacles, suggesting that ISO possesses significant optimization performance. [ABSTRACT FROM AUTHOR]

Spreading and absorption of small liquid drops on porous substrates is of interest in a number of fields ranging from additive manufacturing and composite processing to inkjet printing. In inkjet printing, spreading and absorption processes determine the final area of a printed dot, which is decisive for print quality in terms of coverage and resolution. However, it is not fully understood how substrate and liquid properties influence the involved physical processes and the resultant printed dot area. In this work, the printed dot area of overall 140 paper-liquid pairings representative for the operational window of an inkjet printer is evaluated. The results are explained by a simple model including spreading, absorption, and evaporation. The surface tension and viscosity of the liquids, as well as the pore size and polarity of the substrates were varied systematically to represent the range of uncoated paper-liquid pairings applicable for inkjet printing. Results show that the printed dot area mainly depends on the wettability of the liquid-substrate pairing followed by penetration speed. Evaporation and volume reduction due to roughness filling had little impact. The modeling results are in line with empirical observations showing that the dot area is closely related to the contact angle. [Display omitted] • Evaluation of inkjet printed dots of 140 uncoated paper-liquid pairings. • Substrates and liquids were tailored to cover operation window of an inkjet printer. • Inkjet printed dot area is simulated from spreading, absorption and evaporation. • The printed dot area depends predominantly on the wetting interaction. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) have emerged as a promising solution for effectively handling non-Euclidean data in construction, including building information models (BIM) and scanned point clouds. However, despite their potential, there is a lack of comprehensive scholarly work providing a holistic understanding of the application of GNNs in the construction domain. This paper addresses this gap by conducting a thorough review of 34 publications on GNNs in construction, presenting a comprehensive overview of the current research landscape. By analyzing the existing literature, this paper aims to identify opportunities and challenges for further advancing the application of GNNs in construction. The findings from this review shed light on diverse approaches for constructing graph data from common construction data types and demonstrate the significant potential of GNNs for the industry. Moreover, this paper contributes to the existing body of knowledge by increasing awareness of the current state of GNNs in the construction industry and offering practical recommendations to overcome challenges in real-world practice. [Display omitted] • A systematic review of GNNs research and applications in the construction industry • Identification of GNNs potential in handling the non-Euclidean data commonly found in construction activities. • Various approaches to developing graph data from common data types in construction • Discussion of current applications and potential of GNNs in construction, as well as its challenges and opportunities. [ABSTRACT FROM AUTHOR]

Our goal is to automatically generate CAD 3D models from 2D sketches as part of a design chain where models should be procedural, containing features arranged in a model tree and linked to suitable datums. Current procedural models capture much about the design intent and are easy to edit, but must be created from scratch during the detailed design state—given conceptual sketches as used by designers in the early part of the design process, current sketch-based modeling approaches only output explicit models. Thus, we describe an approach to extract high-level information directly from 2D engineering wireframe sketches and use it to complete a CSG feature tree, which serves as a model tree for a procedural 3D CAD model. Our method extracts procedural model information directly from 2D sketches in the form of a set of features, plus a set of datums and relationships between these features. We detect and analyze features of 2D sketches in isolation, and define the CSG feature tree by the parent–child relationships between features, and combine this information to obtain a complete and consistent CSG feature tree that can be transferred to a 3D modeler, which reconstructs the model. This paper focuses on how to extract the feature datums and the extrusion operation from an input 2D sketch. [Display omitted] • We create a complete CAD model tree from a vectorized sketch of a polyhedral shape • The method extracts the cross-section and auxiliary reference system of each feature • The approach works when line drawing is a connected graph or disconnected subgraphs • Output serves as proof of concept to demonstrate that it can build a procedural model [ABSTRACT FROM AUTHOR]

Aerostatic bearings have been employed as an essential precision engineering element and enabling technology in numerous applications requiring precision and ultraprecision motions. This review paper aims at presenting the state-of-the-art in aerostatic bearings research and development with the emphasis on analytical and computational approaches for design and optimization of bearing performance, and further critically reviewing their future research directions and development trends in the coming decade and beyond. The paper is concluded with the discussion on the future trends and challenges in the aerostatic bearings research, and their applications and potential in precision engineering industries. [ABSTRACT FROM AUTHOR]

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]

• K.S Bfr can store Cd2+ in protein cavities in vitro. • The engineering bacteria with K.S Bfr can enhance the removal of Cd2+ from water. • Bacterioferritin up-regulates the ribosomal metabolic pathway. Cadmium can enter water through various industrial pollutants, posing serious threats to the ecological environment and human health. This study unveiled an effective method for removing Cd2+ from water by introducing bacterioferritin, known as Candidatus Kuenenia stuttgartiensis (K.S Bfr), into engineering bacteria (EB) to facilitate metal ion removal via K.S Bfr storage. Additionally, the paper explores the mechanism behind Cd2+ removal by EB using K.S Bfr. The binding kinetics revealed that EB's Cd2+ binding capacity was significantly higher than that of wild bacteria, and Cd2+ was found to enter the protein cavity of K.S Bfr in vitro. When exposed to Cd2+ concentrations ranging from 0 to 2.5 mM, EB demonstrated a survival rate above 70 %, whereas wild bacteria's survival rate remained below 50 %. The superior Cd2+ removal capacity of EB can be attributed to the intracellular storage capacity of K.S Bfr. Tandem Mass Tags quantitative proteomics analysis showed that EB mitigated cytotoxicity mainly by up-regulating the expression of binding, homeostasis, efflux, and ribosome-related proteins. K.S Bfr was identified as a major pathway for bacteria to remove Cd2+ since it provided independent storage space for Cd2+ and ensured intracellular metal ion homeostasis. This study presents an effective strategy for treating metal wastewater, offering promising new applications for bioremediation. [ABSTRACT FROM AUTHOR]

This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only. • A new dynamic feature engineering method for office building cooling load prediction. • Slow and fast dynamic features engineered from available weather data only. • Control-knowledge based feature to learn on-hours dynamic responses. • Multi-modal approach with eight day-type features to learn occupancy loads. • Deep recurrent learning and AutoML methods compared to the proposed method. [ABSTRACT FROM AUTHOR]