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The aim of this paper is to solve the toxic and harmful problems caused by traditional volatile corrosion inhibitor (VCI) and to analyze the effect of the layered structure on the enhancement of the volatile corrosion inhibition prevention performance of amino acids.

The carbon dots-montmorillonite (DMT) hybrid material is prepared via hydrothermal process. The effect of the DMT-modified alanine as VCI for mild steel is investigated by volatile inhibition sieve test, volatile corrosion inhibition ability test, electrochemical measurement and surface analysis technology. It demonstrates that the DMT hybrid materials can improve the ability of alanine to protect mild steel against atmospheric corrosion effectively. The presence of carbon dots enlarges the interlamellar spacing of montmorillonite and allows better dispersion of alanine. The DMT-modified alanine has higher volatilization ability and an excellent corrosion inhibition of 85.3% for mild steel.

The DMT hybrid material provides a good template for the distribution of VCI, which can effectively improve the vapor-phase antirust property of VCI.

The increased volatilization rate also means increased VCI consumption and higher costs.

Provides a new way of thinking to replace the traditional toxic and harmful VCI.

For the first time, amino acids are combined with nano laminar structures, which are used to solve the problem of difficult volatilization of amino acids.

This study aims to explore entropy evaluation in the bi-directional flow of Casson hybrid nanofluids within a stagnated domain, a topic of significant importance for optimizing thermal systems. The aim is to investigate the behavior of unsteady, magnetized and laminar flow using a parametric model based on the thermo-physical properties of alumina and copper nanoparticles.

The research uses boundary layer approximations and the Keller-box method to solve the derived ordinary differential equations, ensuring numerical accuracy through convergence and stability analysis. A comparison benchmark has been used to authenticate the accuracy of the numerical outcomes.

Results indicate that increasing the Casson fluid parameter (ranging from 0.1 to 1.0) reduces velocity, the Bejan number decreases with higher bidirectional flow parameter (ranging from 0.1 to 0.9) and the Nusselt number increases with higher nanoparticle concentrations (ranging from 1% to 4%).

This study has limitations, including the assumption of laminar flow and the neglect of possible turbulent effects, which could be significant in practical applications.

The findings offer insights for optimizing thermal management systems, particularly in industries where precise control of heat transfer is crucial. The Keller-box simulation method proves to be effective in accurately predicting the behavior of such complex systems, and the entropy evaluation aids in assessing thermodynamic irreversibilities, which can enhance the efficiency of engineering designs.

These findings provide valuable insights into the thermal management of hybrid nanofluid systems, marking a novel contribution to the field.

Recently, there has been a shift toward the embodied energy assessment of buildings. However, the impact of material service life on the life-cycle embodied energy has received little attention. We aimed to address this knowledge gap, particularly in the context of the UAE and investigated the embodied energy associated with the use of concrete and other materials commonly used in residential buildings in the hot desert climate of the UAE.

Using input–output based hybrid analysis, we quantified the life-cycle embodied energy of a villa in the UAE with over 50 years of building life using the average, minimum, and maximum material service life values. Mathematical calculations were performed using MS Excel, and a detailed bill of quantities with >170 building materials and components of the villa were used for investigation.

For the base case, the initial embodied energy was 57% (7390.5 GJ), whereas the recurrent embodied energy was 43% (5,690 GJ) of the life-cycle embodied energy based on average material service life values. The proportion of the recurrent embodied energy with minimum material service life values was increased to 68% of the life-cycle embodied energy, while it dropped to 15% with maximum material service life values.

The findings provide new data to guide building construction in the UAE and show that recurrent embodied energy contributes significantly to life-cycle energy demand. Further, the study of material service life variations provides deeper insights into future building material specifications and management considerations for building maintenance.

This study aims to address the challenge of reducing the build time of a fused deposition modeling (FDM) system while maintaining part strength, proposing a hybrid technique combining photopolymerization and FDM.

For developing the hybrid system, a standard FDM machine was modified to incorporate necessary components so that the whole system can be operated with a single interface; further, the samples were fabricated with conventional and modified process to evaluate the efficacy of the developed system, to determine the extent of time reduction that the proposed methodology can obtain, additionally different sort of 3D models were selected and their build time was compared.

The modified hybrid mechanism can successfully fabricate parts with a modified G-code. The simulation of the technique shows that a reduction of 34%–87% can be achieved for simpler models such as cube while a reduction ranging from 30.6%–87.8% was observed for complex models. An increase in strength of 6.58%, 11.51% and 37.32% was observed in X, Y and Z directions, along with a significant increase in toughness as compared with FDM parts for parts fabricated with the developed mechanism.

The modified mechanism could be used for fast fabrication purposes, which could be very useful for serving situations such as emergency health care, rapid tooling.

This research contributes a novel hybrid technique for additive manufacturing, offering a substantial reduction in build time without compromising mechanical properties, even increasing them.

The purpose of this paper is to explore the emergence of innovation in smart service systems to conceptualize how actor’s relationships through technology-enabled interactions can give birth to novel technologies, processes, strategies and value. The objectives of the study are: to detect the different enablers that activate innovation in smart service systems; and to explore how these can lead dynamically to the emergence of different innovation patterns.

The empirical research adopts an approach based on constructivist grounded theory, performed through observation and semi-structured interviews to investigate the development of innovation in the Italian CTNA (Italian acronym of National Cluster for Aerospace Technology).

The identification and re-elaboration of the novelties that emerged from the analysis of the Cluster allow the elaboration of a diagram that classifies five different shades of innovation, introduced through some related theoretical propositions: technological; process; business model and data-driven; social and eco-sustainable; and practice-based.

The paper embraces a synthesis view that detects the enabling structural and systems dimensions for innovation (the “what”) and the way in which these can be combined to create new technologies, resources, values and social rules (the “how” dimension). The classification of five different kinds of innovation can contribute to enrich extant research on value co-creation and innovation and can shed light on how given technologies and relational strategies can produce varied innovation outcomes according to the diverse stakeholders engaged.

This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries.

Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers. A reference dataset contains comprehensive information regarding mechanical behavior of the lignocellulosic fibers was compiled from previous experimental investigations on mechanical properties for eight different fiber materials. Data encompass three key factors: Density of 0.9–1.6 g/cm³, Diameter of 5.9–1,000 µm, and Microfibrillar angle of 2–49 deg were utilized. Initially, fuzzy clustering technique was utilized for the data. For validating proposed model, ultimate tensile strength and elongation at break were predicted and then examined against unseen new data that had not been used during model development.

The output results demonstrated remarkably accurate and highly acceptable predictions results. The error analysis for the proposed method was discussed by using statistical criteria. The stacked model proved to be effective in significantly reducing level of uncertainty in predicting the mechanical properties, thereby enhancing model’s reliability and precision. The study demonstrates the robustness and efficacy of the stacked method in accurately estimating mechanical properties of lignocellulosic fibers, making it a valuable tool for material scientists and engineers in various applications.

Cellulosic fibers are essential for biomaterials to enhance developing green sustainable bio-products. However, such fibers have diverse characteristics according to their types, chemical composition and structure causing inconsistent mechanical performance. This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries. Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers.

The purpose of this paper is to investigate the surface integrity features, including surface roughness (SR), recast layer (RL), material migration, topography and wire wear pattern in rough and trim-cut wire electric discharge machine (WEDM) of hybrid composite (Al6061-90%/SiC-2.5%/TiB₂-7.5%).

Effects of four important factors, namely, rough-cut history (RCH), pulse on time (T_on), peak current (IP) and wire offset (WO) have been assessed on the responses of interest for trim-cut WEDM. Box–Behnken design (RSM) was used to formulate the experimentation plan. Quantitative indices of surface integrity, namely, SR and RL, and selected samples have been investigated for qualitative analysis, namely, surface topography, material migration and wire wear pattern.

T_on and IP are found to be most significant, whereas RCH and WO are found insignificant for SR. T_on and WO were found to be the most significant factors affecting RL. After trim cut, an RL of thickness 8.26 µm is observed if the initial rough cut has been accomplished at high discharge energy setting. Whereas the best value of RL thickness, i.e. 5.36 µm, can be realized with low level of RCH. A significant decrease in the presence of foreign materials is recorded, indicating its strong correlation with the discharge energy used during machining.

Investigation on surface integrity features for machining of hybrid composite through rough and trim-cut WEDM has been reported by only a limited number of researchers in the past. This study is attempted at fulfilling few vital gaps by addressing the issues such as evaluation of the efficacy of trim cutting under different discharge energy conditions (using RCH), analysis of wire wear pattern in both rough and trim-cut modes and investigation of the wire breakage phenomenon during machining.

This paper aims to research the tribological and dynamic characteristics of aeroengine hybrid ceramic bearings through wear experiments and simulation analysis.

First, the authors carried out wear experiments on Si₃N₄–GCr15 and GCr15–GCr15 friction pairs through the ball-disc wear test rig to explore the tribological properties of their materials. Second, using ANSYS/LS-DYNA simulation software, the dynamic simulation analysis of hybrid bearings was carried out under certain working conditions, and the dynamic contact stress of all-steel bearings of the same size was simulated and compared. Finally, the change of the maximum contact stress of the main bearing under the change of load and rotation speed was studied.

The results show that the Si₃N₄–GCr15 pair has better tribological performance. At the same time, under the conditions of high speed and heavy load, the simulation analysis shows that the contact stress between the ceramic ball and the raceway of the ring is smaller than the steel ball. That is, hybrid bearings have better transient mechanical properties than all-steel bearings. With the speed increasing to 12,000 r/min, the maximum stress point will shift in the inner and outer rings.

In this study, the tribological and transient mechanical properties of Si₃N₄ material were comprehensively analyzed through wear experiments and dynamic simulation analysis, which provided a reference for the design of hybrid bearings for next-generation aeroengines.

This study focuses on the structural performance assessment of hybrid polymer composites for pick-and-place robot grippers used in critical infrastructure. This research involved the creation of composite materials with different nanoparticle concentrations, followed by extensive testing to assess the mechanical properties of the materials, such as strength, stiffness and durability.

The composites comprised bidirectional interply inclined carbon fibers (C), S-glass fibers (SG), E-glass (EG), an epoxy matrix and silica nanoparticles (SNiPs). During construction, the composite materials must be carefully layered using quasi-static sequence techniques (45°C1/45°SG2/45°EG2/45°C1/45°EG2/45°SG2/45°C1) to obtain the epoxy matrix reinforcement and bonding using 0, 2, 4 and 6 wt. % of silica nanoparticles.

According to various test findings, the 4 wt. % of SNiPs added to polymer plates exhibits the maximum strength outcomes. The average results of the tensile and flexural tests for the polymer composite plates with 4 wt. % addition SNiPs were 127.103 MPa and 223.145 MPa, respectively. The average results of the tensile and flexural tests for the plates with 0 wt.% SNiPs were 115.457 MPa and 207.316 MPa, respectively.

The authors hereby attest that the research paper they have submitted is the result of their own independent and unique labor. All of the sources from which the thoughts and passages were derived have been properly credited. The work has not been submitted for publication anywhere and is devoid of any instances of plagiarism.

1. The study enhances the engineering materials for innovative applications.

2. The study explores the mechanical behavior of carbon/S-glass/E-glass fiber composites.

3. Silica nanoparticles were enhancing mechanical characteristics of the composite structure.

The study enhances the engineering materials for innovative applications.

The study explores the mechanical behavior of carbon/S-glass/E-glass fiber composites.

Silica nanoparticles were enhancing mechanical characteristics of the composite structure.

Effective construction safety training has been considered to play a significant role in reducing the incidence of accidents. However, the current safety training methods pay less attention to the relationship between workers' personalized characteristics and their learning needs, which results in workers' low learning participation and poor training effect. The purpose of this paper is to improve the participation and effect of safety training for construction workers with a persona-based approach.

This paper presents a persona-based approach to safety tag generation and training material recommendation. By extracting the demographic characteristics and behavior patterns tags of construction workers, a neural network algorithm is introduced to calculate the learning needs tags of workers, and the collaborative filtering recommendation method is integrated to enrich the innovation of recommendation results. Offline experiments and online experiments are designed to verify the rationality of the proposed method.

The results show that the learning needs of workers are closely related to their background. The proposed method can effectively improve workers' interest in materials and the training effect compared with conventional safety training methods. The research provides a theoretical and practical reference for promoting active safety management and achieving worker-centered safety management.

First, a persona-based approach is introduced to establish a novel framework for solving the problem of personalized construction safety management. Second, an artificial intelligence algorithm is used to automatically extract the learning needs tag values and design a hybrid recommendation method for construction workers' personalized safety training. The collaborative filtering method is integrated to enrich the innovation of recommendation results.