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Rebar is the prime component of reinforced concrete structures, and rebar monitoring is a time-consuming and technical job. With the emergence of the fourth industrial revolution, the construction industry practices have evolved toward digitalization. Still, hesitation remains among stakeholders toward the adoption of advanced technologies and one of the significant reasons is the unavailability of knowledge frameworks and implementation guidelines. This study aims to investigate technical factors impacting automated monitoring of rebar for the understanding, confidence gain and effective implementation by construction industry stakeholders.

A structured study pipeline has been adopted, which includes a systematic literature collection, semistructured interviews, pilot survey, questionnaire survey and statistical analyses via merging two techniques, i.e. structural equation modeling and relative importance index.

The achieved model highlights “digital images” and “scanning” as two main categories being adopted for automated rebar monitoring. Moreover, “external influence”, “data-capturing”, “image quality”, and “environment” have been identified as the main factors under “digital images”. On the other hand, “object distance”, “rebar shape”, “occlusion” and “rebar spacing” have been highlighted as the main contributing factors under “scanning”.

The study provides a base guideline for the construction industry stakeholders to gain confidence in automated monitoring of rebar via vision-based technologies and effective implementation of the progress-monitoring processes. This study, via structured data collection, performed qualitative and quantitative analyses to investigate technical factors for effective rebar monitoring via vision-based technologies in the form of a mathematical model.

Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

The purpose of this study is to develop a magnetic resonance imaging (MRI)-safe iron-free electrical actuator for MR-guided surgical interventions.

The paper deals with the design of an MRI compatible electrical actuator. Three-dimensional electromagnetic and thermal analytical models have been developed to design the actuator. These models have been validated through 3D finite element (FE) computations. The analytical models have been inserted in an optimization procedure that uses genetic algorithms to find the optimal parameters of the actuator.

The analytical models are very fast and precise compared to the FE models. The computation time is 0.1 s for the electromagnetic analytical model and 3 min for the FE one. The optimized actuator does not perturb imaging sequence even if supplied with a current 10 times higher than its rated one. Indeed, the actuator’s magnetic field generated in the imaging area does not exceed 1 ppm of the B₀ field generated by the MRI scanner. The actuator can perform up to 25 biopsy cycles without any risk to the actuator or the patient since he maximum temperature rise of the actuator is about 20°C. The actuator is compact and lightweight compared to its pneumatic counterpart.

The MRI compatible actuator uses the B₀ field generated by scanner as inductor. The design procedure uses magneto-thermal coupled models that can be adapted to the design of a variety actuation systems working in MRI environment.

This study aims to undertake a comprehensive examination of heat transfer by convection in porous systems with top and bottom walls insulated and differently heated vertical walls under a magnetic field. For a specific nanofluid, the study aims to bring out the effects of different segmental heating arrangements.

An existing in-house code based on the finite volume method has provided the numerical solution of the coupled nondimensional transport equations. Following a validation study, different explorations include the variations of Darcy–Rayleigh number (Ra_m = 10–10⁴), Darcy number (Da = 10^–5–10^–1) segmented arrangements of heaters of identical total length, porosity index (ε = 0.1–1) and aspect ratio of the cavity (AR = 0.25–2) under Hartmann number (Ha = 10–70) and volume fraction of φ = 0.1% for the nanoparticles. In the analysis, there are major roles of the streamlines, isotherms and heatlines on the vertical mid-plane of the cavity and the profiles of the flow velocity and temperature on the central line of the section.

The finding of a monotonic rise in the heat transfer rate with an increase in Ra_m from 10 to 10⁴ has prompted a further comparison of the rate at Ra_m equal to 10⁴ with the total length of the heaters kept constant in all the cases. With respect to uniform heating of one entire wall, the study reveals a significant advantage of 246% rate enhancement from two equal heater segments placed centrally on opposite walls. This rate has emerged higher by 82% and 249%, respectively, with both the segments placed at the top and one at the bottom and one at the top. An increase in the number of centrally arranged heaters on each wall from one to five has yielded 286% rate enhancement. Changes in the ratio of the cavity height-to-length from 1.0 to 0.2 and 2 cause the rate to decrease by 50% and increase by 21%, respectively.

Further research with additional parameters, geometries and configurations will consolidate the understanding. Experimental validation can complement the numerical simulations presented in this study.

This research contributes to the field by integrating segmented heating, magnetic fields and hybrid nanofluid in a porous flow domain, addressing existing research gaps. The findings provide valuable insights for enhancing thermal performance, and controlling heat transfer locally, and have implications for medical treatments, thermal management systems and related fields. The research opens up new possibilities for precise thermal management and offers directions for future investigations.

This study aims to design and validate a theoretical model for capacitive imaging (CI) sensors that incorporates the interelectrode shielding and surrounding shielding electrodes. Through experimental verification, the effectiveness of the theoretical model in evaluating CI sensors equipped with shielding electrodes has been demonstrated.

The study begins by incorporating the interelectrode shielding and surrounding shielding electrodes of CI sensors into the theoretical model. A method for deriving the semianalytical model is proposed, using the renormalization group method and physical model. Based on random geometric parameters of CI sensors, capacitance values are calculated using both simulation models and theoretical models. Three different types of CI sensors with varying geometric parameters are designed and manufactured for experimental testing.

The study’s results indicate that the errors of the semianalytical model for the CI sensor are predominantly below 5%, with all errors falling below 10%. This suggests that the semianalytical model, derived using the renormalization group method, effectively evaluates CI sensors equipped with shielding electrodes. The experimental results demonstrate the efficacy of the theoretical model in accurately predicting the capacitance values of the CI sensors.

The theoretical model of CI sensors is described by incorporating the interelectrode shielding and surrounding shielding electrodes into the model. This comprehensive approach allows for a more accurate evaluation of the detecting capability of CI sensors, as well as optimization of their performance.

Studying the shear stress and pressure resulting on the walls of blood vessels, especially during high-pressure cases, which may lead to the explosion or rupture of these vessels, can also lead to the death of many patients. Therefore, it was necessary to try to control the shear and normal stresses on these veins through nanoparticles in the presence of some external forces, such as exposure to some electromagnetic shocks, to reduce the risk of high pressure and stress on those blood vessels. This study aims to examines the shear and normal stresses of electroosmotic-magnetized Sutterby Buongiorno’s nanofluid in a symmetric peristaltic channel with a moderate Reynolds number and curvature. The production of thermal radiation is also considered. Sutterby nanofluids equations of motion, energy equation, nanoparticles concentration, induced magnetic field and electric potential are calculated without approximation using small and long wavelengths with moderate Reynolds numbers.

The Adomian decomposition method solves the nonlinear partial differential equations with related boundary conditions. Graphs and tables show flow features and biophysical factors like shear and normal stresses.

This study found that when curvature and a moderate Reynolds number are present, the non-Newtonian Sutterby fluid raises shear stress across all domains due to velocity decay, resulting in high shear stress. Additionally, modest mobility increases shear stress across all channel domains. The Sutterby parameter causes fluid motion resistance, which results in low energy generation and a decrease in the temperature distribution.

Equations of motion, energy equation, nanoparticle concentration, induced magnetic field and electric potential for Sutterby nano-fluids are obtained without any approximation i.e. the authors take small and long wavelengths and also moderate Reynolds numbers.

This paper aims to develop a new 3D analytical model in cylindrical coordinates to study radial flux eddy current couplers (RFECC) while considering the magnetic edge and 3D curvature effects, and the field reaction due to the induced currents.

The analytical model is developed by combining two formulations. A magnetic scalar potential formulation in the air and the magnets regions and a current density formulation in the conductive region. The magnetic field and eddy currents expressions are obtained by solving the 3D Maxwell equations in 3D cylindrical coordinates with the variable separation method. The torque expression is derived from the field solution using the Maxwell stress tensor. In addition to 3D magnetic edge effects, the proposed model takes into account the reaction field effect due to the induced currents in the conducting part. To show the accuracy of the developed 3D analytical model, its results are compared to those from the 3D finite element simulation.

The obtained results prove the accuracy of the new developed 3D analytical model. The comparison of the 3D analytical model with the 2D simulation proves the strong magnetic edge effects impact (in the axial direction) in these devices which must be considered in the modelling. The new analytical model allows the magnetic edge effects consideration without any correction factor and also presents a good compromise between precision and computation time.

The proposed 3D analytical model presents a considerably reduced computation time compared to 3D finite element simulation which makes it efficient as an accurate design and optimization tool for radial flux eddy current devices.

A new analytical model in 3D cylindrical coordinates has been developed to find the electromagnetic torque in radial flux eddy current couplers. This model considers the magnetic edge effects, the 3D curvature effects and the field reaction (without correction factors) while improving the computation time.

The development of new advanced materials, such as photopolymerizable resins for use in stereolithography (SLA) and Ti6Al4V manufacture via selective laser melting (SLM) processes, have gained significant attention in recent years. Their accuracy, multi-material capability and application in novel fields, such as implantology, biomedical, aviation and energy industries, underscore the growing importance of these materials. The purpose of this study is oriented toward the application of new advanced materials in stent manufacturing realized by 3D printing technologies.

The methodology for designing personalized medical devices, implies computed tomography (CT) or magnetic resonance (MR) techniques. By realizing segmentation, reverse engineering and deriving a 3D model of a blood vessel, a subsequent stent design is achieved. The tessellation process and 3D printing methods can then be used to produce these parts. In this context, the SLA technology, in close correlation with the new types of developed resins, has brought significant evolution, as demonstrated through the analyses that are realized in the research presented in this study. This study undertakes a comprehensive approach, establishing experimentally the characteristics of two new types of photopolymerizable resins (both undoped and doped with micro-ceramic powders), remarking their great accuracy for 3D modeling in die-casting techniques, especially in the production process of customized stents.

A series of analyses were conducted, including scanning electron microscopy, energy-dispersive X-ray spectroscopy, mapping and roughness tests. Additionally, the structural integrity and molecular bonding of these resins were assessed by Fourier-transform infrared spectroscopy–attenuated total reflectance analysis. The research also explored the possibilities of using metallic alloys for producing the stents, comparing the direct manufacturing methods of stents’ struts by SLM technology using Ti6Al4V with stent models made from photopolymerizable resins using SLA. Furthermore, computer-aided engineering (CAE) simulations for two different stent struts were carried out, providing insights into the potential of using these materials and methods for realizing the production of stents.

This study covers advancements in materials and additive manufacturing methods but also approaches the use of CAE analysis, introducing in this way novel elements to the domain of customized stent manufacturing. The emerging applications of these resins, along with metallic alloys and 3D printing technologies, have brought significant contributions to the biomedical domain, as emphasized in this study. This study concludes by highlighting the current challenges and future research directions in the use of photopolymerizable resins and biocompatible metallic alloys, while also emphasizing the integration of artificial intelligence in the design process of customized stents by taking into consideration the 3D printing technologies that are used for producing these stents.

Most of the linear encoders are based on optics. The accuracy and reliability of these encoders are greatly reduced in polluted and noisy environments. Moreover, these encoders have a complex structure and large sensor volume and are thus not suited to small application scenarios and do not have universality. This paper aims to present a new absolute magnetic linear encoder, which has a simple structure, small size and wide application range.

The effect of swing error is analyzed for the sensor structural arrangement. A double-threshold interval algorithm is then proposed to synthesize multiple interval electrical angles into absolute angles and convert them into actual displacement distances.

The final linear encoder measurement range is 15.57 mm, and the resolution reaches ± 2 µm. The effectiveness of the algorithm is demonstrated experimentally.

The linear encoder has good robustness, and high measurement accuracy, which is suitable for industrial production. The linear encoder has been mass-produced and used in an electric power-assisted braking system.

Data-driven marketing is replacing conventional marketing strategies. The modern marketing strategy is based on insights derived from customer behavior information gathered from their facial expressions and neuro-signals. This study explores the potential for face recognition and neuro-marketing in modern-day marketing.

The study conducts an in-depth examination of the extant literature on neuro-marketing and facial recognition marketing. The articles for review are downloaded from the Scopus database, and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is then used to screen and choose the relevant papers. The systematic literature review method is applied to conduct the study.

An extensive review of the literature reveals that the domains of neuro-marketing and face recognition marketing remain understudied. The authors’ review of selected papers delivers five neuro-marketing and facial recognition marketing themes that are essential to modern marketing concepts.

Neuro-marketing and facial recognition marketing are artificial intelligence (AI)-enabled marketing techniques that assist in gaining cognitive insights into human behavior. The findings would be of use to managers in designing marketing strategies to enhance their marketing approach and boost conversion rates.

The uniqueness of this study lies in that it provides an updated review on neuro-marketing and face recognition marketing.