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Nanotechnology (NT) advancements in personal protective textiles (PPT) or personal protective equipment (PPE) have alleviated spread and transmission of this highly contagious viral disease, and enabled enhancement of PPE, thereby fortifying antiviral behavior.

Review of a series of state of the art research papers on the subject matter.

This paper expounds on novel nanotechnological advancements in polymeric textile composites, emerging applications and fight against COVID-19 pandemic.

As a panacea to “public droplet prevention,” textiles have proven to be potentially effective as environmental droplet barriers (EDBs).

PPT in form of healthcare materials including surgical face masks (SFMs), gloves, goggles, respirators, gowns, uniforms, scrub-suits and other apparels play critical role in hindering the spreading of COVID-19 and other “oral-respiratory droplet contamination” both within and outside hospitals.

When used as double-layers, textiles display effectiveness as SFMs or surgical-fabrics, which reduces droplet transmission to <10 cm, within circumference of ∼0.3%.

NT advancements in textiles through nanoparticles, and sensor integration within textile materials have enhanced versatile sensory capabilities, robotics, flame retardancy, self-cleaning, electrical conductivity, flexibility and comfort, thereby availing it for health, medical, sporting, advanced engineering, pharmaceuticals, aerospace, military, automobile, food and agricultural applications, and more. Therefore, this paper expounds on recently emerging trends in nanotechnological influence in textiles for engineering and fight against COVID-19 pandemic.

The purpose of this research is to ascertain the feasibility of fabricating polymer optical fibers (POFs) based textile structures by knitting with Polymethylmethacrylate (PMMA) based optical fibers for textile sensor application. It has long been established that by using the principles of physics, POFs have the capability to function as sensors, detecting strain, temperature and other variables. However, POF applications such as strain and pressure sensing using knitting techniques has since not been very successful due to a number of reasons. Commercially available PMMA-based optical fibers tend to be fragile and susceptible to breakages when subjected to stress during the knitting processes. Also light transmitted within these fibers is prone to leakage due to the curvature that results when optical fibers are interlaced or interlooped within fabric structures.

Using Stoll’s multi-gauge CMS 350 HP knitting machine, five fabric structures namely, 1 × 4 float knit structure, tunnel inlay knit structure, 3:1 fleece fabric and 2:1 fleece fabric structure respectively were used to knit sensor samples. The samples were subsequently tested for length of illumination and sensitivity relative to applied pressure.

The results of this preliminary study establish that embedding plastic optical fibers into a knitted structure during the fabric formation process for soft strain sensor application possible. The best illumination performance was recorded for tunnel inlay structure which had an average of 94 cm course length of POF being illuminated. Sensor sensitivity experiments also establish that the relative spectral intensity of the fiber is sensitive to both light and pressure. Problems encountered and recommendations for further research have also been discussed and proffered.

Due to resource limitations, an innovative technique (use of precision weight set) was used to apply pressure to the sensors. Consequently, information regarding the extent of corresponding sensor deformation has not been used in this initial analysis.

Because the fundamental step toward finding a solution to any engineering problem is the acquisition of reliable data, and considering the fact that most of the popular technologies used for soft textile sensors are still bedeviled with the problem of signal instability and noise, the success of this application thus has the tendency to promote the wide spread adoption of POF sensors for smart apparel applications.

As far as research on soft strain sensors is concerned, to the best of the authors’ knowledge, this is the first study to have attempted to knit deformable sensors using commercially available POFs.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

This paper presents a novel method for identifying damage in reinforced concrete (RC) bridges, utilizing macro-strain data from distributed long-gauge sensors installed on the concrete surface.

The method relies on the principle that heavy vehicles induce larger dynamic vibrations, leading to increased strain and crack formation compared to lighter vehicles. By comparing the absolute macro-strain ratio (AMSR) of a reference sensor with a network of distributed sensors, damage locations can be effectively pinpointed from a single data collection session. Finite-element modeling was employed to validate the method's efficacy, demonstrating that the AMSR ratio increases significantly in the presence of cracks. Experimental validation was conducted on a real-world bridge in Japan, confirming the method's reliability under normal traffic conditions.

This approach offers a practical and efficient means of detecting bridge damage, potentially enhancing the safety and longevity of infrastructure systems.

Original research paper.

This systematic literature review aims to investigate the application and integration of Industry 4.0 (I4.0) technologies in transportation infrastructure construction projects focusing on sustainability pillars.

The study employs a systematic literature review approach, combining qualitative review and quantitative analysis of 142 academic articles published between 2011 and March 2023.

The findings reveal the dominance of Building Information Modelling (BIM) as a central tool for sustainability assessment, while other technologies such as blockchain and autonomous robotics have received limited attention. The adoption of I4.0 technologies, including Internet of Things (IoT) sensors, Augmented Reality (AR), and Big Data, has been prevalent for data-driven analyses, while Unmanned Aerial Vehicle (UAVs) and 3D printing are mainly being integrated either with BIM or in synergy with Artificial Intelligence (AI). We pinpoint critical challenges including high adoption costs, technical barriers, lack of interoperability, and the absence of standardized sustainability benchmarks.

This research distinguishes itself by not only mapping the current integration of I4.0 technologies but also by advocating for standardization and a synergistic human-technology collaborative approach. It offers tailored strategic pathways for diverse types of transportation infrastructure and different project phases, aiming to significantly enhance operational efficiency and sustainability. The study sets a new agenda for leveraging cutting-edge technologies to meet ambitious future sustainability and efficiency goals, making a compelling case for rethinking how these technologies are applied in the construction sector.

The purpose of this study is to design a highly integrated smart glove to enable gesture acquisition and force sensory interactions, and to enhance the realism and immersion of virtual reality interaction experiences.

The smart glove is highly integrated with gesture sensing, force-haptic acquisition and virtual force feedback modules. Gesture sensing realizes the interactive display of hand posture. The force-haptic acquisition and virtual force feedback provide immersive force feedback to enhance the sense of presence and immersion of the virtual reality interaction.

The experimental results show that the average error of the finger bending sensor is only 0.176°, the error of the arm sensor is close to 0 and the maximum error of the force sensing is 2.08 g, which is able to accurately sense the hand posture and force-touch information. In the virtual reality interaction experiments, the force feedback has obvious level distinction, which can enhance the sense of presence and immersion during the interaction.

This paper innovatively proposes a highly integrated smart glove that cleverly integrates gesture acquisition, force-haptic acquisition and virtual force feedback. The glove enhances the sense of presence and immersion of virtual reality interaction through precise force feedback, which has great potential for application in virtual environment interaction in various fields.

The purpose of this paper is to present and implement a task allocation method based on game theory for reconnaissance mission planning of UAVs and USVs system.

In this paper, the decision-making framework via game theory of mission planning is constructed. The mission planning of UAVs–USVs is transformed into a potential game optimization problem by introducing a minimum weight vertex cover model. The modified population-based game-theoretic optimizer (MPGTO) is used to improve the efficiency of solving this complex multi-constraint assignment problem.

Several simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.

Several simulations are carried out to exhibit that the proposed algorithm obtains the superiority on quality and efficiency of mission planning solutions to some existing approaches.

The proposed framework and algorithm are expected to be applied to complex real scenarios with uncertain targets and heterogeneity.

The decision framework via game theory is proposed for the mission planning problem of UAVs–USVs and a MPGTO with swarm evolution, and the adaptive iteration mechanism is presented for ensuring the efficiency and quality of the solution.

Current flexible printed circuit (FPC) assembly relies heavily on manual labor, limiting capacity and increasing costs. Small FPC size makes automation challenging as terminals can be visually occluded. The purpose of this study is to use 3D tactile sensing to mimic human manual mating skills for enabling sensing offset between FPC terminals (FPC-t) and FPC mating slots (FPC-s) under visual occlusion.

The proposed model has three stages: spatial encoding, offset estimation and action strategy. The spatial encoder maps sparse 3D tactile data into a compact 1D feature capturing valid spatial assembly information to enable temporal processing. To compensate for low sensor resolution, consecutive spatial features are input to a multistage temporal convolutional network which estimates alignment offsets. The robot then performs alignment or mating actions based on the estimated offsets.

Experiments are conducted on a Redmi Note 4 smartphone assembly platform. Compared to other models, the proposed approach achieves superior offset estimation. Within limited trials, it successfully assembles FPCs under visual occlusion using three-axis tactile sensing.

A spatial encoder is designed to encode three-axis tactile data into feature maps, overcoming multistage temporal convolution network’s (MS-TCN) inability to directly process such input. Modifying the output to estimate assembly offsets with related motion semantics overcame MS-TCN’s segmentation points output, unable to meet assembly monitoring needs. Training and testing the improved MS-TCN on an FPC data set demonstrated accurate monitoring of the full process. An assembly platform verified performance on automated FPC assembly.

Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.

The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.

Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.

A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.

This paper aims to investigate the impacts of Industry 4.0/5.0 (I4.0/5.0) on the glass manufacturing workplace. Specifically, it studied the workplace, which represents complex manufacturing lines of high variety and volume of products.

A case study based on semi-structured interviews was conducted with managers responsible for I4.0 implementation, and the responses were treated by content analysis.

Findings reinforce I5.0 aspects to be considered in terms of work organization. The interviewees highlight work and human factors as important for technology implementation, which includes workers tasks, skills, nature of work, human resources development, hiring process and organizations strategies. It was also found that knowledge sharing poses a huge challenge.

In lieu of gaps in the literature, this research further discusses management challenges to support digital transformation and impacts on workers and organizations.