Novel thoraco-lumbo-sacral corset design increases Biering-Sorensen back endurance and alters knee and ankle angles during a box lifting task

Research investigating lumbosacral corset designs and their effects are limited and conflicting. The objective was to compare thoraco-lumbo-sacral support corsets (polyester/nylon: TLSSC-poly and neoprene: TLSSC-neo) with a traditional model (TRAD) and Control. Twenty male, university-aged, healthy, recreationally active, participants performed Biering-Sorensen back endurance (BS) test and box lifting tasks (BL:30 repetitions using 20% body mass). Lower and upper erector spinae and hamstrings electromyography (EMG); trunk-hip, knee, and ankle kinematics as well as endurance time were monitored. With BL, the TLSSC-poly (121.4°±17.9) exhibited 1.9% (p = 0.01), 2.7% (p = 0.003), and 3.7% (p = 0.0003) greater knee flexion than TRAD (119.1°±17.5), TLSSC-neo (116.8°±17.4) and Control (120.1°±17.6) respectively. The TLSSC-poly (101.9°± 8.9) demonstrated significant 3.5% (p = 0.005), 2.2% (p = 0.002) and 1.4% (p = 0.01) greater dorsiflexion than TRAD (103.4°±8.7), TLSSC-neo (104.2°±9.8) and Control (105.7°±7.2) respectively. With BS, TLSSC-poly (137.4-s±31.2, 9.7%, p = 0.018) and TLSSC-neo (133.8-s±32.3, 9.2%, p = 0.006) exhibited significantly longer durations than Control (124.8-s±29.8). Relevance to industry: The TLSSC increased BS endurance and TLSSC-poly increased BL knee and ankle angles, possibly providing benefits for workers, with repeated actions over a full work day.     

Effect of working position and cold environment on muscle activation level and fatigue in the upper limb during manual work tasks

Several occupational groups are exposed to periods of low ambient temperatures while performing manual work tasks outdoors. Work tasks typically include heavy lifting, tool handling, and overhead work. This study evaluated the effect of working position and cold environment on muscle activation level (%RMS_max) and fatigue in the upper limb during manual work tasks. Fourteen male participants (25 ± 3 years, 80.9 ± 6.4 kg, 182 ± 5 cm) completed a 2-h test protocol consisting of five test periods alternating with four work periods, wearing identical sets of clothing, under cold (−15 °C) and control (5 °C) conditions. The work periods consisted of manual work at the hip level, manual overhead work, and a lifting exercise. The test periods consisted of isometric maximal voluntary contractions (MVC) and seated rest. Skin temperatures decreased during cold exposure, especially in the extremities. %RMS_max in the forearm was higher in the cold condition both during overhead work and work at the hip level than that for the same work in the control condition, especially at the end of the test when the difference was approximately 25% (equating to 2–3 %RMS_max). For the middle deltoid muscle, the %RMS_max was approximately three times (or 10 %RMS_max) higher during overhead work than work at the hip level, but there was no additional cost of working in the cold. Signs of deltoid muscle fatigue (decrease in electromyography median power frequency and an increase in %RMS_max) were observed during the overhead work periods in both temperature conditions. No decrease in MVC, as a sign of overall muscle fatigue, was observed in either condition.Relevance to industryThis study demonstrated that when wearing suitable cold-weather protective clothing, the adverse effect of work posture is much higher than that of cold on muscle demand and physical strain.     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.     

Hand grip strength for the working-age population in South Korea: Development of an estimation and evaluation model

Although grip strength is frequently measured in clinical settings, methods for evaluating individual grip strength considering physical characteristics are limited. We attempted to develop an easily applicable statistical model to estimate and evaluate the grip strength of Korean workers according to their age, sex, and anthropometric data.Data were collected from the KNHANES (2014–2019). The data were divided into the test and training sets. Potential regression models for estimating grip strength have been suggested based on sex and hand dominance. The performance of each model was compared, and the best model was selected. The estimated grip strength was calculated for each participant. The distribution of the measured to estimated value ratios was presented. The ratios between the dominant and non-dominant hand grip strengths were also calculated.Overall, 21,807 (9652 men and 12,155 women) individuals were included in the dataset. The selected predictors were age, age^2, height, body mass index (BMI), and body mass-to-waist ratio for men and age, age^2, height, BMI, and waist circumference for women. The measured estimated values were 100.0 ± 16.2%, 100.0 ± 16.3% for dominant and non-dominant hands in men and 100.0 ± 18.9% for dominant and non-dominant hands in women. The 95% confidence interval of the dominant to non-dominant hand grip ratio was 84.4–126.7% for men and 82.4–131.3% for women.Grip strength in workers can be screened in comparison to that in the Korean population using the suggested models. This model is an effective method for identifying abnormalities in the upper extremities of Korean workers.     

Development of acoustic denoising learning network for communication enhancement in construction sites

This paper presents a novel denoising approach based on deep learning and signal processing to improve communication efficiency. Construction activities take place when different trades come to the site for overlapped periods to perform their works, which may easily produce hazardous noise levels. The existence of noise affects workers' health issues, especially hearing and rhythm of the heart, and impacts communication efficiency between workers. The proposed approach employs signal processing technique to transform the noisy audio into image and utilize neural networks to extract noisy features and denoise the image. The denoised image is then converted to obtain the denoised audio. Experiments on reducing the side effect of several common noises in construction sites were conducted, compared with the performance of denoising using conventional wavelet transform. Standard objective measures, such as signal-to-noise ratio (SNR), and subjective measures, such as listening tests are used for evaluations. Our experimental results show that the proposed algorithm achieved significant improvements over the traditional method, as evidenced by the following quantitative results of median value: MSE of 0.002, RMSE of 0.049, SNR of 5.7 dB, PSNR of 25.8 dB, and SSR of 8.Results indicate that the proposed algorithm outperforms conventional denoising methods in terms of both objective and subjective evaluation metrics and have the potential to facilitate communication between site workers when facing different noise sources inevitably.     

Counterfactual inference to predict causal knowledge graph for relational transfer learning by assimilating expert knowledge --Relational feature transfer learning algorithm

Transfer learning (TL) is a machine learning (ML) method in which knowledge is transferred from the existing models of related problems to the model for solving the problem at hand. Relational TL enables the ML models to transfer the relationship networks from one domain to another. However, it has two critical issues. One is determining the proper way of extracting and expressing relationships among data features in the source domain such that the relationships can be transferred to the target domain. The other is how to do the transfer procedure. Knowledge graphs (KGs) are knowledge bases that use data and logic to graph-structured information; they are helpful tools for dealing with the first issue. The proposed relational feature transfer learning algorithm (RF-TL) embodies an extended structural equation modelling (SEM) as a method for constructing KGs. Additionally, in fields such as medicine, economics, and law related to people’s lives and property safety and security, the knowledge of domain experts is a gold standard. This paper introduces the causal analysis and counterfactual inference in the TL domain that directs the transfer procedure. Different from traditional feature-based TL algorithms like transfer component analysis (TCA) and CORelation Alignment (CORAL), RF-TL not only considers relations between feature items but also utilizes causality knowledge, enabling it to perform well in practical cases. The algorithm was tested on two different healthcare-related datasets — sleep apnea questionnaire study data and COVID-19 case data on ICU admission — and compared its performance with TCA and CORAL. The experimental results show that RF-TL can generate better transferred models that give more accurate predictions with fewer input features.     

Comparison of OWAS,RULA and REBA for assessing potential work-related musculoskeletal disorders

This study compared three representative observational methods for assessing musculoskeletal loadings: Ovako Working Posture Analysis System (OWAS), Rapid Upper Limb Assessment (RULA), and Rapid Entire Body Assessment (REBA). The comparison was based on 209 cases of upper-body musculoskeletal disorders (MSDs) diagnosed by medical doctors. The most awkward/stressful posture in each participant's tasks was assessed using these techniques. Postural loadings were rated more highly by the RULA than by the OWAS and REBA (p < 0.01). The chi-square test and logistic regression analysis showed that only RULA grand score and action level, and REBA action level were associated with MSD work-relatedness (p < 0.01, p < 0.05, and p < 0.05, respectively). The percentage concordant values of the logistic model for the RULA grand score and action level were 52.4% and 44.8%, respectively, while the percentage concordant value for the REBA action level was 22.1%. Therefore, the RULA may be the best system for estimating the postural loads and work-relatedness of MSDs.Relevance to industryWork-related musculoskeletal disorders are the leading cause of workplace disability in the developed countries. For preventing the disorders, quantification of musculoskeletal loads is required.     

An enhanced 3D model and generative adversarial network for automated generation of horizontal building mask images and cloudless aerial photographs

Information extracted from aerial photographs is widely used in the fields of urban planning and design. An effective method for detecting buildings in aerial photographs is to use deep learning to understand the current state of a target region. However, the building mask images used to train the deep learning model must be manually generated in many cases. To overcome this challenge, a method has been proposed for automatically generating mask images by using textured three-dimensional (3D) virtual models with aerial photographs. Some aerial photographs include clouds, which degrade image quality. These clouds can be removed by using a generative adversarial network (GAN), which leads to improvements in training quality. Therefore, the objective of this research was to propose a method for automatically generating building mask images by using 3D virtual models with textured aerial photographs. In this study, using GAN to remove clouds in aerial photographs improved training quality. A model trained on datasets generated by the proposed method was able to detect buildings in aerial photographs with IoU = 0.651.     

Palette designer: A color-code design tool

The US Federal Aviation Administration (FAA) has developed a standard set of colors for coding information on air traffic control (ATC) displays. A significant complication was that the air traffic controller population includes people who have color-vision deficiencies (CVDs). We wrote a software tool to assist the FAA in selecting a preliminary color set. It accepts a set of luminances and chromaticity coordinates as input and: (1) Draws graphics and calculates color-related figures of merit to predict whether the set will be acceptable for color-normal and CVD users; (2) Flags colors and pairings that violate human factors criteria; and (3) Allows designers to adjust the colors and see the resulting changes immediately. The tool has been used to perform a pilot study for the FAA’s color-set development project and should be useful for designing other color-coding sets, also.     

Risk assessment of China-Pakistan Fiber Optic Project (CPFOP) in the light of Multi-Criteria Decision Making (MCDM)

The China-Pakistan Economic Corridor (CPEC) is considered as an excellent breakthrough for improving the economic and security situation in the region. The estimated worth of CPEC is 62$ billion which is comprising of 49 developmental projects. China-Pakistan Fiber Optic Project (CPFOP) is one of the core projects among these, which will deliver safe route of voice traffic between both countries. CPFOP is greatly beneficial in terms of enhanced security and revenue generation. Currently, Pakistan’s international connectivity is via submarine cables. CPFOP will provide an alternative route for international telecom traffic and also assist in achieving the rapidly growing internet traffic demand in Pakistan. It is estimated that 17 million people will get benefit from this project. However, every project has some undesirable impacts. The aim of this research paper is twofold; 1st to trace out the pros and cons of CPFOP. 2ndly, performing a risk assessment of CPFOP by using Fuzzy VIKOR technique. This approach will help in prioritizing a list of failure modes of Fiber Optic Cable (FOC). Lastly, this paper will help authorities for optimizing and safeguarding national interest in the wake of CPFOP.     

Accuracy improvement of a 3D passive laser tracker for the calibration of industrial robots

The laser tracker has been used as the mainstream instrument for the position accuracy calibration of industrial robots for quite a long time. However, due to the complexity of the built-in dual-axis active servo tracking system, its cost is high and the target reflector has to adjust its pose frequently, so it cannot be popularized in the production and application sites of industrial robots. Based on this drawback, a 3D passive laser tracker (3DPLT) with high precision, simple structure, easy operation and low cost is proposed in this paper. Firstly, the overall structure of the system is designed, and its position error model based on the principle of spherical coordinate measurement and vector transfer method is established. Then, the error parameters are identified by experiments to formulate the error compensation model. Finally, the multi-pose and large-range spatial error compensation verification experiments of the system are carried out on a commercial coordinate measuring machine. The results show that the spatial volumetric errors of the 3DPLT can achieve within 40 μm after compensation with a good repeatability of ±4 μm. A comparison contouring test with a commercial ballbar is also carried out to validate its applicability of robot calibration.     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.     

Polyvinyl-alcohol-based color resist for low-temperature color-filter production

A color resist comprising partially hydrolyzed polyvinyl alcohol, a crosslinker, a photoacid generator, and dyes was designed for color-filter production. The resist was photoexposed, followed by post-exposure baking, development, and curing at 150 °C to yield a solvent-resistant film. The degree of hydrolysis of polyvinyl alcohol affected the preparation of the homogeneous resist solution and the solvent resistance of the cured film. The cured film was insoluble in the resist solvent when the degree of hydrolysis was high, whereas the film displayed insufficient solvent resistance when degree of hydrolysis was low. Nanoindentation experiments of the polyvinyl alcohol film revealed good mechanical properties that were comparable to those of poly(methyl methacrylate). The crosslinking reaction of the polyvinyl alcohol film was analyzed by monitoring the amount of methanol, a volatile compound emitted during the curing of the film; the results suggest that the reaction was likely completed below the process temperature.     

Luminance behaviour of PLEDs with integration of nanomaterials

Incorporation of nanomaterials in device structure is the key to enhance performance of polymer light emitting diodes (PLEDs). The major challenges that impede competence of PLEDs, for application in display technology, are (i) non-availability of stable low work function metals to act as cathode, (ii) presence of charge trapping centers in the polymer chains and (iii) total internal reflection of light at ITO/glass and glass/air interfaces. The foremost problem leads to increase in turn ON voltage of the device and reduction in electron injection from cathode. Low injection and high trapping probability of electrons lead to charge imbalance in the emissive layer and shifting of recombination zone towards cathode. This immensely constrains the formation and radiative decay of excitons in the emissive layer and declines the luminosity of the device. In this review, experimental studies on the integration of nanomaterials in PLED structures to enhance device luminance are presented. The diverse impact of their geometric features, ionization potential, electrical conductivity and refractive index on the carrier transport and light extraction in PLEDs is discussed and a perspective on this evolving research path is provided.     

Engineering knowledge formalization and proposition for informatics development towards a CAD-integrated DfX system for product design

Target design methodologies (DfX) were developed to cope with specific engineering design issues such as cost-effectiveness, manufacturability, assemblability, maintainability, among others. However, DfX methodologies are undergoing the lack of real integration with 3D CAD systems. Their principles are currently applied downstream of the 3D modelling by following the well-known rules available from the literature and engineers’ know-how (tacit internal knowledge).This paper provides a method to formalize complex DfX engineering knowledge into explicit knowledge that can be reused for Advanced Engineering Informatics to aid designers and engineers in developing mechanical products. This research work wants to define a general method (ontology) able to couple DfX design guidelines (engineering knowledge) with geometrical product features of a product 3D model (engineering parametric data). A common layer for all DfX methods (horizontal) and dedicated layers for each DfX method (vertical) allow creating the suitable ontology for the systematic collection of the DfX rules considering each target. Moreover, the proposed framework is the first step for developing (future work) a software tool to assist engineers and designers during product development (3D CAD modelling).A design for assembly (DfA) case study shows how to collect assembly rules in the given framework. It demonstrates the applicability of the CAD-integrated DfX system in the mechanical design of a jig-crane. Several benefits are recognized: (i) systematic collection of DfA rules for informatics development, (ii) identification of assembly issues in the product development process, and (iii) reduction of effort and time during the design review.     

A three-stage multi-objective heterogeneous integrated model with decomposition-reconstruction mechanism and adaptive segmentation error correction method for ship motion multi-step prediction

Reliable and accurate ship motion prediction is essential for ship navigation at sea and marine operations. Although previous studies have yielded rich results in the field of ship motion prediction, most of them have ignored the importance of the dynamic characteristics of ship motion for constructing forecasting models. Besides, the limitations of the single model and the autocorrelation characteristics of the residual series are also unfavorable factors that hinder the forecasting performance. To fill these gaps, a multi-objective heterogeneous integration model based on decomposition-reconstruction mechanism and adaptive segmentation error correction method is proposed in this paper for ship motion multi-step prediction. Specifically, the proposed model is divided into three stages, which are decomposition-reconstruction mechanism, multi-objective heterogeneous integration model and adaptive segmentation error correction method. The effectiveness of the proposed model is verified using four sets of real ship motion data collected from two sites in the South China Sea. The evaluation results show that the proposed model can effectively improve the prediction performance and outperforms other traditional models and state-of-the-art models in the field of ship motion prediction. Prospectively, the model proposed in this study can be used as an effective aid to ship warning systems and has the potential for practical application in ship marine operations.     

A cooperative game theory based user-centered medical device design decision approach under uncertainty

The medical device conceptual design decision-making is a process of coordinating pertinent stakeholders, which will significantly affect the quality of follow-up market competitiveness. However, as the most challenging parts of user-centered design, traditional methods are mainly focusing on determining the priorities of the evaluation criteria and forming the comprehensive value (utility) of the conceptual scheme, may not fully deal with the interaction and interdependent between the conflicts of interest among stakeholders and weigh the ambiguous influence on the overall design expectations, which results in the unstable decision-making results. To overcome this drawback, this paper proposes a cooperative game theory based decision model for device conceptual scheme under uncertainty. The proposed approach consists of three parts: first part is to collect and classify needs of end users and professional users based on predefined evaluation criteria; second part is using rough set theory technique to create criteria correlation diagram and scheme value matrix from users; and third part is developing the fuzzy coalition utility model to maximize the overall desirability through the criteria correlation diagram with the conflict of interests of end and professional users considered, and then selecting the optimal scheme. A case study of blood pressure meter is used to illustrate the proposed approach and the result shows that this approach is more robust compared with the widely used the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach.     

EarlyData knowledge base for material decisions in building design

To make use of the great opportunities for emission reduction in early building design, future emissions need to be calculated when only geometric, but no detailed material information about a building is available. Currently, early design phase life cycle assessments (LCAs) are heavily reliant on assumptions of specific material choices, leading to single point emission values which suggest a precision not representative for an early design stage. By adding knowledge about possible locations and functions of materials within a building to life cycle inventory (LCI) data, the EarlyData knowledge base makes LCA data sets accessible and more transparent. Additionally, “generic building parts” are defined, which describe building parts independently of precise material choices as a combination of layers with specific functions. During evaluation, enriched LCI data and generic building parts enable assessment of a vast number of possible material combinations at once. Thus, instead of single value results for a particular material combination, ranges of results are displayed revealing the building parts with the greatest emission reduction potential. The application of the EarlyData tool is illustrated on a use case comparing a wood building and a concrete building. The database is developed with extensibility in mind, to include other criteria, such as (life cycle) costs.     

Convolutional long short-term memory model for recognizing construction workers’ postures from wearable inertial measurement units

This paper proposes using Deep Neural Networks (DNN) models for recognizing construction workers’ postures from motion data captured by wearable Inertial Measurement Units (IMUs) sensors. The recognized awkward postures can be linked to known risks of Musculoskeletal Disorders among workers. Applying conventional Machine Learning (ML)-based models has shown promising results in recognizing workers’ postures. ML models are limited – they reply on heuristic feature engineering when constructing discriminative features for characterizing postures. This makes further improving the model performance regarding recognition accuracy challenging. In this paper, the authors investigate the feasibility of addressing this problem using a DNN model that, through integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) layers, automates feature engineering and sequential pattern detection. The model’s recognition performance was evaluated using datasets collected from four workers on construction sites. The DNN model integrating one convolutional and two LSTM layers resulted in the best performance (measured by F1 Score). The proposed model outperformed baseline CNN and LSTM models suggesting that it leveraged the advantages of the two baseline models for effective feature learning. It improved benchmark ML models’ recognition performance by an average of 11% under personalized modelling. The recognition performance was also improved by 3% when the proposed model was applied to 8 types of postures across three subjects. These results support that the proposed DNN model has a high potential in addressing challenges for improving the recognition performance that was observed when using ML models.     

Improving the scalability of a rating system for assessing safety of Internet of Toys

Scalability is an important feature for the long term adoption of a rating system that determines the privacy and security of Internet of Toys (IoToys). As technology evolves and innovations are introduced in the IoToy market, the rating system must be capable of including the impact of new factors in the overall safety of the toy. Similarly obsolete factors should be easily removable. The rating system should also account for the difference in the weightage of individual factors. This research enhances the ChildShield rating system proposed by Allana & Chawla (2021) to reflect these additional features. The corresponding consumer label is expanded to include a secondary layer to present supplementary details to the consumer during purchase and use. A case study of grading an IoToy with the enhanced system is conducted in collaboration with a manufacturer and the steps for rating and labelling of IoToys using self-evaluation and guided modes are proposed.