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The SiC reinforced Al composite is perhaps the most successful class of metal matrix composites (MMCs) produced to date. They have found widespread application for aerospace, energy, and military purposes, as well as in other industries – for example, they have been used in electronic packaging, aerospace structures, aircraft and internal combustion engine components, and a variety of recreational products. In all these applications, welding plays a vital role. Little attention has been paid to SiC reinforced aluminium matrix composites joined by gas tungsten arc (GTA) welding. The purpose of this paper is to outline the manufacturing method for producing MMCs, GTA welding of MMCs and pitting corrosion analysis of welded MMCs.

This paper focuses upon production and welding of metal matrix composites. The welded composites have been treated at elevated and cryogenic temperatures for experimental studies. Pitting corrosion analysis of welded plates was carried out as per Box Benkehn Design.

From the results, it should be noted that maximum pitting resistance was observed with MMCs containing 10% SiC treated at cryogenic temperature. Corrosion resistance of welded composites treated at elevated temperature was found to be higher than that of as‐welded and at cryogenic temperature treated composites. The pitting potential increases with increase in % SiC to certain level and decreases with further increase in % SiC. Corrosion potential of composites treated at elevated temperature is high compared to other composites. Maximum pitting resistance is observed when the welding current was kept at 175 amps for 10% addition of SiC in LM25 matrix treated at cryogenic temperature.

The paper outlines the manufacturing method for producing MMCs, GTA welding of MMCs and pitting corrosion analysis of welded MMCs. The results obtained may be helpful for the automobile and aerospace industries.

The aim of this study is to investigate subject independent emotion recognition capabilities of EEG and peripheral physiological signals namely: electroocoulogram (EOG), electromyography (EMG), electrodermal activity (EDA), temperature, plethysmograph and respiration. The experiments are conducted on both modalities independently and in combination. This study arranges the physiological signals in order based on the prediction accuracy obtained on test data using time and frequency domain features.

DEAP dataset is used in this experiment. Time and frequency domain features of EEG and physiological signals are extracted, followed by correlation-based feature selection. Classifiers namely – Naïve Bayes, logistic regression, linear discriminant analysis, quadratic discriminant analysis, logit boost and stacking are trained on the selected features. Based on the performance of the classifiers on the test set, the best modality for each dimension of emotion is identified.

 The experimental results with EEG as one modality and all physiological signals as another modality indicate that EEG signals are better at arousal prediction compared to physiological signals by 7.18%, while physiological signals are better at valence prediction compared to EEG signals by 3.51%. The valence prediction accuracy of EOG is superior to zygomaticus electromyography (zEMG) and EDA by 1.75% at the cost of higher number of electrodes. This paper concludes that valence can be measured from the eyes (EOG) while arousal can be measured from the changes in blood volume (plethysmograph). The sorted order of physiological signals based on arousal prediction accuracy is plethysmograph, EOG (hEOG + vEOG), vEOG, hEOG, zEMG, tEMG, temperature, EMG (tEMG + zEMG), respiration, EDA, while based on valence prediction accuracy the sorted order is EOG (hEOG + vEOG), EDA, zEMG, hEOG, respiration, tEMG, vEOG, EMG (tEMG + zEMG), temperature and plethysmograph.

Many of the emotion recognition studies in literature are subject dependent and the limited subject independent emotion recognition studies in the literature report an average of leave one subject out (LOSO) validation result as accuracy. The work reported in this paper sets the baseline for subject independent emotion recognition using DEAP dataset by clearly specifying the subjects used in training and test set. In addition, this work specifies the cut-off score used to classify the scale as low or high in arousal and valence dimensions. Generally, statistical features are used for emotion recognition using physiological signals as a modality, whereas in this work, time and frequency domain features of physiological signals and EEG are used. This paper concludes that valence can be identified from EOG while arousal can be predicted from plethysmograph.

The future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.

The paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.

Technologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.

Based on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.