Building Information Modelling (BIM) has been extensively studied and applied within the AEC sector, particularly in design and construction. In recent years, Facility Management (FM) processes are becoming more digitalised, thus requiring effective BIM-FM integration. BIM adoption in many countries, such as the UK, Italy and Brazil, has been publicly driven. Generally, adoption was targeted at design and construction implementation, with little effort in framing public action for FM implementation. The lack of an integrated approach for BIM-FM implementation resulted in numerous bespoken implementation approaches that mimic the private sector and hinder knowledge exchange. Therefore, there is a need for assessing and amalgamating knowledge about BIM-FM for public organisations. This research aims to leverage knowledge about BIM-FM in the public domain by analysing and classifying articles published between 2010–2021. The research was carried out through a systematic review and comparative thematic analysis investigating the use of BIM for different public buildings (e.g., schools and hospitals) and the implementation for FM purposes. Research results outline prevalent trends and areas of research from three perspectives: people, process and technology. Results show an increasing number of publications about BIM-FM. However, the divide between BIM-FM for public and private organisations is unequal. BIM-FM research for public organisations is still limited and lacks standardisation. This state-of-the-art review makes an incremental contribution to knowledge by identifying progress, gaps and new industry directions on the subject matter.

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Nowadays, the advancements of wearable consumer devices have become a predominant role in healthcare gadgets. There is always a demand to obtain robust recognition of heterogeneous human activities in complicated IoT environments. The knowledge attained using these recognition models will be then combined with healthcare applications. In this way, the paper proposed a novel deep learning framework to recognize heterogeneous human activities using multimodal sensor data. The proposed framework is composed of four phases: employing dataset and processing, implementation of deep learning model, performance analysis, and application development. The paper utilized the recent KU-HAR database with eighteen different activities of 90 individuals. After preprocessing, the hybrid model integrating Extreme Learning Machine (ELM) and Gated Recurrent Unit (GRU) architecture is used. An attention mechanism is then included for further enhancing the robustness of human activity recognition in the IoT environment. Finally, the performance of the proposed model is evaluated and comparatively analyzed with conventional CNN, LSTM, GRU, ELM, Transformer and Ensemble algorithms. To the end, an application is developed using the Qt framework which can be deployed on any consumer device. In this way, the research sheds light on monitoring the activities of critical patients by healthcare professionals remotely. The proposed ELM-GRUaM model achieved supreme performance in recognizing multimodal human activities with an overall accuracy of 96.71% as compared with existing models.

Nitrification is usually the bottleneck of biological nitrogen removal processes. In SBRs systems, it is not often enough to monitor dissolved oxygen, pH and ORP to spot problems which may occur in nitrification processes. Therefore, automated supervision systems should be designed to include the possibility of monitoring the activity of nitrifying populations. Though the applicability of set-point titration for monitoring biological processes has been widely demonstrated in the literature, the possibility of an automated procedure is still at its early stage of industrial development. In this work, the use of an at-line automated titrator named TITAAN (TITrimetric Automated ANalyser) is presented. The completely automated sensor enables us to track nitrification rate trend with time in an SBR, detecting the causes leading to slower specific nitrification rates. It was also possible to perform early detection of toxic compounds in the influent by assessing their effect on the nitrifying biomass. Nitrifications rates were determined with average errors+/-10% (on 26 tests), never exceeding 20% as compared with UV-spectrophotometric determinations.

The present work was conceived as an investigation on the effects of pulse plating on the microstructure of matt tin coatings froma proprietary acidic methanesulphonate bath. The effects of pulse plating on current efficiency, surface roughness, grain size and orientation of tin deposits were investigated as a function of the duty cycle and the pulse frequency. The impact of pulse plating on the microstructure emerged clearly from the results gathered in this work, though the effect size was relatively limited. The average grain size was found to increase with increasing duty cycle, while the opposite tendency was noticed with increasing frequency. The grain structure, i.e. the cross-section microstructure of the tin deposits, was not influenced to any appreciable degree by pulsed current deposition, remaining columnar irrespective of the pulse plating parameters within our operating range. Grain orientation of direct current as well as high duty cycle pulse plated deposits is a strong (110) texture; this turned out to weaken as either the duty cycle or the frequency was reduced. A relatively stronger impact of pulse plating was that on surface morphology, where surface roughness of pulse plated deposits is reduced with decreasing duty cycle or frequency