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This paper aims to design and optimize the threaded fastener of leakage current particulate matter (PM) sensor. The corresponding air-tight test is conducted to ensure the reliability of the installation strategy with screw connection.

Research on the pressure-deformation curve of seal gasket was conducted and the vibration load of engine was considered for the calculation of the minimum installation pre-tightening force. Simultaneously, the danger threaded section area was calculated, and the carrying capacity was verified. The height of the welding line was studied to ensure the reliability of the application. FEA was carried out to acquire the relationship between local structure size and local stress for continuous improvement of thread connection. The installation torque range was acquired from the torque control principle for the pre-tightening force. The sealing reliability of the connector was proved with leakage.

The air tightness of the thread connector is proved to be fine. When the pre-tightening force is over 8,000 N, and its length reaches 2 mm, the connector has good reliability at ambient temperature. The tightening torque of 60-74 Nm can guarantee the reliable fixing ability of thread connector, and its plastic non-deformation ability in the process of repeated tearing down.

This paper provides an installation strategy and an optimization of PM sensor, which has a positive effect on the study and the manufacture of PM sensor. It is helpful to further develop PM sensor and after-treatment technology. This kind of real-time monitoring PM sensor needs to be studied further to achieve its commercial application.

Internet of Things (IoT) and artificial intelligence are two leading technologies that bought revolution to each and every field of humans using in daily life by making everything smarter than ever. IoT leads to a network of things which creates a self-configuring network. Improving farm productivity is essential to meet the rapidly growing demand for food. In this chapter, the authors have introduced a smart greenhouse by integration of two leading technologies in the market (i.e., Machine Learning and IoT). In proposed model, several sensors are used for data collection and managing the environment of greenhouse. The idea is to propose an IoT and Machine Learning based smart nursery that helps in healthy growing and monitoring of the seed. The structure will be a dome-like structure for observation and isolation of an egg with various sensors like pressure, humidity, temperature, light, moisture, conductivity, air quality, etc. to monitor the nursery internal environment and maintain the control and flow of water and other minerals inside the nursery. The nursery will have a solar panel from which it stores the electricity generated from the sun, a small fan to control the flow of air and pressure. A camera will also be equipped inside the nursery that will use computer vision technology to monitor the health of the plant and will be trained on the past data to notify the user if the plant is diseased or need attention.

The paper aims to develop a particle matter (PM_2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM_2.5 levels is not widely applied in indoor settings. Many reliable methods of monitoring PM_2.5 require either time-consuming or expensive equipment, thus making PM_2.5 monitoring impractical for many settings. The goal of this paper is to identify possible low-cost, low-effort data sources that building managers can use in combination with machine learning (ML) models to approximate the performance of much more costly monitoring devices.

This study identified a variety of data sources, including freely available, public data, data from low-cost sensors and data from expensive, high-quality sensors. This study examined a variety of neural network architectures, including traditional artificial neural networks, generalized recurrent neural networks and long short-term memory neural networks as candidates for the prediction model. The authors trained the selected predictive model using this data and identified data sources that can be cheaply combined to approximate more expensive data sources.

The paper identified combinations of free data sources such as building damper percentages and weather data and low-cost sensors such as Wi-Fi-based occupancy estimator or a Plantower PMS7003 sensor that perform nearly as well as predictions made based on nephelometer data.

This work demonstrates that by combining low-cost sensors and ML, indoor PM_2.5 monitoring can be performed at a drastically reduced cost with minimal error compared to more traditional approaches.

This paper aims to report an insightful portable microfluidic system for rapid and selective sensing of Hg²⁺ in the picomolar (pM) concentration using microcantilever-based piezoresistive sensor. The detection time for various laboratory-based techniques is generally 12–24 h. The majority of modules used in the proposed platform are battery oriented; therefore, they are portable and handy to carry-out on-field investigations.

In this study, the authors have incorporated the benefit of three technologies, i.e. thin-film, nanoparticles (NPs) and micro-electro-mechanical systems, to selectively capture the Hg²⁺ at the pM concentration. The morphology and topography of the proposed sensor are characterized using field emission scanning electron microscopy and verification of the experimental results using energy dispersive X-ray.

The proposed portable microfluidic system is able to perform the detection in 5 min with a limit of detection (LOD) of 0.163 ng (0.81 pM/mL) for Hg²⁺, which perfectly describes its excellent performance over other reported techniques.

A microcantilever-based technology is perfect for on-site detection, and a LOD of 0.163 ng (0.81 pM/mL) is outstanding compared to other techniques, but the fabrication of microcantilever sensor is complex.

Many researchers used NPs for heavy metal ions sensing, but the excess usage and industrialization of NPs are rapidly expanding harmful consequences on the human life and nature. Also, the LOD of the NPs-based method is limited to nanomolar concentration. The suggested microfluidic system used the benefit of thin-film and microcantilever devices to provide advancement over the NPs-based approach and it has a selective sensing in pM concentration.

The purpose of this paper is to demonstrate a novel 3D system-in-package (SiP) approach. This new packaging approach is based on stacked silicon submount technology. As demonstrators, a smart lighting module and a sensor systems were successfully developed by using the fabrication and assembly process described in this paper.

The stacked module consists of multiple layers of silicon submounts which can be designed and fabricated in parallel. The 3D stacking design offers higher silicon efficiency and miniaturized package form factor. This platform consists of silicon submount design and fabrication, module packaging, system assembling and testing and analyzing.

In this paper, a smart light emitting diode system and sensor system will be described based on stacked silicon submount and 3D SiP technology. The integrated smart lighting module meets the optical requirements of general lighting applications. The developed SiP design is also implemented into the miniaturization of particular matter sensors and gas sensor detection system.

SiP has great potential of integrating multiple components into a single compact package, which has potential implementation in intelligent applications.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

The purpose of this paper is a new thin-film based sensor proposed for sensitive and selective detection of mercury (Hg²⁺) ions in water. The thin-film platform is easy to use and quick for heavy metal ions (HMIs) detection in the picomolar range. Ion-selective self-assembled monolayer's (SAM) of thiol used for the detection of HMIs above the Au/Ti top surface.

A thin-film based platform is suitable for the on-field experiments and testing of water samples. HMIs (antigen) and thiol-based SAM (antibody) interaction results change in surface morphology and topography. In this study, the authors have used different characterization techniques to check the selectivity of the proposed method. This change in the morphology and topography of thin-film sensor checked with Fourier-transform infrared spectroscopy, surface-enhanced Raman scattering spectroscopy, atomic force microscopy and scanning electron microscopy with energy dispersive x-ray analysis used for high-resolution images.

This thin-film based platform is straightforward to use and suitable for real-time detection of HMIs at the picomolar range. This thin-film based sensor platform capable of achieving a lower limit of detection (LOD) 27.42 ng/mL (136.56 pM) using SAM of Homocysteine-Pyridinedicarboxylic acid to detect Hg²⁺ ions.

A thin-film based technology is perfect for real-time testing and removal of HMIs, but the LOD is higher as compared to microcantilever-based devices.

The excessive use and commercialization of nanoparticle (NPs) are quickly expanding their toxic impact on health and the environment. The proposed method used the combination of thin-film and NPs, to overcome the limitation of NPs-based technique and have picomolar (136.56 pM) range of HMIs detection. The proposed thin-film-based sensor shows excellent repeatability and the method is highly reliable for toxic Hg²⁺ ions detection. The main advantage of the proposed thin-film sensor is its ability to selectively remove the Hg²⁺ ions from water samples just like a filter and a sensor for detection at picomolar range makes this method best among the other current-state of the art techniques.

This paper aims to propose a new microfluidic portable experimental platform for quick detection of heavy metal ions (HMIs) in picomolar range. The experimental setup uses a microfabricated piezoresistive sensor (MPS) array of eight cantilevers with ion-selective self-assembled monolayer's (SAM).

Most of the components used in this experimental setup are battery operated and, hence, portable to perform the on-field experiments. HMIs (antigen) and thiol-based SAM (antibody) interaction start bending the microcantilever. This results in a change of resistance, which is directly proportional to the surface stress produced due to the mass of targeted HMIs. The authors have used Cysteamine and 4-Mercaptobenzoic acid as a thiol for creating SAM to test the sensitivity and identify the suitable thiol. Some of the cantilevers are blocked using acetyl chloride to use as a reference for error detection.

The portable experimental platform achieves very small detection time of 10-25 min with a lower limit of detection (LOD) 0.762 ng (6.05 pM) for SAM of Cysteamine and 4-Mercaptobenzoic acid to detect Mn2+ ions. This technique has excellent potential and capability to selectively detect Hg2+ ions as low as 2.43 pM/mL using SAM of Homocysteine (Hcys)-Pyridinedicarboxylic acid (PDCA).

As microcantilever is very thin and fragile, it is challenging to apply a surface coating to have selective detection using Nanadispenser. Some of the cantilevers get broken during this process.

The excessive use and commercialization of NPs are quickly expanding their toxic impact on health and the environment. Also, LOD is limited to nanomolar range. The proposed method used the combination of thin-film, NPs, and MEMS-based technology to overcome the limitation of NPs-based technique and have picomolar range of HMIs detection.

This study aims to solve the problem of temperature cross sensitivity of fiber Bragg grating in structural health monitoring, proposing a novel acceleration sensor based on strain chirp effect which is insensitive to temperature.

A kind of M-shaped double cantilever beam structure is developed. The fiber grating is pasted in the gradient strain region of the beam, and the chirp effect is produced under the action of non-uniform stress, and then the change of acceleration is converted into the change of reflection bandwidth to demodulate and eliminate the temperature interference. Through theoretical analysis, simulation and experimental verification with rectangular beam sensor.

The results show that the sinusoidal curvature beam sensor is insensitive to the change of temperature and is more likely to produce chirp effect. The sensitivity is about 317 pm/g, and the natural frequency is 56 Hz.

This paper fulfils an insensitive to temperature changes sensor which has effectively solved the temperature cross-sensitivity problem in building structure health monitoring.

New Zealand’s historical housing stock comprises largely single-storey detached houses, characterised by poor winter comfort with high air infiltration. Challenges with affordability and land use are shifting New Zealand’s housing stock towards double-storey, conjoined medium-density housing (MDH). Reduced external surfaces in this typology should reduce winter heat loss and infiltration, improving winter comfort and health. New concerns arise, however, regarding summertime overheating and poor indoor air quality.

A field study was undertaken where temperature, humidity, airtightness, particulate matter (PM) and total volatile organic compounds (TVOC) were measured in two unoccupied, newly built double-storey, conjoined houses, for several weeks over summer.

The reduced surface area of this typology did not reduce infiltration and demonstrated significant periods of overheating. Internal PM concentrations generally exceeded outdoor concentrations but did not exceed annual average outdoor PM10 guidelines of 20 µg m-3. Infiltration factors (Finf) were closer to more traditional houses. TVOC readings varied widely, but frequently exceeded international guidelines.

The small sample limits the applications of conclusions more widely. Recommendations to investigate a wider sample in different locations with more detailed VOC analysis over all seasons are made.

Improvements to internal environments cannot be guaranteed by housing typology changes alone and must still involve thoughtful environmental design.

Housing typology changes may not improve internal living environments.

A move to the new MDH typology may not achieve expectations of airtightness and thermal improvement. New challenges arise from significant overheating and high TVOC levels, which may lead to new negative health effects.