In-situ detection of microplastics in the aquatic environment: A systematic literature review.

Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the ecosystem. Current laboratory-based microplastic detection methods suffer from various drawbacks, including a lack of standardisation, limited spatial and temporal coverage, high costs, and time-consuming procedures. Consequently, there is a need for the development of in-situ techniques to detect and monitor microplastics to effectively identify and understand their sources, pathways, and behaviours. Herein, we adopt a systematic literature review method to assess the development and application of experimental and field technologies designed for the in-situ detection and monitoring of aquatic microplastics, without the need for sample preparation. Four scientific databases were searched in March 2023, resulting in a review of 62 relevant studies. These studies were classified into seven sensor categories and their working principles were discussed. The sensor classes include optical devices, digital holography, Raman spectroscopy, other spectroscopy, hyperspectral imaging, remote sensing, and other methods. We also looked at how data from these technologies are integrated with machine learning models to develop classifiers capable of accurately characterising the physical and chemical properties of microplastics and discriminating them from other particles. This review concluded that in-situ detection of microplastics in aquatic environments is feasible and can be achieved with high accuracy, even though the methods are still in the early stages of development. Nonetheless, further research is still needed to enhance the in-situ detection of microplastics. This includes exploring the possibility of combining various detection methods and developing robust machine-learning classifiers. Additionally, there is a recommendation for in-situ implementation of the reviewed methods to assess their effectiveness in detecting microplastics and identify their limitations.

Low-Cost Sensors for Monitoring Coastal Climate Hazards: A Systematic Review and Meta-Analysis.

Unequivocal change in the climate system has put coastal regions around the world at increasing risk from climate-related hazards. Monitoring the coast is often difficult and expensive, resulting in sparse monitoring equipment lacking in sufficient temporal and spatial coverage. Thus, low-cost methods to monitor the coast at finer temporal and spatial resolution are imperative for climate resilience along the world's coasts. Exploiting such low-cost methods for the development of early warning support could be invaluable to coastal settlements. This paper aims to provide the most up-to-date low-cost techniques developed and used in the last decade for monitoring coastal hazards and their forcing agents via systematic review of the peer-reviewed literature in three scientific databases: Scopus, Web of Science and ScienceDirect. A total of 60 papers retrieved from these databases through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol were analysed in detail to yield different categories of low-cost sensors. These sensors span the entire domain for monitoring coastal hazards, as they focus on monitoring coastal zone characteristics (e.g., topography), forcing agents (e.g., water levels), and the hazards themselves (e.g., coastal flooding). It was found from the meta-analysis of the retrieved papers that terrestrial photogrammetry, followed by aerial photogrammetry, was the most widely used technique for monitoring different coastal hazards, mainly coastal erosion and shoreline change. Different monitoring techniques are available to monitor the same hazard/forcing agent, for instance, unmanned aerial vehicles (UAVs), time-lapse cameras, and wireless sensor networks (WSNs) for monitoring coastal morphological changes such as beach erosion, creating opportunities to not only select but also combine different techniques to meet specific monitoring objectives. The sensors considered in this paper are useful for monitoring the most pressing challenges in coastal zones due to the changing climate. Such a review could be extended to encompass more sensors and variables in the future due to the systematic approach of this review. This study is the first to systematically review a wide range of low-cost sensors available for the monitoring of coastal zones in the context of changing climate and is expected to benefit coastal researchers and managers to choose suitable low-cost sensors to meet their desired objectives for the regular monitoring of the coast to increase climate resilience.

A Mathematical Model of Estradiol Production from Ultrasound Data for Bovine Ovarian Follicles.

In this paper, we present a new way to assess the concentration of estradiol (E2) and Insulin Growth Factor-1 (IGF) based on the results from ultrasound scans combined with mathematical models. The IGF1 model is based on the progesterone (P4) concentration, which can be estimated with models calculating P4 level based on the size/volume of corpus luteum (CL) measured during ultrasound scans. At this moment little is known about the underlying reasons for double ovulation and silent heat occurrences. Both of these are linked to the level of IGF1: double ovulations are linked to higher IGF1 levels and and silent heat is linked to lower E2 to P4 ratio. These models can help to improve understanding of the related concentrations of E2 and IGF1. Currently, it is known that diet and genetic factors have an impact on ovulation rates and silent heat. In this study, we also examine the decline of the production of E2 in vivo by atretic follicles throughout the process of atresia. This is the first recorded quantitative description of this decline.

NIR-Based Intelligent Sensing of Product Yield Stress for High-Value Bioresorbable Polymer Processing.

PLA (polylactide) is a bioresorbable polymer used in implantable medical and drug delivery devices. Like other bioresorbable polymers, PLA needs to be processed carefully to avoid degradation. In this work we combine in-process temperature, pressure, and NIR spectroscopy measurements with multivariate regression methods for prediction of the mechanical strength of an extruded PLA product. The potential to use such a method as an intelligent sensor for real-time quality analysis is evaluated based on regulatory guidelines for the medical device industry. It is shown that for the predictions to be robust to processing at different times and to slight changes in the processing conditions, the fusion of both NIR and conventional process sensor data is required. Partial least squares (PLS), which is the established 'soft sensing' method in the industry, performs the best of the linear methods but demonstrates poor reliability over the full range of processing conditions. Conversely, both random forest (RF) and support vector regression (SVR) show excellent performance for all criteria when used with a prior principal component (PC) dimension reduction step. While linear methods currently dominate for soft sensing of mixture concentrations in highly conservative, regulated industries such as the medical device industry, this work indicates that nonlinear methods may outperform them in the prediction of mechanical properties from complex physicochemical sensor data. The nonlinear methods show the potential to meet industrial standards for robustness, despite the relatively small amount of training data typically available in high-value material processing.

A Literature Review of Modelling and Experimental Studies of Water Treatment by Adsorption Processes on Nanomaterials.

A significant growth in the future demand for water resources is expected. Hence researchers have focused on finding new technologies to develop water filtration systems by using experimental and simulation methods. These developments were mainly on membrane-based separation technology, and photocatalytic degradation of organic pollutants which play an important role in wastewater treatment by means of adsorption technology. In this work, we provide valuable critical review of the latest experimental and simulation methods on wastewater treatment by adsorption on nanomaterials for the removal of pollutants. First, we review the wastewater treatment processes that were carried out using membranes and nanoparticles. These processes are highlighted and discussed in detail according to the rate of pollutant expulsion, the adsorption capacity, and the effect of adsorption on nanoscale surfaces. Then we review the role of the adsorption process in the photocatalytic degradation of pollutants in wastewater. We summarise the comparison based on decomposition ratios and degradation efficiency of pollutants. Therefore, the present article gives an evidence-based review of the rapid development of experimental and theoretical studies on wastewater treatment by adsorption processes. Lastly, the future direction of adsorption methods on water filtration processes is indicated.

Death Processes in Bovine Theca and Granulosa Cells Modelled and Analysed Using a Systems Biology Approach.

In this paper, newly discovered mechanisms of atresia and cell death processes in bovine ovarian follicles are investigated. For this purpose the mRNA expression of receptor interacting protein kinases 1 and 3 (RIPK1 and RIPK3) of the granulosa and theca cells derived from healthy and atretic follicles are studied. The follicles were assigned as either healthy or atretic based on the estradiol to progesterone ratio. A statistically significant difference was recorded for the mRNA expression of a RIPK1 and RIPK3 between granulosa cells from healthy and atretic follicles. To further investigate this result a systems biology approach was used. The genes playing roles in necroptosis, apoptosis and atresia were chosen and a network was created based on human genes annotated by the IMEx database in Cytoscape to identify hubs and bottle-necks. Moreover, correlation networks were built in the Cluepedia plug-in. The networks were created separately for terms describing apoptosis and programmed cell death. We demonstrate that necroptosis (RIPK-dependent cell death pathway) is an alternative mechanism responsible for death of bovine granulosa and theca cells. We conclude that both apoptosis and necroptosis occur in the granulosa cells of dominant follicles undergoing luteinisation and in the theca cells from newly selected follicles.

Hyperbolic source location of crack related acoustic emission in bone.

Little work has been done on the localization of microcracks in bone using acoustic emission. Microcrack localization is useful to study the fracture process in bone and to prevent fractures in patients. Locating microcracks that occur before fracture allows one to predict where fracture will occur if continued stress is applied to the bone. Two source location algorithms were developed to locate microcracks on rectangular bovine bone samples. The first algorithm uses a constant velocity approach which has some difficulty dealing with the anisotropic nature of bone. However, the second algorithm uses an iterative technique to estimate the correct velocity for the acoustic emission source location being located. In tests with simulated microcracks, the constant velocity algorithm achieves a median error of 1.78 mm (IQR 1.51 mm) and the variable velocity algorithm improves this to a median error of 0.70 mm (IQR 0.79 mm). An experiment in which the bone samples were loaded in a three point bend test until they fractured showed a good correlation between the computed location of detected microcracks and where the final fracture occurred. Microcracks can be located on bovine bone samples using acoustic emission with good accuracy and precision.