Computed tomography (CT) is a widely utilized noninvasive diagnostic tool in clinical practice. However, the commonly employed small molecular iodinated contrast agents (ICAs) in clinical CT imaging have limitations such as nonspecific distribution in body, rapid clearance through kidneys, etc., leading to a narrow imaging time window. In contrast, existing nano-sized ICAs face challenges like structural uncertainty, poor reproducibility, low iodine content, and uniformity issues. In this study, a novel approach is presented utilizing the aggregation-induced emission luminogen (AIEgen) to design and fabricate a kind of monocomponent nano-sized ICA (namely, BioDHU-CT NPs) that exhibits a unique aggregation effect upon activation. The small sized BioDHU-CT nanoparticles exhibit excellent tumor targeting capabilities and can release ICA modified with AIEgen with a high release efficiency up to 88.45%, under the activation of reactive oxygen species highly expressed in tumor regions. The released ICA performs in situ aggregation capability in the tumor region, which can enhance the retention efficiency of CT contrast agents, extending the imaging time window and improving the imaging quality in tumor regions.

Here, we present an improved water quality index (WQI) model for assessment of coastal water quality using Cork Harbour, Ireland, as the case study. The model involves the usual four WQI components - selection of water quality indicators for inclusion, sub-indexing of indicator values, sub-index weighting and sub-index aggregation - with improvements to make the approach more objective and data-driven and less susceptible to eclipsing and ambiguity errors. The model uses the machine learning algorithm, XGBoost, to rank and select water quality indicators for inclusion based on relative importance to overall water quality status. Of the ten indicators for which data were available, transparency, dissolved inorganic nitrogen, ammoniacal nitrogen, BOD5, chlorophyll, temperature and orthophosphate were selected for summer, while total organic nitrogen, dissolved inorganic nitrogen, pH, transparency and dissolved oxygen were selected for winter. Linear interpolation functions developed using national recommended guideline values for coastal water quality are used for sub-indexing of water quality indicators and the XGBoost rankings are used in combination with the rank order centroid weighting method to determine sub-index weight values. Eight sub-index aggregation functions were tested - five from existing WQI models and three proposed by the authors. The computed indices were compared with those obtained using a multiple linear regression (MLR) approach and R2 and RMSE used as indicators of aggregation function performance. The weighted quadratic mean function (R2 = 0.91, RMSE = 4.4 for summer; R2 = 0.97, RMSE = 3.1 for winter) and the unweighted arithmetic mean function (R2 = 0.92, RMSE = 3.2 for summer; R2 = 0.97, RMSE = 3.2 for winter) proposed by the authors were identified as the best functions and showed reduced eclipsing and ambiguity problems compared to the others.

Considering that the population is aging rapidly, the demand for technology for aging-at-home, which can provide reliable, unobtrusive monitoring of human activity, is expected to expand. This research focuses on improving the solution of the posture detection problem, which is a part of fall detection system. Fall detection, using depth maps obtained by the Microsoft Kinect sensor, is a two-stage method. We concentrate on the first stage of the system, that is, pose recognition from a depth map. For lying pose detection, a new hybrid FRSystem is proposed. In the system, two rule sets are investigated, the first one created based on a domain knowledge and the second induced based on the rough set theory. Additionally, two inference aggregation approaches are considered with and without the knowledge measure. The results indicate that the new axiomatic definition of knowledge measures, which we propose has a positive impact on the effectiveness of inference and the rule induction method reducing the number of rules in a set maintains it.

A tool to quantify the pollution potential of leachate, termed the revised leachate pollution index (r-LPI), has been developed. It was developed using the fuzzy Delphi analytic hierarchy process (FDAHP). The formulation entails four major steps: parameter selection, weight calculation, normalization of parameters, and aggregation of the parameters. Eleven leachate parameters categorized into three criteria were selected using the fuzzy Delphi method (FDM). The relative weights of the parameters and the criteria were computed using the fuzzy analytic hierarchy process (FAHP), and rating curves were used for normalization of the parameters. The selection of an aggregation function is one of the most critical steps in the development of a composite indicator. In this study, the concept of r-LPI was briefly discussed and 14 different aggregation functions were examined to estimate the pollution potential of landfill leachate. Based on accountability and non-accountability of weights of the parameters, ambiguity, eclipsing, and constant functional behavior, 8 aggregation functions were eliminated. The remaining six aggregation functions were subjected to sensitivity analysis. Furthermore, information lost due to aggregation of parameters was quantified. Based on the findings, it was concluded that the weighted additive function effectively quantifies the pollution potential of landfill leachate and thus recommended for the r-LPI.

Multi-criteria decision analysis is a quantitative approach to the drug benefit-risk assessment which allows for consistent comparisons by summarising all benefits and risks in a single score. The multi-criteria decision analysis consists of several components, one of which is the utility (or loss) score function that defines how benefits and risks are aggregated into a single quantity. While a linear utility score is one of the most widely used approach in benefit-risk assessment, it is recognised that it can result in counter-intuitive decisions, for example, recommending a treatment with extremely low benefits or high risks. To overcome this problem, alternative approaches to the scores construction, namely, product, multi-linear and Scale Loss Score models, were suggested. However, to date, the majority of arguments concerning the differences implied by these models are heuristic. In this work, we consider four models to calculate the aggregated utility/loss scores and compared their performance in an extensive simulation study over many different scenarios, and in a case study. It is found that the product and Scale Loss Score models provide more intuitive treatment recommendation decisions in the majority of scenarios compared to the linear and multi-linear models, and are more robust to the correlation in the criteria.

Assessing water supply capacity is crucial to meet stakeholders\' needs, notably in the Mediterranean region. This region has been identified as a climate change hot spot, and as a region where water demand is continuously increasing due to population growth and the expansion of irrigated areas. The Hérault River catchment (2500 km(2), France) is a typical example and a negative trend in discharge has been observed since the 1960s. In this context, local stakeholders need first to understand the processes controlling the evolution of water resources and demands in the past to latter evaluate future water supply capacity and anticipate the tensions users could be confronted to in the future. A modelling framework is proposed at a 10-day time step to assess whether water resources have been able to meet water demands over the last 50 years. Water supply was evaluated using hydrological modelling and a dam management model. Water demand dynamics were estimated for the domestic and agricultural sectors. A water supply capacity index is computed to assess the extent and the frequency to which water demand has been satisfied at the sub-basin scale. Simulated runoff dynamics were in good agreement with observations over the calibration and validation periods. Domestic water demand has increased considerably since the 1980s and is characterized by a seasonal peak in summer. Agricultural demand has increased in the downstream sub-basins and decreased upstream where irrigated areas have decreased. As a result, although most water demands were satisfied between 1961 and 1980, irrigation requirements in summer have sometimes not been satisfied since the 1980s. This work is the first step toward evaluating possible future changes in water allocation capacity in the catchment, using future climate change, dam management and water use scenarios.