Using Surrogate Parameters to Enhance Monitoring of Community Wastewater Management System Performance for Sustainable Operations.

Community wastewater management systems (CWMS) are small-scale wastewater treatment systems typically in regional and rural areas with less sophisticated treatment processes and often managed by local governments or communities. Research and industrial applications have demonstrated that online UV-Vis sensors have great potential for improving wastewater monitoring and treatment processes. Existing studies on the development of surrogate parameters with models from spectral data for wastewater were largely limited to lab-based.

Applications of Online UV-Vis Spectrophotometer for Drinking Water Quality Monitoring and Process Control: A Review.

Water quality monitoring is an essential component of water quality management for water utilities for managing the drinking water supply. Online UV-Vis spectrophotometers are becoming popular choices for online water quality monitoring and process control, as they are reagent free, do not require sample pre-treatments and can provide continuous measurements. The advantages of the online UV-Vis sensors are that they can capture events and allow quicker responses to water quality changes compared to conventional water quality monitoring.

Toward Unique and Unbiased Causal Effect Estimation From Data With Hidden Variables.

Causal effect estimation from observational data is a crucial but challenging task. Currently, only a limited number of data-driven causal effect estimation methods are available. These methods either provide only a bound estimation of causal effects of treatment on the outcome or generate a unique estimation of the causal effect but making strong assumptions on data and having low efficiency.

Experimental verification of the relationship between first-order coherence and linear steerability.

Coherence and steerability are two essential characteristics of quantum systems. For a two-qubit state, the first-order coherence and the maximal violation of linear steering inequality are used to operationally measure the degree of coherence and steerability, respectively. Recently, a complementary relation between first-order coherence and linear steerability has been proposed.

Evaluation of the impact of suspended particles on the UV absorbance at 254 nm (UV) measurements using a submersible UV-Vis spectrophotometer.

There is an increasing need to use online instrumentation for continuous monitoring of water quality. However, industrial applications using online instruments, such as submersible UV-Vis spectrophotometers, may require the use of alternative techniques to remove particle effect rather than performing a physical filtration step. Some submersible UV-Vis spectrophotometers have built-in generic particle compensation algorithms to remove the filtration step.

The influence of rainfall intensity and duration on sediment pathways and subsequent clogging in permeable pavements.

The trapping of sediments within permeable pavements during infiltration is an important process that contributes to their water quality treatment performance. However, this process also leads to clogging, which decreases the infiltration capacity of the pavement. With different rainfall intensities and durations, this study investigates the amount and size of sediment passing through a porous paver, as well as through the gravel-filled gaps that separate adjacent pavers.

Authenticity and credibility aware detection of adverse drug events from social media.

Objectives: Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media.

Authenticity and credibility aware detection of adverse drug events from social media.

Objectives: Adverse drug events (ADEs) are among the top causes of hospitalization and death. Social media is a promising open data source for the timely detection of potential ADEs. In this paper, we study the problem of detecting signals of ADEs from social media.

Supervised signal detection for adverse drug reactions in medication dispensing data.

Motivation: Adverse drug reactions (ADRs) are one of the leading causes of morbidity and mortality and thus should be detected early to reduce consequences on health outcomes. Medication dispensing data are comprehensive sources of information about medicine uses that can be utilized for the signal detection of ADRs. Sequence symmetry analysis (SSA) has been employed in previous studies to detect signals of ADRs from medication dispensing data, but it has a moderate sensitivity and tends to miss some ADR signals.

Building Diversified Multiple Trees for classification in high dimensional noisy biomedical data.

Purpose: It is common that a trained classification model is applied to the operating data that is deviated from the training data because of noise. This paper will test an ensemble method, Diversified Multiple Tree (DMT), on its capability for classifying instances in a new laboratory using the classifier built on the instances of another laboratory.

Methods: DMT is tested on three real world biomedical data sets from different laboratories in comparison with four benchmark ensemble methods, AdaBoost, Bagging, Random Forests, and Random Trees.

Detecting signals of detrimental prescribing cascades from social media.

Motivation: Prescribing cascade (PC) occurs when an adverse drug reaction (ADR) is misinterpreted as a new medical condition, leading to further prescriptions for treatment. Additional prescriptions, however, may worsen the existing condition or introduce additional adverse effects (AEs). Timely detection and prevention of detrimental PCs is essential as drug AEs are among the leading causes of hospitalization and deaths.

A Probabilistic Approach to Mitigate Composition Attacks on Privacy in Non-Coordinated Environments.

Organizations share data about individuals to drive business and comply with law and regulation. However, an adversary may expose confidential information by tracking an individual across disparate data publications using quasi-identifying attributes (e.g.