Carbamazepine (CBZ) is one of the most common emerging contaminants released to the aquatic environment through domestic and pharmaceutical wastewater. Due to its high persistence through ...conventional degradation treatments, CBZ is considered a typical indicator for anthropogenic activities. This study tested the removal of CBZ through two different clay-based purification techniques: adsorption of relatively large concentrations (20–500 μmol L−1) and photocatalysis of lower concentrations (<20 μmol L−1). The sorption mechanism was examined by FTIR measurements, exchangeable cations released, and colloidal charge of the adsorbing clay materials. Photocatalysis was performed in batch experiments under various conditions. Despite the neutral charge of carbamazepine, the highest adsorption was observed on negatively charged montmorillonite-based clays. Desorption tests indicate that adsorbed CBZ is not released by washing. The adsorption/desorption processes were confirmed by ATR-FTIR analysis of the clay-CBZ particles. A combination of synthetic montmorillonite or hectorite with low H2O2 concentrations under UVC irradiation exhibits efficient homo-heterogeneous photodegradation at μM CBZ levels. The two techniques presented in this study suggest solutions for both industrial and municipal wastewater, possibly enabling water reuse.
We propose a new pruning constraint when mining frequent temporal patterns to be used as classification and prediction features, the Semantic Adjacency Criterion SAC, which filters out temporal ...patterns that contain potentially semantically contradictory components, exploiting each medical domain’s knowledge. We have defined three SAC versions and tested them within three medical domains (oncology, hepatitis, diabetes) and a frequent-temporal-pattern discovery framework. Previously, we had shown that using SAC enhances the repeatability of discovering the same temporal patterns in similar proportions in different patient groups within the same clinical domain. Here, we focused on SAC’s computational implications for pattern discovery, and for classification and prediction, using the discovered patterns as features, by four different machine-learning methods: Random Forests, Naïve Bayes, SVM, and Logistic Regression. Using SAC resulted in a significant reduction, across all medical domains and classification methods, of up to 97% in the number of discovered temporal patterns, and in the runtime of the discovery process, of up to 98%. Nevertheless, the highly reduced set of only semantically transparent patterns, when used as features, resulted in classification and prediction models whose performance was at least as good as the models resulting from using the complete temporal-pattern set.
Abstract Objective Clinicians and medical researchers alike require useful, intuitive, and intelligent tools to process large amounts of time-oriented multiple-patient data from multiple sources. For ...analyzing the results of clinical trials or for quality assessment purposes, an aggregated view of a group of patients is often required. To meet this need, we designed and developed the VISualizatIon of Time-Oriented RecordS (VISITORS) system, which combines intelligent temporal analysis and information visualization techniques. The VISITORS system includes tools for intelligent retrieval, visualization, exploration, and analysis of raw time-oriented data and derived (abstracted) concepts for multiple patient records. To derive meaningful interpretations from raw time-oriented data (known as temporal abstraction s), we used the knowledge-based temporal-abstraction method. Methods The main module of the VISITORS system is an interactive, ontology-based exploration module, which enables the user to visualize raw data and abstract (derived) concepts for multiple patient records, at several levels of temporal granularity; to explore these concepts; and to display associations among raw and abstract concepts. A knowledge-based delegate function is used to convert multiple data points into one delegate value representing each temporal granule. To select the population of patients to explore, the VISITORS system includes an ontology-based temporal-aggregation specification language and a graphical expression-specification module. The expressions, applied by an external temporal mediator, retrieve a list of patients, a list of relevant time intervals, and a list of time-oriented patients’ data sets, by using an expressive set of time and value constraints. Results Functionality and usability evaluation of the interactive exploration module was performed on a database of more than 1000 oncology patients by a group of 10 users—five clinicians and five medical informaticians. Both types of users were able in a short time (mean of 2.5 ± 0.2 min per question) to answer a set of clinical questions, including questions that require the use of specialized operators for finding associations among derived temporal abstractions, with high accuracy (mean of 98.7 ± 2.4 on a predefined scale from 0 to 100). There were no significant differences between the response times and between accuracy levels of the exploration of the data using different time lines, i.e., absolute (i.e., calendrical) versus relative (referring to some clinical key event). A system usability scale (SUS) questionnaire filled out by the users demonstrated the VISITORS system to be usable (mean score for the overall group: 69.3), but the clinicians’ usability assessment was significantly lower than that of the medical informaticians. Conclusions We conclude that intelligent visualization and exploration of longitudinal data of multiple patients with the VISITORS system is feasible, functional, and usable.
Hybrid semiconductor–metal nanoparticles (HNPs) manifest unique, synergistic electronic and optical properties as a result of combining semiconductor and metal physics via a controlled interface. ...These structures can exhibit spatial charge separation across the semiconductor–metal junction upon light absorption, enabling their use as photocatalysts. The combination of the photocatalytic activity of the metal domain with the ability to generate and accommodate multiple excitons in the semiconducting domain can lead to improved photocatalytic performance because injecting multiple charge carriers into the active catalytic sites can increase the quantum yield. Herein, we show a significant metal domain size dependence of the charge carrier dynamics as well as the photocatalytic hydrogen generation efficiencies under nonlinear excitation conditions. An understanding of this size dependence allows one to control the charge carrier dynamics following the absorption of light. Using a model hybrid semiconductor–metal CdS–Au nanorod system and combining transient absorption and hydrogen evolution kinetics, we reveal faster and more efficient charge separation and transfer under multiexciton excitation conditions for large metal domains compared to small ones. Theoretical modeling uncovers a competition between the kinetics of Auger recombination and charge separation. A crossover in the dominant process from Auger recombination to charge separation as the metal domain size increases allows for effective multiexciton dissociation and harvesting in large metal domain HNPs. This was also found to lead to relative improvement of their photocatalytic activity under nonlinear excitation conditions.
A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, ...parameters, events and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse and sharing of that knowledge.
The
knowledge-based temporal abstraction method decomposes the temporal abstraction
task into five
subtasks. These subtasks are solved by five domain-independent
temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific
knowledge types: structural, classification (functional), temporal semantic (logical) and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed.
The knowledge-based temporal abstraction method has been implemented in the
RÉSUMÉ system and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.
Today, cardiac implantable electronic devices (CIEDs), such as pacemakers and implantable cardioverter defibrillators (ICDs), play an increasingly important role in healthcare ecosystems as patient ...life support devices. Physicians control, program and configure CIEDs on a regular basis using a dedicated programmer device. The programmer device is open to external connections (e.g., USB, Bluetooth, etc.), and thus it is exposed to a variety of cyber-attacks by which an attacker can manipulate the programmer device's operations and consequently harm the patient. In this paper, we present CardiWall, a novel detection and prevention system designed to protect ICDs from cyber-attacks aimed at the programmer device. Our system has six different layers of protection, leveraging medical experts' knowledge, statistical methods, and machine learning algorithms. We evaluated the CardiWall system extensively in two comprehensive experiments. For the evaluation, we gathered data for a period of four years and used 775 benign clinical commands that are related to hundreds of different patients (obtained from different programmer devices located at Barzilai University Medical center) and 28 malicious clinical commands (created by two cardiology experts from different hospitals). The evaluation results show that only two out of the six layers proposed in CardiWall system provided a high detection capability associated with high rates of true positive, and low rates of false positive. With the configuration that provided the best harmonic mean of sensitivity and specificity (HMSS), CardiWall achieved a high true positive rate (TPR) of 91.4% and a very low false positive rate (FPR) of 1%, with an AUC of 94.7%.
Biomedical data, in particular electronic medical records data, include a large number of variables sampled in irregular fashion, often including both time point and time intervals, thus providing ...several challenges for analysis and data mining. Classification of multivariate time series data is a challenging task, but is often necessary for medical care or research. Increasingly, temporal abstraction, in which a series of raw-data time points is abstracted into a set of symbolic time intervals, is being used for classification of multivariate time series. In this paper, we introduce a novel supervised discretization method, geared towards enhancement of classification accuracy, which determines the cutoffs that will best discriminate among classes through the distribution of their states. We present a framework for classification of multivariate time series analysis, which implements three phases: (1) application of a temporal-abstraction process that transforms a series of raw time-stamped data points into a series of symbolic time intervals (based on either unsupervised or supervised temporal abstraction); (2) mining these time intervals to discover frequent temporal-interval relation patterns (TIRPs), using versions of Allen’s 13 temporal relations; (3) using the patterns as features to induce a classifier. We evaluated the framework, focusing on the comparison of three versions of the new, supervised, temporal discretization for classification (TD4C) method, each relying on a different symbolic-state distribution-distance measure among outcome classes, to several commonly used unsupervised methods, on real datasets in the domains of diabetes, intensive care, and infectious hepatitis. Using only three abstract temporal relations resulted in a better classification performance than using Allen’s seven relations, especially when using three symbolic states per variable. Similarly when using the horizontal support and mean duration as the TIRPs feature representation, rather than a binary (existence) representation. The classification performance when using the three versions of TD4C was superior to the performance when using the unsupervised (EWD, SAX, and KB) discretization methods.
Hybrid nanoparticles combine two or more disparate materials on the same nanosystem and represent a powerful approach for achieving advanced materials with multiple functionalities stemming from the ...unusual materials combinations. This review focuses on recent advances in the area of semiconductor–metal hybrid nanoparticles. Synthesis approaches offering high degree of control over the number of components, their compositions, shapes, and interfacial characteristics are discussed, including examples of advanced architectures. Progress in hybrid nanoscale inorganic cage structures prepared by a selective edge growth mechanism of the metal onto the semiconductor nanocrystal is also presented. The combined and often synergistic properties of the hybrid nanoparticles are described with emphasis on optical properties, electronic structure, electrical characteristics, and light induced charge separation effects. Progress toward the application of hybrid nanoparticles in photocatalysis is overviewed. We conclude with a summary and point out some challenges for further development and understanding of semiconductor–metal hybrid nanoparticles. This progress shows promise for application of hybrid nanoparticles in photocatalysis, catalysis, optical components, and electronic devices.
Nanochemistry provides powerful synthetic tools allowing one to combine different materials on a single nanostructure, thus unfolding numerous possibilities to tailor their properties toward diverse ...functionalities. Herein, we review the progress in the field of semiconductor–metal hybrid nanoparticles (HNPs) focusing on metal–chalcogenides–metal combined systems. The fundamental principles of their synthesis are discussed, leading to a myriad of possible hybrid architectures including Janus zero-dimensional quantum dot-based systems and anisotropic quasi 1D nanorods and quasi-2D platelets. The properties of HNPs are described with particular focus on emergent synergetic characteristics. Of these, the light-induced charge-separation effect across the semiconductor–metal nanojunction is of particular interest as a basis for the utilization of HNPs in photocatalytic applications. The extensive studies on the charge-separation behavior and its dependence on the HNPs structural characteristics, environmental and chemical conditions, and light excitation regime are surveyed. Combining the advanced synthetic control with the charge-separation effect has led to demonstration of various applications of HNPs in different fields. A particular promise lies in their functionality as photocatalysts for a variety of uses, including solar-to-fuel conversion, as a new type of photoinitiator for photopolymerization and 3D printing, and in novel chemical and biomedical uses.
Classification of multivariate time series data, often including both time points and intervals at variable frequencies, is a challenging task. We introduce the KarmaLegoSification (KLS) framework ...for classification of multivariate time series analysis, which implements three phases: (1) application of a temporal abstraction process that transforms a series of raw time-stamped data points into a series of symbolic time intervals; (2) mining these symbolic time intervals to discover frequent time-interval-related patterns (TIRPs), using Allen’s temporal relations; and (3) using the TIRPs as features to induce a classifier. To efficiently detect multiple TIRPs (features) in a single entity to be classified, we introduce a new algorithm, SingleKarmaLego, which can be shown to be superior for that purpose over a Sequential TIRPs Detection algorithm. We evaluated the KLS framework on datasets in the domains of diabetes, intensive care, and infectious hepatitis, assessing the effects of the various settings of the KLS framework. Discretization using Symbolic Aggregate approXimation (SAX) led to better performance than using the equal-width discretization (EWD); knowledge-based cut-off definitions when available were superior to both. Using three abstract temporal relations was superior to using the seven core temporal relations. Using an epsilon value larger than zero tended to result in a slightly better accuracy when using the SAX discretization method, but resulted in a reduced accuracy when using EWD, and overall, does not seem beneficial. No feature selection method we tried proved useful. Regarding feature (TIRP) representation, mean duration performed better than horizontal support, which in turn performed better than the default Binary (existence) representation method.
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