Nurses’ reactions to alarms in a neonatal intensive care unit

Neonatal intensive care units (NICUs), like other intensive care units, are intended to provide immediate responses to any change in the patients condition. Patient-monitoring alarms and alarms from other medical equipment are very common in these units, and most alarms have no clinical significance. This study addresses the question of how alarms affect nurses actions by measuring the occurrence of alarms from different causes in a NICU, recording the nurses reactions, and analyzing the relationship between the alarms and the actions. The results show that nurses often do not respond directly to alarms, but, rather, use them as additional sources of information in their ongoing flow of actions. The probabilities for their responding to an alarm depend on the causes of the alarm, its duration, and the characteristics of the patient. These findings support the view that experienced nurses dynamically adjust their activities according to the information they receive from alarm systems and other sources, and that they combine their reactive actions with the periodic performance of routine tasks.     

Supporting SAT based BMC on Finite Path Models

The standard translation of a Bounded Model Checking (BMC) instance into a satisfiability problem, (a.k.a SAT), might produce misleading results in the case when the model under verification contains finite paths. Models with finite paths might be produced unknowingly when using modern verification languages such as PSL-Sugar [Property Specification Language: Reference Manual. Version 1.1, Accellera, June 2004]. Specifically, the use of language constructs such as restrict, assume etc. might lead to such models. Thus the user may receive misleading results from SAT based tools.In this paper we describe in what circumstances the finite path problem occurs and present an improved translation of the BMC problem into a SAT instance. The new translation does not suffer from the discussed shortcoming. Our translation is only slightly longer then the usual one introducing one extra Boolean variable in the model.We also show that this translation may improve the SAT solver runtime even for models without finite paths.     

The psychology of ongoing threat: Relative risk appraisal, the September 11 attacks, and terrorism-related fears.

There are now replicated findings that posttraumatic stress disorder (PTSD) symptoms related to the September 11, 2001, attacks occurred in large numbers of persons who did not fit the traditional definition of exposure to a traumatic event. These data are not explained by traditional epidemiologic "bull's eye" disaster models, which assume the psychological effects are narrowly, geographically circumscribed, or by existing models of PTSD onset. In this article, the authors develop a researchable model to explain these and other terrorism-related phenomena by synthesizing research and concepts from the cognitive science, risk appraisal, traumatic stress, and anxiety disorders literatures. They propose the new term relative risk appraisal to capture the psychological function that is the missing link between the event and subjective response in these and other terrorism-related studies to date. Relative risk appraisal highlights the core notion from cognitive science that human perception is an active, multidimensional process, such that for unpredictable societal threats, proximity to the event is only one of several factors that influence behavioral responses. Addressing distortions in relative risk appraisal effectively could reduce individual and societal vulnerability to a wide range of adverse economic and ethnopolitical consequences to terrorist attacks. The authors present ways in which these concepts and related techniques can be helpful in treating persons with September 11- or terrorism-related distress or psychopathology. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Standardization and modularization driven by minimizing overall process effort

Faster product development is a major goal for companies in competitive markets. Product platform architectures support planning for addressing diverse markets and fulfilling future market desires. Applying standardization or modularization on product platform components leverages current product design effort across future products. This work introduces a method—SMDP (standardization and modularization driven by process effort)—for focusing engineering effort when applying standardization or modularization on product platform components. SMDP calculates the total design effort from current to future generations of the platform following standardization or modularization of components. By comparing the total design cost of different simulations, we can direct the design team to standardization or modularization opportunities. The contribution of this work is in using an estimation of design effort as the basis for decision in contrast to commonly used static measures of components' interactions. Such a computational approach allows conducting sensitivity studies that address the subjective nature of various estimations needed for exercising SMDP. SMDP is illustrated in a product platform design of an external-drum plate-setter for the digital prepress printing market.     

Particle Size,Surface Coating,and PEGylation Influence the Biodistribution of Quantum Dots in Living Mice

This study evaluates the influence of particle size, PEGylation, and surface coating on the quantitative biodistribution of near‐infrared‐emitting quantum dots (QDs) in mice. Polymer‐ or peptide‐coated ⁶⁴Cu‐labeled QDs 2 or 12 nm in diameter, with or without polyethylene glycol (PEG) of molecular weight 2000, are studied by serial micropositron emission tomography imaging and region‐of‐interest analysis, as well as transmission electron microscopy and inductively coupled plasma mass spectrometry. PEGylation and peptide coating slow QD uptake into the organs of the reticuloendothelial system (RES), liver and spleen, by a factor of 6–9 and 2–3, respectively. Small particles are in part renally excreted. Peptide‐coated particles are cleared from liver faster than physical decay alone would suggest. Renal excretion of small QDs and slowing of RES clearance by PEGylation or peptide surface coating are encouraging steps toward the use of modified QDs for imaging living subjects.     

Automated network troubleshooting knowledge acquisition

Troubleshooting knowledge acquisition is a notorious network maintenance expert systems development bottleneck. We present an improved methodology to generate automatically a skeleton of network troubleshooting knowledge base given the data about network topology, test costs, and network component failure likelihood. Our methodology uses AO ^* search where a suitable modification of the Huffman code procedure is found to be an admissible heuristic. Our heuristic uses synergistically information about both component failure rates and test costs while relaxing topology constraints. The resulting expert system (XTAR) minimizes expected troubleshooting cost faster and learns better troubleshooting techniques during its operation.     

Oxidation of organic pollutants in aqueous solutions by nanosized copper oxide catalysts

The catalytic activity of copper oxide nanoparticles was investigated for the removal of organic pollutants in aqueous solutions, using hydrogen peroxide as an oxidant. Complete degradation of both alachlor and phenanthrene was achieved after 20 min. The kinetics of the reaction was found to be pseudo-first-order with respect to the pollutant. The influence on the reaction kinetics of different catalyst samples, consisting of the same material but of different origin and different particle properties, was examined. The effects of several factors such as irradiation, oxidant concentration, ionic strength and pH on the reaction were also investigated. The catalysis is not photo-induced and can be performed without UV–vis irradiation. In particular, an optimal oxidant concentration was determined for the studied system. The presence of salts was found to inhibit the alachlor degradation rate. The addition of high concentrations of oxidant or salt results in pseudo-zero-order kinetics. However, NaCl at very high concentrations (>1 M) was found to cause a dramatic increase in reaction rate. The catalysis is efficient over a wide range of pH values.     

A new cohesive zone model for mixed mode interface fracture in bimaterials

A new two-dimensional cohesive zone model which is suitable for the prediction of mixed mode interface fracture in bimaterials is presented. The model accounts for the well known fact that the interfacial fracture toughness is not a constant, but a function of the mode mixity. Within the framework of this model, the cohesive energy and the cohesive strength are not chosen to be constant, but rather functions of the mode mixity. A polynomial cohesive zone model is derived in light of analytical and experimental observations of interface cracks. The validity of the new cohesive law is examined by analyzing double cantilever beam and Brazilian disk specimens. The methodology to determine the parameters of the model is outlined and a failure criterion for a pair of ceramic clays is suggested.     

“Andromaly”: a behavioral malware detection framework for android devices

This article presents Andromaly—a framework for detecting malware on Android mobile devices. The proposed framework realizes a Host-based Malware Detection System that continuously monitors various features and events obtained from the mobile device and then applies Machine Learning anomaly detectors to classify the collected data as normal (benign) or abnormal (malicious). Since no malicious applications are yet available for Android, we developed four malicious applications, and evaluated Andromaly’s ability to detect new malware based on samples of known malware. We evaluated several combinations of anomaly detection algorithms, feature selection method and the number of top features in order to find the combination that yields the best performance in detecting new malware on Android. Empirical results suggest that the proposed framework is effective in detecting malware on mobile devices in general and on Android in particular.