Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques.

Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algorithms to augment AKI recognition using the novel biomarker, neutrophil gelatinase associated lipocalin (NGAL), combined with contemporary biomarkers such as N-terminal pro B-type natriuretic peptide (NT-proBNP), urine output (UOP), and plasma creatinine. Machine learning approaches including logistic regression (LR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and deep neural networks (DNN) were used in this study. The AI/ML algorithm helped predict AKI 61.8 (32.5) hours faster than the Kidney Disease and Improving Global Disease Outcomes (KDIGO) criteria for burn and non-burned trauma patients. NGAL was analytically superior to traditional AKI biomarkers such as creatinine and UOP. With ML, the AKI predictive capability of NGAL was further enhanced when combined with NT-proBNP or creatinine. The use of AI/ML could be employed with NGAL to accelerate detection of AKI in at-risk burn and non-burned trauma patients

A Facile and Practical p-Toluenesulfonic Acid Catalyzed Route to Dicoumarols Containing an Aroyl group

New and known dicoumarols may be efficiently synthesized employing p-toluenesulfonic acid (p-TSA) as a solid acid catalyst from the reaction of 4-hydroxycoumarin with aryl glyoxal in water. This method offers direct access to structurally diverse coumarin derivatives in moderate to good yields (up to 65%). A total of five new compounds were synthesized.KEYWORDS Dicoumarol, p-toluenesulfonic acid, aryl glyoxal, 4-hydroxycoumarin.PDF and Supp files attache

Studies of Sea Surface Temperature and Chlorophyll-a Variations in East Coast of Peninsular Malaysia

This paper attempts to classify the oceanographic conditions of the fishing grids in east coast of Peninsular Malaysia using surface chlorophyll-a content and sea surface temperature (SST) data from satellite. The variation of SST and chlorophyll-a content in the South China Sea is greatly affected by the monsoon system. Analysis results showed that both SST and chlorophyll-a variations of the fishing grids are closely related to their geographical locations. The classification using chlorophyll-a on the fishing grids give a clearer variation compared to SST. Hierarchical cluster analysis gave a better means of understanding the variations of these oceanographic conditions and the relationship among the fishing grids. However, to understand how these variations of oceanographic condition affect the marine fisheries catch in Malaysian Exclusive Economic Zone (EEZ), further studies should be conducted using longer time scale data

Psychological Security and Its Relationship to Empathy Among a Sample of Early Childhood in Jubail Industrial City

The current research aims at revealing the relationship between psychological security and empathy in the stage of early childhood at the Jubail Industrial City. Its significance can be attributed to the importance of developing empathy among children, enlightening the community and educators about the importance of psychological security and its relationship to empathy among children in the early childhood stage. An analytical descriptive approach was employed as it suits the nature of the current research. A random sample comprising 204 children in the early childhood stage. Having applied the psychological security [1] and empathy scales [2] to the research sample, the following result was reached. There is a statistically significant correlation between psychological security and empathy in a sample of children in the early childhood stage in Jubail Industrial City

Common Statistical Concepts in the Supervised Machine Learning Arena

One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be objectively assessed without appropriate statistical measurements. The scope of statistics within the ML realm is rather broad and cannot be adequately covered in a single review article. Therefore, here we will mainly focus on the common statistical concepts that pertain to supervised ML (i.e. classification and regression) along with their interdependencies and certain limitations

Effect of Exposure Conditions on the Long-Term Dielectric Properties of Mortar Samples Containing ASR Gel

Alkali-silica reaction (ASR) is a chemical reaction between alkalis present in portland cement and amorphous or otherwise disordered siliceous minerals in particular aggregates. Through this reaction, reactive silica binds with hydroxyl and alkali ions and forms a gel, known as ASR gel. Recently, microwave materials characterization techniques have shown great potential for detecting ASR in mortar. However, the comprehensive understanding of variables that affect the extent of ASR in mortar and their interaction with microwave signals, in particular the effect of environmental exposure conditions requires more investigations. Therefore, parameters related to these conditions must be considered when using microwave techniques for ASR detection and evaluation. In this paper, the effect of exposure conditions on ASR gel formation and microwave dielectric properties of mortar samples is investigated. To this end, extended measurements of the complex dielectric constants of three different sets of mortar samples are presented at S-band (2.6-3.95 GHz). The samples were cast with potentially reactive ASR-aggregates and subjected to different environmental conditions. The results show slightly different permittivities for the differently stored samples, potentially indicating different amount of ASR gel. This observation was corroborated through UV fluorescence microscopy, where different amounts of ASR gel were observed in the samples. Moreover, the results indicate that ASR gel evolution may be better tracked through loss factor measurements, while pre-existing-gel may be better detected through permittivity measurements

SAT based Enforcement of Domotic Effects in Smart Environments

The emergence of economically viable and efficient sensor technology provided impetus to the development of smart devices (or appliances). Modern smart environments are equipped with a multitude of smart devices and sensors, aimed at delivering intelligent services to the users of smart environments. The presence of these diverse smart devices has raised a major problem of managing environments. A rising solution to the problem is the modeling of user goals and intentions, and then interacting with the environments using user defined goals. `Domotic Effects' is a user goal modeling framework, which provides Ambient Intelligence (AmI) designers and integrators with an abstract layer that enables the definition of generic goals in a smart environment, in a declarative way, which can be used to design and develop intelligent applications. The high-level nature of domotic effects also allows the residents to program their personal space as they see fit: they can define different achievement criteria for a particular generic goal, e.g., by defining a combination of devices having some particular states, by using domain-specific custom operators. This paper describes an approach for the automatic enforcement of domotic effects in case of the Boolean application domain, suitable for intelligent monitoring and control in domotic environments. Effect enforcement is the ability to determine device configurations that can achieve a set of generic goals (domotic effects). The paper also presents an architecture to implement the enforcement of Boolean domotic effects, and results obtained from carried out experiments prove the feasibility of the proposed approach and highlight the responsiveness of the implemented effect enforcement architectur

Holographic Dark Energy from a Modified GBIG Scenario

We construct a holographic dark energy model in a braneworld setup that gravity is induced on the brane embedded in a bulk with Gauss-Bonnet curvature term. We include possible modification of the induced gravity and its coupling with a canonical scalar field on the brane. Through a perturbational approach to calculate the effective gravitation constant on the brane, we examine the outcome of this model as a candidate for holographic dark energy.Comment: 13 pages, accepted for publication in IJMP

Capacity enrichment OCDMA based on algorithm of novel flexible cross correlation (FCC) address code

The flexible cross-correlation (FCC) address code for Spectral-Amplitude Coding Optical Code-Division Multiple-Access (SACOCDMA) systems has been developed.The FCC code has advantages, such as flexible cross-correlation property at any given number of users and weights, as well as effectively suppressed the impact of phase-induced intensity noise (PIIN) and multiple-access interference (MAI) cancellation property.The results revealed that the FCC code can accommodate 150 users, where FCC code offers 66 %, 172 %, 650 % and 900 % improvement as a contrast to 90, 55, 20 and 15 number of users for dynamic cyclic shift (DCS), modified double weight (MDW), modified frequency hopping (MFH) and Hadamard codes, respectively, for a permissible bit error rate (BER) of 10−9

Node re-ordering as a means of anomaly detection in time-evolving graphs

© Springer International Publishing AG 2016. Anomaly detection is a vital task for maintaining and improving any dynamic system. In this paper, we address the problem of anomaly detection in time-evolving graphs, where graphs are a natural representation for data in many types of applications. A key challenge in this context is how to process large volumes of streaming graphs. We propose a pre-processing step before running any further analysis on the data, where we permute the rows and columns of the adjacency matrix. This pre-processing step expedites graph mining techniques such as anomaly detection, PageRank, or graph coloring. In this paper, we focus on detecting anomalies in a sequence of graphs based on rank correlations of the reordered nodes. The merits of our approach lie in its simplicity and resilience to challenges such as unsupervised input, large volumes and high velocities of data. We evaluate the scalability and accuracy of our method on real graphs, where our method facilitates graph processing while producing more deterministic orderings. We show that the proposed approach is capable of revealing anomalies in a more efficient manner based on node rankings. Furthermore, our method can produce visual representations of graphs that are useful for graph compression