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Abstract: The use of condoms in sexual assault cases has become increasingly common due to the heightened awareness of the use of DNA as evidence in criminal investigations. The ability to identify and differentiate the polymers and additives found in lubricant residues can provide investigators leads and insights as to the perpetrator of a sexual assault. Matrix-assisted laser desorption/ionization–time of flight-mass spectrometry (MALDI–TOF-MS) is ideal for detecting condom lubricants and additives; the instrument is capable of surveying analytes across a wide mass range and is a preferred technique for the analysis of polymers. Three MALDI–TOF-MS methods directed toward the detection and differentiation of condom and personal lubricant residues, as well as their mixtures with biological fluids, were developed and compared: (a) a sample premixed with aqueous matrix; (b) a sample premixed with an ionic liquid matrix; and (c) a layering method that incorporates a cationization reagent. Of the three, the layered method that utilized sodium chloride as a cationization reagent showed the best sensitivity and selectivity. This method allowed for the segregation of the various lubricant formulas into a discrete number of groups. Infrared spectroscopy was used to support and clarify the MALDI data. Principal component analysis was used to further demonstrate the ability of this method to segregate various lubricant types into a limited number of classes. Additionally, lubricant residues could be detected in the presence of biological fluids down to a fraction of a percent. [Copyright &y& Elsevier]

Data-driven soft sensors have been widely used to measure key variables for industrial processes. Soft sensors using deep learning models have attracted considerable attention and shown superior predictive performance. However, if a soft sensor encounters an unexpected situation in inferring data or if noisy input data is used, the estimated value derived by a standard soft sensor using deep learning may at best be untrustworthy. This problem can be mitigated by expressing a degree of uncertainty about the trustworthiness of the estimated value produced by the soft sensor. To address this issue of uncertainty, we propose using an uncertainty-aware soft sensor that uses Bayesian recurrent neural networks (RNNs). The proposed soft sensor uses a RNN model as a backbone and is then trained using Bayesian techniques. The experimental results demonstrated that such an uncertainty-aware soft sensor increases the reliability of predictive uncertainty. In comparisons with a standard soft sensor, it shows a capability to use uncertainties for interval prediction without compromising predictive performance. [ABSTRACT FROM AUTHOR]

Introduction. Proper ambulance use is important not only due to the patient's transport quality but also because of the need for efficient use of limited resources allotted by the system. Therefore, this study was conducted to check for overuse or underuse of the ambulance system by patients who visited the emergency department (ED). Methods. In this study, a secondary data analysis was conducted using the existing database of the National Emergency Department Information System with all patients who visited EDs over the three-year study period from 2016 to 2018. The study subjects were classified into the following groups: (1) appropriate Emergency Medical Services (EMS) usage; (2) appropriate no EMS usage; (3) underuse; and (4) overuse groups. Results. Of 18,298,535 patients, 11,668,581 (63.77%) were classified under the appropriate usage group, while 6,629,954 (36.23%) were classified under the inappropriate usage group. In the appropriate EMS usage group, there were 2,408,845 (13.16%) patients. In the appropriate no EMS usage group, there were 9,259,706 (50.60%) patients. As for the inappropriate usage group, there were 5,147,352 (28.13%) patients categorized under the underuse group. On the other hand, there were 1,482,602 (8.10%) patients under the overuse group. Conclusion. There are many patients who use ambulances appropriately, but there are still many overuse and underuse. Guidelines on ambulance use are necessary for the efficient use of emergency medical resources and for the safety of patients. [ABSTRACT FROM AUTHOR]

Identifying bacterial species is essential to epidemiological surveillance. However, the determination of bacterial species is a tedious and labor‐intensive process. Various machine learning methods have been used for identifying bacterial species with mass spectral fingerprints. Although machine learning methods achieve real‐time identification without human experts, it still requires data preprocessing. To address this issue, we proposed a unified solution for the identification of bacterial species with a convolutional neural network. The neural network automatically determined species according to their mass spectra without the preprocessing steps. The convolutional and pooling layers in the neural network could replace the binning, baseline correction, and scaling procedures. Moreover, because of the explainable structure, the model could identify important regions of spectra to discriminate each bacterial species. We used spectral samples obtained from the fatty acid methyl esters of 10 samples from 16 bacterial species (a total of 16) to demonstrate the usefulness of the proposed method by comparing it with existing classification methods preceded by preprocessing. The comparison results confirmed that the proposed method outperformed the alternatives in terms of classification accuracy and robustness. Moreover, the classification results of the proposed method are interpretable. Identifying bacterial species is essential to epidemiological surveillance. However, the determination of bacterial species is labor‐intensive. Various machine learning methods have been used for identifying bacterial species with mass spectral fingerprints. Although machine learning methods achieve real‐time identification without human experts, it still requires data preprocessing. We proposed a unified solution for the identification of bacterial species with a convolutional neural network. The comparison results confirmed that the proposed method outperformed the alternatives in terms of classification accuracy and robustness. [ABSTRACT FROM AUTHOR]

• A new graph-based clustering method using node cutting was proposed. • We present theoretical results that guarantee the robustness in selecting nodes. • The optimal cluster number can be determined by adjusting a sparsity parameter. • Comparison studies show that the proposed method outperformed existing methods. Graph-based clustering is an efficient method for identifying clusters in local and nonlinear data patterns. Among the existing methods, spectral clustering is one of the most prominent algorithms. However, this method is vulnerable to noise and outliers. This study proposes a robust graph-based clustering method that removes the data nodes of relatively low density. The proposed method calculates the pseudo-density from a similarity matrix, and reconstructs it using a sparse regularization model. In this process, noise and the outer points are determined and removed. Unlike previous edge cutting-based methods, the proposed method is robust to noise while detecting clusters because it cuts out irrelevant nodes. We use a simulation and real-world data to demonstrate the usefulness of the proposed method by comparing it to existing methods in terms of clustering accuracy and robustness to noisy data. The comparison results confirm that the proposed method outperforms the alternatives. [ABSTRACT FROM AUTHOR]

In many industries, statistical process monitoring techniques play a key role in improving processes through variation reduction and defect prevention. Modern large-scale industrial processes require appropriate monitoring techniques that can efficiently address high-dimensional nonlinear processes. Such processes have been successfully monitored with several latent variable-based methods. However, because these monitoring methods use Hotelling's T 2 statistics in the reduced space, a normality assumption underlies the construction of these tools. This assumption has limited the use of latent variable-based monitoring charts in both nonlinear and nonnormal situations. In this study, we propose a variational autoencoder (VAE) as a monitoring method that can address both nonlinear and nonnormal situations in high-dimensional processes. VAE is appropriate for T 2 charts because it causes the reduced space to follow a multivariate normal distribution. The effectiveness and applicability of the proposed VAE-based chart were demonstrated through experiments on simulated data and real data from a thin-film-transistor liquid-crystal display process. • We propose a variational autoencoder (VAE)-based process monitoring technique. • VAE is a nonlinear feature extraction method that appropriate for T 2 charts. • VAE chart can reduce both unwanted false alarms and misdetections in process control. • VAE charts outperform the existing latent variable-based control charts. [ABSTRACT FROM AUTHOR]

The identification of microorganisms is very important in different fields and alternative methods are necessary for a rapid and simple identification. The use of fatty acids for bacterial identification is gaining attention as phenotypic characteristics are reflective of the genotype and are more easily analyzed. In this work, gas chromatography-vacuum ultraviolet spectroscopy (GC-VUV) was used to determine bacteria fatty acid methyl esters (FAMEs), to identify and discriminate different environmental bacteria based on their fatty acid profile. Microorganisms were grown in agar and their fatty acids extracted, saponified, and esterified before analysis. Unique FAME profiles were obtained for each microorganism mainly composed of branched, cyclopropane, hydroxy, saturated, and unsaturated fatty acid methyl esters. S. maltophilia showed a higher diversity of fatty acids while Bacillus species showed higher complexity in terms of branched-chain FAMEs, with several iso and anteiso forms. 12 different bacteria genera and 15 species were successfully differentiated based on their fatty acid profiles after performing PCA and cluster analysis. Some difficult to differentiate species, such as Bacillus sp., which are genetically very similar, were differentiated with the developed method. [ABSTRACT FROM AUTHOR]

Airplane deicing is a safety measure to eliminate/prevent icing on airplanes that can lead to airflow disruption and emergency conditions. Aircraft deicing/anti-icing fluids (ADF) are high in glycol content. At Dallas Fort Worth International Airport (DFW), the major aircraft deicing activities are conducted at designated deicing pads called Source Isolation Deicing Sites, where ADF run-off can be captured and conveyed into the airport’s glycol collection system. A portion of ADF drips from the aircraft during taxiing and shears off the aircraft during take-off, entering nearby waterways without treatment. Glycol acts as a nutrient for bacteria in the airport’s receiving waterways, resulting in an increase in bacterial growth and a subsequent reduction in dissolved oxygen (DO), potentially endangering aquatic life. This paper proposes a prototype data-driven deicing activities management framework for DFW to address the complexity of airport deicing operations and its impacts. The proposed framework uses stochastic dynamic programming (SDP) to assign airplanes in each hour to deicing pad locations, so as to maximize DO in the receiving waters, subject to airport constraints. Some data were artificially generated using the available knowledge of airport operations. The state transition equations in SDP were estimated. The proposed framework was demonstrated using three cases during major deicing events. Improvements in DO compared with actual DO recorded in the data were mixed; however, the results motivated DFW to implement a new data collection process to replace the artificially-generated data, so that a more accurate optimization could be conducted in the future. [ABSTRACT FROM AUTHOR]

Player evaluation is a key component of the question-answering (QA) system in sports. Since existing player evaluation methods heavily rely on game statistics, they cannot capture the qualitative impact of each player during a game, which can be exploited using news articles after the game. In this paper, we propose a deep learning-based player evaluation model by combining both quantitative game statistics and the qualitative analyses provided by news articles. Players are classified as positive or negative based on their performance during certain periods, and news articles in the same period are annotated using the player's class. Then, the relationship between news articles and the annotated polarity is investigated by a deep neural network, which can deal with the high dimensionality of the text data. Since there is no explicit polarity label for news articles, we use the change in game statistics in target periods to annotate related sentences. The proposed system is applied to a Korean professional baseball league (KBO) and it is shown to be capable of understanding the sentence polarity of news articles on player performances. [ABSTRACT FROM AUTHOR]

Dynamic programming (DP) is a mathematical programming approach for optimizing a system that changes over time and is a common approach for developing intelligent systems. Expert systems that are intelligent must be able to adapt dynamically over time. An optimal DP policy identifies the optimal decision dependent on the current state of the system. Hence, the decisions controlling the system can intelligently adapt to changing system states. Although DP has existed since Bellman introduced it in 1957, exact DP policies are only possible for problems with low dimension or under very limiting restrictions. Fortunately, advances in computational power have given rise to approximate DP (ADP). However, most ADP algorithms are still computationally-intractable for high-dimensional problems. This paper specifically considers continuous-state DP problems in which the state variables are multicollinear. The issue of multicollinearity is currently ignored in the ADP literature, but in the statistics community it is well known that high multicollinearity leads to unstable (high variance) parameter estimates in statistical modeling. While not all real world DP applications involve high multicollinearity, it is not uncommon for real cases to involve observed state variables that are correlated, such as the air quality ozone pollution application studied in this research. Correlation is a common occurrence in observed data, including sources in meteorology, energy, finance, manufacturing, health care, etc. ADP algorithms for continuous-state DP achieve an approximate solution through discretization of the state space and model approximations. Typical state space discretizations involve full-dimensional grids or random sampling. The former option requires exponential growth in the number of state points as the state space dimension grows, while the latter option is typically inefficient and requires an intractable number of state points. The exception is computationally-tractable ADP methods based on a design and analysis of computer experiments (DACE) approach. However, the DACE approach utilizes ideal experimental designs that are (nearly) orthogonal, and a multicollinear state space will not be appropriately represented by such ideal experimental designs. While one could directly build approximations over the multicollinear state space, the issue of unstable model approximations remains unaddressed. Our approach for handling multicollinearity employs data mining methods for two purposes: (1) to reduce the dimensionality of a DP problem and (2) to orthogonalize a multicollinear DP state space and enable the use of a computationally-efficient DACE-based ADP approach. Our results demonstrate the risk of ignoring high multicollinearity, quantified by high variance inflation factors representing model instability. Our comparisons using an air quality ozone pollution case study provide guidance on combining feature selection and feature extraction to guarantee orthogonality while achieving over 95% dimension reduction and good model accuracy. [ABSTRACT FROM AUTHOR]

Spatiotemporal surveillance, especially in detection of emerging outbreaks is of particular importance. When an outbreak spreads across some areas, the incidence rate at the center of the outbreak area might be expected to be much higher than the rate at its edge. However, to the best of our knowledge, all existing methods assume a uniformly increasing rate across the entire area of the outbreak. The purpose of this study is to compare the performance of the spatiotemporal surveillance methods such as multivariate cumulative sum (MCUSUM) or multivariate exponentially weighted moving average (MEWMA) when the changes in size are nonhomogeneous. Monte Carlo simulations were conducted to examine the properties of these spatiotemporal surveillance methods and compared them in terms of the detection speed and the identification rate under various scenarios. The results showed that when nonhomogeneous change sizes are involved, the MCUSUM method taking into account spatial nonhomogeneity of increase rates yields a better identification than the method ignoring such change size pattern although the detection speeds are similar. Further, a case study for the detection of male thyroid cancer data in New Mexico in the United States was performed to demonstrate the applicability of these methods. [ABSTRACT FROM PUBLISHER]

Extracting useful and meaningful patterns from large volumes of text data is of growing importance. In the present study we analyze vast amounts of prescription data, generated from the book of oriental medicine to identify the relationships between the symptoms and the associated medicines used to treat these symptoms. The oriental medicine book used in this study (called Bangyakhappyeon) contains a large number of prescriptions to treat about 54 categorized symptoms and lists the corresponding herbal materials. We used an association rule algorithm combined with network analysis and found useful and informative relationships between the symptoms and medicines. [ABSTRACT FROM AUTHOR]

Abstract: Objective: This pilot study was designed to determine if metabolic effects in different brain regions (left and right parietal lobes, midbrain) caused by 3 d of food consumption without methionine or cysteine could be detected by proton magnetic resonance spectroscopy. Methods: Healthy individuals 18 to 36 y old (n = 8) were studied by magnetic resonance spectroscopy after receiving a diet with adequate sulfur amino acids (SAAs) or with zero SAA for 3 d. Pulse sequences were used to selectively measure glutathione (GSH), and linear combination modeling of spectra was used to measure other high-abundance brain metabolites and expressed relative to creatine (Cr). Results: Although dietary SAAs are required to maintain GSH, the 3-d SAA insufficiency resulted in no significant change in GSH/Cr in the three brain regions. Principal component analysis of 16 metabolites measured by linear combination modeling showed that the metabolic pattern in the midbrain, but not in the parietal lobes, was distinguished according to the dietary SAAs. Multivariate statistical analysis showed that the major discriminating factors were signals of glutamate/Cr, (glutamate + glutamine)/Cr, and myoinositol/Cr. Correlation analyses between midbrain metabolites and GSH-related metabolites in plasma showed that midbrain glutamate/Cr had an inverse correlation with plasma cystine. Conclusion: The data show that magnetic resonance spectroscopy is a non-invasive tool suitable for nutritional assessment and suggest that nutritional imbalance caused by 3 d of SAA-free food more selectively affects the midbrain than the parietal lobes. [Copyright &y& Elsevier]

Abstract: The main objective of the present paper is to characterize smoking behavior among older adults by assessing the psychological distress, physical health status, alcohol use, and demographic variables in relations to the current smoking. We targeted 466 senior American smokers who are 65 years of age or older from the 2006 National Survey on Drug Use and Health (NSDUH, 2006). We employed a decision tree algorithm to conduct classification analysis to find the relationship between the average numbers of cigarette use per day. The results showed that the most important explanatory variable for prediction of the average number of cigarette use per day is the age when first started smoking cigarettes every day, followed by education level, and psychological distress. These results suggest that social workers need to provide more customized and individualized intervention to older adults. [Copyright &y& Elsevier]

Abstract: Malaria is a devastating mosquito-borne disease, which affects hundreds of millions of people each year. It is transmitted predominantly by Anopheles gambiae, whose females must be >10 days old to become infective. In this study, cuticular lipids from a laboratory strain of this mosquito species were analyzed using a mass spectrometry method to evaluate their utility for age, sex and mating status differentiation. Matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS), in conjunction with an acenaphthene/silver nitrate matrix preparation, was shown to be 100% effective in classifying A. gambiae females into 1, 7–10, and 14 days of age. MALDI-MS analysis, supported by multivariate statistical methods, was also effective in detecting cuticular lipid differences between the sexes and between virgin and mated females. The technique requires further testing, but the obtained results suggest that MALDI-MS cuticular lipid spectra could be used for age grading of A. gambiae females with precision greater than with other available methods. [Copyright &y& Elsevier]

Abstract: This paper presents a statistical analysis on the environmental impact of airport deicing activities at Dallas-Fort Worth (D/FW) International Airport. The focus of this paper is on aircraft deicing, which typically uses a spray of aircraft deicing and anti-icing fluids (ADAF). ADAF has a high concentration of ethylene/propylene/diethylene glycol, which shears off airfoil surfaces during takeoff and drips to the runways during taxiing. A significant portion of the glycol runs off and mixes with the airport’s receiving waters during heavy deicing periods, resulting in bacterial growth that causes an increase in chemical oxygen demand (COD) and a subsequent reduction in dissolved oxygen (DO) in the receiving waters. In this study, statistical methods for data mining were employed to evaluate the impact of airport deicing activities on COD and DO in the receiving waters immediately surrounding D/FW Airport. In particular, decision tree models were developed to determine important explanatory variables for predicting levels of COD and DO in the airport’s waterways. The decision tree modeling and analysis of COD determined north–south wind, glycol usage at a specific deicing pad, and monitoring site to be significant explanatory variables. The impact of glycol usage on DO was apparent as every decision tree at least one group with a median DO below 4.0mg/l, and these low-DO groups were associated with high glycol usage. These results are crucial to D/FW Airport in their goal to minimize the potential adverse impact of deicing activities on the water quality in waterways proximate to the airport. The advantages of data-driven modeling and analysis are its cost-effectiveness due to its potential to be implemented without making major changes in physical systems, ease of application, and usefulness in making future management decisions. [Copyright &y& Elsevier]

The article focuses on a simulation model for evaluating patient-to-nurse assignments and enabling nurses to use an evidence-based practice (EBP). It mentions about the traditional nursing system under which nurse budgeting is done before planning nurses' schedule and workloads and informs that the simulation model was developed after collecting data on nurses' workflow and patient in a North Texas hospital. It reveals that the data collection based on the simulation model revealed that the nurses liked the speed and appropriateness with which patient-to-nurse assignments are determined. Also discussed are the limitations of the use of an evidence-based approach of patient-to-nurse assignments.

• We propose a predictive traffic control system for the autonomous vehicle routing problem. • We develop an applicable control system to complement the existing routing algorithm. • Proposed method can predict and prevent traffic congestion. • A real times simulation presents the utility and superiority of the proposed method. This study aimed to resolve a real-world traffic problem in a large-scale plant. Autonomous vehicle systems (AVSs), which are designed to use multiple vehicles to transfer materials, are widely used to transfer wafers in semiconductor manufacturing. Traffic control is a significant challenge with AVSs because all vehicles must be monitored and controlled in real time, to cope with uncertainties such as congestion. However, existing traffic control systems, which are primarily designed and controlled by human experts, are insufficient to prevent heavy congestion that impedes production. In this study, we developed a traffic control system based on machine learning predictions, and a routing method that dynamically determines AVS routes with reduced congestion rates. We predicted congestion for critical bottleneck areas, and utilized the predictions for adaptive routing control of all vehicles to avoid congestion. We conducted an experimental evaluation to compare the predictive performance of four popular algorithms. We performed a simulation study based on data from semiconductor fabrication to demonstrate the utility and superiority of the proposed method. The experimental results showed that AVSs with the proposed approach outperformed the existing approach in terms of delivery time, transfer time, and queuing time. We found that adopting machine learning-based traffic control can enhance the performance of existing AVSs and reduce the burden on the human experts who monitor and control AVSs. [ABSTRACT FROM AUTHOR]