bluuebunny/crossref_metadata_2025 huggingface dataset:
An Approach for Managing Manufacturing Assets through Radio Frequency Energy Harvesting
Authors: Tahir Muhammad Ashhal , Ramis Ferrer Borja , Martinez Lastra Jose Luis
Published: January 2019

The manufacturing industry requests novel solutions that will permit enterprises to stay competitive in the market. This leads to decisions being made based on different technologies that are focused on real-time accurate measurement and monitoring of manufacturing assets. In the context of traceability, radio frequency identification (RFID) tags have been traditionally used for tracking, monitoring, and collecting data of various manufacturing resources operating along the value chain. RFID tags and microelectromechanical systems (MEMS) sensors enable the monitoring of manufacturing assets by providing real-time data. Such devices are usually powered by batteries that need regular maintenance, which in turn leads to delays that affect the overall manufacturing process time. This article presents a low-cost approach to detect and measure radio frequency (RF) signals in assembly lines for optimizing the manufacturing operations in the manufacturing industry. Through the detection and measurement of RF signals, the RF energy can be harvested at certain locations on the assembly line. Then, the harvested energy can be supplied to the MEMS sensors, minimizing the regular maintenance for checking and replacing batteries. This leads to an increase in the operational efficiency and an overall reduction in operational and maintenance costs.
A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging
Authors: Xin Jing , Gao Kaiyuan , Shan Mao , Yan Bo , Liu Ding
Published: January 2019

Ultra-wideband (UWB) sensors have been widely used in multi-robot systems for cooperative tracking and positioning purposes due to their advantages such as high ranging accuracy and good real-time performance. In order to reduce the influence of non-line-of-sight (NLOS) UWB communication caused by the presence of obstacles on ranging accuracy in indoor environments, the paper proposes a novel Bayesian filtering approach for UWB ranging error mitigation. Nonparametric UWB sensor models, namely received signal strength (RSS) model and time of arrival (TOA) model, are constructed to capture the probabilistic noise characteristics under the influence of different obstruction conditions and materials within a typical indoor environment. The proposed Bayesian filtering approach can be used either as a standalone error mitigation approach for peer-to-peer (P2P) ranging, or as a part of a higher level Bayesian state estimation framework. Experiments were conducted to validate and evaluate the proposed approach in two configurations, i.e., inter-robot ranging, and mobile robot tracking in a wireless sensor network. The experimental results show that the proposed method can accurately identify the line-of-sight (LOS) and NLOS scenarios with wood and metal obstacles in a probabilistic representation and effectively improve the ranging/tracking accuracy. In addition, the low computational overhead of the approach makes it attractive in real-time systems.
Characterization of a Robust 3D- and Inkjet-Printed Capacitive Position Sensor for a Spectrometer Application
Authors: Faller Lisa-Marie , Lenzhofer Martin , Hirschl Christina , Kraft Martin , Zangl Hubert
Published: January 2019

An inkjet- and 3D-printed capacitive sensor system with an all-digital and flexible sensor read-out hardware is reported. It enables spectrometer devices with significantly reduced device outlines and costs. The sensor is developed as multilayer inkjet-printed electrode structure on a 3D-printed copper housing. Very high required position resolutions of r e s p o s < 50 nm and a wide measurement range of r m = 1000 μ m at an offset of d 0 = 1000 μ m in the considered spectrometers motivate this work. The read-out hardware provides high sampling rates of up to r s ≈ 10 ns and enables the generation of trigger signals, i.e., the mirror control signal, without a time lag. The read-out circuitry is designed as a carrier frequency system, which enables flexible choices of bandwidth and measurement signal frequency. It thus allows for separation in frequency from coupling parasitics, i.e., other frequencies present in the device under test, and makes the read-out quasi-noise-immune.
Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering
Authors: Rawassizadeh Reza , Dobbins Chelsea , Akbari Mohammad , Pazzani Michael
Published: January 2019

Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering approach can be used as an annotation on raw sensor data. It improves information retrieval by reducing the search space and is based on searching only the related clusters. To further improve behavior quantification, the third algorithm identifies contrasting events within a cluster content. Two large real-world smartphone datasets have been used to evaluate our algorithms and demonstrate the utility and resource efficiency of our approach to search.
A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults
Authors: Tegou Thomas , Kalamaras Ilias , Tsipouras Markos , Giannakeas Nikolaos , Votis Kostantinos , Tzovaras Dimitrios
Published: January 2019

Indoor localization systems have already wide applications mainly for providing localized information and directions. The majority of them focus on commercial applications providing information such us advertisements, guidance and asset tracking. Medical oriented localization systems are uncommon. Given the fact that an individual’s indoor movements can be indicative of his/her clinical status, in this paper we present a low-cost indoor localization system with room-level accuracy used to assess the frailty of older people. We focused on designing a system with easy installation and low cost to be used by non technical staff. The system was installed in older people houses in order to collect data about their indoor localization habits. The collected data were examined in combination with their frailty status, showing a correlation between them. The indoor localization system is based on the processing of Received Signal Strength Indicator (RSSI) measurements by a tracking device, from Bluetooth Beacons, using a fingerprint-based procedure. The system has been tested in realistic settings achieving accuracy above 93% in room estimation. The proposed system was used in 271 houses collecting data for 1–7-day sessions. The evaluation of the collected data using ten-fold cross-validation showed an accuracy of 83% in the classification of a monitored person regarding his/her frailty status (Frail, Pre-frail, Non-frail).
Human Physical Activity Recognition Using Smartphone Sensors
Authors: Voicu Robert-Andrei , Dobre Ciprian , Bajenaru Lidia , Ciobanu Radu-Ioan
Published: January 2019

Because the number of elderly people is predicted to increase quickly in the upcoming years, “aging in place” (which refers to living at home regardless of age and other factors) is becoming an important topic in the area of ambient assisted living. Therefore, in this paper, we propose a human physical activity recognition system based on data collected from smartphone sensors. The proposed approach implies developing a classifier using three sensors available on a smartphone: accelerometer, gyroscope, and gravity sensor. We have chosen to implement our solution on mobile phones because they are ubiquitous and do not require the subjects to carry additional sensors that might impede their activities. For our proposal, we target walking, running, sitting, standing, ascending, and descending stairs. We evaluate the solution against two datasets (an internal one collected by us and an external one) with great effect. Results show good accuracy for recognizing all six activities, with especially good results obtained for walking, running, sitting, and standing. The system is fully implemented on a mobile device as an Android application.
Three-Dimensional Face Reconstruction Using Multi-View-Based Bilinear Model
Authors: Tian Liang , Liu Jing , Guo Wei
Published: January 2019

Face reconstruction is a popular topic in 3D vision system. However, traditional methods often depend on monocular cues, which contain few feature pixels and only use their location information while ignoring a lot of textural information. Furthermore, they are affected by the accuracy of the feature extraction method and occlusion. Here, we propose a novel facial reconstruction framework that accurately extracts the 3D shapes and poses of faces from images captured at multi-views. It extends the traditional method using the monocular bilinear model to the multi-view-based bilinear model by incorporating the feature prior constraint and the texture constraint, which are learned from multi-view images. The feature prior constraint is used as a shape prior to allowing us to estimate accurate 3D facial contours. Furthermore, the texture constraint extracts a high-precision 3D facial shape where traditional methods fail because of their limited number of feature points or the mostly texture-less and texture-repetitive nature of the input images. Meanwhile, it fully explores the implied 3D information of the multi-view images, which also enhances the robustness of the results. Additionally, the proposed method uses only two or more uncalibrated images with an arbitrary baseline, estimating calibration and shape simultaneously. A comparison with the state-of-the-art monocular bilinear model-based method shows that the proposed method has a significantly higher level of accuracy.
Damage Identification in Bridges by Processing Dynamic Responses to Moving Loads: Features and Evaluation
Authors: Zhu Xiang , Cao Maosen , Ostachowicz Wieslaw , Xu Wei
Published: January 2019

The detection of damage in bridges subjected to moving loads has attracted increasing attention in the field of structural health monitoring. Processing the dynamic responses induced by moving loads to characterize damage is the key to identifying damage in bridges. On this topic, various methods of processing dynamic responses to moving loads have been developed in recent decades, with respective strengths and weaknesses. These methods appear in different applications and literatures and their features have not been comprehensively surveyed to form a profile of this special area. To address this issue, this study presents a comprehensive survey of methods for identifying damage by processing dynamic responses of cracked bridges subjected to moving loads. First, methods utilizing the Fourier transform to process dynamic responses to moving loads for damage detection in bridges are examined. Second, methods using wavelet transform to process the dynamic responses to moving loads for damage characterization are examined. Third, methods of employing the Hilbert-Huang transform to process the dynamic responses to moving loads for damage identification are examined. Fourth, methods of dynamic response-driven heuristic interrogation of damage in bridges subjected to moving loads are examined. Finally, we recommend future research directions for advancing the development of damage identification relying on processing dynamic responses to moving loads. This study provides a profile of the state-of-the-art and state-of-the-use of damage identification in bridges based on dynamic responses to moving loads, with the primary aim of helping researchers find crucial points for further exploration of theories, methods, and technologies for damage detection in bridges subjected to moving loads.
Traffic Class Prioritization-Based Slotted-CSMA/CA for IEEE 802.15.4 MAC in Intra-WBANs
Authors: Masud Farhan , Abdullah Abdul Hanan , Altameem Ayman , Abdul-Salaam Gaddafi , Muchtar Farkhana
Published: January 2019

This paper proposes an improved Traffic Class Prioritization based Carrier Sense Multiple Access/Collision Avoidance (TCP-CSMA/CA) scheme for prioritized channel access to heterogenous-natured Bio-Medical Sensor Nodes (BMSNs) for IEEE 802.15.4 Medium Access Control (MAC) in intra-Wireless Body Area Networks (WBANs). The main advantage of the scheme is to provide prioritized channel access to heterogeneous-natured BMSNs of different traffic classes with reduced packet delivery delay, packet loss, and energy consumption, and improved throughput and packet delivery ratio (PDR). The prioritized channel access is achieved by assigning a distinct, minimized and prioritized backoff period range to each traffic class in every backoff during contention. In TCP-CSMA/CA, the BMSNs are distributed among four traffic classes based on the existing patient’s data classification. The Backoff Exponent (BE) starts from 1 to remove the repetition of the backoff period range in the third, fourth, and fifth backoffs. Five moderately designed backoff period ranges are proposed to assign a distinct, minimized, and prioritized backoff period range to each traffic class in every backoff during contention. A comprehensive verification using NS-2 was carried out to determine the performance of the TCP-CSMA/CA in terms of packet delivery delay, throughput, PDR, packet loss ratio (PLR) and energy consumption. The results prove that the proposed TCP-CSMA/CA scheme performs better than the IEEE 802.15.4 based PLA-MAC, eMC-MAC, and PG-MAC as it achieves a 47% decrease in the packet delivery delay and a 63% increase in the PDR.
Liquid Droplet Microresonators
Authors: Giorgini Antonio , Avino Saverio , Malara Pietro , De Natale Paolo , Gagliardi Gianluca
Published: January 2019

We provide here an overview of passive optical micro-cavities made of droplets in the liquid phase. We focus on resonators that are naturally created and suspended under gravity thanks to interfacial forces, illustrating simple ways to excite whispering-gallery modes in various slow-evaporation liquids using free-space optics. Similar to solid resonators, frequency locking of near-infrared and visible lasers to resonant modes is performed exploiting either phase-sensitive detection of the leakage cavity field or multiple interference between whispering-gallery modes in the scattered light. As opposed to conventional micro-cavity sensors, each droplet acts simultaneously as the sensor and the sample, whereby the internal light can detect dissolved compounds and particles. Optical quality factors up to 107–108 are observed in liquid-polymer droplets through photon lifetime measurements. First attempts in using single water droplets are also reported. These achievements point out their huge potential for direct spectroscopy and bio-chemical sensing in liquid environments. Finally, the first experiments of cavity optomechanics with surface acoustic waves in nanolitre droplets are presented. The possibility to perform studies of viscous-elastic properties points to a new paradigm: a droplet device as an opto-fluid-mechanics laboratory on table-top scale under controlled environmental conditions.
Measurement Matrix Construction for Large-area Single Photon Compressive Imaging
Authors: Wang Hui , Yan Qiurong , Li Bing , Yuan Chenglong , Wang Yuhao
Published: January 2019

We have developed a single photon compressive imaging system based on single photon counting technology and compressed sensing theory, using a photomultiplier tube (PMT) photon counting head as the bucket detector. This system can realize ultra-weak light imaging with the imaging area up to the entire digital micromirror device (DMD) working region. The measurement matrix in this system is required to be binary due to the two working states of the micromirror corresponding to two controlled elements. And it has a great impact on the performance of the imaging system, because it involves modulation of the optical signal and image reconstruction. Three kinds of binary matrix including sparse binary random matrix, m sequence matrix and true random number matrix are constructed. The properties of these matrices are analyzed theoretically with the uncertainty principle. The parameters of measurement matrix including sparsity ratio, compressive sampling ratio and reconstruction time are verified in the experimental system. The experimental results show that, the increase of sparsity ratio and compressive sampling ratio can improve the reconstruction quality. However, when the increase is up to a certain value, the reconstruction quality tends to be saturated. Compared to the other two types of measurement matrices, the m sequence matrix has better performance in image reconstruction.
Hand Movement Classification Using Burg Reflection Coefficients
Authors: Ramírez-Martínez Daniel , Alfaro-Ponce Mariel , Pogrebnyak Oleksiy , Aldape-Pérez Mario , Argüelles-Cruz Amadeo-José
Published: January 2019

Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.
An Approach to Reinforce Multipath TCP with Path-Aware Information
Authors: Nguyen Kien , Golam Kibria Mirza , Ishizu Kentaro , Kojima Fumihide , Sekiya Hiroo
Published: January 2019

Multipath TCP (MPTCP), which enables the use of multiple wireless links (e.g., Wi-Fi and LTE) for data transmissions, is an excellent technology for evolving multi-homing devices in mobile wireless networks. This paper explores concepts and feasibility of realizing MPTCP with path awareness (PA), in which the path-aware information is leveraged to reinforce the MPTCP transmissions. In particular, when aware of a network situation, a device can facilitate a mechanism that dynamically shifts the MPTCP traffic to a single path and vice versa. As a result, MPTCP with PA could solve the significant problem of negative aggregation benefit when the MPTCP throughput over divergent paths is worse than the best one of single-path TCP. We illustrate the feasibility of the proposed concept through our new implementation of a so-called MPTCP-LA (i.e., MPTCP with Loss Awareness). MPTCP-LA keeps the aggregation benefits non-negative by temporarily switching an MPTCP transmission on a path to a standby condition when the on-device observed loss reaches a threshold. We extensively evaluate MPTCP-LA in comparison to the standard MPTCP in an emulated environment. The results show that MPTCP-LA has better performance regarding enhancing throughput and saving networking resources.
Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping
Authors: Burgués Javier , Hernández Victor , Lilienthal Achim , Marco Santiago
Published: January 2019

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments.
An Adversarial-Risk-Analysis Approach to Counterterrorist Online Surveillance
Authors: Gil César , Parra-Arnau Javier
Published: January 2019

The Internet, with the rise of the IoT, is one of the most powerful means of propagating a terrorist threat, and at the same time the perfect environment for deploying ubiquitous online surveillance systems. This paper tackles the problem of online surveillance, which we define as the monitoring by a security agency of a set of websites through tracking and classification of profiles that are potentially suspected of carrying out terrorist attacks. We conduct a theoretical analysis in this scenario that investigates the introduction of automatic classification technology compared to the status quo involving manual investigation of the collected profiles. Our analysis starts examining the suitability of game-theoretic-based models for decision-making in the introduction of this technology. We propose an adversarial-risk-analysis (ARA) model as a novel way of approaching the online surveillance problem that has the advantage of discarding the hypothesis of common knowledge. The proposed model allows us to study the rationality conditions of the automatic suspect detection technology, determining under which circumstances it is better than the traditional human-based approach. Our experimental results show the benefits of the proposed model. Compared to standard game theory, our ARA-based model indicates in general greater prudence in the deployment of the automatic technology and exhibits satisfactory performance without having to relax crucial hypotheses such as common knowledge and therefore subtracting realism from the problem, although at the expense of higher computational complexity.
Integrated Management of Energy, Wellbeing and Health in the Next Generation of Smart Homes
Authors: EL Jaouhari Saad , Jose Palacios-Garcia Emilio , Anvari-Moghaddam Amjad , Bouabdallah Ahmed
Published: January 2019

This contribution proposes an implementation for next generation smart homes, where heterogeneous data, coming from multiple sensors (medical, wellbeing, energy, contextual, etc.) and house equipment (smart fridge, smart TV, etc.), need to be managed, secured and visualized. As a first step, it focuses only on energy and health data. However, it aims to lay the foundations to manage any type of information towards the development of smart interactions with the house, which might include artificial intelligence and machine learning. These data are securely collected using a central Web of Things gateway, located inside the smart home. For the e-health part, a set of possible use-cases is provided, along with the current progress of the implantation. In this regard, the main idea is to link the next generation smart homes with external medical entities in order to provide, first, quick intervention in the event of an abnormality being detected, and to be able to provide basic medical services such as remote consultations with a doctor for a particular health issue. This vision can be very promising, particularly in rural areas, where access to medical services is difficult. As for the energy part, the aim is to collect users’ energy consumption inside the smart home, which can be supplied from different sources (heat, water, gas, or electricity), and to enable the use of advanced algorithms to predict and manage local energy consumption and production (if any). This approach combines data collected from smart meters, operational information of the smart energy devices (the status of smart plugs), user’s requests and external network signals such as energy prices. By using a home energy management system that accepts such input parameters, the operation of in-home devices and appliances can be optimally controlled according to different objectives (e.g., minimizing energy costs and maximizing user’s comfort level).
A Secure and Efficient Group Key Agreement Scheme for VANET
Authors: Liu Lianhai , Wang Yujue , Zhang Jingwei , Yang Qing
Published: January 2019

A vehicular ad hoc network (VANET) is a special mobile ad hoc network that provides vehicle collaborative security applications using intervehicle communication technology. The method enables vehicles to exchange information (e.g., emergency brake). In VANET, there are many vehicle platoon driving scenes, where vehicles with identical attributes (location, organization, etc.) are organized as a group. However, this organization causes the issue of security threats (message confidentiality, identity privacy, etc.) because of an unsafe wireless communication channel. To protect the security and privacy of group communication, it is necessary to design an effective group key agreement scheme. By negotiating a dynamic session secret key using a fixed roadside unit (RSU), which has stronger computational ability than the on-board unit (OBU) equipped on the vehicle, the designed scheme can help to provide more stable communication performance and speed up the encryption and decryption processes. To effectively implement the anonymous authentication mechanism and authentication efficiency, we use a batch authentication scheme and a shared secret key mechanism among the vehicles, RSUs and trusted authority (TA). We design an efficient group secret key agreement scheme, which satisfies the above communication and security requirements, protects the privacy of vehicles, and traces the real identity of the vehicle at a time when it is necessary. Computational analysis shows that the proposed scheme is secure and more efficient than existing schemes.
A Fourier Descriptor of 2D Shapes Based on Multiscale Centroid Contour Distances Used in Object Recognition in Remote Sensing Images
Authors: Zheng Yan , Guo Baolong , Chen Zhijie , Li Cheng
Published: January 2019

A shape descriptor is an effective tool for describing the shape feature of an object in remote sensing images. Researchers have put forward a lot of excellent descriptors. The discriminability of some descriptors is very strong in the experiments, but usually their computational cost is large, which makes them unsuitable to be used in practical applications. This paper proposes a new descriptor-FMSCCD (Fourier descriptor based on multiscale centroid contour distance)—which is a frequency domain descriptor based on the CCD (centroid contour distance) method, multiscale description, and Fourier transform. The principle of FMSCCD is simple, and the computational cost is very low. What is commendable is that its discriminability is still strong, and its compatibility with other features is also great. Experiments on three databases demonstrate its strong discriminability and operational efficiency.
Alcohol Drinking by Husbands/Partners Is Associated with Higher Intimate Partner Violence against Women in Angola
Authors: Yaya Sanni , Ghose Bishwajit
Published: January 2019

Intimate partner violence (IPV), as the most prevalent form of violence against women, is a commonly encountered phenomenon across sub-Saharan African countries, including Angola. As a fast-growing economy, Angola is experiencing a booming alcohol industry and persistent IPV and women’s rights issues, along with weak prohibition and enforcement against this practice. However, so far, there is no systematic research investigating the predictors of IPV in Angola and whether spousal alcohol drinking has any relationship with women’s experience of IPV. Therefore, in this study, we aimed to assess the predictors of IPV (defined as physical, emotional, and sexual violence) among Angolan women with a special focus on their partners’ alcohol drinking habit. Cross-sectional data on 7669 women aged 15–49 years from the Angola Demographic and Health Survey were used for this study. Data were analyzed using descriptive and logistic regression methods. Results indicated that physical IPV (32.3%, 95% Confidence Interval = 30.3 to 34.5) was most prevalent, followed by emotional (27.3%, 95% CI = 25.3 to 29.4) and sexual IPV (7.4%, 95% CI = 6.6 to 8.4). In the multivariate analysis, higher education and household wealth status showed protective effects against certain forms of IPV. Alcohol drinking by husbands/partners was associated with significantly higher odds of experiencing physical [OR = 2.950; 95% CI = 2.632, 3.306], emotional [OR = 2.470; 95% CI = 2.187,2.789], and sexual IPV [OR = 2.729; 95% CI = 2.220, 3.354] among women. Women who reported experiencing physical IPV had increased odds of drinking alcohol [OR = 1.474; 95% CI = 1.290, 1.684] compared with those who did not. These findings reflect the widespread prevalence of IPV in sub-Saharan African countries. Special focus should be given to married men with alcohol drinking habits to reduce women’s vulnerability to IPV and dependence on alcohol use.
Tracking Poisson Parameter for Non-Stationary Discontinuous Time Series with Taylor’s Abnormal Fluctuation Scaling
Authors: Sakoda Gen , Takayasu Hideki , Takayasu Misako
Published: January 2019

We propose a parameter estimation method for non-stationary Poisson time series with the abnormal fluctuation scaling, known as Taylor’s law. By introducing the effect of Taylor’s fluctuation scaling into the State Space Model with the Particle Filter, the underlying Poisson parameter’s time evolution is estimated correctly from given non-stationary time series data with abnormally large fluctuations. We also developed a discontinuity detection method which enables tracking the Poisson parameter even for time series including sudden discontinuous jumps. As an example of application of this new general method, we analyzed Point-of-Sales data in convenience stores to estimate change of probability of purchase of commodities under fluctuating number of potential customers. The effectiveness of our method for Poisson time series with non-stationarity, large discontinuities and Taylor’s fluctuation scaling is verified by artificial and actual time series.
Meta-Control in Pigeons (Columba livia) and the Role of the Commissura Anterior
Authors: Ünver Emre , Xiao Qian , Güntürkün Onur
Published: January 2019

Meta-control describes an interhemispheric response conflict that results from the perception of stimuli that elicit a different reaction in each hemisphere. The dominant hemisphere for the perceived stimulus class often wins this competition. There is evidence from pigeons that meta-control results from interhemispheric response conflicts that prolong reaction time when the animal is confronted with conflicting information. However, recent evidence in pigeons also makes it likely that the dominant hemisphere can slow down the subdominant hemisphere, such that meta-control could instead result from the interhemispheric speed differences. Since both explanations make different predictions for the effect of commissurotomy, we tested pigeons in a meta-control task both before and after transection of the commissura anterior. This fiber pathway is the largest pallial commissura of the avian brain. The results revealed a transient phase in which meta-control possibly resulted from interhemispheric response conflicts. In subsequent sessions and after commissurotomy, however, the results suggest interhemispheric speed differences as a basis for meta-control. Furthermore, they reveal that meta-control is modified by interhemispheric transmission via the commissura anterior, although it does not seem to depend on it.
Anomalies of the Portal Venous System in Dogs and Cats as Seen on Multidetector-Row Computed Tomography: An Overview and Systematization Proposal
Authors: Bertolini Giovanna
Published: January 2019

This article offers an overview of congenital and acquired vascular anomalies involving the portal venous system in dogs and cats, as determined by multidetector-row computed tomography angiography. Congenital absence of the portal vein, portal vein hypoplasia, portal vein thrombosis and portal collaterals are described. Portal collaterals are further discussed as high- and low-flow connections and categorized in hepatic arterioportal malformation, arteriovenous fistula, end-to-side and side-to-side congenital portosystemic shunts, acquired portosystemic shunts, cavoportal and porto-portal collaterals. Knowledge of different portal system anomalies helps understand the underlying physiopathological mechanism and is essential for surgical and interventional approaches.
Household Behavior with Respect to Meat Consumption: Differences between Households with and without Children. Vet. Sci. 2017, 4, 53
Authors: Merlino Valentina , Borra Danielle , Verduna Tibor , Massaglia Stefano
Published: January 2019

Due to an error during production, the order of the first and last names of the authors are incorrect in the published paper [...]
Illuminating the Neural Circuits Underlying Orienting of Attention
Authors: Posner Michael I. , Niell Cristopher M.
Published: January 2019

Human neuroimaging has revealed brain networks involving frontal and parietal cortical areas as well as subcortical areas, including the superior colliculus and pulvinar, which are involved in orienting to sensory stimuli. Because accumulating evidence points to similarities between both overt and covert orienting in humans and other animals, we propose that it is now feasible, using animal models, to move beyond these large-scale networks to address the local networks and cell types that mediate orienting of attention. In this opinion piece, we discuss optogenetic and related methods for testing the pathways involved, and obstacles to carrying out such tests in rodent and monkey populations.
Physics-Informed Data-Driven Models to Predict Surface Runoff Water Quantity and Quality in Agricultural Fields
Authors: Liang Jing , Li Wenzhe , Bradford Scott A. , Šimůnek Jiří
Published: January 2019

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models (PBMs), which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with PBMs, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. Here we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport. A large number of numerical simulations was then carried out to develop a database containing information about the impact of various relevant factors on surface runoff quantity and quality, such as different weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices. Finally, the resulting database was used to train data-driven models. Several Machine Learning techniques were explored to find input-output functional relations. The results indicate that the Neural Network model with two hidden layers performed the best among selected data-driven models, accurately predicting runoff water quantity and quality over a wide range of parameters.
Processing discharge summaries in general practice: a qualitative interview study with GPs and practice managers
Authors: Spencer Rachel A , Rodgers Sarah , Salema Ndeshi , Campbell Stephen M , Avery Anthony J
Published: January 2019

Background

Discharge summaries are essential for communicating patient information from secondary care to general practice on hospital discharge. Although there has been extensive research into their design and completion in secondary care, very little is known about primary care processing of these documents.

Aim

To explore what general practice staff think are the factors associated with failure to respond to actions requested in discharge summaries and what practices do to mitigate this.

Design & setting

Semi-structured interviews were undertaken with primary care staff in three geographical regions of England.

Method

Interviews with 10 practice managers and 10 GPs (one of each at each of the 10 practices) were undertaken to explore management of discharge summaries.

Results

Five themes emerged from the interviews. The 'secondary care factors' theme describes participants’ perspectives on the design of summaries, which are inconsistent and often require improvement. The 'safety features of processing systems' theme focuses on document handling in primary care. A theme devoted to 'medicines reconciliation' followed. 'Error and harm as a result of faulty processing' is a theme describing ‘human error’ and other factors that participants believed contributed to failure to respond to requested actions. Finally, the 'strategies for safety improvement' theme describes initiatives to prevent failures of safer transitions of care.

Conclusion

Correct processing of discharge summaries is essential to ensure patients experience a safe transition of care and not just a hospital discharge. Based on the interview findings, strategies to mitigate against faults in the processing of discharge summaries have been suggested to enhance safer transitions of care.
starry: Analytic Occultation Light Curves
Authors: Luger Rodrigo , Agol Eric , Foreman-Mackey Daniel , Fleming David P. , Lustig-Yaeger Jacob , Deitrick Russell
Published: January 2019

We derive analytic, closed form, numerically stable solutions for the total flux received from a spherical planet, moon, or star during an occultation if the specific intensity map of the body is expressed as a sum of spherical harmonics. Our expressions are valid to arbitrary degree and may be computed recursively for speed. The formalism we develop here applies to the computation of stellar transit light curves, planetary secondary eclipse light curves, and planet–planet/planet–moon occultation light curves, as well as thermal (rotational) phase curves. In this paper, we also introduce starry, an open-source package written in C++ and wrapped in Python that computes these light curves. The algorithm in starry is six orders of magnitude faster than direct numerical integration and several orders of magnitude more precise. starry also computes analytic derivatives of the light curves with respect to all input parameters for use in gradient-based optimization and inference, such as Hamiltonian Monte Carlo (HMC), allowing users to quickly and efficiently fit observed light curves to infer properties of a celestial body’s surface map. (Please see https://github.com/rodluger/starry, https://rodluger.github.io/starry/, and https://doi.org/10.5281/zenodo.1312286).
Nebular-phase Spectra of Superluminous Supernovae: Physical Insights from Observational and Statistical Properties
Authors: Nicholl Matt , Berger Edo , Blanchard Peter K. , Gomez Sebastian , Chornock Ryan
Published: January 2019

We study the spectroscopic evolution of superluminous supernovae (SLSNe) later than 100 days after maximum light. We present new data for Gaia16apd and SN 2017egm and analyze these with a larger sample comprising 41 spectra of 12 events. The spectra become nebular within 2–4 e-folding times after light-curve peak, with the rate of spectroscopic evolution correlated to the light-curve timescale. Emission lines are identified with well-known transitions of oxygen, calcium, magnesium, sodium, and iron. SLSNe are differentiated from other SNe Ic by a prominent O i
λ7774 line and higher ionization states of oxygen. The iron-dominated region around 5000 Å is more similar to broad-lined SNe Ic than to normal SNe Ic. Principal component analysis shows that five “eigenspectra” capture ≳70% of the variance, while a clustering analysis shows no clear evidence for multiple SLSN subclasses. Line velocities are 5000–8000 km s−1 and show stratification of the ejecta. O i
λ7774 likely arises in a dense inner region that also produces calcium emission, while [O i] λ6300 comes from farther out until 300–400 days. The luminosities of O i
λ7774 and Ca ii suggest significant clumping, in agreement with previous studies. Ratios of [Ca ii] λ7300/[O i] λ6300 favor progenitors with relatively massive helium cores, likely ≳6

, though more modeling is required here. SLSNe with broad light curves show the strongest [O i] λ6300, suggesting larger ejecta masses. We show how the inferred velocity, density, and ionization structure point to a central power source.
High-redshift Massive Quiescent Galaxies Are as Flat as Star-forming Galaxies: The Flattening of Galaxies and the Correlation with Structural Properties in CANDELS/3D-HST
Authors: Hill Allison R. , van der Wel Arjen , Franx Marijn , Muzzin Adam , Skelton Rosalind E. , Momcheva Iva , van Dokkum Pieter , Whitaker Katherine E.
Published: January 2019

We investigate the median flattening of galaxies at 0.2 < z < 4.0 in all five CANDELS/3D-HST fields via the apparent axis ratio, q. We separate the sample into bins of the redshift, stellar mass, Sérsic index, size, and UVJ-determined star-forming state to discover the most important drivers of the median q (q
med). Quiescent galaxies at z < 1 and M
* > 1011
M
⊙ are rounder than those at lower masses, consistent with the hypothesis that they have grown significantly through dry merging. The massive quiescent galaxies at higher redshifts become flatter and are as flat as star-forming massive galaxies at 2.5 < z < 3.5, consistent with formation through direct transformations or wet mergers. We find that in quiescent galaxies, correlations with q
med and M
*, and z and r

e
are driven by the evolution in the Sérsic index (n), consistent with the growing accumulation of minor mergers at lower redshifts. Interestingly, n does not drive these trends fully in star-forming galaxies. Instead, the strongest predictor of q in star-forming galaxies is the effective radius, where larger galaxies are flatter. Our findings suggest that q
med is the tracing bulge-to-total ratio, which would explain why smaller/more massive star-forming galaxies are rounder than their extended/less massive analogs, although it is unclear why the Sérsic index correlates more weakly with flattening for star-forming galaxies than for quiescent galaxies.
On the Persistence of QPOs during the SGR 1806−20 Giant Flare
Authors: Miller M. Coleman , Chirenti Cecilia , Strohmayer Tod E.
Published: January 2019

The discovery of quasi-periodic brightness oscillations (QPOs) in the X-ray emission accompanying the giant flares of the soft gamma-ray repeaters SGR 1806–20 and SGR 1900+14 has led to intense speculation about their nature and what they might reveal about the interiors of neutron stars. Here we take a fresh look at the giant flare data for SGR 1806–20, and in particular we analyze short segments of the post-peak emission using a Bayesian procedure, which has not previously been applied to these data. We find at best weak evidence that any QPO persists for more than ∼1 s; instead, almost all the data are consistent with a picture in which there are numerous independently excited modes that decay within a few tenths of a second. This has interesting implications for the rapidity of decay of the QPO modes, which could occur by the previously suggested mechanism of coupling to the MHD continuum. The strongest QPOs favor certain rotational phases, which might suggest special regions of the crust or of the magnetosphere. We also find several previously unreported QPOs in these data, which may help in tracking down their origin.
The Fundamental Plane of Black Hole Accretion and Its Use as a Black Hole-Mass Estimator
Authors: Gültekin Kayhan , King Ashley L. , Cackett Edward M. , Nyland Kristina , Miller Jon M. , Matteo Tiziana Di , Markoff Sera , Rupen Michael P.
Published: January 2019

We present an analysis of the fundamental plane of black hole accretion, an empirical correlation of the mass of a black hole (M), its 5 GHz radio continuum luminosity (νL

ν
), and its 2–10 keV X-ray power-law continuum luminosity (L

X
). We compile a sample of black holes with primary, direct black hole-mass measurements that also have sensitive, high-spatial-resolution radio and X-ray data. Taking into account a number of systematic sources of uncertainty and their correlations with the measurements, we use Markov chain Monte Carlo methods to fit a mass-predictor function of the form

log
(
M

/

10

8

M

⊙

)
=

μ

0

+

ξ

μ
R

log
(

L

R

/

10

38

erg

s

−
1

)

 + 

ξ

μ
X

log
(

L

X

/

10

40

erg

s

−
1

)

. Our best-fit results are μ
0 = 0.55 ± 0.22, ξ

μR
 = 1.09 ± 0.10, and

ξ

μ
X

=
−

0.59

−
0.15

+
0.16

with the natural logarithm of the Gaussian intrinsic scatter in the log-mass direction

ln

ϵ

μ

=
−

0.04

−
0.13

+
0.14

. This result is a significant improvement over our earlier mass scaling result because of the increase in active galactic nuclei sample size (from 18 to 30), improvement in our X-ray binary sample selection, better identification of Seyferts, and improvements in our analysis that takes into account systematic uncertainties and correlated uncertainties. Because of these significant improvements, we are able to consider potential influences on our sample by including all sources with compact radio and X-ray emission but ultimately conclude that the fundamental plane can empirically describe all such sources. We end with advice for how to use this as a tool for estimating black hole masses.