bluuebunny/crossref_metadata_2025 huggingface dataset:
Who Is (Likely) Peer-Reviewing Your Papers? A Partial Insight into the World’s Top Reviewers
Authors: Pomponi Francesco , D’Amico Bernardino , Rye Tom
Published: March 2019

Scientific publishing is experiencing unprecedented growth in terms of outputs across all fields. Inevitably this creates pressure throughout the system on a number of entities. One key element is represented by peer-reviewers, whose demand increases at an even higher pace than that of publications, since more than one reviewer per paper is needed and not all papers that get reviewed get published. The relatively recent Publons platform allows for unprecedented insight into the usual ‘blindness’ of the peer-review system. At a time where the world’s top peer-reviewers are announced and celebrated, we have taken a step back in order to attempt a partial mapping of their profiles to identify trends and key dimensions of this community of ‘super-reviewers’. This commentary focuses necessarily on a limited sample due to manual processing of data, which needs to be done within a single day for the type of information we seek. In investigating the numbers of performed reviews vs. academic citations, our analysis suggests that most reviews are carried out by relatively inexperienced academics. For some of these early career academics, peer-reviewing seems to be the only activity they engage with, given the high number of reviews performed (e.g., three manuscripts per day) and the lack of outputs (zero academic papers and citations in some cases). Additionally, the world’s top researchers (i.e., highly-cited researchers) are understandably busy with research activities and therefore far less active in peer-reviewing. Lastly, there seems to be an uneven distribution at a national level between scientific outputs (e.g., publications) and reviews performed. Our analysis contributes to the ongoing global discourse on the health of scientific peer-review, and it raises some important questions for further discussion.
The Ecosystem of Repository Migration
Authors: Hardesty Juliet L. , Homenda Nicholas
Published: March 2019

Indiana University was an early adopter of the Fedora repository, developing it as a home for heterogeneous digital library content from a variety of collections with unique content models. After joining the Hydra Project, now known as Samvera, in 2012, development progressed on a variety of applications that formed the foundation for digital library services using the Fedora 4 repository. These experiences have shaped migration planning to move from Fedora 3 to Fedora 4 for this large and inclusive set of digital content. Moving to Fedora 4 is not just a repository change; it is an ecosystem shift. End user interfaces for access, management systems for collection managers, and data structures are all impacted. This article shares what Indiana University has learned about migrating to Fedora 4 to help others work through their own migration considerations. This article is also meant to inspire the Fedora repository development community to offer ways to further ease migration work, sustaining Fedora users moving forward, and inviting new Fedora users to try the software and become involved in the community.
“Scientific Writing for Impact Is a Learned Skill—It Can Be Enhanced with Training”: An Interview with Patrick O’Connor
Authors: Li Yongyan , O’Connor Patrick
Published: March 2019

Dr Patrick O’Connor is a scientist based in Adelaide, Australia [...]
Spatially Integrated Social Sciences with Qualitative GIS to Support Impact Assessment in Mining Communities
Authors: Lechner Alex Mark , Owen John , Ang Michelle , Kemp Deanna
Published: March 2019

Spatially integrated social science is a broad term used to describe the integration of space and place in social science research using Geographic Information Systems (GIS). It includes qualitative GIS approaches, such as geo-ethnology and geo-narratives, which combine qualitative social data with GIS and represent an emerging approach with significant potential for facilitating new insights into the dynamic interactions between mining companies and host communities. Mine operations are unique in their complexity, both in terms of the dynamic and diverse nature of issues and the requirement to integrate knowledge, theories, and approaches from a range of disciplines. In this paper we describe the potential for spatially integrated social science using qualitative GIS to understand the social impacts of mining. We review current literature and propose a framework that incorporates quantitative and qualitative knowledge across social and biophysical domains within a multi-user approach. We provide examples to illustrate how our approach could support past, present, and future assessment of socio-environmental systems in large-scale mining. We conclude by discussing the need for a multi-disciplinary approach to support decision makers and local stakeholders in considering complex social and environmental scenarios.
The Detection and Characterization of Arctic Sea Ice Leads with Satellite Imagers
Authors: Hoffman Jay P. , Ackerman Steven A. , Liu Yinghui , Key Jeffrey R.
Published: March 2019

Sea ice leads (fractures) play a critical role in the exchange of mass and energy between the ocean and atmosphere in the polar regions. The thinning of Arctic sea ice over the last few decades will likely result in changes in lead distributions, so monitoring their characteristics is increasingly important. Here we present a methodology to detect and characterize sea ice leads using satellite imager thermal infrared window channels. A thermal contrast method is first used to identify possible sea ice lead pixels, then a number of geometric and image analysis tests are applied to build a subset of positively identified leads. Finally, characteristics such as width, length and orientation are derived. This methodology is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) observations for the months of January through April over the period of 2003 to 2018. The algorithm results are compared to other satellite estimates of lead distribution. Lead coverage maps and statistics over the Arctic illustrate spatial and temporal lead patterns.
Rock Location and Quantitative Analysis of Regolith at the Chang’e 3 Landing Site Based on Local Similarity Constraint
Authors: Hu Bin , Wang Deli , Zhang Ling , Zeng Zhaofa
Published: March 2019

Structural analysis of lunar regolith not only provides important information about lunar geology but also provides a reference for future lunar sample return missions. The Lunar Penetrating Radar (LPR) onboard China’s Chang’E-3 (CE-3) provides a unique opportunity for mapping the subsurface structure and the near-surface stratigraphic structure of the regolith. The problem of rock positioning and regolith-basement interface highlighting is meaningful. In this paper, we propose an adaptive rock extraction method based on local similarity constraints to achieve the rock location and quantitative analysis for regolith. Firstly, a processing pipeline is designed to image the LPR CH-2 A and B data. Secondly, we adopt an f-x EMD (empirical mode decomposition)-based dip filter to extract low-wavenumber components in the two data. Then, we calculate the local similarity spectrum between the filtered CH-2 A and B. After a soft threshold function, we pick the local maximums in the spectrum as the location of each rock. Finally, according to the extracted result, on the one hand, the depth of regolith is obtained, and on the other hand, the distribution information of the rocks in regolith, which changes with the path and the depth, is also revealed.
Phaseless Radar Coincidence Imaging with a MIMO SAR Platform
Authors: Diebold Aaron V. , Imani Mohammadreza F. , Smith David R.
Published: March 2019

The correlation-based synthetic aperture radar imaging technique, termed radar coincidence imaging, is extended to a fully multistatic multiple-input multiple-output (MIMO) synthetic aperture radar (SAR) configuration. Within this framework, we explore two distinct processing schemes: incoherent processing of intensity data, obtained using asynchronous receivers and inspired by optical ghost imaging works, and coherent processing with synchronized array elements. Improvement in resolution and image quality is demonstrated in both cases using numerical simulations that model an airborne MIMO SAR system at microwave frequencies. Finally, we explore methods for reducing measurement times and computational loads through compressive and gradient image reconstruction using phaseless data.
Feature Comparison and Optimization for 30-M Winter Wheat Mapping Based on Landsat-8 and Sentinel-2 Data Using Random Forest Algorithm
Authors: He Yuanhuizi , Wang Changlin , Chen Fang , Jia Huicong , Liang Dong , Yang Aqiang
Published: March 2019

Winter wheat cropland is one of the most important agricultural land-cover types affected by the global climate and human activity. Mapping 30-m winter wheat cropland can provide beneficial reference information that is necessary for understanding food security. To date, machine learning algorithms have become an effective tool for the rapid identification of winter wheat at regional scales. Algorithm implementation is based on constructing and selecting many features, which makes feature set optimization an important issue worthy of discussion. In this study, the accurate mapping of winter wheat at 30-m resolution was realized using Landsat-8 Operational Land Imager (OLI), Sentinel-2 Multispectral Imager (MSI) data, and a random forest algorithm. This paper also discusses the optimal combination of features suitable for cropland extraction. The results revealed that: (1) the random forest algorithm provided robust performance using multi-features (MFs), multi-feature subsets (MFSs), and multi-patterns (MPs) as input parameters. Moreover, the highest accuracy (94%) for winter wheat extraction occurred in three zones, including: pure farmland, urban mixed areas, and forest areas. (2) Spectral reflectance and the crop growth period were the most essential features for crop extraction. The MFSs combined with the three to four feature types enabled the high-precision extraction of 30-m winter wheat plots. (3) The extraction accuracy of winter wheat in three zones with multiple geographical environments was affected by certain dominant features, including spectral bands (B), spectral indices (S), and time-phase characteristics (D). Therefore, we can improve the winter wheat mapping accuracy of the three regional types by improving the spectral resolution, constructing effective spectral indices, and enriching vegetation information. The results of this paper can help effectively construct feature sets using the random forest algorithm, thus simplifying the feature construction workload and ensuring high-precision extraction results in future winter wheat mapping research.
Room Temperature Hydrogen Gas Sensing via Reversible Hydrogenation of Electrochemically Deposited Polycarbazole on Interdigitated Pt Transducers
Authors: Stolarczyk Agnieszka , Jarosz Tomasz , Procek Marcin
Published: March 2019

In this study, polycarbazole (PCz) is presented as a receptor structure for chemoresistive hydrogen sensors. The fabrication of the proposed sensors via electropolymerisation of PCz on interdigitated Pt electrodes is an inexpensive, cost-efficient, and repeatable method. Preliminary results presented in this work show that PCz-based sensors are sensitive to hydrogen gas in the range of 1–4% in air at room temperature. Notably, responses are both relatively high (from approximately 280% for 1% of H2) and rapid (response and recovery times for 1% H2 from 5 s and up to 32 s, respectively). Results of PCz structures on Pt and Au electrodes prove that the application of Pt electrodes is crucial for observation of sensing effect. A sensing mechanism based on reversible hydrogenation of PCz is proposed to explain the sensor operating principles.
Point-of-Need DNA Testing for Detection of Foodborne Pathogenic Bacteria
Authors: Vidic Jasmina , Vizzini Priya , Manzano Marisa , Kavanaugh Devon , Ramarao Nalini , Zivkovic Milica , Radonic Vasa , Knezevic Nikola , Giouroudi Ioanna , Gadjanski Ivana
Published: March 2019

Foodborne pathogenic bacteria present a crucial food safety issue. Conventional diagnostic methods are time-consuming and can be only performed on previously produced food. The advancing field of point-of-need diagnostic devices integrating molecular methods, biosensors, microfluidics, and nanomaterials offers new avenues for swift, low-cost detection of pathogens with high sensitivity and specificity. These analyses and screening of food items can be performed during all phases of production. This review presents major developments achieved in recent years in point-of-need diagnostics in land-based sector and sheds light on current challenges in achieving wider acceptance of portable devices in the food industry. Particular emphasis is placed on methods for testing nucleic acids, protocols for portable nucleic acid extraction and amplification, as well as on the means for low-cost detection and read-out signal amplification.
CoSiM-RPO: Cooperative Routing with Sink Mobility for Reliable and Persistent Operation in Underwater Acoustic Wireless Sensor Networks
Authors: Ali Munsif , Khan Anwar , Aurangzeb Khursheed , Ali Ihsan , Mahmood Hasan , Haider Syed Irtaza , Bhatti Naeem
Published: March 2019

An efficient algorithm for the persistence operation of data routing is crucial due to the uniqueness and challenges of the aqueous medium of the underwater acoustic wireless sensor networks (UA-WSNs). The existing multi-hop algorithms have a high energy cost, data loss, and less stability due to many forwarders for a single-packet delivery. In order to tackle these constraints and limitations, two algorithms using sink mobility and cooperative technique for UA-WSNs are devised. The first one is sink mobility for reliable and persistence operation (SiM-RPO) in UA-WSNs, and the second is the enhanced version of the SiM-RPO named CoSiM-RPO, which utilizes the cooperative technique for better exchanging of the information and minimizes data loss probability. To cover all of the network through mobile sinks (MSs), the division of the network into small portions is accomplished. The path pattern is determined for MSs in a manner to receive data even from a single node in the network. The MSs pick the data directly from the nodes and check them for the errors. When erroneous data are received at the MS, then the relay cooperates to receive correct data. The proposed algorithm boosts the network lifespan, throughput, delay, and stability more than the existing counterpart schemes.
A Deep Convolutional Neural Network Inspired by Auditory Perception for Underwater Acoustic Target Recognition
Authors: Yang Honghui , Li Junhao , Shen Sheng , Xu Guanghui
Published: March 2019

Underwater acoustic target recognition (UATR) using ship-radiated noise faces big challenges due to the complex marine environment. In this paper, inspired by neural mechanisms of auditory perception, a new end-to-end deep neural network named auditory perception inspired Deep Convolutional Neural Network (ADCNN) is proposed for UATR. In the ADCNN model, inspired by the frequency component perception neural mechanism, a bank of multi-scale deep convolution filters are designed to decompose raw time domain signal into signals with different frequency components. Inspired by the plasticity neural mechanism, the parameters of the deep convolution filters are initialized randomly, and the is n learned and optimized for UATR. The n, max-pooling layers and fully connected layers extract features from each decomposed signal. Finally, in fusion layers, features from each decomposed signal are merged and deep feature representations are extracted to classify underwater acoustic targets. The ADCNN model simulates the deep acoustic information processing structure of the auditory system. Experimental results show that the proposed model can decompose, model and classify ship-radiated noise signals efficiently. It achieves a classification accuracy of 81.96%, which is the highest in the contrast experiments. The experimental results show that auditory perception inspired deep learning method has encouraging potential to improve the classification performance of UATR.
Corrosion Monitoring Method of Porous Aluminum Alloy Plate Hole Edges Based on Piezoelectric Sensors
Authors: Dai Wei , Wang Xiangyu , Zhang Meng , Zhang Weifang , Wang Rongqiao
Published: March 2019

Corrosion damage to the aircraft structure can significantly reduce the safety performance and endanger flight safety. Especially when the corrosion occurs in a stress concentration region, such as hole edges, it can easily threaten the entire structure. In this paper, an on-line imaging qualitative monitoring algorithm based on piezoelectric sensors is proposed for detecting hole edge corrosion damage of porous aluminum alloy structures. The normalized amplitude is used to characterize the correlation between the initial Lamb wave signal and the damage signal, which is as an image reconstruction parameter in the algebraic iterative probability reconstruction algorithm. Moreover, a homogenization algorithm is proposed to process the reconstruction results. The experimental results of single hole and double hole corrosion for porous aluminum alloy plate show that the method can effectively achieve the location and quantification of corrosion damage to one and two holes of the porous structure.
Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat
Authors: Zhang Jiayi , Liu Xia , Liang Yan , Cao Qiang , Tian Yongchao , Zhu Yan , Cao Weixing , Liu Xiaojun
Published: March 2019

Rapid and effective acquisition of crop growth information is a crucial step of precision agriculture for making in-season management decisions. Active canopy sensor GreenSeeker (Trimble Navigation Limited, Sunnyvale, CA, USA) is a portable device commonly used for non-destructively obtaining crop growth information. This study intended to expand the applicability of GreenSeeker in monitoring growth status and predicting grain yield of winter wheat (Triticum aestivum L.). Four field experiments with multiple wheat cultivars and N treatments were conducted during 2013–2015 for obtaining canopy normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) synchronized with four agronomic parameters: leaf area index (LAI), leaf dry matter (LDM), leaf nitrogen concentration (LNC), and leaf nitrogen accumulation (LNA). Duration models based on NDVI and RVI were developed to monitor these parameters, which indicated that NDVI and RVI explained 80%, 68–70%, 10–12%, and 67–73% of the variability in LAI, LDM, LNC and LNA, respectively. According to the validation results, the relative root mean square error (RRMSE) were all <0.24 and the relative error (RE) were all <23%. Considering the variation among different wheat cultivars, the newly normalized vegetation indices rNDVI (NDVI vs. the NDVI for the highest N rate) and rRVI (RVI vs. the RVI for the highest N rate) were calculated to predict the relative grain yield (RY, the yield vs. the yield for the highest N rate). rNDVI and rRVI explained 77–85% of the variability in RY, the RRMSEs were both <0.13 and the REs were both <6.3%. The result demonstrates the feasibility of monitoring growth parameters and predicting grain yield of winter wheat with portable GreenSeeker sensor.
Deep Learning for Joint Adaptations of Transmission Rate and Payload Length in Vehicular Networks
Authors: Elwekeil Mohamed , Wang Taotao , Zhang Shengli
Published: March 2019

Recently, vehicular networks have emerged to facilitate intelligent transportation systems (ITS). They enable vehicles to communicate with each other in order to provide various services such as traffic safety, autonomous driving, and entertainments. The vehicle-to-vehicle (V2V) communication channel is doubly selective, where the channel changes within the transmission bandwidth and the frame duration. This necessitates robust algorithms to provide reliable V2V communications. In this paper, we propose a scheme that provides joint adaptive modulation, coding and payload length selection (AMCPLS) for V2V communications. Our AMCPLS scheme selects both the modulation and coding scheme (MCS) and the payload length of transmission frames for V2V communication links, according to the V2V channel condition. Our aim is to achieve both reliability and spectrum efficiency. Our proposed AMCPLS scheme improves the V2V effective throughput performance while satisfying a predefined frame error rate (FER). Furthermore, we present a deep learning approach that exploits deep convolutional neural networks (DCNN) for implementing the proposed AMCPLS. Simulation results reveal that the proposed DCNN-based AMCPLS approach outperforms other competing machine learning algorithms such as k-nearest neighbors (k-NN) and support vector machines (SVM) in terms of FER, effective throughput, and prediction time.
Intelligent and Dynamic Ransomware Spread Detection and Mitigation in Integrated Clinical Environments
Authors: Fernández Maimó Lorenzo , Huertas Celdrán Alberto , Perales Gómez Ángel L. , García Clemente Félix J. , Weimer James , Lee Insup
Published: March 2019

Medical Cyber-Physical Systems (MCPS) hold the promise of reducing human errors and optimizing healthcare by delivering new ways to monitor, diagnose and treat patients through integrated clinical environments (ICE). Despite the benefits provided by MCPS, many of the ICE medical devices have not been designed to satisfy cybersecurity requirements and, consequently, are vulnerable to recent attacks. Nowadays, ransomware attacks account for 85% of all malware in healthcare, and more than 70% of attacks confirmed data disclosure. With the goal of improving this situation, the main contribution of this paper is an automatic, intelligent and real-time system to detect, classify, and mitigate ransomware in ICE. The proposed solution is fully integrated with the ICE++ architecture, our previous work, and makes use of Machine Learning (ML) techniques to detect and classify the spreading phase of ransomware attacks affecting ICE. Additionally, Network Function Virtualization (NFV) and Software Defined Networking (SDN)paradigms are considered to mitigate the ransomware spreading by isolating and replacing infected devices. Different experiments returned a precision/recall of 92.32%/99.97% in anomaly detection, an accuracy of 99.99% in ransomware classification, and promising detection and mitigation times. Finally, different labelled ransomware datasets in ICE have been created and made publicly available.
Research on Optical Fiber Sensor Based on Underwater Deformation Measurement
Authors: Chen Jiawang , Cao Chen , Huang Yue , Zhang Yonglei , Ge Yongqiang
Published: March 2019

With the increase in the scale and complexity of underwater engineering, safety problems caused by underwater deformation have become increasingly prominent. Although the intensity fiber curvature sensor can be used for curvature monitoring on the ground, its sensing mechanism is still under investigation. This paper establishes the mathematical model of optical power relative loss and bending radius during deformation of the fiber sensitive region and uses the optical power meter to measure light intensity loss in the sensitive region, which verifies the correctness of the model and reveals the sensing mechanism of the intensity fiber curvature sensor, then optimizes the sensor signal conditioning circuit, applies the sensor to the single-point deformation curvature measurement, and analyzes its measurement error and accuracy. It is proved that the linear measurement range of the sensor is improved when compared with existing similar products.
An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China
Authors: Yang Shaoyuan , Lei Liping , Zeng Zhaocheng , He Zhonghua , Zhong Hui
Published: March 2019

Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO2 emissions. Quantifying anthropogenic CO2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO2 emissions by an artificial neural network using column-average dry air mole fraction of CO2 (XCO2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO2 anomalies (dXCO2) derived from XCO2 and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO2 emissions especially in the areas with high anthropogenic CO2 emissions. Our results indicate that XCO2 data from satellite observations can be applied in estimating anthropogenic CO2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO2 uptake and emissions, from satellite observations.
Adaptive Target Birth Intensity Multi-Bernoulli Filter with Noise-Based Threshold
Authors: Hu Xiaolong , Ji Hongbing , Liu Long
Published: March 2019

Adaptively modeling the target birth intensity while maintaining the filtering efficiency is a challenging issue in multi-target tracking (MTT). Generally, the target birth probability is predefined as a constant and only the target birth density is considered in existing adaptive birth models, resulting in deteriorated target tracking accuracy, especially in the target appearing cases. In addition, the existing adaptive birth models also give rise to a decline in operation efficiency on account of the extra birth modeling calculations. To properly adapt the real variation of the number of newborn targets and improve the multi-target tracking performance, a novel fast sequential Monte Carlo (SMC) adaptive target birth intensity cardinality balanced multi-target multi-Bernoulli (CBMeMBer) filter is proposed in this paper. Through adaptively conducting the target birth probability in a pre-processing step, which incorporates the information of current measurements to correct the pre-setting of the target birth probability, the proposed filter can truly adapt target birth cases and achieve better tracking accuracy. Moreover, the implementation efficiency can be improved significantly by employing a measurement noise-based threshold in the likelihood calculations of the multi-target updating. Simulation results verify the effectiveness of the proposed filter.
Real-Time Eyeblink Detector and Eye State Classifier for Virtual Reality (VR) Headsets (Head-Mounted Displays, HMDs)
Authors: Alsaeedi Nassr , Wloka Dieter
Published: March 2019

The aim of the study is to develop a real-time eyeblink detection algorithm that can detect eyeblinks during the closing phase for a virtual reality headset (VR headset) and accordingly classify the eye’s current state (open or closed). The proposed method utilises analysis of a motion vector for detecting eyelid closure, and a Haar cascade classifier (HCC) for localising the eye in the captured frame. When the downward motion vector (DMV) is detected, a cross-correlation between the current region of interest (eye in the current frame) and a template image for an open eye is used for verifying eyelid closure. A finite state machine is used for decision making regarding eyeblink occurrence and tracking the eye state in a real-time video stream. The main contributions of this study are, first, the ability of the proposed algorithm to detect eyeblinks during the closing or the pause phases before the occurrence of the reopening phase of the eyeblink. Second, realising the proposed approach by implementing a valid real-time eyeblink detection sensor for a VR headset based on a real case scenario. The sensor is used in the ongoing study that we are conducting. The performance of the proposed method was 83.9% for accuracy, 91.8% for precision and 90.40% for the recall. The processing time for each frame took approximately 11 milliseconds. Additionally, we present a new dataset for non-frontal eye monitoring configuration for eyeblink tracking inside a VR headset. The data annotations are also included, such that the dataset can be used for method validation and performance evaluation in future studies.
White Box Implementations Using Non-Commutative Cryptography
Authors: Marin Leandro
Published: March 2019

In this paper, we present a method to create a safe arithmetic that can be used to obfuscate implementations that require operations over commutative groups. The method is based on the structure of the endomorphisms of certain extensions of the original commutative group. The endomorphisms of a commutative group are non-commutative (in general), thus we can use a non-commutative group to emulate the arithmetic of a commutative one. The techniques presented in this paper are very flexible and the programmer has a wide variety of options to obfuscate the algorithms. The system can be parameterized using conjugations, thus it is possible to generate a different arithmetic for each instance of the program with a change in the security parameters, even in cases in which this number is huge (for example, in IoT applications). The security of this method is based not only on the difficulty of the conjugacy search problem (in a harder version because only partial information about the groups is known by the attacker), but also in a number of extra options that can be chosen by the programmer. The paper explains the general method, analyzes its algebraic properties and provides detailed examples based on the vector spaces over F 2 and XOR operators.
A Multilayer Fusion Light-Head Detector for SAR Ship Detection
Authors: Gui Yunchuan , Li Xiuhe , Xue Lei
Published: March 2019

Synthetic aperture radar (SAR) ship detection is a heated and challenging problem. Traditional methods are based on hand-crafted feature extraction or limited shallow-learning features representation. Recently, with the excellent ability of feature representation, deep neural networks such as faster region based convolution neural network (FRCN) have shown great performance in object detection tasks. However, several challenges limit the applications of FRCN in SAR ship detection: (1) FRCN with a fixed receptive field cannot match the scale variability of multiscale SAR ship objects, and the performance degrade when the objects are small; (2) as a two-stage detector, FRCN performs an intensive computation and leads to low-speed detection; (3) when the background is complex, the imbalance of easy and hard examples will lead to a high false detection. To tackle the above issues, we design a multilayer fusion light-head detector (MFLHD) for SAR ship detection. Instead of using a single feature map, shallow high-resolution and deep semantic feature are combined to produce region proposal. In detection subnetwork, we propose a light-head detector with large-kernel separable convolution and position sensitive pooling to improve the detection speed. In addition, we adapt focal loss to loss function and training more hard examples to reduce the false alarm. Extensive experiments on SAR ship detection dataset (SSDD) show that the proposed method achieves superior performance in SAR ship detection both in accuracy and speed.
Measurement of an Analyte Concentration in Test Solution by Using Helmholtz Resonator for Biosensor Applications
Authors: Chen Yugang , Park Yong-Hwa
Published: March 2019

In this paper, an indirect method of measuring an analyte concentration in a test solution using the resonant frequency change of a Helmholtz resonator is proposed, using a novel architecture of Helmholtz resonator filled with two kinds of fluids (fixed fluid and test solution). Since the analyte concentration yields changes of density and sound speed of the test solution, the resonant frequency of the proposed Helmholtz resonator is affected by the analyte concentration of the test solution. From this effect, the analyte concentration of the test solution can be measured by the spectrum of acoustic resonance of the Helmholtz resonator. The experiment was done using a 3D-printed Helmholtz resonator system with an acoustic power source and detectors, which is consistent with analytical results and showed that the analyte concentration can be measured with higher sensitivity compared to conventional cantilever-type sensors. As an example application, the possibility of measuring glucose concentration of human blood was demonstrated, showing higher sensitivity and relatively low frequency range compared to previous resonance based methods.
Feasibility of Home-Based Automated Assessment of Postural Instability and Lower Limb Impairments in Parkinson’s Disease
Authors: Ferraris Claudia , Nerino Roberto , Chimienti Antonio , Pettiti Giuseppe , Cau Nicola , Cimolin Veronica , Azzaro Corrado , Priano Lorenzo , Mauro Alessandro
Published: March 2019

A self-managed, home-based system for the automated assessment of a selected set of Parkinson’s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson’s Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson’s disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects’ performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, indicate that the system is feasible for an objective and automated assessment of Parkinson’s disease at home, and it could be the basis for the development of neuromonitoring and neurorehabilitation applications in a telemedicine framework.
The Role of Public Administration in Sustainable Urban Development: Evidence from Italy
Authors: Malandrino Ornella , Sica Daniela , Supino Stefania
Published: March 2019

The commitment to improve energy and environmental performance in public administration is essential for the development models geared towards lasting sustainability. The Public Administration (PA) in Italy, in particular, plays a key role to affirm models oriented towards energy and environmental sustainability, thanks to a wider and more innovative approach. The PA has a dual consumer/user role, public heritage, and decision maker/planner, in promoting energy efficiency at the local level, in the light of specific problems and peculiarities. For several decades, initiatives have been launched at various levels to improve energy and environmental performance in the public administration. The aim of this study is to outline, from a critical perspective, the state of the art policy makers in Italy about energy efficiency measures in public administration. There are, however, many cultural, value-related, financial, technological, institutional, and operational issues in the PA sector that limit investments in energy efficiency. It should be noted that if, on the one hand, the PA shows a lack of knowledge and is unwilling to adopt practices and systemic tools for monitoring and control, and on the other, in terms of bureaucracy, the system appears to be far too complicated and costly. The multiple benefits linked to improved energy performance would therefore require that the PA rethink their organizational and functional models, put in place more flexible and less bureaucratic forms of management and a more dynamic, pervasive, and proactive approach towards initiatives that promote energy efficiency. The research is a contribution towards identifying the driving forces behind potential systems and tools for energy efficiency in the PA, highlighting both critical elements and opportunities, and in particular, the limits deriving from the technological, managerial, and organizational options available for energy efficiency enhancement in the sector of the Italian PA.
Bodybuilding and Fitness Doping in Transition. Historical Transformations and Contemporary Challenges
Authors: Andreasson Jesper , Johansson Thomas
Published: March 2019

This article describes and analyses the historical development of gym and fitness culture in general and doping use in this context in particular. Theoretically, the paper utilises the concept of subculture and explores how a subcultural response can be used analytically in relation to processes of cultural normalisation as well as marginalisation. The focus is on historical and symbolic negotiations that have occurred over time, between perceived expressions of extreme body cultures and sociocultural transformations in society—with a perspective on fitness doping in public discourse. Several distinct phases in the history of fitness doping are identified. First, there is an introductory phase in the mid-1950s, in which there is an optimism connected to modernity and thoughts about scientifically-engineered bodies. Secondly, in the 1960s and 70s, a distinct bodybuilding subculture is developed, cultivating previously unseen muscular male bodies. Thirdly, there is a critical phase in the 1980s and 90s, where drugs gradually become morally objectionable. The fourth phase, the fitness revolution, can be seen as a transformational phase in gym culture. The massive bodybuilding body is replaced with the well-defined and moderately muscular fitness body, but at the same time there are strong commercialised values which contribute to the development of a new doping market. Finally, it is possible to speculate on the development of a fifth phase, in which fitness doping is increasingly being filtered into mainstream gym and fitness culture, influencing the fitness doping demography.
ROUTINE: The Development of a Physical Activity Promoting Journey Planner Web App
Authors: Füssl Elisabeth , Jaunig Johannes , Titze Sylvia
Published: March 2019

Behavior change apps are widespread, but the scientific base of the app-concept is rarely disclosed. The aim of this article is to present the methodological approach used for the development of a so-called “fitness feedback demonstrator” within an already existing journey planner web app to motivate people to increase their physical activity behavior while using public transport. Firstly, we introduce the behavior change theories applied for the design of the feedback, followed by the analysis of focus-group discussions about the desired content of the fitness feedback. Secondly, we describe how we conducted a field test to measure the number of steps taken when using public transport. Finally, we used the feedback from potential users in terms of design/attractiveness and comprehensibility of the added fitness information. The “fitness feedback demonstrator” is a good practical example of how to make use of the different research expertise to develop a theory-based tool to encourage persons to integrate physical activity into their daily routines.
The Digital Divide among Parents and Their Emerging Adult Children: Intergenerational Accounts of Technologically Assisted Family Communication
Authors: Barrie Courtney K. , Bartkowski John P. , Haverda Timothy
Published: March 2019

A great deal of previous research has examined the profound influence of digital communication technology (e.g., texting, videoconferencing, social media usage) on family life. However, few inquiries have explored the contours of technologically assisted communication using qualitative data collected from various family members. Our study breaks new ground by using interview data collected from a split sample of parents and their emerging adult children (interviewed separately) to investigate intergenerational accounts of technologically assisted family communication. Using insights from various theoretical perspectives, we analyze thirty in-depth interviews with middle-aged parents (ages 39–62) and their corresponding emerging adult children (ages 18–29) who use technology as a significant means of communicating with one another. Our analyses reveal two overarching patterns. Discordant accounts reflect disparate intergenerational views of technologically assisted family communication. By contrast, concordant accounts provide evidence of shared intergenerational reflections on technology’s role in family life. These patterns are explained by family life complexities, technology use experiences, and intergenerational norms of communication. Our study confirms that communication technology plays a multifarious role in family life across generational lines. Implications of these findings and promising avenues for future research are discussed.
Time Course of Recovery Following Resistance Exercise with Different Loading Magnitudes and Velocity Loss in the Set
Authors: Pareja-Blanco Fernando , Villalba-Fernández Antonio , Cornejo-Daza Pedro , Sánchez-Valdepeñas Juan , González-Badillo Juan
Published: March 2019

The aim of this study was to compare the time course of recovery following four different resistance exercise protocols in terms of loading magnitude (60% vs. 80% 1RM—one-repetition maximum) and velocity loss in the set (20% vs. 40%). Seventeen males performed four different protocols in full squat exercise, which were as follows: (1) 60% 1RM with a velocity loss of 20% (60-20), (2) 60% 1RM with a velocity loss of 40% (60-40), (3) 80% 1RM with a velocity loss of 20% (80-20), and (4) 80% 1RM with a velocity loss of 40% (80-40). Movement velocity against the load that elicited a 1 m·s−1 velocity at baseline measurements (V1-load), countermovement jump (CMJ) height, and sprint time at 20 m (T20) were assessed at Pre, Post, 6 h-Post, 24 h-Post, and 48 h-Post. Impairments in V1-load were significantly higher for 60-40 than other protocols at Post (p < 0.05). The 60-20 and 80-40 protocols exhibited significant performance impairments for V1-load at 6 h-Post and 24 h-Post, respectively (p < 0.05). CMJ height remained decreased for 60-20 and 60-40 until 24 h-Post (p < 0.001–0.05). Regarding T20, the 80-40 protocol resulted in higher performance than 60-40 at 24 h-Post and the 80-20 protocol induced a greater performance than 60-40 protocol at 48 h-Post (p < 0.05). A higher velocity loss during the set (40%) and a lower relative load (60% 1RM) resulted in greater fatigue and slower rate of recovery than lower velocity loss (20%) and higher relative load (80% 1RM).
The Humanmade Paradise: Exploring the Perceived Dimensions and Their Associations with Aesthetic Pleasure for Liu Yuan, a Chinese Classical Garden
Authors: Li Xin , Xia Bing , Lusk Anne , Liu Xing , Lu Ning
Published: March 2019

Investigating how people perceive Chinese Classical Gardens (CCGs) and their appeal are important issues of landscape studies. By considering the Liu-Yuan Garden, we developed a measurement instrument to investigate the perceived dimensions of landscape appreciation. Based on literature review, the descriptive items to measure how people perceived Liu-Yuan Garden were developed. Then, the explorative factor analysis and confirmatory factor analysis were employed, and six dimensions were extracted and validated including “Adoration”, “Nostalgia”, “Liveliness”, “Exquisiteness”, “Hedonic Value”, and “Placeness”. Finally, the Ordinary Linear Regression method was employed to estimate how the aesthetic appreciation is influenced by these perceived dimensions. The result revealed that four of these dimensions were significantly correlated with aesthetic pleasure. The influences of these perceived dimensions on aesthetic pleasure were measured and compared. The results indicated that “Placeness” provided the most important influence on aesthetic pleasure, whereas “Liveliness” was the least influential of the four related dimensions.
Assessing Land Use Changes in Polish Territories: Patterns, Directions and Socioeconomic Impacts on Territorial Management
Authors: Castanho Rui Alexandre , Naranjo Gómez José Manuel , Kurowska-Pysz Joanna
Published: March 2019

Bearing in mind the relationships between the territorial management and the achievement of sustainable development, studies regarding the land use changes are seen as pivotal. What is more, they can also enable us to understand the dynamics and variables for proper territorial management. Therefore, a retrospective study has been carried out regarding the land use changes in the Polish territory in the years: 1990, 2000, and 2012, by means of GIS (Geographic Information Systems) tools such as the CLC (Corine Land Cover). Moreover, and considering the complex dynamics, patterns, and particularities that territories presents, a deep analysis of those land uses is critical for the identification of barriers and opportunities for long-term sustained development. The study enabled us to identify the land use changes in the last decades in the Polish territory—allowing us to establish a relationship and identification of the associated barriers and opportunities within the socioeconomic sphere.
Correlative Impact of Shading Strategies and Configurations Design on Pedestrian-Level Thermal Comfort in Traditional Shophouse Neighbourhoods, Southern China
Authors: Yin Shi , Lang Werner , Xiao Yiqiang , Xu Zhao
Published: March 2019

Traditional shophouse neighbourhoods (TSNs) in southern China respond well to the local hot and humid climate through proper street configurations and the integration of different shading strategies. Investigating the impact of shading strategies and configurations in TSNs on outdoor thermal comfort is valuable for guiding current urban design. Three street canyons in a TSN of Guangzhou with different shading strategies were selected as basic cases for microclimatic measurement in the summer season, i.e., alleys, streets with arcade for pedestrians, and streets with high-density greenery. After validating their simulation models in ENVI-met, five groups of parametric simulations were generated by varying the canyon aspect ratio (CHW), the canyon axis orientation, arcade proportion (AHW), and the tree-covered area (TCA). Using the physiological equivalent temperature (PET) to assess the above results, the correlative impact of different variations on pedestrian’s thermal comfort and their corresponding favourable ranges are summarized. The findings suggest that: (a) only in alleys and arcade streets, the pedestrian-level thermal comfort was significantly influenced by canyon axis orientation. (b) The thermal stress for pedestrians increased dramatically when the CHW was lower than 1.5 in alleys and 0.78 in boulevards (in TCA = 89%), while the CHW higher than 1 indicated a remarkable reduction on the PET for pedestrians in arcades. (c) The pedestrians started losing the protection from shading strategy to thermal stress when the AHW was higher than 1.33 (in canyon with CHW = 1) or the TCA was lower than 33% (in canyon with CHW = 0.78).