bluuebunny/crossref_metadata_2025 huggingface dataset:
Automated Method of Road Extraction from Aerial Images Using a Deep Convolutional Neural Network
Authors: Alshaikhli Tamara , Liu Wen , Maruyama Yoshihisa
Published: November 2019

Updating road networks using remote sensing imagery is among the most important topics in city planning, traffic management and disaster management. As a good alternative to manual methods, which are considered to be expensive and time consuming, deep learning techniques provide great improvements in these regards. One of these techniques is the use of deep convolution neural networks (DCNNs). This study presents a road segmentation model consisting of a skip connection of U-net and residual blocks (ResBlocks) in the encoding part and convolution layers (Conv. layer) in the decoding part. Although the model uses fewer residual blocks in the encoding part and fewer convolution layers in the decoding part, it produces better image predictions in comparison with other state-of-the-art models. This model automatically and efficiently extracts road networks from high-resolution aerial imagery in an unexpansive manner using a small training dataset.
Concrete Crack Identification and Image Mosaic Based on Image Processing
Authors: Tian Furui , Zhao Ying , Che Xiangqian , Zhao Yagebai , Xin Dabo
Published: November 2019

Crack assessment is an essential process in bridge detection. In general, most non-contact crack detection techniques are not suitable for widespread use. The reason for this is that they all need to position the ruler at the inspection site in advance or calibrate the camera unit pixel size at a certain distance in a very intricate process. However, the object distance method in this paper can complete the calculation using only the crack image and the working distance, which are provided by an acquisition system equipped with a camera and laser range finder. First, the object distance method and the scale method are compared by calculating the crack width, and the results show that the object distance method is the more accurate method. Then, a double edge pixel statistical method is proposed to calculate the crack length, which solves the problem of redundant and missing pixels. In addition, the conventional mosaic algorithm is improved to realize an image mosaic for the more efficient splicing of crack images. Finally, a series of laboratory tests were conducted to verify the proposed approach. The experiments showed that the precision of crack length extraction can reach 92%, and the improved algorithm stitching precision can reach 98%.
Study on Dynamic Strain Distribution in Rock Broken by Abrasive Slurry Jet
Authors: Wang Fengchao , Han Weigang , Ma Zhihui , Guo Chuwen
Published: November 2019

To reveal the stress and strain field in rock under abrasive slurry jet (ASJ) impacting, this paper carried out experiments to measure the dynamic rock strain using a novel strain measurement approach, which is the non-contact strain measurement system. Moreover, it was compared with the theoretical analysis of stress propagation in rock. The results showed that dynamic strain will propagate within rock in the form of spherical waves, whose maximum value is proportional to the jet pressure and inversely proportional to the square of the propagation distance. In the process of ASJ impacting rock, strain will increase from zero to a maximum value in milliseconds. Dynamic strain in rock is fluctuates due to the release of internal stress caused by the rock crushing, which can reflect that rock will be broken by ASJ in the form of stepped-failure. The paper reveals the dynamic strain distribution in rock broken by ASJ and provides a new idea and method for studying the rock failure mechanism under ASJ impacting.
Key Technologies and Development Trends of 5G Optical Networks
Authors: Chang Shu-Hao
Published: November 2019

With the development of 5G, 5G optical networks have gradually received increasing attention from scholars. However, most studies have focused on discussing the technical or market aspect. Furthermore, their findings have not provided a panorama of the technologies in the 5G domain, nor have they provided a detailed understanding of the key technologies and development trends. An optical network is an indispensable type of infrastructure for the development of 5G. Therefore, defining key technologies in this domain is particularly crucial. The present study used patents for 5G optical networks as the basis of its analysis and constructed a technology network using a network analysis method. Research results indicated that the key technologies provided by 5G optical networks include wireless communication network facilities and local resource management (H04W88 and H04W72), selection arrangements for multiplex systems (H04Q11), and arrangements enabling multiple uses of the transmission path (H04L5). The maturation of optical component technology has paved the way for multiplex communication system technology to flourish and made it one of the key technologies in the development of 5G. Additionally, an analysis of top patentees revealed that information technology companies are the main force in developing 5G optical network technologies. Thus, driven by the market, 5G optical communication has become the technical focus of the private sector. In this study, the researchers constructed a technology network model to explore the technology development trends, and the results may serve as a reference for the government in observing emerging technologies.
A Promising Technological Approach to Improve Indoor Air Quality
Authors: Maggos Thomas , Binas Vassiliοs , Siaperas Vasileios , Terzopoulos Antypas , Panagopoulos Panagiotis , Kiriakidis George
Published: November 2019

Indoor Air quality (IAQ) in private or public environments is progressively recognized as a critical issue for human health. For that purpose the poor IAQ needs to be mitigated and immediate drastic measures must be taken. In environmental science and especially in advanced oxidation processes and technologies (AOPs-AOTs), photocatalysis has gained considerable interest among scientists as a tool for IAQ improvement. In the current study an innovative paint material was developed which exhibits intense photocatalytic activity under direct and diffused visible light for the degradation of air pollutants, suitable for indoor use. A laboratory and a real scale study were performed using the above innovative photo-paint. The lab test was performed in a special design photo-reactor while the real scale in a military’s medical building. Nitrogen Oxide (NO) and Toluene concentration was monitored between “reference” rooms (without photo paint) and “green” rooms (with photo-paint) in order to estimate the photocatalytic efficiency of the photo-paint to degrade the above pollutants. Results of the study showed a decrease up to 60% and 16% for NO and toluene respectively under lab scale tests while an improvement of air quality up to 19% and 5% under real world conditions was achieved.
Evaluation and Projection of Extreme Precipitation over Northern China in CMIP5 Models
Authors: Rao Xiaoqiang , Lu Xi , Dong Wenjie
Published: November 2019

This study evaluates 32 climate models from CMIP5 compared with a daily gridded observation dataset of extreme precipitation indices including total extreme precipitation (R95p), maximum consecutive five days of precipitation (RX5day) and wet days larger than 10 mm of precipitation (R10mm) over Northern China during the historical period (1986–2005). Results show the majority models have good performance on spatial distribution but overestimate the amplitude of precipitation over Northern China. Most models can also capture interannual variation of R95p and RX5d, but with poor simulations on R10mm. Considering both spatial and temporal factors, the best multi-model ensemble (Group 1) has been selected and improved by 42%, 34%, and 37% for R95p, RX5d, and R10mm, respectively. Projection of extreme precipitation indicates that the fastest-rising region is in Northwest China due to the enhanced rainfall intensity. However, the uncertainty analysis shows the increase of extreme rainfall over Northwest China has a low confidence level. The projection of increasing extreme rainfall over Northeast China from Group 1 due to the longer extreme rainfall days is more credible. The weak subtropical high and southwest winds from Arabian Sea lead to the low wet biases from Group 1 and the cyclonic anomalies over Northeast China, which result in more extreme precipitation.
Estimation of Low-Flow in South Korean River Basins Using a Canonical Correlation Analysis and Neural Network (CCA-NN) Based Regional Frequency Analysis
Authors: Jung Kichul , Kim Eunji , Kang Boosik
Published: November 2019

Low-flow quantiles at ungauged locations are generally estimated based on hydrological methods, such as the drainage area ratio and frequency analysis methods. In practice, the drainage area ratio approach is a popular but simple linear model. When hydrologically nonlinear characteristics govern the runoff process, the linear approach leads to significant bias. This study was conducted to develop an improved nonlinear approach using a canonical correlation analysis and neural network (CCA-NN)-based regional frequency analysis (RFA) for low-flow estimation. The jackknife technique was utilized to validate the two methods. The approaches were applied to 33 river basins in South Korea. In this work, we focused on two-year and five-year return periods. For the two-year return period, the BIAS, RMSE, and R2 were 0.013, 0.511, and 0.408 with the RFA, respectively, and −0.042, 1.042, and 0.114 with the drainage area ratio method, respectively; whereas for the five-year return period, the respective indices were −0.018, 0.316, and 0.573 with RFA, respectively, and 0.166, 0.536, and 0.044 with the drainage area ratio method, respectively. RFA outperformed the drainage area ratio method based on its high prediction accuracy and ability to avoid the bias problem. This study indicates that machine learning-based nonlinear techniques have the potential for use in estimating reliable low-flows at ungauged sites.
Seasonal Variations and Chemical Predictors of Oxidative Potential (OP) of Particulate Matter (PM), for Seven Urban French Sites
Authors: Calas Aude , Uzu Gaëlle , Besombes Jean-Luc , Martins Jean M.F. , Redaelli Matteo , Weber Samuël , Charron Aurelie , Albinet Alexandre , Chevrier Florie , Brulfert Guillaume , Mesbah Boualem , Favez Olivier , Jaffrezo Jean-Luc
Published: November 2019

Epidemiological studies suggest that the main part of chronic effects from air pollution is likely to be linked with particulate matter (PM). Oxidative potential (OP) of PM is gaining strong interest as a promising health exposure metric. This study combined atmospheric detailed composition results obtained for seven different urban background environments over France to examine any possible common feature in OP seasonal variations obtained using two assays (acid ascorbic (AA) and dithiothreitol (DTT)) along a large set of samples ( N > 700 ). A remarkable homogeneity in annual cycles was observed with a higher OP activity in wintertime at all investigated sites. Univariate correlations were used to link the concentrations of some major chemical components of PM and their OP. Four PM components were identified as OP predictors: OC, EC, monosaccharides and Cu. These species are notably emitted by road transport and biomass burning, targeting main sources probably responsible for the measured OP activity. The results obtained confirm that the relationship between OP and atmospheric pollutants is assay- and location-dependent and, thus, the strong need for a standardized test, or set of tests, for further regulation purposes.
Mutation S115T in IMP-Type Metallo-β-Lactamases Compensates for Decreased Expression Levels Caused by Mutation S119G
Authors: Zhang Charles J. , Faheem Mohammad , Dang Paulie , Morris Monica N. , Kumar Pooja , Oelschlaeger Peter
Published: November 2019

(1) Background: Metallo-β-lactamases (MBLs) have raised concerns due to their ability to inactivate carbapenems and newer generation cephalosporins and the absence of clinically available MBL inhibitors. Their genes are often transferred horizontally, and the number of MBL variants has grown exponentially, with many newer variants showing enhanced enzyme activity or stability. In this study, we investigated a closely related group of variants from the IMP family that all contain the combination of mutations S115T and S119G relative to IMP-1. (2) Methods: The effects of each individual mutation and their combination in the IMP-1 sequence background in comparison to IMP-1 were investigated. Their ability to confer resistance and their in-cell expression levels were determined. All enzymes were purified, and their secondary structure and thermal stability were determined with circular dichroism. Their Zn(II) content and kinetic constants with a panel of β-lactam antibiotics were determined. (3) Results: All four enzymes were viable and conferred resistance to all antibiotics tested except aztreonam. However, the single-mutant enzymes were slightly deficient, IMP-1S115T due to decreased enzyme activity and IMP-1-S119G due to decreased thermal stability and expression, while the double mutant did not show these defects. (4) Conclusions: These observations suggest that S119G was acquired due to its increased enzyme activity and S115T to suppress the thermal stability and expression defect introduced by S119G.
Temozolomide in Glioblastoma Therapy: Role of Apoptosis, Senescence and Autophagy. Comment on Strobel et al., Temozolomide and Other Alkylating Agents in Glioblastoma Therapy. Biomedicines 2019, 7, 69
Authors: Kaina Bernd
Published: November 2019

Temozolomide, a DNA methylating drug, is currently being used first-line in glioblastoma therapy. Although the mode of action of this so-called SN1 alkylating agent is well described, including the types of induced DNA damage triggering the DNA damage response and survival and death pathways, some researchers expressed doubt that data mostly obtained by in vitro models can be translated into the in vivo situation. In experimental settings, high doses of the agent are often used, which are likely to activate responses triggered by base N-alkylations instead of O6-methylguanine (O6MeG), which is the primary cytotoxic lesion induced by low doses of temozolomide and other methylating drugs in O6-methylguanine-DNA methyltransferase (MGMT) repair incompetent cells. However, numerous studies provided compelling evidence that O6MeG is not only a mutagenic, but also a powerful toxic lesion inducing DNA double-strand breaks, apoptosis, autophagy and cellular senescence. MGMT, repairing the lesion through methyl group transfer, is a key node in protecting cells against all these effects and has a significant impact on patient’s survival following temozolomide therapy, supporting the notion that findings obtained on a molecular and cellular level can be translated to the therapeutic setting in vivo. This comment summarizes the current knowledge on O6MeG-triggered pathways, including dose dependence and the question of thresholds, and comes up with the conclusion that data obtained on cell lines using low dose protocols are relevant and apoptosis, autophagy and senescence are therapeutically important endpoints.
Optical Biosensors for Therapeutic Drug Monitoring
Authors: Garzón Vivian , Pinacho Daniel , Bustos Rosa-Helena , Garzón Gustavo , Bustamante Sandra
Published: November 2019

Therapeutic drug monitoring (TDM) is a fundamental tool when administering drugs that have a limited dosage or high toxicity, which could endanger the lives of patients. To carry out this monitoring, one can use different biological fluids, including blood, plasma, serum, and urine, among others. The help of specialized methodologies for TDM will allow for the pharmacodynamic and pharmacokinetic analysis of drugs and help adjust the dose before or during their administration. Techniques that are more versatile and label free for the rapid quantification of drugs employ biosensors, devices that consist of one element for biological recognition coupled to a signal transducer. Among biosensors are those of the optical biosensor type, which have been used for the quantification of different molecules of clinical interest, such as antibiotics, anticonvulsants, anti-cancer drugs, and heart failure. This review presents an overview of TDM at the global level considering various aspects and clinical applications. In addition, we review the contributions of optical biosensors to TDM.
Intracerebral Hemorrhage: Blood Components and Neurotoxicity
Authors: Madangarli Neha , Bonsack Frederick , Dasari Rajaneekar , Sukumari–Ramesh Sangeetha
Published: November 2019

Intracerebral hemorrhage (ICH) is a subtype of stroke which is associated with the highest mortality and morbidity rates of all strokes. Although it is a major public health problem, there is no effective treatment for ICH. As a consequence of ICH, various blood components accumulate in the brain parenchyma and are responsible for much of the secondary brain damage and ICH-induced neurological deficits. Therefore, the strategies that could attenuate the blood component-induced neurotoxicity and improve hematoma resolution are highly needed. The present article provides an overview of blood-induced brain injury after ICH and emphasizes the need to conduct further studies elucidating the mechanisms of hematoma resolution after ICH.
Neuroinflammation in Post-Traumatic Epilepsy: Pathophysiology and Tractable Therapeutic Targets
Authors: Sharma Rishabh , Leung Wai Lam , Zamani Akram , O’Brien Terence J. , Casillas Espinosa Pablo M. , Semple Bridgette D.
Published: November 2019

Epilepsy is a common chronic consequence of traumatic brain injury (TBI), contributing to increased morbidity and mortality for survivors. As post-traumatic epilepsy (PTE) is drug-resistant in at least one-third of patients, there is a clear need for novel therapeutic strategies to prevent epilepsy from developing after TBI, or to mitigate its severity. It has long been recognized that seizure activity is associated with a local immune response, characterized by the activation of microglia and astrocytes and the release of a plethora of pro-inflammatory cytokines and chemokines. More recently, increasing evidence also supports a causal role for neuroinflammation in seizure induction and propagation, acting both directly and indirectly on neurons to promote regional hyperexcitability. In this narrative review, we focus on key aspects of the neuroinflammatory response that have been implicated in epilepsy, with a particular focus on PTE. The contributions of glial cells, blood-derived leukocytes, and the blood–brain barrier will be explored, as well as pro- and anti-inflammatory mediators. While the neuroinflammatory response to TBI appears to be largely pro-epileptogenic, further research is needed to clearly demonstrate causal relationships. This research has the potential to unveil new drug targets for PTE, and identify immune-based biomarkers for improved epilepsy prediction.
Virtual-Reality-Induced Visual Perturbations Impact Postural Control System Behavior
Authors: Chander Harish , Kodithuwakku Arachchige Sachini N. K. , Hill Christopher M. , Turner Alana J. , Deb Shuchisnigdha , Shojaei Alireza , Hudson Christopher , Knight Adam C. , Carruth Daniel W.
Published: November 2019

Background: Virtual reality (VR) is becoming a widespread tool in rehabilitation, especially for postural stability. However, the impact of using VR in a “moving wall paradigm” (visual perturbation), specifically without and with anticipation of the perturbation, is unknown. Methods: Nineteen healthy subjects performed three trials of static balance testing on a force plate under three different conditions: baseline (no perturbation), unexpected VR perturbation, and expected VR perturbation. The statistical analysis consisted of a 1 × 3 repeated-measures ANOVA to test for differences in the center of pressure (COP) displacement, 95% ellipsoid area, and COP sway velocity. Results: The expected perturbation rendered significantly lower (p < 0.05) COP displacements and 95% ellipsoid area compared to the unexpected condition. A significantly higher (p < 0.05) sway velocity was also observed in the expected condition compared to the unexpected condition. Conclusions: Postural stability was lowered during unexpected visual perturbations compared to both during baseline and during expected visual perturbations, suggesting that conflicting visual feedback induced postural instability due to compensatory postural responses. However, during expected visual perturbations, significantly lowered postural sway displacement and area were achieved by increasing the sway velocity, suggesting the occurrence of postural behavior due to anticipatory postural responses. Finally, the study also concluded that VR could be used to induce different postural responses by providing visual perturbations to the postural control system, which can subsequently be used as an effective and low-cost tool for postural stability training and rehabilitation.
Antineoplastic Effect of a Combined Mitotane Treatment/Ionizing Radiation in Adrenocortical Carcinoma: A Preclinical Study
Authors: Cerquetti Lidia , Bucci Barbara , Carpinelli Giulia , Lardo Pina , Proietti Antonella , Saporito Raffaele , Rindi Guido , Petrangeli Elisa , Toscano Vincenzo , Stigliano Antonio
Published: November 2019

Mitotane (MTT) is an adrenolytic drug used in adjuvant and advanced treatments of adrenocortical carcinoma (ACC). Ionizing radiation (IR) is also used in adrenal cancer treatment, even though its biological action remains unknown. To provide a reliable in vivo preclinical model of ACC, we used mouse xenografts bearing human ACC to test the effects of MTT and IR alone and in combination. We evaluated tumor growth inhibition by the RECIST criteria and analyzed the cell cycle by flow cytometry (FCM). In the xenograft ACC model treated with MTT/IR in combination, we observed a marked inhibition of tumor growth, with strong tumor regression (p < 0.0001) compared to MTT and IR given alone (p < 0.05). The MTT results confirm its antisteroidogenic activity (p < 0.05) in the xenograft ACC model, revealing its ability to render cancer cells more prone to radiotherapy treatment. In addition, to explain the biological effect of these treatments on the Mismatch Repair System (MMR), we interfered with the MSH2 gene expression in untreated and MTT/IR-treated H295R and SW13 cell lines. Moreover, we observed that upon treatment with MTT/IR to induce DNA damage, MSH2 gene inhibition in both the H295R and SW13 cell lines did not allow DNA damage repair, thus inducing cell death. In conclusion, MTT seems to have a radiosensitizing property and, when given in combination with IR, is able to promote neoplastic growth inhibition, leading to a significant reduction in tumor size due to cell death.
Response to Lenvatinib Is Associated with Optimal RelativeDose Intensity in Hepatocellular Carcinoma: Experience in Clinical Settings
Authors: Sasaki Ryu , Fukushima Masanori , Haraguchi Masafumi , Miuma Satoshi , Miyaaki Hisamitsu , Hidaka Masaaki , Eguchi Susumu , Matsuo Satoshi , Tajima Kazuaki , Matsuzaki Toshihisa , Hashimoto Satsuki , Ooba Kazuo , Kugiyama Yuki , Yatsuhashi Hiroshi , Motoyoshi Yasuhide , Shigeno Masaya , Kinoshita Noboru , Nakao Kazuhiko
Published: November 2019

Background: Lenvatinib is currently available as the first-line treatment for advanced unresectable hepatocellular carcinoma. We evaluated the relationship between its relative dose intensity (RDI) and response in clinical settings. Methods: From March 2018 to May 2019, 93 patients were administered lenvatinib at the Nagasaki University Hospital and its related facilities. Among these, 81 patients (66 men, 15 women, median age 72.0) who received lenvatinib were analyzed retrospectively. Results: Fourteen patients were Child–Pugh grade B, and 15 had received other systemic therapy. According to Response Evaluation Criteria in Solid Tumors (RECIST), the objective response (OR) rate was 17.3%. The overall survival (OS) was significantly better in the OR group (p = 0.011). There was a significant difference in RDI between the OR and non-OR groups (p < 0.05). The area under the receiver operating characteristics curve for OR prediction by the 4, 8, 12, and 16-week RDI were 0.666, 0.747, 0.731, and 0.704, respectively. In the 8-week RDI ≥67.0% group, OS was significantly better than in the 8-week RDI <67.0% group (p = 0.003). Conclusions: Because a sufficient RDI is required to achieve an OR, it is strongly recommended that lenvatinib should be administered to patients with good hepatic function and status.
Brain Tumors in NF1 Children: Influence on Neurocognitive and Behavioral Outcome
Authors: Taddei Matilde , Erbetta Alessandra , Esposito Silvia , Saletti Veronica , Bulgheroni Sara , Riva Daria
Published: November 2019

Neurofibromatosis type-1 (NF1) is a monogenic tumor-predisposition syndrome creating a wide variety of cognitive and behavioral abnormalities, such as decrease in cognitive functioning, deficits in visuospatial processing, attention, and social functioning. NF1 patients are at risk to develop neurofibromas and other tumors, such as optic pathway gliomas and other tumors of the central nervous system. Few studies have investigated the impact of an additional diagnosis of brain tumor on the cognitive outcome of children with NF1, showing unclear results and without controlling by the effect of surgery, radio- or chemotherapy. In the present mono-institutional study, we compared the behavioral and cognitive outcomes of 26 children with neurofibromatosis alone (NF1) with two age-matched groups of 26 children diagnosed with NF1 and untreated optic pathway glioma (NF1 + OPG) and 19 children with NF1 and untreated other central nervous system tumors (NF1 + CT). NF1 + CT and NF1 + OPG showed significantly impaired cognitive abilities compared to NF1 group, with weaknesses in visuo-spatial abilities, visual scanning and verbal working memory, while general verbal abilities are preserved. Moreover, NF1 + OPG patients present more frequent internalizing problems and increased oppositional-deviant behaviors. These results suggest that the co-diagnosis of a brain tumor in NF1 children may partially worsen the cognitive and emotional outcome.
Application of Pullulan and Chitosan Multilayer Coatings in Fresh Papayas
Authors: Zhang Linyun , Huang Chongxing , Zhao Hui
Published: November 2019

In this work, some multilayer coatings (two-layer, four-layer or six-layer) based on pullulan and chitosan for protecting papayas were prepared by the layer-by-layer technique. The papayas were coated by immersion and stored at 25 °C, 50% relative humidity or up to 14 days. Uncoated and monolayer-coated papayas were used as controls. The pullulan/chitosan coatings decreased the papaya weight loss, softening, color change (b*, ΔE), and pH, retarded the fall of titratable acidity and vitamin C, and maintained respiratory rate and soluble solid contents. Sensory quality evaluation demonstrated that pullulan/chitosan coatings effectively preserved papaya flavor and overall acceptance. In general, the four-layer coatings provided the best fruit preservation. In conclusion, multilayer pullulan/chitosan coatings are efficient in maintaining the post-harvest quality and prolonging the shelf life of fresh papaya.
Smart Health and Safety Equipment Monitoring System for Distributed Workplaces
Authors: Al-Dulaimi Jabbar , Cosmas John , Abbod Maysam
Published: November 2019

This paper presents a design and prototype of an IoT-based health and safety monitoring system using MATLAB GUI. This system, which is called the Smart Health and Safety Monitoring System, is aimed at reducing the time, cost and manpower requirements of distributed workplaces. The proposed system is a real-time control and monitoring system that can access on-line the status of consumable devices in the workplace via the internet and prioritise the critically high location that need replenishing. The system dynamically updates the status of all location, such as first aid boxes, earplug dispensers and fire extinguishers. Simulation results of the proposed system gives shorter path, time and cost in comparison to manual maintenance systems.
Quantification of Carotid Intraplaque Hemorrhage: Comparison between Manual Segmentation and Semi-Automatic Segmentation on Magnetization-Prepared Rapid Acquisition with Gradient-Echo Sequences
Authors: Song Young Ju , Kwak Hyo Sung , Chung Gyung Ho , Jo Seongil
Published: November 2019

Purpose: Carotid intraplaque hemorrhage (IPH) increases risk of territorial cerebral ischemic events, but different sequences or criteria have been used to diagnose or quantify carotid IPH. The purpose of this study was to compare manual segmentation and semi-automatic segmentation for quantification of carotid IPH on magnetization-prepared rapid acquisition with gradient-echo (MPRAGE) sequences. Methods: Forty patients with 16–79% carotid stenosis and IPH on MPRAGE sequences were reviewed by two trained radiologists with more than five years of specialized experience in carotid plaque characterization with carotid plaque MRI. Initially, the radiologists manually viewed the IPH based on the MPRAGE sequence. IPH volume was then measured by three different semi-automatic methods, with high signal intensity 150%, 175%, and 200%, respectively, above that of adjacent muscle on the MPRAGE sequence. Agreement on measurements between manual segmentation and semi-automatic segmentation was assessed using the intraclass correlation coefficient (ICC). Results: There was near-perfect agreement between manual segmentation and the 150% and 175% criteria for semi-automatic segmentation in quantification of IPH volume. The ICC of each semi-automatic segmentation were as follows: 150% criteria: 0.861, 175% criteria: 0.809, 200% criteria: 0.491. The ICC value of manual vs. 150% criteria and manual vs. 175% criteria were significantly better than the manual vs. 200% criteria (p < 0.001). Conclusions: The ICC of 150% and 175% criteria for semi-automatic segmentation are more reliable for quantification of IPH volume. Semi-automatic classification tools may be beneficial in large-scale multicenter studies by reducing image analysis time and avoiding bias between human reviewers.
A Smoothed Algorithm with Convergence Analysis under Generalized Maximum Correntropy Criteria in Impulsive Interference
Authors: Qu Hua , Shi Youwei , Zhao Jihong
Published: November 2019

The generalized maximum correntropy criterion (GMCC) algorithm is computationally simple and robust against impulsive noise but it suffers from slow convergence speed as it is derived and based on stochastic gradient, which only use the current data sample. In order to deal with this issue, a smoothed GMCC algorithm (SGMCC) is proposed. In the SGMCC algorithm, instead of taking the exponential weighted average of gradient vector to approximate the expectation of the gradient vector, we take the exponential weighted average of the variable step-size so that the SGMCC algorithm can be viewed as a sign GMCC algorithm with smoothed variable step-size. Moreover, convergence performance analyses are derived in terms of variable step-size, mean-square stability and steady-state behavior to demonstrate the robustness of the proposed algorithm. At last, simulation comparisons show that the proposed algorithm is robust against impulsive noise and converges fast with lower computational complexity. Also, for the steady-state behavior, simulation results verify that the simulated value matches well with the theoretical one.
Unidimensional Continuous-Variable Quantum Key Distribution with Untrusted Detection under Realistic Conditions
Authors: Huang Luyu , Zhang Yichen , Chen Ziyang , Yu Song
Published: November 2019

A unidimensional continuous-variable quantum key distribution protocol with untrusted detection is proposed, where the two legitimate partners send unidimensional modulated or Gaussian-modulated coherent states to an untrusted third party, i.e., Charlie, to realize the measurement. Compared with the Gaussian-modulated coherent-state protocols, the unidimensional modulated protocols take the advantage of easy modulation, low cost, and only a small number of random numbers required. Security analysis shows that the proposed protocol cannot just defend all detectors side channels, but also achieve great performance under certain conditions. Specifically, three cases are discussed in detail, including using unidimensional modulated coherent states in Alice’s side, in Bob’s side, and in both sides under realistic conditions, respectively. Under the three conditions, we derive the expressions of the secret key rate and give the optimal gain parameters. It is found that the optimal performance of the protocol is achieved by using unidimensional modulated coherent states in both Alice’s and Bob’s side. The resulting protocol shows the potential for long-distance secure communication using the unidimensional quantum key distribution protocol with simple modulation method and untrusted detection under realistic conditions.
Causal Analysis of Learning Performance Based on Bayesian Network and Mutual Information
Authors: Chen Jing , Feng Jun , Hu Jingzhao , Sun Xia
Published: November 2019

Over the past few years, online learning has exploded in popularity due to the potentially unlimited enrollment, lack of geographical limitations, and free accessibility of many courses. However, learners are prone to have poor performance due to the unconstrained learning environment, lack of academic pressure, and low interactivity. Personalized intervention design with the learners’ background and learning behavior factors in mind may improve the learners’ performance. Causality strictly distinguishes cause from outcome factors and plays an irreplaceable role in designing guiding interventions. The goal of this paper is to construct a Bayesian network to make causal analysis and then provide personalized interventions for different learners to improve learning. This paper first constructs a Bayesian network based on background and learning behavior factors, combining expert knowledge and a structure learning algorithm. Then the important factors in the constructed network are selected using mutual information based on entropy. At last, we identify learners with poor performance using inference and propose personalized interventions, which may help with successful applications in education. Experimental results verify the effectiveness of the proposed method and demonstrate the impact of factors on learning performance.
Voronoi Decomposition of Cardiovascular Dependency Structures in Different Ambient Conditions: An Entropy Study
Authors: Bajic Dragana , Skoric Tamara , Milutinovic-Smiljanic Sanja , Japundzic-Zigon Nina
Published: November 2019

This paper proposes a method that maps the coupling strength of an arbitrary number of signals D, D ≥ 2, into a single time series. It is motivated by the inability of multiscale entropy to jointly analyze more than two signals. The coupling strength is determined using the copula density defined over a [0 1]D copula domain. The copula domain is decomposed into the Voronoi regions, with volumes inversely proportional to the dependency level (coupling strength) of the observed joint signals. A stream of dependency levels, ordered in time, creates a new time series that shows the fluctuation of the signals’ coupling strength along the time axis. The composite multiscale entropy (CMSE) is then applied to three signals, systolic blood pressure (SBP), pulse interval (PI), and body temperature (tB), simultaneously recorded from rats exposed to different ambient temperatures (tA). The obtained results are consistent with the results from the classical studies, and the method itself offers more levels of freedom than the classical analysis.
Commodity Revenues, Agricultural Sector and the Magnitude of Deindustrialization: A Novel Multisector Perspective
Authors: Sadik-Zada Elkhan Richard , Loewenstein Wilhelm , Hasanli Yadulla
Published: November 2019

This study puts forward a model of a multisector economy and embeds it in a novel theoretical framework to address the relationship between commodity revenues and manufacturing output with a special focus on the role of the agricultural sector. The three-sector model lays the groundwork for analyzing policy choices in more complex sectoral settings. Based on the theoretical analysis, the study identifies the weight of the individual economic sectors in the public revenue generation as a determinant of the magnitude of rent seeking epitomized in the crowding out effect of investments in manufacturing. We find that enclave agriculture contributes to the deindustrialization pressure in the face of natural resource windfalls. The central finding of the multisector analysis is the conclusion that not diversification per se but rather a diversification with the substantial domestic factor or market orientation has the capability to limit the magnitude of deindustrialization. For the empirical validation of the theoretical findings, the study employs fixed effects, fully modified OLS, dynamic common correlated effects estimators and dynamic fixed effects estimators for the dataset of 113 developing and transition economies for 1963–2014 period. The estimations reveal that natural resource revenues correspond with a higher level of the manufacturing sector output. In the economies with a low level of economic diversification, commodity bonanza leads however to the shrinkage of the manufacturing. In the commodity revenue dependent settings, nevertheless, agricultural sector exports have a negative impact on the performance of the manufacturing sector. These findings are in line with the predictions of the theoretical model.
USB Artifact Analysis Using Windows Event Viewer, Registry and File System Logs
Authors: Neyaz Ashar , Shashidhar Narasimha
Published: November 2019

A USB mass storage device yields a lot of artifacts when connected to a system. These artifacts are persistent in nature and are retained even after the system has been shut down and the information they contain may assist in carrying out forensic analysis on a suspect system. In this paper, we demonstrate how Windows Event Viewer can be used to find forensic artifacts in a suspect system for investigative purposes. We also discuss the potential that Windows registry holds to identify USB devices’ information that have been connected to the system, to corroborate our findings from Windows Event Viewer. Finally, we use the Windows 10 file system to extract log details that contain the setup information of a USB device that was connected to the system the very first time, and obtain the necessary identifiers and time stamp details.
Li-ion Battery Modeling and State of Charge Estimation Method Including the Hysteresis Effect
Authors: Antonucci Vincenzo , Artale Giovanni , Brunaccini Giovanni , Caravello Giuseppe , Cataliotti Antonio , Cosentino Valentina , Di Cara Dario , Ferraro Marco , Guaiana Salvatore , Panzavecchia Nicola , Sergi Francesco , Tinè Giovanni
Published: November 2019

In this paper, a new approach to modeling the hysteresis phenomenon of the open circuit voltage (OCV) of lithium-ion batteries and estimating the battery state of charge (SoC) is presented. A characterization procedure is proposed to identify the battery model parameters, in particular, those related to the hysteresis phenomenon and the transition between charging and discharging conditions. A linearization method is used to obtain a suitable trade-off between the model accuracy and a low computational cost, in order to allow the implementation of SoC estimation on common hardware platforms. The proposed characterization procedure and the model effectiveness for SoC estimation are experimentally verified using a real grid-connected storage system. A mixed algorithm is adopted for SoC estimation, which takes into account both the traditional Coulomb counting method and the developed model. The experimental comparison with the traditional approach and the obtained results show the feasibility of the proposed approach for accurate SoC estimation, even in the presence of low-accuracy measurement transducers.
Fuel Cell Hybrid Model for Predicting Hydrogen Inflow through Energy Demand
Authors: Casteleiro-Roca José-Luis , Barragán Antonio Javier , Manzano Francisca Segura , Calvo-Rolle José Luis , Andújar José Manuel
Published: November 2019

Hydrogen-based energy storage and generation is an increasingly used technology, especially in renewable systems because they are non-polluting devices. Fuel cells are complex nonlinear systems, so a good model is required to establish efficient control strategies. This paper presents a hybrid model to predict the variation of H2 flow of a hydrogen fuel cell. This model combining clusters’ techniques to get multiple Artificial Neural Networks models whose results are merged by Polynomial Regression algorithms to obtain a more accurate estimate. The model proposed in this article use the power generated by the fuel cell, the hydrogen inlet flow, and the desired power variation, to predict the necessary variation of the hydrogen flow that allows the stack to reach the desired working point. The proposed algorithm has been tested on a real proton exchange membrane fuel cell, and the results show a great precision of the model, so that it can be very useful to improve the efficiency of the fuel cell system.
A Novel Compact dq-Reference Frame Model for Inverter-Based Microgrids
Authors: Macana Carlos A. , Mojica-Nava Eduardo , Pota Hemanshu R. , Guerrero Josep M. , Vasquez Juan C.
Published: November 2019

The development and the experimental validation of a novel dynamic model of an islanded three-phase Inverter-based Microgrid (IMG) is presented in this paper. The proposed model reproduces the relevant system dynamics without excessive complexity and enough accuracy. The dynamics of the IMG are captured with a compact and scalable dynamic model, considering inverter based distributed generators with d-current droop primary and proportional resonant inner controllers. The complete development of the model, the practical assumptions, and the accurate proportional power sharing of the primary control technique are shown. The accuracy performance was verified in experiments performed at the Aalborg Intelligent Microgrids Laboratory for an islanded IMG case.
Security Analysis of Multi-Antenna NOMA Networks Under I/Q Imbalance
Authors: Li Xingwang , Zhao Mengle , Zhang Changsen , Khan Wali Ullah , Wu Jun , Rabie Khaled M. , Kharel Rupak
Published: November 2019

This paper investigates the reliability and security performance of the downlink non-orthogonal multiple access (NOMA) networks over Nakagami-m fading channels, where the base station (BS) aims to communicate with multi-antenna NOMA users in the presence of a multi-antenna eavesdropper. To be more practical, a detrimental factor at both transmitter and receiver is considered, namely in-phase and quadrature-phase imbalance (IQI). To further improve the reliability and security of the considered networks, the selection combining (SC) algorithm at the receiver is taken into account. More specifically, the exact analytical expressions for the outage probability (OP) and the intercept probability (IP) are derived in closed-form. To obtain a better understanding of the influence for the IQI parameters on the system performance, the asymptotic behaviors for the outage probabilities (OPs) in the high signal-to-noise ratio (SNR) region are analyzed. Based on the asymptotic results, the diversity order of the considered system are obtained and discussed. The numerical results are presented to verify the validity of the theoretical analysis.
SSKM: Scalable and Secure Key Management Scheme for Group Signature Based Authentication and CRL in VANET
Authors: Lim Kiho , Liu Weihua , Wang Xiwei , Joung Jingon
Published: November 2019

The security in vehicular ad hoc networks (VANETs) has become a large consideration in safeguarding growing applications and intelligent transport systems. A group signature, a popular authentication approach for VANETs, can be implemented to protect vehicular communications against malicious users. However, the issue of securely distributing group keys to fast-moving vehicular nodes arises. The growing size of the certificate revocation list (CRL) has provided the corresponding complication in its management and distribution in VANETs. In this paper, an efficient key management protocol for group signature based authentication is proposed. A group is extended to a domain with various roadside units forming a hierarchical topology. Our proposed scheme provides a secure method to deliver group keys to vehicular nodes, ensuring the security requirements. Similarly, through utilizing the two Bloom filters in our hierarchical topology, an efficient and scalable vehicle revocation mechanism can be achieved that can minimize the CRL size. Our experiment results demonstrate a scalable, efficient, and secure key distribution scheme in vehicular networking. Moreover, an effective CRL management mechanism can be accomplished using the hierarchical topology.
An Efficient and Unique TF/IDF Algorithmic Model-Based Data Analysis for Handling Applications with Big Data Streaming
Authors: Iwendi Celestine , Ponnan Suresh , Munirathinam Revathi , Srinivasan Kathiravan , Chang Chuan-Yu
Published: November 2019

As the field of data science grows, document analytics has become a more challenging task for rough classification, response analysis, and text summarization. These tasks are used for the analysis of text data from various intelligent sensing systems. The conventional approach for data analytics and text processing is not useful for big data coming from intelligent systems. This work proposes a novel TF/IDF algorithm with the temporal Louvain approach to solve the above problem. Such an approach is supposed to help the categorization of documents into hierarchical structures showing the relationship between variables, which is a boon to analysts making essential decisions. This paper used public corpora, such as Reuters-21578 and 20 Newsgroups for massive-data analytic experimentation. The result shows the efficacy of the proposed algorithm in terms of accuracy and execution time across six datasets. The proposed approach is validated to bring value to big text data analysis. Big data handling with map-reduce has led to tremendous growth and support for tasks like categorization, sentiment analysis, and higher-quality accuracy from the input data. Outperforming the state-of-the-art approach in terms of accuracy and execution time for six datasets ensures proper validation.
Cooling Benefits of an Extensive Green Roof and Sensitivity Analysis of Its Parameters in Subtropical Areas
Authors: Zhang Yu , Zhang Lei , Ma Luyao , Meng Qinglin , Ren Peng
Published: November 2019

The present study aims to further demonstrate the cooling benefits of an extensive green roof (EGR) and fill the gap existing in the literature in terms of a sensitivity analysis of an EGR, especially in subtropical areas. First, onsite measurements were performed. The results indicated that the peak air temperatures in the chamber with the EGR were 4.0 °C and 1.9 °C lower, respectively, compared to those in the chamber with a bare roof on sunny and rainy days. Moreover, the EGR decreased the daily electricity consumption from air conditioning by up to 16.7% on sunny days and 6.7% on cloudy days. Second, the measured values were employed to validate the green roof module (GRM) in EnergyPlus. The results demonstrated that the GRM yielded accurate results in quantifying the cooling benefits of the EGR. Finally, we selected 16 factors of the EGR, each with four levels, to perform the sensitivity analysis. Range and variance analyses revealed that the factors that most significantly impacted the EGR performance were the R-value of roof construction, substrate (soil) thickness, the thermal conductivity of dry substrate, the leaf area index, leaf emissivity, and the solar absorptance of the substrate. These factors contributed 90.8% to the performance index.