bluuebunny/crossref_metadata_2025 huggingface dataset:
Demystifying Brain Tumor Segmentation Networks: Interpretability and Uncertainty Analysis
Authors: Natekar Parth , Kori Avinash , Krishnamurthi Ganapathy
Published: February 2020

The accurate automatic segmentation of gliomas and its intra-tumoral structures is important not only for treatment planning but also for follow-up evaluations. Several methods based on 2D and 3D Deep Neural Networks (DNN) have been developed to segment brain tumors and to classify different categories of tumors from different MRI modalities. However, these networks are often black-box models and do not provide any evidence regarding the process they take to perform this task. Increasing transparency and interpretability of such deep learning techniques is necessary for the complete integration of such methods into medical practice. In this paper, we explore various techniques to explain the functional organization of brain tumor segmentation models and to extract visualizations of internal concepts to understand how these networks achieve highly accurate tumor segmentations. We use the BraTS 2018 dataset to train three different networks with standard architectures and outline similarities and differences in the process that these networks take to segment brain tumors. We show that brain tumor segmentation networks learn certain human-understandable disentangled concepts on a filter level. We also show that they take a top-down or hierarchical approach to localizing the different parts of the tumor. We then extract visualizations of some internal feature maps and also provide a measure of uncertainty with regards to the outputs of the models to give additional qualitative evidence about the predictions of these networks. We believe that the emergence of such human-understandable organization and concepts might aid in the acceptance and integration of such methods in medical diagnosis.
Pseudomonas azotoformans Belonging to Pseudomonas fluorescens Group as Causative Agent of Blue Coloration in Carcasses of Slaughterhouse Rabbits
Authors: Circella Elena , Schiavone Antonella , Barrasso Roberta , Camarda Antonio , Pugliese Nicola , Bozzo Giancarlo
Published: February 2020

The study describes the finding of an abnormal blue-tinged color found on rabbit carcasses in the refrigeration cell of two butcher shops in Apulia Region. The carcasses were from an industrial rabbitry for production of meat with a regularly authorized slaughterhouse. Pseudomonas azotoformans, a microorganism included in Pseudomonas fluorescens group, was isolated from samples collected by the altered carcasses, showing the growth of uniform bacterial colonies with fluorescent pigmentation. The bacterium was also isolated from an additional water sample and from the labelling gun collected in the slaughterhouse, whilst the knives used for slaughtering resulted negative. Chromatic alteration was experimentally reproduced on new carcasses using a 108 cfu/mL bacterial suspension prepared with the isolated strain. Due to their resistance characteristics, members of P. fluorescens group are very difficult to eradicate once introduced into the production environment. Therefore, their presence, even if not considered a public health problem, should be monitored by food industry operators in self-control plans.
Genomewide Mapping of Selection Signatures and Genes for Extreme Feather Pecking in Two Divergently Selected Laying Hen Lines
Authors: Iffland Hanna , Wellmann Robin , Schmid Markus , Preuß Siegfried , Tetens Jens , Bessei Werner , Bennewitz Jörn
Published: February 2020

Feather pecking (FP) is a longstanding serious problem in commercial flocks of laying hens. It is a highly polygenic trait and the genetic background is still not completely understood. In order to find genomic regions influencing FP, selection signatures between laying hen lines divergently selected for high and low feather pecking were mapped using the intra-population iHS and the inter-population FST approach. In addition, the existence of an extreme subgroup of FP hens (EFP) across both selected lines has been demonstrated by fitting a mixture of negative binomial distributions to the data and calculating the posterior probability of belonging to the extreme subgroup (pEFP) for each hen. A genomewide association study (GWAS) was performed for the traits pEFP and FP delivered (FPD) with a subsequent post GWAS analysis. Mapping of selection signatures revealed no clear regions under selection. GWAS revealed a region on Chromosome 1, where the existence of a QTL influencing FP is likely. The candidate genes found in this region are a part of the GABAergic system, which has already been linked to FP in previous studies. Despite the polygenic nature of FP, selection on these candidate genes may reduce FP.
Implications for Conservation of Collection of Mediterranean Spur-Thighed Tortoise as Pets in Morocco: Residents’ Perceptions, Habits, and Knowledge
Authors: Segura Amalia , Delibes-Mateos Miguel , Acevedo Pelayo
Published: February 2020

The trading and collection of wild animals as pets may be cause for concern regarding animal welfare and species conservation. These concerns can be exemplified by Mediterranean spur-thighed tortoise (Testudo graeca), a long-living species whose use as pets is long established. The human dimension plays a major role in the wildlife for the pet collection, and is particularly important in countries like Morocco, where this might pose a threat to the conservation of the species involved. This study, which is based on a questionnaire survey (n = 480 participants), documents the fact that many people in Morocco keep tortoises as pets: 55% of the participants in the survey and over two tortoises/person. Importantly, most captive tortoises, particularly juveniles, had been collected directly from wild populations by their owners (42%, n = 264). In general, the tortoise owners had limited knowledge of their tortoises’ habits and requirements, although rural people were more likely to acknowledge that the tortoise is a wild and threatened species. Our study reveals that non-commercial collection is a common activity in Morocco that may threaten wild tortoise populations and hence species conservation, and it could have consequences regarding the welfare of the animals. We were also able to identify the profile of people towards whom education campaigns should be directed in order to reduce the number of tortoises collected from wild populations. Additional field research should also be conducted to quantify the impact of pet collection on wild tortoise populations.
Porous Doped Carbons from Anthracite for High-Performance Supercapacitors
Authors: Deng Jie , Peng Zhu , Xiao Zhe , Song Shuang , Dai Hui , Li Luming
Published: February 2020

Carbon-based materials, as some of the most important electrode materials for supercapacitors (SC), have spurred enormous attentions. Now, it is highly desirable but remains an open challenge to design stable and high-capacity carbons for further enhancing supercapacitive function. Here, a facile chemical activation recipe is introduced to develop biomass-derived functional carbons using cheap and abundant natural resources, anthracite, as the heteroatom-rich carbon sources, and potassium hydroxide (KOH) as activator. These porous carbons have high BET surface areas of roughly 2814 m2 g−1, large pore volumes of up to 1.531 cm3 g−1, and a high porosity that combines micro- and small-sized mesopores. The optimal nanocarbon features two additional outstanding virtues: an appropriate N-doping level (2.77%) and a uniform pore size distribution in the narrow range of 1–4 nm. Synergy of the above unique structural traits and desirable chemical composition endows resultant samples with the much boosted supercapacitive property with remarkable specific capacitance at varied current densities (e.g., 325 F g−1 at 0.5 A/g), impressive energy/power density, and long cycling stability over 5000 cycles at 10 A g−1 (92% capacity retention). When constructing the symmetric supercapacitor utilizing a common neutral Na2SO4 electrolyte that can strongly circumvent the corrosion effect occurring in the strong acid/alkaline solutions, both an elevated operation voltage at 1.8 V and a fascinating energy density of 23.5 Wh kg−1 are attained. The current study paves the way to explore the stable, efficient, and high-voltage SC assembled by the anthracite-derived porous doped nanocarbons for a wide spectrum of applications like automobiles, vehicle devices, and so on.
Ozone Layer Evolution in the Early 20th Century
Authors: Egorova Tatiana , Rozanov Eugene , Arsenovic Pavle , Sukhodolov Timofei
Published: February 2020

The ozone layer is well observed since the 1930s from the ground and, since the 1980s, by satellite-based instruments. The evolution of ozone in the past is important because of its dramatic influence on the biosphere and humans but has not been known for most of the time, except for some measurements of near-surface ozone since the end of the 19th century. This gap can be filled by either modeling or paleo reconstructions. Here, we address ozone layer evolution during the early 20th century. This period was very interesting due to a simultaneous increase in solar and anthropogenic activity, as well as an observed but not explained substantial global warming. For the study, we exploited the chemistry-climate model SOCOL-MPIOM driven by all known anthropogenic and natural forcing agents, as well as their combinations. We obtain a significant global scale increase in the total column ozone by up to 12 Dobson Units and an enhancement of about 20% of the near-surface ozone over the Northern Hemisphere. We conclude that the total column ozone changes during this period were mainly driven by enhanced solar ultra violet (UV) radiation, while near-surface ozone followed the evolution of anthropogenic ozone precursors. This finding can be used to constrain the solar forcing magnitude.
The Heat Health Warning System in Germany—Application and Warnings for 2005 to 2019
Authors: Matzarakis Andreas , Laschewski Gudrun , Muthers Stefan
Published: February 2020

During intense heat episodes, the human population suffers from an increased morbidity and mortality. In order to minimize such negative health impacts, the general public and the public health authorities are informed and warned by means of an advanced procedure known as a “heat health warning system” (HHWS). It is aimed at triggering interventions and at taking preventive measures. The HHWS in Germany has been in operation since 2005. The present work is aimed at showing the updated structure of an advanced HHWS that has been developed further several times during its 15 years of operation. This is to impart knowledge to practitioners about the concept of the system. In Germany, dangerous heat episodes are predicted on the basis of the numerical weather forecast. The perceived temperature as an appropriate thermal index is calculated and used to assess the levels of heat stress. The thermo-physiologically based procedure contains variable thresholds taking into account the short time acclimatization of the people. The forecast system further comprises the nocturnal indoor conditions, the specific characteristics of the elderly population, and the elevation of a region. The heat warnings are automatically generated, but they are published with possible adjustments and a compulsory confirmation by the biometeorology forecaster. Preliminary studies indicate a reduction in the heat related outcomes. In addition, the extensive duration of the strongest heat wave in summer 2018, which lasted three weeks, highlights the necessity of the HHWS to protect human health and life.
Dual Networks for High-Precision and High-Speed Registration of Brain Electron Microscopy Images
Authors: Shu Chang , Xin Tong , Zhou Fangxu , Chen Xi , Han Hua
Published: February 2020

It remains a mystery as to how neurons are connected and thereby enable use to think, and volume reconstruction from series of microscopy sections of brains is a vital technique in determining this connectivity. Image registration is a key component; the aim of image registration is to estimate the deformation field between two images. Current methods choose to directly regress the deformation field; however, this task is very challenging. It is common to trade off computational complexity with precision when designing complex models for deformation field estimation. This approach is very inefficient, leading to a long inference time. In this paper, we suggest that complex models are not necessary and solve this dilemma by proposing a dual-network architecture. We divide the deformation field prediction problem into two relatively simple subproblems and solve each of them on one branch of the proposed dual network. The two subproblems have completely opposite properties, and we fully utilize these properties to simplify the design of the dual network. These simple architectures enable high-speed image registration. The two branches are able to work together and make up for each other’s drawbacks, and no loss of accuracy occurs even when simple architectures are involved. Furthermore, we introduce a series of loss functions to enable the joint training of the two networks in an unsupervised manner without introducing costly manual annotations. The experimental results reveal that our method outperforms state-of-the-art methods in fly brain electron microscopy image registration tasks, and further ablation studies enable us to obtain a comprehensive understanding of each component of our network.
Immunotherapy by Immune Checkpoint Inhibitors and Nuclear Medicine Imaging: Current and Future Applications
Authors: Decazes Pierre , Bohn Pierre
Published: February 2020

Immunotherapy by using immune checkpoint inhibitors is a revolutionary development in oncology. Medical imaging is also impacted by this new therapy, particularly nuclear medicine imaging (also called radionuclide imaging), which uses radioactive tracers to visualize metabolic functions. Our aim was to review the current applications of nuclear medicine imaging in immunotherapy, along with their limitations, and the perspectives offered by this imaging modality. Method: Articles describing the use of radionuclide imaging in immunotherapy were researched using PubMed by April 2019 and analyzed. Results: More than 5000 articles were analyzed, and nearly 100 of them were retained. Radionuclide imaging, notably 18F-FDG PET/CT, already has a major role in many cancers for pre-therapeutic and therapeutic evaluation, diagnoses of adverse effects, called immune-related adverse events (IrAE), and end-of-treatment evaluations. However, these current applications can be hindered by immunotherapy, notably due to atypical response patterns such as pseudoprogression, which is defined as an increase in the size of lesions, or the visualization of new lesions, followed by a response, and hyperprogression, which is an accelerated tumor growth rate after starting treatment. To overcome these difficulties, new opportunities are offered, particularly therapeutic evaluation criteria adapted to immunotherapy and immuno-PET allowing us to predict responses to immunotherapy. Moreover, some new technological solutions are also promising, such as radiomic analyses and body composition on associated anatomical images. However, more research has to be done, notably for the diagnosis of hyperprogression and pseudoprogression. Conclusion: Immunotherapy, by its major impact on cancer and by the new patterns generated on images, is revolutionary in the field of medical images. Nuclear medicine imaging is already established and will be able to help meet new challenges through its plasticity.
Monocarboxylate Transporter 4 Regulates Glioblastoma Motility and Monocyte Binding Ability
Authors: Lai Sheng-Wei , Lin Hui-Jung , Liu Yu-Shu , Yang Liang-Yo , Lu Dah-Yuu
Published: February 2020

Glioblastoma (GBM) is characterized by severe hypoxic and acidic stress in an abnormal microenvironment. Monocarboxylate transporter (MCT)4, a pH-regulating protein, plays an important role in pH homeostasis of the glycolytic metabolic pathways in cancer cells. The present study showed that GBM exposure to hypoxic conditions increased MCT4 expression. We further analyzed the glioma patient database and found that MCT4 was significantly overexpressed in patients with GBM, and the MCT4 levels positively correlated with the clinico-pathological grades of gliomas. We further found that MCT4 knockdown abolished the hypoxia-enhanced of GBM cell motility and monocyte adhesion. However, the overexpression of MCT4 promoted GBM cell migration and monocyte adhesion activity. Our results also revealed that MCT4-regulated GBM cell motility and monocyte adhesion are mediated by activation of the serine/threonine-specific protein kinase (AKT), focal adhesion kinase (FAK), and epidermal growth factor receptor (EGFR) signaling pathways. Moreover, hypoxia mediated the acetylated signal transducer and activator of transcription (STAT)3 expression and regulated the transcriptional activity of hypoxia inducible factor (HIF)-1α in GBM cell lines. In a GBM mouse model, MCT4 was significantly increased in the tumor necrotic tissues. These findings raise the possibility for the development of novel therapeutic strategies targeting MCT4.
Multiparametric MRI for Prostate Cancer Detection: New Insights into the Combined Use of a Radiomic Approach with Advanced Acquisition Protocol
Authors: Monti Serena , Brancato Valentina , Di Costanzo Giuseppe , Basso Luca , Puglia Marta , Ragozzino Alfonso , Salvatore Marco , Cavaliere Carlo
Published: February 2020

Prostate cancer (PCa) is a disease affecting an increasing number of men worldwide. Several efforts have been made to identify imaging biomarkers to non-invasively detect and characterize PCa, with substantial improvements thanks to multiparametric Magnetic Resonance Imaging (mpMRI). In recent years, diffusion kurtosis imaging (DKI) was proposed to be directly related to tissue physiological and pathological characteristic, while the radiomic approach was proven to be a key method to study cancer imaging phenotypes. Our aim was to compare a standard radiomic model for PCa detection, built using T2-weighted (T2W) and Apparent Diffusion Coefficient (ADC), with an advanced one, including DKI and quantitative Dynamic Contrast Enhanced (DCE), while also evaluating differences in prediction performance when using 2D or 3D lesion segmentation. The obtained results in terms of diagnostic accuracy were high for all of the performed comparisons, reaching values up to 0.99 for the area under a receiver operating characteristic curve (AUC), and 0.98 for both sensitivity and specificity. In comparison, the radiomic model based on standard features led to prediction performances higher than those of the advanced model, while greater accuracy was achieved by the model extracted from 3D segmentation. These results provide new insights into active topics of discussion, such as choosing the most convenient acquisition protocol and the most appropriate postprocessing pipeline to accurately detect and characterize PCa.
TGF-β Regulates Collagen Type I Expression in Myoblasts and Myotubes via Transient Ctgf and Fgf-2 Expression
Authors: Hillege Michèle , Galli Caro Ricardo , Offringa Carla , de Wit Gerard , Jaspers Richard , Hoogaars Willem
Published: February 2020

Transforming Growth Factor β (TGF-β) is involved in fibrosis as well as the regulation of muscle mass, and contributes to the progressive pathology of muscle wasting disorders. However, little is known regarding the time-dependent signalling of TGF-β in myoblasts and myotubes, as well as how TGF-β affects collagen type I expression and the phenotypes of these cells. Here, we assessed effects of TGF-β on gene expression in C2C12 myoblasts and myotubes after 1, 3, 9, 24 and 48 h treatment. In myoblasts, various myogenic genes were repressed after 9, 24 and 48 h, while in myotubes only a reduction in Myh3 expression was observed. In both myoblasts and myotubes, TGF-β acutely induced the expression of a subset of genes involved in fibrosis, such as Ctgf and Fgf-2, which was subsequently followed by increased expression of Col1a1. Knockdown of Ctgf and Fgf-2 resulted in a lower Col1a1 expression level. Furthermore, the effects of TGF-β on myogenic and fibrotic gene expression were more pronounced than those of myostatin, and knockdown of TGF-β type I receptor Tgfbr1, but not receptor Acvr1b, resulted in a reduction in Ctgf and Col1a1 expression. These results indicate that, during muscle regeneration, TGF-β induces fibrosis via Tgfbr1 by stimulating the autocrine signalling of Ctgf and Fgf-2.
Effect of Dietary Silk Peptide on Obesity, Hyperglycemia, and Skeletal Muscle Regeneration in High-Fat Diet-Fed Mice
Authors: Lee Kippeum , Jin Heegu , Chei Sungwoo , Oh Hyun-Ji , Lee Jeong-Yong , Lee Boo-Yong
Published: February 2020

Obesity is associated with excess body fat accumulation that can cause hyperglycemia and reduce skeletal muscle function and strength, which characterize the development of sarcopenic obesity. In this study, we aimed to determine the mechanism whereby acid-hydrolyzed silk peptide (SP) prevents high-fat diet (HFD)-induced obesity and whether it regulates glucose uptake and muscle differentiation using in vivo and in vitro approaches. Our findings demonstrate that SP inhibits body mass gain and the expression of adipogenic transcription factors in visceral adipose tissue (VAT). SP also had an anti-diabetic effect in VAT and skeletal muscle because it upregulated glucose transporter type 4 (GLUT4) and uncoupling protein 3 (UCP3) expression. Furthermore, SP reduced ubiquitin proteasome and promoted myoblast determination protein 1 (MyoD)/myogenic factor 4 (myogenin) expression, implying that it may have potential for the treatment of obesity-induced hyperglycemia and obesity-associated sarcopenia.
Evolving Cell-Based and Cell-Free Clinical Strategies for Treating Severe Human Liver Diseases
Authors: Cernigliaro Viviana , Peluso Rossella , Zedda Beatrice , Silengo Lorenzo , Tolosano Emanuela , Pellicano Rinaldo , Altruda Fiorella , Fagoonee Sharmila
Published: February 2020

Liver diseases represent a major global health issue, and currently, liver transplantation is the only viable alternative to reduce mortality rates in patients with end-stage liver diseases. However, scarcity of donor organs and risk of recidivism requiring a re-transplantation remain major obstacles. Hence, much hope has turned towards cell-based therapy. Hepatocyte-like cells obtained from embryonic stem cells or adult stem cells bearing multipotent or pluripotent characteristics, as well as cell-based systems, such as organoids, bio-artificial liver devices, bioscaffolds and organ printing are indeed promising. New approaches based on extracellular vesicles are also being investigated as cell substitutes. Extracellular vesicles, through the transfer of bioactive molecules, can modulate liver regeneration and restore hepatic function. This review provides an update on the current state-of-art cell-based and cell-free strategies as alternatives to liver transplantation for patients with end-stage liver diseases.
Design of Gelatin Pouches for the Preservation of Flaxseed Oil during Storage
Authors: Kchaou Hela , Jridi Mourad , Nasri Moncef , Debeaufort Frédéric
Published: February 2020

Flaxseed oil (FO) is composed mainly of polyunsaturated fatty acids that are very sensitive to oxidation induced by oxygen, temperature, and light. To overcome this problem, gelatin-based films containing glucose are crosslinked via the Maillard reaction (MR) at 120 °C and used for the conception of pouches in which the FO was packaged. The prepared pouches, as well as the oil alone, are incubated at 50 °C for 21 days. The results show that the peroxide index of the oil stored in the gelatin-based pouches is almost stable and decreases on the 21st day. The specific extinction coefficients prove a more pronounced degradation of the non-package oil (control). In addition, the results of thiobarbituric acid reactive substances (TBARs) test reveal higher values in the control, with a tendency to continuously increase up until the 21st day. However, the oil stored in the pouches reveals less TBARs content, decreasing with oxidation time. The non-heated glucose-supplemented pouches showed the best results, suggesting a moderate and gradual development of the MR at 50 °C. The application of pouches based on gelatin films seems to be an effective and interesting tool for protecting FO against lipid oxidation, acting as perfectly biodegradable and sustainable containers for small doses.
Improving Internet of Things (IoT) Security with Software-Defined Networking (SDN)
Authors: Al Hayajneh Abdullah , Bhuiyan Md Zakirul Alam , McAndrew Ian
Published: February 2020

There has been an increase in the usage of Internet of Things (IoT), which has recently become a rising area of interest as it is being extensively used for numerous applications and devices such as wireless sensors, medical devices, sensitive home sensors, and other related IoT devices. Due to the demand to rapidly release new IoT products in the market, security aspects are often overlooked as it takes time to investigate all the possible vulnerabilities. Since IoT devices are internet-based and include sensitive and confidential information, security concerns have been raised and several researchers are exploring methods to improve the security among these types of devices. Software defined networking (SDN) is a promising computer network technology which introduces a central program named ‘SDN Controller’ that allows overall control of the network. Hence, using SDN is an obvious solution to improve IoT networking performance and overcome shortcomings that currently exist. In this paper, we (i) present a system model to effectively use SDN with IoT networks; (ii) present a solution for mitigating man-in-the-middle attacks against IoT that can only use HTTP, which is a critical attack that is hard to defend; and (iii) implement the proposed system model using Raspberry Pi, Kodi Media Center, and Openflow Protocol. Our system implementation and evaluations show that the proposed technique is more resilient to cyber-attacks.
Effect of Brazing Parameters on the Microstructure and Properties of SiC Ceramic Joint with Zr-Cu Filler Metal
Authors: Zhou Bofang , Wang Jinfeng , Feng Keqin , Cai Yuchen , Chen Sitan
Published: February 2020

The microstructure and mechanical properties of brazing SiC ceramic with Zr-Cu filler metal under different brazing parameters (brazing temperature, holding time) were investigated. The phase of the joint reaction interface between Zr-Cu filler metal and SiC ceramic was characterized by XRD, the microstructure and fracture morphology of the brazing SiC ceramic joint were analyzed by SEM with EDS, and the strength of the joint was evaluated by compression shear test. The results show that the brazing join between SiC ceramic and Zr-Cu filler metal can be realized at the brazing temperature of 1100 °C~1300 °C, and the main products of interface reaction are ZrC and Zr2Si. The shear strength of the joint increases with the brazing temperature, and reaches the highest at 1200 °C. The thickness of interface reaction layer increases with the increase of holding time at brazing temperature of 1200 °C. Thickness of the interface reaction layer is 2.9 μm when the joint is holding for 20 min, and the maximum shear strength of the corresponding brazed SiC ceramic joint is 57 MPa.
Scintillator Crystals: Structure, Characterization and Models for Better Performances
Authors: Rinaldi Daniele , Montalto Luigi
Published: February 2020

The majority of instrumentation and systems for radiation detection are based on scintillators [...]
A Multiple Rényi Entropy Based Intrusion Detection System for Connected Vehicles
Authors: Yu Ki-Soon , Kim Sung-Hyun , Lim Dae-Woon , Kim Young-Sik
Published: February 2020

In this paper, we propose an intrusion detection system based on the estimation of the Rényi entropy with multiple orders. The Rényi entropy is a generalized notion of entropy that includes the Shannon entropy and the min-entropy as special cases. In 2018, Kim proposed an efficient estimation method for the Rényi entropy with an arbitrary real order α . In this work, we utilize this method to construct a multiple order, Rényi entropy based intrusion detection system (IDS) for vehicular systems with various network connections. The proposed method estimates the Rényi entropies simultaneously with three distinct orders, two, three, and four, based on the controller area network (CAN)-IDs of consecutively generated frames. The collected frames are split into blocks with a fixed number of frames, and the entropies are evaluated based on these blocks. For a more accurate estimation against each type of attack, we also propose a retrospective sliding window method for decision of attacks based on the estimated entropies. For fair comparison, we utilized the CAN-ID attack data set generated by a research team from Korea University. Our results show that the proposed method can show the false negative and positive errors of less than 1% simultaneously.
Multiscale Entropy as a New Feature for EEG and fNIRS Analysis
Authors: Angsuwatanakul Thanate , O’Reilly Jamie , Ounjai Kajornvut , Kaewkamnerdpong Boonserm , Iramina Keiji
Published: February 2020

The present study aims to apply multiscale entropy (MSE) to analyse brain activity in terms of brain complexity levels and to use simultaneous electroencephalogram and functional near-infrared spectroscopy (EEG/fNIRS) recordings for brain functional analysis. A memory task was selected to demonstrate the potential of this multimodality approach since memory is a highly complex neurocognitive process, and the mechanisms governing selective retention of memories are not fully understood by other approaches. In this study, 15 healthy participants with normal colour vision participated in the visual memory task, which involved the making the executive decision of remembering or forgetting the visual stimuli based on his/her own will. In a continuous stimulus set, 250 indoor/outdoor scenes were presented at random, between periods of fixation on a black background. The participants were instructed to make a binary choice indicating whether they wished to remember or forget the image; both stimulus and response times were stored for analysis. The participants then performed a scene recognition test to confirm whether or not they remembered the images. The results revealed that the participants intentionally memorising a visual scene demonstrate significantly greater brain complexity levels in the prefrontal and frontal lobe than when purposefully forgetting a scene; p < 0.05 (two-tailed). This suggests that simultaneous EEG and fNIRS can be used for brain functional analysis, and MSE might be the potential indicator for this multimodality approach.
Blind Mesh Assessment Based on Graph Spectral Entropy and Spatial Features
Authors: Lin Yaoyao , Yu Mei , Chen Ken , Jiang Gangyi , Chen Fen , Peng Zongju
Published: February 2020

With the wide applications of three-dimensional (3D) meshes in intelligent manufacturing, digital animation, virtual reality, digital cities and other fields, more and more processing techniques are being developed for 3D meshes, including watermarking, compression, and simplification, which will inevitably lead to various distortions. Therefore, how to evaluate the visual quality of 3D mesh is becoming an important problem and it is necessary to design effective tools for blind 3D mesh quality assessment. In this paper, we propose a new Blind Mesh Quality Assessment method based on Graph Spectral Entropy and Spatial features, called as BMQA-GSES. 3D mesh can be represented as graph signal, in the graph spectral domain, the Gaussian curvature signal of the 3D mesh is firstly converted with Graph Fourier transform (GFT), and then the smoothness and information entropy of amplitude features are extracted to evaluate the distortion. In the spatial domain, four well-performing spatial features are combined to describe the concave and convex information and structural information of 3D meshes. All the extracted features are fused by the random forest regression to predict the objective quality score of the 3D mesh. Experiments are performed successfully on the public databases and the obtained results show that the proposed BMQA-GSES method provides good correlation with human visual perception and competitive scores compared to state-of-art quality assessment methods.
Decision Tree-Based Sensitive Information Identification and Encrypted Transmission System
Authors: Liu Shuang , Yang Ziheng , Li Yi , Wang Shuiqing
Published: February 2020

With the advent of the information age, the effective identification of sensitive information and the leakage of sensitive information during the transmission process are becoming increasingly serious issues. We designed a sensitive information recognition and encryption transmission system based on a decision tree. By training sensitive data to build a decision tree, unknown data can be classified and identified. The identified sensitive information can be marked and encrypted to achieve intelligent recognition and protection of sensitive information. This lays the foundation for the development of an information recognition and encryption transmission system.
Deep Residual Learning for Nonlinear Regression
Authors: Chen Dongwei , Hu Fei , Nian Guokui , Yang Tiantian
Published: February 2020

Deep learning plays a key role in the recent developments of machine learning. This paper develops a deep residual neural network (ResNet) for the regression of nonlinear functions. Convolutional layers and pooling layers are replaced by fully connected layers in the residual block. To evaluate the new regression model, we train and test neural networks with different depths and widths on simulated data, and we find the optimal parameters. We perform multiple numerical tests of the optimal regression model on multiple simulated data, and the results show that the new regression model behaves well on simulated data. Comparisons are also made between the optimal residual regression and other linear as well as nonlinear approximation techniques, such as lasso regression, decision tree, and support vector machine. The optimal residual regression model has better approximation capacity compared to the other models. Finally, the residual regression is applied into the prediction of a relative humidity series in the real world. Our study indicates that the residual regression model is stable and applicable in practice.
Optimization to Limit the Effects of Underloaded Generator Sets in Stand-Alone Hybrid Ship Grids
Authors: German-Galkin Sergey , Tarnapowicz Dariusz , Matuszak Zbigniew , Jaskiewicz Marek
Published: February 2020

For the safety of the supply, diesel generator (DG) sets are used in various stand-alone power systems using variable-speed generators. The stand-alone hybrid grid system presented in this article, with a wind generator and a diesel generator, but also the system of a ship’s network, serves as an example. To ensure the safety of the ship’s exploitation, the parallel operation of two stand-alone power supplies is required. In parallel operation with the required symmetrical active power load (regardless of the load size), the internal combustion engine of the DG set is often underloaded. This leads to deterioration of its technical properties and, consequently, to a negative impact on the environment. This article presents an analysis of the stand-alone hybrid power system of a ship’s grid consisting of a DG with a speed and voltage regulator and a shaft generator of variable speed—a permanent magnet synchronous generator (PMSG). The possibility of controlling the active and reactive power distribution between the DG and shaft generator (SG) was also studied. Control over the mechatronic SG–DG system limits the harmful influence of the DG on the environment and, most of all, improves the technical qualities of the engine of the DG system, which is often underloaded. Analytic studies of the system were performed, and simulation results of the mechatronic model are presented.
Optimal Battery Sizing for Electric Truck Delivery
Authors: Baek Donkyu , Chen Yukai , Chang Naehyuck , Macii Enrico , Poncino Massimo
Published: February 2020

Finding the cost-optimal battery size in the context of parcel delivery with Electric Vehicles (EVs) requires solving a tradeoff between using the largest possible battery (so as to maximize the number of deliveries over a given time) and the relative costs (initial investment plus the unnecessary increase of the truck weight during delivery). In this paper, we propose a framework for the optimal battery sizing for parcel delivery with an electric truck; we implement an electric truck simulator including a nonlinear battery model to evaluate revenue, battery cost, charging cost, and overall profit for annual delivery. Our framework finds the cost-optimal battery size for different parcel weight distributions and customer location distributions. We analyze the effect of battery sizing on the profit, which is up to 56%.
Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation
Authors: Wang Longda , Wang Xingcheng , Sheng Zhao , Lu Senkui
Published: February 2020

In this paper, an improved multi-objective shark smell optimization algorithm using composite angle cosine is proposed for automatic train operation (ATO). Specifically, when solving the problem that the automatic train operation velocity trajectory optimization easily falls into local optimum, the shark smell optimization algorithm with strong searching ability is adopted, and composite angle cosine is incorporated. In addition, the dual-population evolution mechanism is adopted to restrain the aggregation phenomenon in shark population at the end of the iteration to suppress the local convergence. Correspondingly, the composite angle cosine, considering the numerical difference and preference difference, is used as the evaluation index, which ameliorates the shortcoming that the traditional evaluation index is not objective and reasonable. Finally, the Matlab/simulation and hardware-in-the-loop simulation (HILS) results for automatic train operation show that the improved optimization algorithm proposed in this paper has better optimization performance.
Assessment of a Hydrokinetic Energy Converter Based on Vortex-Induced Angular Oscillations of a Cylinder
Authors: Malefaki Iro , Konstantinidis Efstathios
Published: February 2020

Vortex-induced oscillations offer a potential means to harness hydrokinetic energy even at low current speeds. In this study, we consider a novel converter where a cylinder undergoes angular oscillations with respect to a pivot point, in contrast to most previous configurations, where the cylinder undergoes flow-induced oscillations transversely to the incident free stream. We formulate a theoretical model to deal with the coupling of the hydrodynamics and the structural dynamics, and we numerically solve the resulting nonlinear equation of cylinder motion in order to assess the performance of the energy converter. The hydrodynamical model utilizes a novel approach where the fluid forces acting on the oscillating cylinder are split into components acting along and normal to the instantaneous relative velocity between the moving cylinder and the free stream. Contour plots illustrate the effects of the main design parameters (in dimensionless form) on the angular response of the cylinder and the energy efficiency of the converter. Peak efficiencies of approximately 20% can be attained by optimal selection of the main design parameters. Guidelines on the sizing of actual converters are discussed.
Environmental Sustainability of Bioenergy Strategies in Western Kenya to Address Household Air Pollution
Authors: Carvalho Ricardo Luís , Yadav Pooja , García-López Natxo , Lindgren Robert , Nyberg Gert , Diaz-Chavez Rocio , Krishna Kumar Upadhyayula Venkata , Boman Christoffer , Athanassiadis Dimitris
Published: February 2020

Over 640 million people in Africa are expected to rely on solid-fuels for cooking by 2040. In Western Kenya, cooking inefficiently persists as a major cause of burden of disease due to household air pollution. Efficient biomass cooking is a local-based renewable energy solution to address this issue. The Life-Cycle Assessment tool Simapro 8.5 is applied for analyzing the environmental impact of four biomass cooking strategies for the Kisumu County, with analysis based on a previous energy modelling study, and literature and background data from the Ecoinvent and Agrifootprint databases applied to the region. A Business-As-Usual scenario (BAU) considers the trends in energy use until 2035. Transition scenarios to Improved Cookstoves (ICS), Pellet-fired Gasifier Stoves (PGS) and Biogas Stoves (BGS) consider the transition to wood-logs, biomass pellets and biogas, respectively. An Integrated (INT) scenario evaluates a mix of the ICS, PGS and BGS. In the BGS, the available biomass waste is sufficient to be upcycled and fulfill cooking demands by 2035. This scenario has the lowest impact on all impact categories analyzed followed by the PGS and INT. Further work should address a detailed socio-economic analysis of the analyzed scenarios.
Exergetic Analysis of a New Direct Steam Generation Solar Plant Using Screw Expanders
Authors: Iodice Paolo , Langella Giuseppe , Amoresano Amedeo
Published: February 2020

Screw expanders are volumetric machines particularly suitable in energy conversion with steam–liquid mixtures and for the exploitation of low temperature heat sources. This study explored the main criteria to evaluate the thermodynamic advantages and exergetic assessment of an innovative solar electricity generation system: Screw expanders are utilized as power machines and parabolic trough collectors as a thermal source. Such a direct steam generation solar system, which is based on the Rankine cycle, offers benefits in comparison with usual power plants with dynamic expanders: The best exploitation of low temperature heat sources, satisfactory thermal efficiency with steam–liquid mixtures, lower evaporation pressures, and reduced size. Under real working conditions, screw expanders can work at part-load operating conditions; thus, the chief purpose of the present paper was to analyze the exergetic advantages of the planned solar power system when solar radiation and off-design working conditions fluctuate. Initially, the polytropic expansion phase with a specific numerical model is described to evaluate the energy losses that affect the thermodynamic performance of screw expanders when installed in the planned renewable energy power plant. Subsequently, to explore the exergy harnessing in the exhausted steam at off-design working conditions and then to appraise the maximum exergetic efficiency of the proposed screw expander-based solar thermal electricity plant, numerical optimization was performed in a broad range of evaporation and condensation temperatures.
Solubility and Diffusivity of Polar and Non-Polar Molecules in Polyethylene-Aluminum Oxide Nanocomposites for HVDC Applications
Authors: Holder Shima L. , Karlsson Mattias E. , Olsson Richard T. , Hedenqvist Mikael S. , Nilsson Fritjof
Published: February 2020

The best commercial high-voltage insulation material of today is (crosslinked) ultra-pure low-density polyethylene (LDPE). A 100-fold decrease in electrical conductivity can be achieved by adding 1–3 wt.% of well-dispersed inorganic nanoparticles to the LDPE. One hypothesis is that the nanoparticle surfaces attract ions and polar molecules, thereby cleaning the surrounding polymer, and thus reducing the conductivity. LDPE-based nanocomposites with 1–12 wt.% octyl-coated aluminum oxide nanoparticles were prepared and the sorption and desorption of one polar compound (acetophenone, a crosslinking by-product) and one non-polar compound of a similar size (limonene) were examined. Since the uptake of acetophenone increased linearly with increasing filler content, whereas the uptake of limonene decreased, the surface attraction hypothesis was strengthened. The analytical functions for predicting composite solubility as a function of particle size and filler fraction were derived using experimental solubility measurements and Monte Carlo simulations.
How Does the Spillover among Natural Gas, Crude Oil, and Electricity Utility Stocks Change over Time? Evidence from North America and Europe
Authors: Zhang Wenting , He Xie , Nakajima Tadahiro , Hamori Shigeyuki
Published: February 2020

Our study analyzes the return and volatility spillover among the natural gas, crude oil, and electricity utility stock indices in North America and Europe from 4 August 2009 to 16 August 2019. First, in time domain, both total return and volatility spillover are stronger in Europe than in North America. Furthermore, compared to natural gas, crude oil has a greater volatility spillover on the electricity utility stock indices in North America and Europe. Second, in frequency domain, most of the return spillover occurs in the short-term, while most of the volatility spillover occurs over a longer period. Third, the rolling analyses indicate that the return and volatility from 2009 to late 2013 remained stable in North America and Europe, which may be a result of the 2008 global financial crisis, and started to fluctuate after late 2013 due to some extreme events, indicating that extreme events can significantly influence spillover effects. Moreover, investors should monitor current events to diversify their portfolios properly and hedge their risks.
Surviving the Energy Transition: Development of a Proposal for Evaluating Sustainable Business Models for Incumbents in Germany’s Electricity Market
Authors: Wagner Oliver , Adisorn Thomas , Tholen Lena , Kiyar Dagmar
Published: February 2020

In the light of Germany’s chosen path towards the energy transition, the regulatory framework has changed considerably. New players have succeeded in entering the market, and renewable energies have become increasingly competitive. Greater electrification of the transport and heating sectors will be needed in the future to achieve national climate targets. Against this background, Germany’s big energy companies need to be sure that their sales will increase. However, they were unable to anticipate this development, and made strategic mistakes in the past. The development of sustainable business models in line with the energy transition failed to materialize. Now it is becoming increasingly clear that companies must create new business models to survive in the long term. These business models have to keep with the tradition, whilst meeting the needs of low-carbon power supplies. In this paper, we will examine the past and future challenges of the four energy companies and develop a proposal for evaluating sustainable business models. For this purpose, we use the multi-level perspective to categorize developments in the electricity market over the last 50 years, and then apply a multi-criteria analysis to derive five suitable business models from the results.