bluuebunny/crossref_metadata_2025 huggingface dataset:
On the Use of Learnheuristics in Vehicle Routing Optimization Problems with Dynamic Inputs
Authors: Arnau Quim , Juan Angel A. , Serra Isabel
Published: December 2018

Freight transportation is becoming an increasingly critical activity for enterprises in a global world. Moreover, the distribution activities have a non-negligible impact on the environment, as well as on the citizens’ welfare. The classical vehicle routing problem (VRP) aims at designing routes that minimize the cost of serving customers using a given set of capacitated vehicles. Some VRP variants consider traveling times, either in the objective function (e.g., including the goal of minimizing total traveling time or designing balanced routes) or as constraints (e.g., the setting of time windows or a maximum time per route). Typically, the traveling time between two customers or between one customer and the depot is assumed to be both known in advance and static. However, in real life, there are plenty of factors (predictable or not) that may affect these traveling times, e.g., traffic jams, accidents, road works, or even the weather. In this work, we analyze the VRP with dynamic traveling times. Our work assumes not only that these inputs are dynamic in nature, but also that they are a function of the structure of the emerging routing plan. In other words, these traveling times need to be dynamically re-evaluated as the solution is being constructed. In order to solve this dynamic optimization problem, a learnheuristic-based approach is proposed. Our approach integrates statistical learning techniques within a metaheuristic framework. A number of computational experiments are carried out in order to illustrate our approach and discuss its effectiveness.
Hadoop vs. Spark: Impact on Performance of the Hammer Query Engine for Open Data Corpora
Authors: Pelucchi Mauro , Psaila Giuseppe , Toccu Maurizio
Published: December 2018

The Hammer prototype is a query engine for corpora of Open Data that provides users with the concept of blind querying. Since data sets published on Open Data portals are heterogeneous, users wishing to find out interesting data sets are blind: queries cannot be fully specified, as in the case of databases. Consequently, the query engine is responsible for rewriting and adapting the blind query to the actual data sets, by exploiting lexical and semantic similarity. The effectiveness of this approach was discussed in our previous works. In this paper, we report our experience in developing the query engine. In fact, in the very first version of the prototype, we realized that the implementation of the retrieval technique was too slow, even though corpora contained only a few thousands of data sets. We decided to adopt the Map-Reduce paradigm, in order to parallelize the query engine and improve performances. We passed through several versions of the query engine, either based on the Hadoop framework or on the Spark framework. Hadoop and Spark are two very popular frameworks for writing and executing parallel algorithms based on the Map-Reduce paradigm. In this paper, we present our study about the impact of adopting the Map-Reduce approach and its two most famous frameworks to parallelize the Hammer query engine; we discuss various implementations of the query engine, either obtained without significantly rewriting the algorithm or obtained by completely rewriting the algorithm by exploiting high level abstractions provided by Spark. The experimental campaign we performed shows the benefits provided by each studied solution, with the perspective of moving toward Big Data in the future. The lessons we learned are collected and synthesized into behavioral guidelines for developers approaching the problem of parallelizing algorithms by means of Map-Reduce frameworks.
PCA-Based Multiple-Trait GWAS Analysis: A Powerful Model for Exploring Pleiotropy
Authors: Zhang Wengang , Gao Xue , Shi Xinping , Zhu Bo , Wang Zezhao , Gao Huijiang , Xu Lingyang , Zhang Lupei , Li Junya , Chen Yan
Published: December 2018

Principal component analysis (PCA) is a potential approach that can be applied in multiple-trait genome-wide association studies (GWAS) to explore pleiotropy, as well as increase the power of quantitative trait loci (QTL) detection. In this study, the relationship of test single nucleotide polymorphisms (SNPs) was determined between single-trait GWAS and PCA-based GWAS. We found that the estimated pleiotropic quantitative trait nucleotides (QTNs) β * ^ were in most cases larger than the single-trait model estimations ( β 1 ^ and β 2 ^ ). Analysis using the simulated data showed that PCA-based multiple-trait GWAS has improved statistical power for detecting QTL compared to single-trait GWAS. For the minor allele frequency (MAF), when the MAF of QTNs was greater than 0.2, the PCA-based model had a significant advantage in detecting the pleiotropic QTNs, but when its MAF was reduced from 0.2 to 0, the advantage began to disappear. In addition, as the linkage disequilibrium (LD) of the pleiotropic QTNs decreased, its detection ability declined in the co-localization effect model. Furthermore, on the real data of 1141 Simmental cattle, we applied the PCA model to the multiple-trait GWAS analysis and identified a QTL that was consistent with a candidate gene, MCHR2, which was associated with presoma muscle development in cattle. In summary, PCA-based multiple-trait GWAS is an efficient model for exploring pleiotropic QTNs in quantitative traits.
Battle of the Sexes in Best of Breed: Sex Influences Dogs’ Success in the Show Ring
Authors: Wilson Bethany J. , Kasbarian Alicia J. , Dhand Navneet , McGreevy Paul D.
Published: December 2018

Much of the research on pedigree dog breeding has been directed towards understanding the implications of reduced genetic diversity and the prevalence of inherited disorders. An example is the potential role of the popular sire effect in perpetuating genetic defects. If male dogs are more likely than bitches to be identified as examples of members of a breed that align with breed standard, they may be selected for breeding earlier. This may contribute to the influence of individual males and contribute to popular sire effect. Conversely, if breed standards are written in a sex-neutral fashion, and if dogs are entered, exhibited, and judged in a sex-neutral fashion, then we would expect the success of female dogs in the show ring to be equal to that of their male counterparts. With a focus on toy and giant breeds, the current pilot study collated samples of dog show results to explore relationships between sex and the likelihood of success in the show ring. It focused on toy and giant breeds to explore any differences in equity, if it existed, at either end of the size and concomitant age-at-maturation spectrum. For the purpose of this study, toy breeds were those that weigh < 10 kg at maturity while giant breed dogs were those that exceed 45 kg. Within these two clusters, the least (n = 3) and most popular (n = 3) breeds were then selected to explore any potential role of sex on success in the show ring. The popularity of breeds was determined using the numbers of dogs registered with the Australian National Kennel Council. Using results from dog shows (n = 18) from 2015 to 2016, data on 1,080 dogs were obtained. Within these 12 breeds for the 18 shows, there were 137 Best of Breed (BOB) titles awarded: Pug (n = 18), Toy Poodle (n = 18), Bullmastiff (n = 14), Rottweiler (n = 17), Fox Terrier (Smooth) (n = 18), Bloodhound (n = 3), Schnauzer (miniature) (n = 15), Great Dane (n = 17), Norfolk Terrier (n = 10), Norwich Terrier (n = 5), Central Asian Shepherd Dog (n = 2). Despite the near parity of male and female dogs being exhibited, of these 137 titles, 86 (62.8%) were awarded to male dogs (at least 41 individuals) and 51 (37.2%) to female dogs (at least 32 individuals) showing that male dogs are more likely to win BOB titles (χ2 = 9.4455, df = 1, p-value = 0.002117). Among the toy subset of breeds, this effect was higher (χ2 = 6.798, df = 1, p-value = 0.009126) than among the giant breed subset, for whom the advantage to male dogs did not reach statistical significance versus χ2 = 3.0967, df = 1, p-value = 0.07845). This suggests that judges find the male dogs more appealing, presumably because they are more aligned with breed standards.
Combatting Antibiotic Resistance Together: How Can We Enlist the Help of Industry?
Authors: Edwards Suzanne E. , Morel Chantal M. , Busse Reinhard , Harbarth Stephan
Published: December 2018

The development of antibiotics needs to be supported through new financial stimuli, including help from the public sector. In exchange for public support, industry should be asked to do what is in their power to help curb the inappropriate use of antibiotics. This work discusses key areas through which industry has an important influence on antibiotic consumption and where agreements can be made alongside financial incentives, even those intended to stimulate very early research. As long as the traditional unit sale-based business model for antibiotics remains in place, profit-making incentives will likely undermine efforts to sell and utilize antibiotics in a sustainable manner. In the short-term, while we try to come to a consensus on how best to fix the market, we need measures to prevent major over-selling and inappropriate promotion—especially for new, badly needed antibiotics that reach the market. This paper explores ways in which the pharmaceutical industry could help buttress sustainable antibiotic use while we search for more long-term, constructive, mutually-beneficial ways to organize the market.
Recognition of Acoustic Signals of Commutator Motors
Authors: Glowacz Adam
Published: December 2018

Most faults can stop a motor, and time is lost in fixing the damaged motor. This is a reason why it is essential to develop fault-detection methods. This paper describes the acoustic-based fault detection of two commutator motors: the commutator motor of an electric impact drill and the commutator motor of a blender. Acoustic signals were recorded by a smartphone. Five states of the electric impact drill and three states of the blender were analysed: for the electric impact drill, these states were healthy, damaged gear train, faulty fan with five broken rotor blades, faulty fan with 10 broken rotor blades, and shifted brush (motor off); for the blender, these states were healthy, faulty fan with two broken rotor blades, and faulty fan with five broken rotor blades. A feature extraction method, MSAF-RATIO-27-MULTIEXPANDED-4-GROUPS (Method of Selection of Amplitudes of Frequency Ratio of 27% Multiexpanded 4 Groups), was developed and used for the computation of feature vectors. The nearest mean (NM) and support vector machine (SVM) classifiers were used for data classification. Analysis of the recognition of acoustic signals was carried out. The analysed value of TEEID (the total efficiency of recognition of the electric impact drill) was equal to 96% for the NM classifier and 88.8% for SVM. The analysed value of TEB (the total efficiency of recognition of the blender) was equal to 100% for the NM classifier and 94.11% for SVM.
Effects of Vehicular Communication on Risk Assessment in Automated Driving Vehicles
Authors: Shin Donghoon , Park Kangmun , Park Manbok
Published: December 2018

This paper proposes a human-centered risk assessment algorithm designed to find the intervention moment of drive mode and active safety mode while monitoring threat vehicles ahead to overcome effects of vehicular communication on risk assessment in automated driving vehicle. Although a conventional radar system is known to be best fitted on-board ranging sensor in terms of longitudinal safety, it is generally not enough for a reliable automated driving because of sensing uncertainty of the traffic environments and incomplete perception results due to sensor limitations. This can be overcome by implementing vehicle-to-vehicle (V2V) communication which provides complementary source of target vehicle’s dynamic behavior. Using V2V communication with vehicle internal and surround information obtained from the on-board sensor system, future vehicle motion has been predicted. With accurately predicted motion of a remote vehicle, a collision risk and the automated drive mode are determined by incorporating human factor. Effects of the V2V communication on a human-centered risk assessment algorithm have been investigated through a safe triangle analysis. The computer simulation studies have been conducted in order to validate the performance of the proposed algorithm. It has been shown that the V2V communication with the proposed risk assessment algorithm allows a faster drive mode decision and active safety intervention moment.
Collective Attestation for Manageable IoT Environments
Authors: Lee JongHyup
Published: December 2018

The swarm attestation methods have been proposed to detect illegitimate modifications in a large network efficiently. However, they do not provide the scalable identification of detected devices, which is critical to keep a swarm network trustworthy in the practical uses. In this paper, we propose a lightweight attestation method with efficient scalable identification of target devices. The proposed approach, called CAMIE (Collective Attestation for Manageable IoT Environments), combines binary-embedded tag generation and regional reports to build the region-based attestation result. During the attestation process, the swarm network generates merged region summaries. An authorized verifier can infer the individual device information from the regional attestation results. In this way, CAMIE can achieve the scalable identification of compromised devices with significantly less overhead.
Optimal Irregular Wind Farm Design for Continuous Placement of Wind Turbines with a Two-Dimensional Jensen-Gaussian Wake Model
Authors: Wang Longyan , Zhou Yunkai , Xu Jian
Published: December 2018

Optimal design of wind turbine placement in a wind farm is one of the most effective tools to reduce wake power losses by alleviating the wake effect in the wind farm. In comparison to the discrete grid-based wind farm design method, the continuous coordinate method has the property of continuously varying the placement of wind turbines, and hence, is far more capable of obtaining the global optimum solutions. In this paper, the coordinate method was applied to optimize the layout of a real offshore wind farm for both simplified and realistic wind conditions. A new analytical wake model (Jensen-Gaussian model) taking into account the wake velocity variation in the radial direction was employed for the optimization study. The means of handling the irregular real wind farm boundary were proposed to guarantee that the optimized wind turbine positions are feasible within the wind farm boundary, and the discretization method was applied for the evaluation of wind farm power output under Weibull distribution. By investigating the wind farm layout optimization under different wind conditions, it showed that the total wind farm power output increased linearly with an increasing number of wind turbines. Under some particular wind conditions (e.g., constant wind speed and wind direction, and Weibull distribution), almost the same power losses were obtained under the wake effect of some adjacent wind turbine numbers. A common feature of the wind turbine placements regardless of the wind conditions was that they were distributed along the wind farm boundary as much as possible in order to alleviate the wake effect.
Dark Spot Detection in SAR Images of Oil Spill Using Segnet
Authors: Guo Hao , Wei Guo , An Jubai
Published: December 2018

Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However, some natural phenomena (such as waves, ocean currents, and low wind belts, as well as human factors) may change the backscatter intensity on the surface of the sea, resulting in uneven intensity, high noise, and blurred boundaries of oil slicks or lookalikes. In this paper, Segnet is used as a semantic segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of an oil spill. The effectiveness of the method is demonstrated through the comparison with fully convolutional networks (FCN), an initiator of semantic segmentation models, and some other segmentation methods. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show a higher robustness under high noise and fuzzy boundary conditions.
A Novel Design of Through-Hole Depth On-Machine Optical Measuring Equipment for Automatic Drilling and Riveting
Authors: Wu Nianhan , Zhao Wu , Wang Xin , Tao Ye , Hou Zhengmeng
Published: December 2018

In the aerospace manufacturing industry, it is impossible to achieve precise and efficient automatic drilling and riveting for largescale composite board parts. The bottleneck is that the depth detection of rivet holes still relies on manual operation, which seriously affects the assembly efficiency and stability of composite board parts. In order to realize accurate and efficient on-machine automatic measurement for through holes in the automatic drilling and riveting process of largescale composite board parts, this paper presents a novel hole depth measuring device. Its mechanical structure is developed based on our newly designed measurement scheme and optical path, the purpose of which is to convert the hole depth data into displacement data of the probe motion. Its electrical hardware consists of three units: a laser transceiver unit to pick up laser spots; a displacement measuring unit to capture the probe movement in real time; and a driving unit to achieve motion control of the probe. Finally, the experimental results indicated that the proposed method and device are capable of performing automatic measurements for through-hole depth. In addition, factors affecting the measuring accuracy and stability of the device are initially analyzed and discussed, which lay a foundation for subsequent research on error compensation and probe calibration.
Absolute Phase Retrieval Using One Coded Pattern and Geometric Constraints of Fringe Projection System
Authors: Yang Xu , Zeng Chunnian , Luo Jie , Lei Yu , Tao Bo , Chen Xiangcheng
Published: December 2018

Fringe projection technologies have been widely used for three-dimensional (3D) shape measurement. One of the critical issues is absolute phase recovery, especially for measuring multiple isolated objects. This paper proposes a method for absolute phase retrieval using only one coded pattern. A total of four patterns including one coded pattern and three phase-shift patterns are projected, captured, and processed. The wrapped phase, as well as average intensity and intensity modulation, are calculated from three phase-shift patterns. A code word encrypted into the coded pattern can be calculated using the average intensity and intensity modulation. Based on geometric constraints of fringe projection system, the minimum fringe order map can be created, upon which the fringe order can be calculated from the code word. Compared with the conventional method, the measurement depth range is significantly improved. Finally, the wrapped phase can be unwrapped for absolute phase map. Since only four patterns are required, the proposed method is suitable for real-time measurement. Simulations and experiments have been conducted, and their results have verified the proposed method.
Collapse Mechanisms of Nascent and Aged Sea Spray Aerosol Proxy Films
Authors: Carter-Fenk Kimberly A. , Allen Heather C.
Published: December 2018

Sea spray aerosol (SSA) is highly enriched in marine-derived organic compounds during seasons of high biological productivity, and saturated fatty acids comprise one of the most abundant classes of molecules. Fatty acids and other organic compounds form a film on SSA surfaces, and SSA particle surface-area-to-volume ratios are altered during aging in the marine boundary layer (MBL). To understand SSA surface organization and its role during dynamic atmospheric conditions, an SSA proxy fatty acid film and its individual components stearic acid (SA), palmitic acid (PA), and myristic acid (MA) are studied separately using surface pressure–area ( Π − A ) isotherms and Brewster angle microscopy (BAM). The films were spread on an aqueous NaCl subphase at pH 8.2, 5.6, and 2.0 to mimic nascent to aged SSA aqueous core composition in the MBL, respectively. We show that the individual fatty acid behavior differs from that of the SSA proxy film, and at nascent SSA pH the mixture yields a monolayer with intermediate rigidity that folds upon film compression to the collapse state. Acidification causes the SSA proxy film to become more rigid and form 3D nuclei. Our results reveal film morphology alterations, which are related to SSA reflectivity, throughout various stages of SSA aging and provide a better understanding of SSA impacts on climate.
Label-Free SERS Discrimination and In Situ Analysis of Life Cycle in Escherichia coli and Staphylococcus epidermidis
Authors: Paccotti Niccolò , Boschetto Francesco , Horiguchi Satoshi , Marin Elia , Chiadò Alessandro , Novara Chiara , Geobaldo Francesco , Giorgis Fabrizio , Pezzotti Giuseppe
Published: December 2018

Surface enhanced Raman spectroscopy (SERS) has been proven suitable for identifying and characterizing different bacterial species, and to fully understand the chemically driven metabolic variations that occur during their evolution. In this study, SERS was exploited to identify the cellular composition of Gram-positive and Gram-negative bacteria by using mesoporous silicon-based substrates decorated with silver nanoparticles. The main differences between the investigated bacterial strains reside in the structure of the cell walls and plasmatic membranes, as well as their biofilm matrix, as clearly noticed in the corresponding SERS spectrum. A complete characterization of the spectra was provided in order to understand the contribution of each vibrational signal collected from the bacterial culture at different times, allowing the analysis of the bacterial populations after 12, 24, and 48 h. The results show clear features in terms of vibrational bands in line with the bacterial growth curve, including an increasing intensity of the signals during the first 24 h and their subsequent decrease in the late stationary phase after 48 h of culture. The evolution of the bacterial culture was also confirmed by fluorescence microscope images.
Recent Progress in Surface Plasmon Resonance Biosensors (2016 to Mid-2018)
Authors: Gorodkiewicz Ewa , Lukaszewski Zenon
Published: December 2018

More than 50 papers on surface plasmon resonance biosensors, published between 2016 and mid-2018, are reviewed. Papers concerning the determination of large particles such as vesicles, exosomes, cancer cells, living cells, stem cells, and microRNA are excluded, as these are covered by a very recent review. The reviewed papers are categorized into five groups, depending on the degree of maturity of the reported solution; ranging from simple marker detection to clinical application of a previously developed biosensor. Instrumental solutions and details of biosensor construction are analyzed, including the chips, receptors, and linkers used, as well as calibration strategies. Biosensors with a sandwich structure containing different nanoparticles are considered separately, as are SPR (Surface Plasmon Resonance) applications for investigating the interactions of biomolecules. An analysis is also made of the markers determined using the biosensors. In conclusion, there is shown to be a growing number of SPR applications in the solution of real clinical problems.
Alterations in Motor Cortical Representation of Muscles Following Incomplete Spinal Cord Injury in Humans
Authors: Fassett Hunter J. , Turco Claudia V. , El-Sayes Jenin , Nelson Aimee J.
Published: December 2018

(1) Background: The primary motor cortex (M1) experiences reorganization following spinal cord injury (SCI). However, there is a paucity of research comparing bilateral M1 organization in SCI and questions remain to be answered. We explored the presence of somatotopy within the M1 representation of arm muscles, and determined whether anatomical shifts in these representations occur, and investigated the symmetry in organization between the two hemispheres.; (2) Methods: Transcranial magnetic stimulation (TMS) was used to map the representation of the biceps, flexor carpi radialis and abductor pollicis brevis (APB) bilaterally in nine individuals with chronic incomplete cervical spinal cord injury and nine aged- and handed-matched uninjured controls. TMS was delivered over a 6 × 5 point grid that encompassed M1 using an intensity specific to the resting motor threshold for each muscle tested.; (3) Results: Results indicate that, compared to controls, muscle representations in SCI are shifted medially but preserve a general somatotopic arrangement, and that territory dedicated to the APB muscle is greater.; (4) Conclusions: These findings demonstrate differences in the organization of M1 between able-bodied controls and those with incomplete cervical SCI. This altered organization may have future implications in understanding the functional deficits observed in SCI and rehabilitation techniques aimed at restoring function.
Evaluating the Impact of the Morphological Transformation of Urban Sites on the Urban Thermal Microenvironment
Authors: Ali Salman , Li Baofeng
Published: December 2018

Tropical cities currently face issues of climate change resulting from rapid urbanization and the impact of urban morphological transformations on the microclimate. The analysis of urban physical forms and patterns is a realisticmethodfor quantifying these impacts. This work examined the impact of morphological transformations of an urban site in Wuhan, China, on the microthermal environment at different time periods. We also quantified and compared the impact of four urban site morphologies on ambient air temperature. The morphological changes of the study site were inferred from Google Earth images acquired at different time points in 2006 and 2013. ENVI-met simulation software was used to compare the changes in temperature at the selected site by specific date. The year- and time-based analysis of existing urban morphologies and their impact on the microurban thermal environment shows that the overall minimum and maximum values of morning and afternoon ambient air temperature are nearly the same for the 2013 and 2006 morphologies. The maximum temperature difference was observed in the afternoon (14:00), with an average difference of approximately 2 °C in the east. The findings of this research could provide a useful guide for optimizing the transformation of urban site planning and design and a suitable method for assessing the impact of built-up areas on the environment.
RNA-Sequencing Analysis of Adrenocortical Carcinoma, Pheochromocytoma and Paraganglioma from a Pan-Cancer Perspective
Authors: Crona Joakim , Backman Samuel , Welin Staffan , Taïeb David , Hellman Per , Stålberg Peter , Skogseid Britt , Pacak Karel
Published: December 2018

Adrenocortical carcinoma (ACC) and pheochromocytoma and paraganglioma (PPGL) are defined by clinicopathological criteria and can be further sub-divided based on different molecular features. Whether differences between these molecular subgroups are significant enough to re-challenge their current clinicopathological classification is currently unknown. It is also not fully understood to which other cancers ACC and PPGL show similarity to. To address these questions, we included recent RNA-Seq data from the Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) datasets. Two bioinformatics pipelines were used for unsupervised clustering and principal components analysis. Results were validated using consensus clustering model and interpreted according to previous pan-cancer experiments. Two datasets consisting of 3319 tumors from 35 disease categories were studied. Consistent with the current classification, ACCs clustered as a homogenous group in a pan-cancer context. It also clustered close to neural crest derived tumors, including gliomas, neuroblastomas, pancreatic neuroendocrine tumors, and PPGLs. Contrary, some PPGLs mixed with pancreatic neuroendocrine tumors or neuroblastomas. Thus, our unbiased gene-expression analysis of PPGL did not overlap with their current clinicopathological classification. These results emphasize some importances of the shared embryological origin of these tumors, all either related or close to neural crest tumors, and opens for investigation of a complementary categorization based on gene-expression features.
EphA3 Pay-Loaded Antibody Therapeutics for the Treatment of Glioblastoma
Authors: Offenhäuser Carolin , Al-Ejeh Fares , Puttick Simon , Ensbey Kathleen S. , Bruce Zara C. , Jamieson Paul R. , Smith Fiona M. , Stringer Brett W. , Carrington Benjamin , Fuchs Adrian V. , Bell Craig A. , Jeffree Rosalind , Rose Stephen , Thurecht Kristofer J. , Boyd Andrew W. , Day Bryan W.
Published: December 2018

The EphA3 receptor has recently emerged as a functional tumour-specific therapeutic target in glioblastoma (GBM). EphA3 is significantly elevated in recurrent disease, is most highly expressed on glioma stem cells (GSCs), and has a functional role in maintaining self-renewal and tumourigenesis. An unlabelled EphA3-targeting therapeutic antibody is currently under clinical assessment in recurrent GBM patients. In this study, we assessed the efficacy of EphA3 antibody drug conjugate (ADC) and radioimmunotherapy (RIT) approaches using orthotopic animal xenograft models. Brain uptake studies, using positron emission tomography/computed tomography (PET/CT) imaging, show EphA3 antibodies are effectively delivered across the blood-tumour barrier and accumulate at the tumour site with no observed normal brain reactivity. A robust anti-tumour response, with no toxicity, was observed using EphA3, ADC, and RIT approaches, leading to a significant increase in overall survival. Our current research provides evidence that GBM patients may benefit from pay-loaded EphA3 antibody therapies.
Alkali-Free Zn–Al Layered Double Hydroxide Catalysts for Triglyceride Transesterification
Authors: Tajuddin Nazrizawati A. , Manayil Jinesh C. , Isaacs Mark A. , Parlett Christopher M.A. , Lee Adam F. , Wilson Karen
Published: December 2018

Zn–Al layered double hydroxides (LDHs) of general formula [Zn2+(1−x)Al3+x(OH)2]x+(CO32−)x/2·yH2O are promising solid base catalysts for the transesterification of lipids to biofuels. However, conventional synthetic routes employ alkali hydroxide/carbonate precipitants which may contaminate the final LDH catalyst and biofuel. The use of (NH3)2CO3 and NH3OH as precipitants affords alkali-free Zn–Al-LDHs spanning a wide composition range. The hydrothermal reconstruction of calcined Zn–Al-LDHs offers superior solid basicity and catalytic activity for the transesterification of C4–C18 triglycerides with methanol, compared with cold liquid phase or vapour phase reconstruction. Hydrothermally activated Zn3.3–Al-LDH was stable towards leaching during transesterification.
Activation of Polyamine Catabolism by N1,N11-Diethylnorspermine in Hepatic HepaRG Cells Induces Dedifferentiation and Mesenchymal-Like Phenotype
Authors: Ivanova Olga N. , Snezhkina Anastasiya V. , Krasnov George S. , Valuev-Elliston Vladimir T. , Khomich Olga A. , Khomutov Alexey R. , Keinanen Tuomo A. , Alhonen Leena , Bartosch Birke , Kudryavtseva Anna V. , Kochetkov Sergey N. , Ivanov Alexander V.
Published: December 2018

Tumorigenesis is accompanied by the metabolic adaptation of cells to support enhanced proliferation rates and to optimize tumor persistence and amplification within the local microenvironment. In particular, cancer cells exhibit elevated levels of biogenic polyamines. Inhibitors of polyamine biosynthesis and inducers of their catabolism have been evaluated as antitumor drugs, however, their efficacy and safety remain controversial. Our goal was to investigate if drug-induced modulation of polyamine metabolism plays a role in dedifferentiation using differentiated human hepatocyte-like HepaRG cell cultures. N1,N11-diethylnorspermine (DENSpm), a potent inducer of polyamine catabolism, triggered an epithelial-mesenchymal transition (EMT)-like dedifferentiation in HepaRG cultures, as shown by down-regulation of mature hepatocytes markers and upregulation of classical EMT markers. Albeit the fact that polyamine catabolism produces H2O2, DENSpm-induced de-differentiation was not affected by antioxidants. Use of a metabolically stable spermidine analogue showed furthermore, that spermidine is a key regulator of hepatocyte differentiation. Comparative transcriptome analyses revealed, that the DENSpm-triggered dedifferentiation of HepaRG cells was accompanied by dramatic metabolic adaptations, exemplified by down-regulation of the genes of various metabolic pathways and up-regulation of the genes involved in signal transduction pathways. These results demonstrate that polyamine metabolism is tightly linked to EMT and differentiation of liver epithelial cells.
Cost-Effective Surface Modification of Carbon Cloth Electrodes for Microbial Fuel Cells by Candle Soot Coating
Authors: Chen Bor-Yann , Tsao Yuan-Ting , Chang Shih-Hang
Published: December 2018

This study explored an economically-feasible and environmentally friendly attempt to provide more electrochemically promising carbon cloth anodes for microbial fuel cells (MFCs) by modifying them with candle soot coating. The sponge-like structure of the deposited candle soot apparently increased the surface areas of the carbon cloths for bacterial adhesion. The super-hydrophilicity of the deposited candle soot was more beneficial to bacterial propagation. The maximum power densities of MFCs configured with 20-s (13.6 ± 0.9 mW·m−2), 60-s (19.8 ± 0.2 mW·m−2), and 120-s (17.6 ± 0.8 mW·m−2) candle-soot-modified carbon cloth electrodes were apparently higher than that of an MFC configured with an unmodified electrode (10.2 ± 0.2 mW·m−2). The MFCs configured with the 20- and 120-s candle-soot-modified carbon cloth electrodes exhibited lower power densities than that of the MFC with the 60-s candle-soot-modified carbon cloth electrode. This suggested that the insufficient residence time of candle soot led to an incomplete formation of the hydrophilic surface, whereas protracted candle sooting would lead to a thick deposited soot film with a smaller conductivity. The application of candle soot for anode modification provided a simple, rapid, cost-effective, and environment-friendly approach to enhancing the electron-transfer capabilities of carbon cloth electrodes. However, a postponement in the MFC construction may lead to a deteriorated hydrophilicity of the candle-soot-modified carbon cloth.
Characteristics of a Surgical Snare Using Microwave Energy
Authors: Sugiyama Masashi , Saito Kazuyuki
Published: December 2018

Currently, minimally invasive treatments that insert various treatment devices into an endoscope are actively being performed. A high-frequency (HF) snare is commonly used as an energy device inserted into an endoscope. However, using a high-frequency snare, problems usually occur, such as the obstruction of the visual field caused by smoke. On the other hand, microwave heating produces less smoke and provides a better visual field. In this study, a snare using microwave energy inserted into an endoscope is proposed, and its characteristics are evaluated.
Quiescent-Interval Single-Shot Magnetic Resonance Angiography
Authors: Saini Aman , Wallace Alex , Albadawi Hassan , Naidu Sailendra , Alzubaidi Sadeer , Knuttinen M. Grace , Panda Anshuman , Oklu Rahmi
Published: December 2018

Lower extremity peripheral arterial disease (PAD) is a chronic, debilitating disease with a significant global burden. A number of diagnostic imaging techniques exist, including computed tomography angiography (CTA) and contrast-enhanced magnetic resonance angiography (CEMRA), to aid in PAD diagnosis and subsequent treatment planning. Due to concerns of renal toxicity or nephrogenic systemic fibrosis (NSF) for iodinated and gadolinium-based contrasts, respectively, a number of non-enhanced MRA (NEMRA) protocols are being increasingly used in PAD diagnosis. These techniques, including time of flight and phase contrast MRA, have previously demonstrated poor image quality, long acquisition times, and/or susceptibility to artifacts when compared to existing contrast-enhanced techniques. In recent years, Quiescent-Interval Single-Shot (QISS) MRA has been developed to overcome these limitations in NEMRA methods, with promising results. Here, we review the various screening and diagnostic tests currently used for PAD. The various NEMRA protocols are discussed, followed by a comprehensive review of the literature on QISS MRA to date. A particular emphasis is placed on QISS MRA feasibility studies and studies comparing the diagnostic accuracy and image quality of QISS MRA versus other diagnostic imaging techniques in PAD.
Anthropomorphic Breast and Head Phantoms for Microwave Imaging
Authors: Joachimowicz Nadine , Duchêne Bernard , Conessa Christophe , Meyer Olivier
Published: December 2018

This paper deals with breast and head phantoms fabricated from 3D-printed structures and liquid mixtures whose complex permittivities are close to that of the biological tissues within a large frequency band. The goal is to enable an easy and safe manufacturing of stable-in-time detailed anthropomorphic phantoms dedicated to the test of microwave imaging systems to assess the performances of the latter in realistic configurations before a possible clinical application to breast cancer imaging or brain stroke monitoring. The structure of the breast phantom has already been used by several laboratories to test their measurement systems in the framework of the COST (European Cooperation in Science and Technology) Action TD1301-MiMed. As for the tissue mimicking liquid mixtures, they are based upon Triton X-100 and salted water. It has been proven that such mixtures can dielectrically mimic the various breast tissues. It is shown herein that they can also accurately mimic most of the head tissues and that, given a binary fluid mixture model, the respective concentrations of the various constituents needed to mimic a particular tissue can be predetermined by means of a standard minimization method.
Bayesian 3D X-ray Computed Tomography with a Hierarchical Prior Model for Sparsity in Haar Transform Domain
Authors: Wang Li , Mohammad-Djafari Ali , Gac Nicolas , Dumitru Mircea
Published: December 2018

In this paper, a hierarchical prior model based on the Haar transformation and an appropriate Bayesian computational method for X-ray CT reconstruction are presented. Given the piece-wise continuous property of the object, a multilevel Haar transformation is used to associate a sparse representation for the object. The sparse structure is enforced via a generalized Student-t distribution ( S t g ), expressed as the marginal of a normal-inverse Gamma distribution. The proposed model and corresponding algorithm are designed to adapt to specific 3D data sizes and to be used in both medical and industrial Non-Destructive Testing (NDT) applications. In the proposed Bayesian method, a hierarchical structured prior model is proposed, and the parameters are iteratively estimated. The initialization of the iterative algorithm uses the parameters of the prior distributions. A novel strategy for the initialization is presented and proven experimentally. We compare the proposed method with two state-of-the-art approaches, showing that our method has better reconstruction performance when fewer projections are considered and when projections are acquired from limited angles.
A Comprehensive Evaluation of Graph Kernels for Unattributed Graphs
Authors: Zhang Yi , Wang Lulu , Wang Liandong
Published: December 2018

Graph kernels are of vital importance in the field of graph comparison and classification. However, how to compare and evaluate graph kernels and how to choose an optimal kernel for a practical classification problem remain open problems. In this paper, a comprehensive evaluation framework of graph kernels is proposed for unattributed graph classification. According to the kernel design methods, the whole graph kernel family can be categorized in five different dimensions, and then several representative graph kernels are chosen from these categories to perform the evaluation. With plenty of real-world and synthetic datasets, kernels are compared by many criteria such as classification accuracy, F1 score, runtime cost, scalability and applicability. Finally, quantitative conclusions are discussed based on the analyses of the extensive experimental results. The main contribution of this paper is that a comprehensive evaluation framework of graph kernels is proposed, which is significant for graph-classification applications and the future kernel research.
Reply to the Comments on: Tian Zhao et al. The Principle of Least Action for Reversible Thermodynamic Processes and Cycles. Entropy 2018, 20, 542
Authors: Guo Zeng-Yuan , Zhao Tian , Hua Yu-Chao
Published: December 2018

The purpose of this reply is to provide a discussion and closure for the comment paper by Dr. Bormashenko on the present authors’ article, which discussed the application of the principle of least action in reversible thermodynamic processes and cycles. Dr. Bormashenko’s questions and misunderstandings are responded to, and the differences between the present authors’ work and Lucia’s are also presented.
A New Kinematic Model and Control Strategy for a Mobile Platform for Transporting Lightweight Manipulators
Authors: Feliu-Talegon Daniel , San-Millan Andres , Feliu-Batlle Vicente
Published: December 2018

This work is concerned with the mechanical design and the description of the different components of a new mobile base for a lightweight mobile manipulator. These kinds of mobile manipulators are normally composed of multiple lightweight links mounted on a mobile platform. This work is focused on the description of the mobile platform, the development of a new kinematic model and the design of a control strategy for the system. The proposed kinematic model and control strategy are validated by means of experimentation using the real prototype. The workspace of the system is also defined.
Robust Day-Ahead Forecasting of Household Electricity Demand and Operational Challenges
Authors: Gerossier Alexis , Girard Robin , Bocquet Alexis , Kariniotakis George
Published: December 2018

In the recent years, several short-term forecasting models of household electricity demand have been proposed in the literature. This is partly due to emerging smart-grid applications, which require these kinds of forecasts to manage systems such as smart homes, prosumer aggregations, etc., and partly thanks to the availability of data from smart meters, which enable the development of such models. Since most models are academically developed, they often do not address challenges related to their implementation in a real-world environment. In the latter case, several issues arise, related to data quality and availability, which affect the operational performance and robustness of a forecasting system. In this paper, we design a hierarchical forecasting framework based on a total of 5 probabilistic models of varying complexity, after analyzing the respective performance and advantages of the models with an offline dataset. This multi-layered framework is necessary to address the various problematic situations occurring in practice and abide by the requirements for a real-world deployment. The forecasting system is deployed in a real-world case and evaluated here on data from 20 households. Field data, comprising forecasts and measurements, are analyzed for each household. A detailed comparison is drawn between the online and offline performances. Since a notable degradation is observed in the operational environment, we discuss at length the reasons for such an effect. We determine that the exact settings of the training and test periods are marginally responsible, but that the main cause is the intrinsic evolution of the demand time series, which hinders the forecasting performance. This evolution is due to unknown household characteristics that need to be monitored to provide more adaptable models.
A GIS-Based Method for Identification of Wide Area Rooftop Suitability for Minimum Size PV Systems Using LiDAR Data and Photogrammetry
Authors: Palmer Diane , Koumpli Elena , Cole Ian , Gottschalg Ralph , Betts Thomas
Published: December 2018

Knowledge of roof geometry and physical features is essential for evaluation of the impact of multiple rooftop solar photovoltaic (PV) system installations on local electricity networks. The paper starts by listing current methods used and stating their strengths and weaknesses. No current method is capable of delivering accurate results with publicly available input data. Hence a different approach is developed, based on slope and aspect using aircraft-based Light Detection and Ranging (LiDAR) data, building footprint data, GIS (Geographical Information Systems) tools, and aerial photographs. It assesses each roof’s suitability for PV deployment. That is, the characteristics of each roof are examined for fitting of at least a minimum size solar power system. In this way the minimum potential solar yield for region or city may be obtained. Accuracy is determined by ground-truthing against a database of 886 household systems. This is the largest validation of a rooftop assessment method to date. The method is flexible with few prior assumptions. It can generate data for various PV scenarios and future analyses.
Performance and Exhaust Emission Optimization of a Dual Fuel Engine by Response Surface Methodology
Authors: Kamaraj Raj Kumar , Gowthami Thankachi Raghuvaran Jinu , Panimayam Arul Franco , Allasi Haiter Lenin
Published: December 2018

A central composite face-centered design (CCFD) was employed to examine the optimal conditions for the compression ratio of the Sardine Fish Oil Methyl Ester (SFOME) blend to the Thermal cracked Cashew Shell Nut Liquid T-CSNL blend by simultaneously considering the brake thermal efficiency (BTE), the brake specific fuel consumption (BSFC), carbon monoxide (CO), hydrocarbon (HC), and nitrogen oxide (NOx) emissions as response variables. The experimental results obtained were analyzed with the help of Design Expert software, and output response predictions were fitted with a polynomial quadratic model of the second degree. The maximum overall desirability obtained for the entire model was 0.7506 with a compression ratio of 19.31 and blend ratios of 20% for SFOME and 15.72% for T-CSNL by volume proportion. Under optimum conditions, it was found that the predicted and experimental results were very similar, and it can be concluded that the quadratic model of second-order can precisely predict the performance and emission characteristics of engines.