bluuebunny/crossref_metadata_2025 huggingface dataset:
Review of Substrate Integrated Waveguide Circuits for Beam-Forming Networks Working in X-Band
Authors: Venanzoni Giuseppe , Mencarelli Davide , Morini Antonio , Farina Marco , Prudenzano Francesco
Published: March 2019

A review of substrate integrated waveguide (SIW) components designed for the use in beam-forming networks working in X-Band is presented. The proposed devices are four-port and six-port couplers and magic tees for the network. The devices take full advantage of the use of SIW technology in order to reduce size, weight, and cost. The design processes of all devices are exposed, and the experimental results of prototypes show the feasibility of these solutions.
Consensus Algorithms Based Multi-Robot Formation Control under Noise and Time Delay Conditions
Authors: Wei Heng , Lv Qiang , Duo Nanxun , Wang GuoSheng , Liang Bing
Published: March 2019

In recent years, the formation control of multi-mobile robots has been widely investigated by researchers. With increasing numbers of robots in the formation, distributed formation control has become the development trend of multi-mobile robot formation control, and the consensus problem is the most basic problem in the distributed multi-mobile robot control algorithm. Therefore, it is very important to analyze the consensus of multi-mobile robot systems. There are already mature and sophisticated strategies solving the consensus problem in ideal environments. However, in practical applications, uncertain factors like communication noise, communication delay and measurement errors will still lead to many problems in multi-robot formation control. In this paper, the consensus problem of second-order multi-robot systems with multiple time delays and noises is analyzed. The characteristic equation of the system is transformed into a quadratic polynomial of pure imaginary eigenvalues using the frequency domain analysis method, and then the critical stability state of the maximum time delay under noisy conditions is obtained. When all robot delays are less than the maximum time delay, the system can be stabilized and achieve consensus. Compared with the traditional Lyapunov method, this algorithm has lower conservativeness, and it is easier to extend the results to higher-order multi-robot systems. Finally, the results are verified by numerical simulation using MATLAB/Simulink. At the same time, a multi-mobile robot platform is built, and the proposed algorithm is applied to an actual multi-robot system. The experimental results show that the proposed algorithm is finally able to achieve the consensus of the second-order multi-robot system under delay and noise interference.
Ultrasonic Tomographic Technique and Its Applications
Authors: Takiguchi Takashi
Published: March 2019

X-ray tomography and magnetic resonance imaging (MRI) are excellent techniques for non-destructive or non-invasive inspections, however, they have shotcomings including the expensive cost in both the devices themselves and their protection facilities, the harmful side effects of the X-rays to human bodies and to the environment. In view of this argument, it is necessary to develop new, inexpensive, safe and reliable tomographic techniques, especially in medical imaging and non-destructive inspections. There are new tomographic techniques under development such as optical tomography, photo-acoustic tomography, ultrasonic tomography and so on, from which we take ultrasonic tomography as the topic in this paper. We introduce a review of the known ultrasonic tomographic techniques and discuss their future development.
Image Shadow Removal Using End-To-End Deep Convolutional Neural Networks
Authors: Fan Hui , Han Meng , Li Jinjiang
Published: March 2019

Image degradation caused by shadows is likely to cause technological issues in image segmentation and target recognition. In view of the existing shadow removal methods, there are problems such as small and trivial shadow processing, the scarcity of end-to-end automatic methods, the neglecting of light, and high-level semantic information such as materials. An end-to-end deep convolutional neural network is proposed to further improve the image shadow removal effect. The network mainly consists of two network models, an encoder–decoder network and a small refinement network. The former predicts the alpha shadow scale factor, and the latter refines to obtain sharper edge information. In addition, a new image database (remove shadow database, RSDB) is constructed; and qualitative and quantitative evaluations are made on databases such as UIUC, UCF and newly-created databases (RSDB) with various real images. Using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) for quantitative analysis, the algorithm has a big improvement on the PSNR and the SSIM as opposed to other methods. In terms of qualitative comparisons, the network shadow has a clearer and shadow-free image that is consistent with the original image color and texture, and the detail processing effect is much better. The experimental results show that the proposed algorithm is superior to other algorithms, and it is more robust in subjective vision and objective quantization.
Regional Frequency Analysis of Precipitation Extremes and Its Spatio-Temporal Patterns in the Hanjiang River Basin, China
Authors: Hao Wenlong , Hao Zhenchun , Yuan Feifei , Ju Qin , Hao Jie
Published: March 2019

Extreme events such as rainstorms and floods are likely to increase in frequency due to the influence of global warming, which is expected to put considerable pressure on water resources. This paper presents a regional frequency analysis of precipitation extremes and its spatio-temporal pattern characteristics based on well-known index-flood L-moments methods and the application of advanced statistical tests and spatial analysis techniques. The results indicate the following conclusions. First, during the period between 1969 and 2015, the annual precipitation extremes at Fengjie station show a decreasing trend, but the Wuhan station shows an increasing trend, and the other 24 stations have no significant trend at a 5% confidence level. Secondly, the Hanjiang River Basin can be categorized into three homogenous regions by hierarchical clustering analysis with the consideration of topography and mean precipitation in these areas. The GEV, GNO, GPA and P III distributions fit better for most of the basin and MARE values range from 3.19% to 6.41% demonstrating that the best-fit distributions for each homogenous region is adequate in predicting the quantiles estimates. Thirdly, quantile estimates are reliable enough when the return period is less than 100 years, however estimates for a higher return period (e.g., 1000 years) become unreliable. Further, the uncertainty of quantiles estimations is growing with the growing return periods and the estimates based on R95P series have a smaller uncertainty to describe the extreme precipitation in the Hanjiang river basin (HJRB). Furthermore, In the HJRB, most of the extreme precipitation events (more than 90%) occur during the rainy season between May and October, and more than 30% of these extreme events concentrate in July, which is mainly impacted by the sub-tropical monsoon climate. Finally, precipitation extremes are mainly concentrated in the areas of Du River, South River and Daba Mountain in region I and Tianmen, Wuhan and Zhongxiang stations in region III, located in the upstream of Danjiangkou Reservoir and Jianghan Plain respectively. There areas provide sufficient climate conditions (e.g., humidity and precipitation) responsible for the occurring floods and will increase the risk of natural hazards to these areas.
Influence of the Heights of Low-Level Jets on Power and Aerodynamic Loads of a Horizontal Axis Wind Turbine Rotor
Authors: Zhang Xuyao , Yang Congxin , Li Shoutu
Published: March 2019

The influence of the heights of low-level jets (LLJs) on the rotor power and aerodynamic loads of a horizontal axis wind turbine were investigated using the fatigue, aerodynamics, structures, and turbulence code. The LLJ and shear inflow wind fields were generated using an existing wind speed spectral model. We found that the rotor power predicted by the average wind speed of the hub height is higher than the actual power in relatively weak and shallow LLJ inflow conditions, especially when the LLJ height is located inside the rotor-swept area. In terms of aerodynamic loads, when the LLJ height is located inside the rotor-swept area, the root mean square (RMS) rotor thrust coefficient and torque coefficient increase, while the RMS rotor unbalanced aerodynamic load coefficients, including lateral force, longitudinal force, tilt moment, and yaw moment, decreased. This means that the presence of both positive and negative wind shear in the rotor-swept area not only increases the rotor power but also reduces the unbalanced aerodynamic loads, which is beneficial to the operation of wind turbine. Power spectrum analysis shows no obvious difference in the power spectrum characteristics of the rotor torque and thrust in LLJ inflow conditions with different heights.
Interrelationship between Lab, Space, Astrophysical, Magnetic Fusion, and Inertial Fusion Plasma Experiments
Authors: Koepke Mark E.
Published: March 2019

The objectives of this review are to articulate geospace, heliospheric, and astrophysical plasma physics issues that are addressable by laboratory experiments, to convey the wide range of laboratory experiments involved in this interdisciplinary alliance, and to illustrate how lab experiments on the centimeter or meter scale can develop, through the intermediary of a computer simulation, physically credible scaling of physical processes taking place in a distant part of the universe over enormous length scales. The space physics motivation of laboratory investigations and the scaling of laboratory plasma parameters to space plasma conditions, having expanded to magnetic fusion and inertial fusion experiments, are discussed. Examples demonstrating how laboratory experiments develop physical insight, validate or invalidate theoretical models, discover unexpected behavior, and establish observational signatures for the space community are presented. The various device configurations found in space-related laboratory investigations are outlined.
The Relationship between Plasma Taurine Levels and Diabetic Complications in Patients with Type 2 Diabetes Mellitus
Authors: Sak Duygu , Erdenen Fusun , Müderrisoglu Cuneyt , Altunoglu Esma , Sozer Volkan , Gungel Hulya , Guler Pınar Akca , Sak Tuncer , Uzun Hafize
Published: March 2019

Background: Taurine has an active role in providing glucose homeostasis and diabetes causes a decline in taurine levels. This paper investigates the relationship between taurine and diabetic complications, patients’ demographic features, and biochemical parameters. Methods: Fifty-nine patients with type 2 diabetes mellitus (T2DM), and 28 healthy control subjects between the ages of 32 and 82 were included in the study. The mean age of subjects was 55.6 ± 10.3 and mean diabetes duration was 10.2 ± 6.0 years. The most commonly accompanying comorbidity was hypertension (HT) (64.5%, n = 38), and the most frequent diabetic complication was neuropathy (50.8%, n = 30). Plasma taurine concentrations were measured by an enzyme-linked immunoassay (ELISA) kit. Results: Plasma taurine concentrations were significantly lower in diabetic patients (0.6 ± 0.1 mmol/L) than controls (0.8 ± 0.2 mmol/L) and in hypertensive (0. 6 ± 0.1 mmol/L) patients (p = 0.000, p = 0.027 respectively). Conclusion: Plasma taurine levels were decreased in patients with T2DM and this was not related to FBG, HbA1c, and microalbuminuria. With regard to complications, we only found a correlation with neuropathy. We suggest that taurine levels may be more important in the development of diabetes; however, it may also have importance for the progression of the disease and the subsequent complications. We further assert that taurine measurement at different times may highlight whether there is a causal relationship in the development of complications.
The Main Metabolites of Fluorouracil + Adriamycin + Cyclophosphamide (FAC) Are Not Major Contributors to FAC Toxicity in H9c2 Cardiac Differentiated Cells
Authors: Reis-Mendes Ana , Carvalho Félix , Remião Fernando , Sousa Emília , Bastos Maria de Lourdes , Costa Vera Marisa
Published: March 2019

In the clinical practice, the combination of 5-fluorouracil (5-FU) + Adriamycin (also known as doxorubicin, DOX) + cyclophosphamide (CYA) (known as FAC) is used to treat breast cancer. The FAC therapy, however, carries some serious risks, namely potential cardiotoxic effects, although the mechanisms are still unclear. In the present study, the role of the main metabolites regarding FAC-induced cardiotoxicity was assessed at clinical relevant concentrations. Seven-day differentiated H9c2 cells were exposed for 48 h to the main metabolites of FAC, namely the metabolite of 5-FU, α-fluoro-β-alanine (FBAL, 50 or 100 μM), of DOX, doxorubicinol (DOXOL, 0.2 or 1 μM), and of CYA, acrolein (ACRO, 1 or 10 μM), as well as to their combination. The parent drugs (5-FU 50 μM, DOX 1 μM, and CYA 50 μM) were also tested isolated or in combination with the metabolites. Putative cytotoxicity was evaluated through phase contrast microscopy, Hoechst staining, membrane mitochondrial potential, and by two cytotoxicity assays: the reduction of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) and the neutral red (NR) lysosomal incorporation. The metabolite DOXOL was more toxic than FBAL and ACRO in the MTT and NR assays. When in combination, neither FBAL nor ACRO increased DOXOL-induced cytotoxicity. No nuclear condensation was observed for any of the tested combinations; however, a significant mitochondrial potential depolarization after FBAL 100 μM + DOXOL 1 μM + ACRO 10 μM or FBAL 100 μM + DOXOL 1 μM exposure was seen at 48 h. When tested alone DOX 1 μM was more cytotoxic than all the parent drugs and metabolites in both the cytotoxicity assays performed. These results demonstrated that DOXOL was the most toxic of all the metabolites tested; nonetheless, the metabolites do not seem to be the major contributors to FAC-induced cardiotoxicity in this cardiac model.
Family Socioeconomic Position at Birth and School Bonding at Age 15: Blacks’ Diminished Returns
Authors: Assari Shervin
Published: March 2019

Life course epidemiological studies have documented the effects of family socioeconomic position (SEP) at birth on youth developmental processes and outcomes decades later. According to the minorities’ diminished returns (MDR) theory, however, family SEP at birth generates smaller returns for Black compared to White families. Using 15 years of follow up data of a national sample of American families, this study investigated racial differences in the effect of family income at birth on subsequent school bonding of the adolescent at age 15. The fragile families and child well-being study (FFCWS) is a 15-year prospective longitudinal study of 495 White and 1436 Black families from the birth of their child. Family SEP (income to needs ratio) at birth was the independent variable. Youth school bonding at age 15 was the main outcome. Linear regressions were applied for data analysis, with race as the focal moderator. In the pooled sample, in addition to each race, higher family SEP at birth was associated with higher school bonding of the youth at age 15. Race altered the effects of family SEP at birth on youth school bonding at age 15, indicating smaller protective effects for Black compared to White youth. Race stratified regressions also showed the effect of family SEP at birth on age 15 school bonding for White youth, but not Black youth. Tangible outcomes that follow economic resources at birth are disproportionately smaller for Black families compared to those for White families. Merely equalizing SEP is not enough for the elimination of racial inequalities in youth outcomes. Policies should reduce societal and structural barriers that commonly cause diminished returns of SEP for Black families. Policy evaluations should aim for most effective policies that have the potential to equalize Blacks’ and Whites’ chances for gaining tangible developmental and health outcomes from identical SEP resources.
The Spatial and Temporal Variability of the Indoor Environmental Quality during Three Simulated Office Studies at a Living Lab
Authors: Clements Nicholas , Zhang Rongpeng , Jamrozik Anja , Campanella Carolina , Bauer Brent
Published: March 2019

The living lab approach to building science research provides the ability to accurately monitor occupants and their environment and use the resulting data to evaluate the impact that various components of the built environment have on human comfort, health, and well-being. A hypothesized benefit of the living lab approach is the ability to simulate the real indoor environment in an experimentally controlled setting over relatively long periods of time, overcoming a significant hurdle encountered in many chamber-type experimental designs that rarely reflect typical indoor environments. Here, we present indoor environmental quality measurements from a network of sensors as well as building system design and operational data demonstrating the ability of a living lab to realistically simulate a wide range of environmental conditions in an office setting by varying air temperature, lighting, façade control, and sound masking in a series of three human subject experiments. The temporal variability of thermal and lighting conditions was assessed on an hourly basis and demonstrated the significant impact of façade design and control on desk-level measurements of both factors. Additional factors, such as desk layout and building system design (e.g., luminaires, speaker system), also contributed significantly to spatial variability in air temperature, lighting, and sound masking exposures, and this variability was reduced in latter experiments by optimizing desk layout and building system design. While ecologically valid experimental conditions are possible with a living lab, a compromise between realism and consistency in participant experience must often be found by, for example, using an atypical desk layout to reduce spatial variability in natural light exposure. Based on the experiences from these three studies, experimental design and environmental monitoring considerations for future office-based living lab experiments are explored.
Plasma Oxidation Printing into DLC and Graphite for Surface Functionalization
Authors: Aizawa Tatsuhiko , Wasa Kenji , Nogami Yoshiro
Published: March 2019

A diamond-like carbon (DLC) film, coated on a AISI420-J2 stainless steel substrate and vertically aligned graphite (VAG), was structured by high-density plasma oxidation to work as a DLC-punch for micro-stamping and DLC-nozzle array for micro-dispensing, in addition to acting as a copper-plated thermal spreader, respectively. Thick DLC films were micro-patterned by maskless lithography and directly plasma-etched to remove the unmasked regions. Thick VAG (Ca plates were micro-patterned by screen-printing and selectively etched to activate the surface. Raman spectroscopy as well as electric resistivity measurement proved that there was no degradation of VAG by this surface activation. Wet plating was utilized to prove that copper wettability was improved by this surface treatment.
Novel Molecular Characterization of Colorectal Primary Tumors Based on miRNAs
Authors: Conde Elisa , Pascual Alejandro , Prieto-Cuadra Daniel , Laza Val F. , Molina-Cerrillo Javier , Ramos-Muñoz Miren Edurne , Rodríguez-Serrano Esperanza Macarena , Soto José Luis , Carrato Alfredo , García-Bermejo María Laura , Guillén-Ponce Carmen
Published: March 2019

microRNAs (miRNA) expression in colorectal (CR) primary tumours can facilitate a more precise molecular characterization. We identified and validated a miRNA profile associated with clinical and histopathological features that might be useful for patient stratification. In situ hybridization array using paraffin-embedded biopsies of CR primary tumours were used to screen 1436 miRNAs. 17 miRNAs were selected for validation by quantitative reverse transcription polymerase chain reaction (qRT-PCR) (n = 192) and were further correlated with clinical and histopathological data. We demonstrated that miRNAs associated to Colorectal Cancer (CRC) diagnosis age (over 50s and 60s) included miR-1-3p, miR-23b-3p, miR-27b-3p, miR-143-3p, miR-145-5p and miR-193b-5p. miR-23b-3p and miR-24-3p discriminated between Lynch Syndrome and sporadic CRC. miR-10a-5p, miR-20a-5p, miR-642b and Let-7a-5p were associated to stroma abundance. miR-642b and Let-7a-5p were associated with to peritumoral inflammation abundance. miR-1-3p, miR-143-3p and miR-145-5p correlated with mucinous component. miR-326 correlated with tumour location (right or left sided). miR-1-3p associated with tumour grade. miR-20a-5p, miR-193b-5p, miR-320a, miR-326 and miR-642b-3p associated to tumour stage and progression. Remarkably, we also demonstrated that miR-1-3p and miR-326 expression significantly associated with patient overall survival (OS). Hierarchical clustering and bioinformatics analysis indicated that selected miRNAs could re-classify the patients and work cooperatively, modulating common target genes involved in colorectal cancer key signalling pathways. In conclusion, molecular characterization of CR primary tumours based on miRNAs could lead to more accurate patient reclassification and may be useful for efficient patient management.
Cancer Biogenesis in Ribosomopathies
Authors: Sulima Sergey O. , Kampen Kim R. , De Keersmaecker Kim
Published: March 2019

Ribosomopathies are congenital diseases with defects in ribosome assembly and are characterized by elevated cancer risks. Additionally, somatic mutations in ribosomal proteins have recently been linked to a variety of cancers. Despite a clear correlation between ribosome defects and cancer, the molecular mechanisms by which these defects promote tumorigenesis are unclear. In this review, we focus on the emerging mechanisms that link ribosomal defects in ribosomopathies to cancer progression. This includes functional “onco-specialization” of mutant ribosomes, extra-ribosomal consequences of mutations in ribosomal proteins and ribosome assembly factors, and effects of ribosomal mutations on cellular stress and metabolism. We integrate some of these recent findings in a single model that can partially explain the paradoxical transition from hypo- to hyperproliferation phenotypes, as observed in ribosomopathies. Finally, we discuss the current and potential strategies, and the associated challenges for therapeutic intervention in ribosome-mutant diseases.
Deciphering Nuclear Mechanobiology in Laminopathy
Authors: Hah Jungwon , Kim Dong-Hwee
Published: March 2019

Extracellular mechanical stimuli are translated into biochemical signals inside the cell via mechanotransduction. The nucleus plays a critical role in mechanoregulation, which encompasses mechanosensing and mechanotransduction. The nuclear lamina underlying the inner nuclear membrane not only maintains the structural integrity, but also connects the cytoskeleton to the nuclear envelope. Lamin mutations, therefore, dysregulate the nuclear response, resulting in abnormal mechanoregulations, and ultimately, disease progression. Impaired mechanoregulations even induce malfunction in nuclear positioning, cell migration, mechanosensation, as well as differentiation. To know how to overcome laminopathies, we need to understand the mechanisms of laminopathies in a mechanobiological way. Recently, emerging studies have demonstrated the varying defects from lamin mutation in cellular homeostasis within mechanical surroundings. Therefore, this review summarizes recent findings highlighting the role of lamins, the architecture of nuclear lamina, and their disease relevance in the context of nuclear mechanobiology. We will also provide an overview of the differentiation of cellular mechanics in laminopathy.
Green Infrastructure Financing as an Imperative to Achieve Green Goals
Authors: Zimmerman Rae , Brenner Ryan , Llopis Abella Jimena
Published: March 2019

Green infrastructure (GI) has increasingly gained popularity for achieving adaptation and mitigation goals associated with climate change and extreme weather events. To continue implementing GI, financial tools are needed for upfront project capital or development costs and later for maintenance. This study’s purpose is to evaluate financing tools used in a selected GI dataset and to assess how those tools are linked to various GI technologies and other GI project characteristics like cost and size. The dataset includes over 400 GI U.S. projects, comprising a convenience sample, from the American Society of Landscape Architects (ASLA). GI project characteristics were organized to answer a number of research questions using descriptive statistics. Results indicated that the number of projects and overall cost shares were mostly located in a few states. Grants were the most common financial tool with about two-thirds of the projects reporting information on financial tools receiving grant funding. Most projects reported financing from only one tool with a maximum of three tools. Projects primarily included multiple GI technologies averaging three and a maximum of nine. The most common GI technologies were bioswales, retention, rain gardens, and porous pavements. These findings are useful for decision-makers evaluating funding support for GI.
Large Area Graphene Deposition on Hydrophobic Surfaces, Flexible Textiles, Glass Fibers and 3D Structures
Authors: Jia Guobin , Plentz Jonathan , Dellith Jan , Dellith Andrea , Wahyuono Ruri Agung , Andrä Gudrun
Published: March 2019

Graphene and its derivatives have many superior electrical, thermal, mechanical, chemical, and structural properties, and promise for many applications. One of the issues for scalable applications is the lack of a simple, reliable method that allows the deposit of a well-ordered monolayer using low-cost graphene flakes onto target substrates with different surface properties. Another issue is the adhesion of the deposited graphene thin film, which has not been well investigated yet. Following our former finding of a double self-assembly (DSA) process for efficient deposition of a monolayer of graphene flakes (MGFs), in this work we demonstrate that the DSA process can be applied even on very challenging samples including highly hydrophobic polytetrafluoroethylene (PTFE), flexible textiles, complex 3D objects, and thin glass fibers. Additionally, we tested adhesion of the graphene flakes on the flat glass substrate by scotch tape peel test of the MGFs. The results show that the graphene flakes adhere quite well on the flat glass substrate and most of the graphene flakes stay on the glass. These findings may trigger many large-scale applications of low-cost graphene feedstocks and other 2D materials.
Thermal Transformation of Caffeic Acid on the Nanoceria Surface Studied by Temperature Programmed Desorption Mass-Spectrometry, Thermogravimetric Analysis and FT–IR Spectroscopy
Authors: Nastasiienko Nataliia , Palianytsia Borys , Kartel Mykola , Larsson Mats , Kulik Tetiana
Published: March 2019

The studies of pyrolysis of caffeic acid (CA) and its surface complexes is important for the development of technologies of heterogeneous catalytic pyrolysis of plant- and wood- based renewable biomass components. In this work, the structure and thermal transformations of the surface complexes of CA on the surface of nanoceria were investigated using Fourier transform–infrared (FT–IR) spectroscopy, thermogravimetric analysis (TGA) and temperature-programmed desorption mass spectrometry (TPD MS). It was found that CA on the surface of cerium dioxide forms several types of complexes: bidentate carboxylates, monodentate carboxylates and complexes formed as a result of interaction with phenolic hydroxyl groups. This is due to the ability of nanosized cerium dioxide to generate basic hydroxyl groups that can deprotonate phenolic groups to form phenolates on the surface. The main pyrolysis products were identified. The possible ways of forming 3,4-dihydroxyphenylethylene, acetylene carboxylic acid, pyrocatechol and phenol from surface complexes of CA were suggested. It was established that on the nanoceria surface effectively occur the decarboxylation, decarbonylation, and dehydration reactions of the CA, which are the desirable processes in biomass conversion technologies.
Sorption Membranes and Filter for Water Purification and Disinfection in Outdoor Conditions
Authors: Zhuravlev Igor
Published: March 2019

The aim of this article was to develop a simple, cheap, light, highly efficient sorption membrane and filtering device for the purification of naturally polluted water with application in outdoor conditions. Sorption membranes have been prepared from cellulose pulp using paper and pulp technology. The sorbents were introduced into paper pulp as a filler (as activated carbon) or were chemically grafted to cellulose fibers. The absorption ability of such membranes assembled in a filter device has been investigated. The removal of weighted small mechanical particles, microorganisms, dissolved organic contaminants and colloids, and the undesirable ion impurities by the filter was investigated. Using a 10 × 10 cm filter, purification and disinfection of 18 liters of natural polluted water from a lake was done. Purification of dirty water occurs with gravity from a tank (bucket).
Energy Response of Silicon Drift Detectors for Kaonic Atom Precision Measurements
Authors: Miliucci Marco , Iliescu Mihail , Amirkhani Aidin , Bazzi Massimiliano , Curceanu Catalina , Fiorini Carlo , Scordo Alessandro , Sirghi Florin , Zmeskal Johann
Published: March 2019

Novel, large-area silicon drift detectors (SDDs) have been developed to perform precision measurements of kaonic atom X-ray spectroscopy, for the study the K ¯ N strong interaction in the low-energy regime. These devices have special geometries, field configurations and read-out electronics, resulting in excellent performances in terms of linearity, stability and energy resolution. In this work the SDDs energy response in the energy region between 4000 eV and 12,000 eV is reported, revealing a stable linear response within 1 eV and good energy resolution.
Determination of Crystal Growth Geometry Factors and Nucleation Site Densities of Electrodeposited Ferromagnetic Cobalt Nanowire Arrays
Authors: Saeki Ryusei , Ohgai Takeshi
Published: March 2019

The time-dependence of electrochemical reduction current, which was observed during the one-dimensional (1-D) crystal growth of ferromagnetic cobalt nanowire arrays, was analyzed by Johnson–Mehl–Avrami–Kolmogorov (JMAK) theory. Textured hcp-Co nanowire arrays were synthesized by potentio-static electrochemical reduction of Co2+ ions in anodized aluminum oxide (AAO) nanochannel films. Crystal growth geometry factor n in the JMAK equation was determined to be ca. 1. Hence, the electrochemical crystal growth process of a numerical nanowires array can be explained by 1-D geometry. The crystal nucleation frequency factor, k in JMAK equation was estimated to be the range between 10−4 and 10−3. Our experimental results revealed that the crystal nucleation site density Nd increased up to 2.7 × 10−8 nm−3 when increasing the overpotential for cobalt electrodeposition by shifting the cathode potential down to −0.85 V vs. Ag/AgCl. The (002) crystal orientation of hcp-Co nanowire arrays was, remarkably, observed by decreasing Nd. Spontaneous magnetization behavior was observed in the axial direction of nanowires. By decreasing the overpotential for cobalt electrodeposition, the coercivity of the nanocomposite film increased and reached up to 1.88 kOe, with a squareness of ca. 0.9 at room temperature.
Salmonella Infection in Chronic Inflammation and Gastrointestinal Cancer
Authors: Zha Lang , Garrett Shari , Sun Jun
Published: March 2019

Salmonella not only causes acute infections, but can also cause patients to become chronic “asymptomatic” carriers. Salmonella has been verified as a pathogenic factor that contributes to chronic inflammation and carcinogenesis. This review summarizes the acute and chronic Salmonella infection and describes the current research progress of Salmonella infection contributing to inflammatory bowel disease and cancer. Furthermore, this review explores the underlying biological mechanism of the host signaling pathways manipulated by Salmonella effector molecules. Using experimental animal models, researchers have shown that Salmonella infection is related to host biological processes, such as host cell transformation, stem cell maintenance, and changes of the gut microbiota (dysbiosis). Finally, this review discusses the current challenges and future directions in studying Salmonella infection and its association with human diseases.
A New Dictionary Construction Based Multimodal Medical Image Fusion Framework
Authors: Zhou Fuqiang , Li Xiaosong , Zhou Mingxuan , Chen Yuanze , Tan Haishu
Published: March 2019

Training a good dictionary is the key to a successful image fusion method of sparse representation based models. In this paper, we propose a novel dictionary learning scheme for medical image fusion. First, we reinforce the weak information of images by extracting and adding their multi-layer details to generate the informative patches. Meanwhile, we introduce a simple and effective multi-scale sampling to implement a multi-scale representation of patches while reducing the computational cost. Second, we design a neighborhood energy metric and a multi-scale spatial frequency metric for clustering the image patches with a similar brightness and detail information into each respective patch group. Then, we train the energy sub-dictionary and detail sub-dictionary, respectively by K-SVD. Finally, we combine the sub-dictionaries to construct a final, complete, compact and informative dictionary. As a main contribution, the proposed online dictionary learning can not only obtain an informative as well as compact dictionary, but can also address the defects, such as superfluous patch issues and low computation efficiency, in traditional dictionary learning algorithms. The experimental results show that our algorithm is superior to some state-of-the-art dictionary learning based techniques in both subjective visual effects and objective evaluation criteria.
Evaluating the Transient Energy Dissipation in a Centrifugal Impeller under Rotor-Stator Interaction
Authors: Tao Ran , Zhao Xiaoran , Wang Zhengwei
Published: March 2019

In fluid machineries, the flow energy dissipates by transforming into internal energy which performs as the temperature changes. The flow-induced noise is another form that flow energy turns into. These energy dissipations are related to the local flow regime but this is not quantitatively clear. In turbomachineries, the flow regime becomes pulsating and much more complex due to rotor-stator interaction. To quantitatively understand the energy dissipations during rotor-stator interaction, the centrifugal air pump with a vaned diffuser is studied based on total energy modeling, turbulence modeling and acoustic analogy method. The numerical method is verified based on experimental data and applied to further simulation and analysis. The diffuser blade leading-edge site is under the influence of impeller trailing-edge wake. The diffuser channel flow is found periodically fluctuating with separations from the blade convex side. Stall vortex is found on the diffuser blade trailing-edge near outlet. High energy loss coefficient sites are found in the undesirable flow regions above. Flow-induced noise is also high in these sites except in the stall vortex. Frequency analyses show that the impeller blade frequency dominates in the diffuser channel flow except in the outlet stall vortexes. These stall vortices keep their own stall frequency which is about 1/5 impeller frequency with high energy loss coefficient but low noise level. Results comparatively prove the energy dissipation mechanism in the centrifugal air pump under rotor-stator interaction. Results also provide the quantitative basis for turbomachinery’s loss reduction design.
An Entrepreneurship Venture for Training K–12 Teachers to Use Engineering as a Context for Learning
Authors: Kukreti Anant R. , Broering Jack
Published: March 2019

In this paper, the authors present their experiences from participating in a National Science Foundation (NSF) I-Corps L training program established for business startups, using Blank’s Lean LaunchPad, Osterwalder’s Business Model Canvas, and associated tools. They used the entrepreneurial skills acquired through this training to scale-up their emerging innovation, the Cincinnati Engineering Enhanced Math and Science Program (CEEMS), which had been developed, implemented, and evaluated with successful results over a period of seven years in a targeted 14 school-district partnership in Greater Cincinnati. The overriding goal was to improve student learning and success rates in K–12 math and science courses by helping to accelerate the process of bringing effective educational innovation, CEEMS, to scale. In CEEMS, teachers were trained in using challenge-based learning (CBL) and the engineering design process (EDP), teaching pedagogies to transform their classrooms into student-centered, hands-on learning environments, while also assisting students to improve their evaluation scores related to science, math, and engineering instruction. CEEMS teachers acquired the necessary skills through coursework, professional development (PD) workshops, and longitudinal professional guidance provided by assigned coaches over a period of two years to become proficient in developing CBL–EDP curriculum, teaching it, and assessing student learning and reflecting after teaching. The authors have documented how they used customer market research conducted during the I-Corps L training to define their minimum viable product (MVP) to duplicate the successful CEEMS methodology through a condensed (≤16 week) self-paced, completely online training program with virtual coaching support. The authors also describe the process they used to move forward very quickly from an MVP to a more complete product offering, its branding, the process of trademarking it, and finally licensing it to an established non-profit organization (NPO) for future marketing. Details of the whole experience are presented with the hope that it will serve as a useful guide for other venture creators.
Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting
Authors: Fan Guo-Feng , Guo Yan-Hui , Zheng Jia-Mei , Hong Wei-Chiang
Published: March 2019

In this paper, the historical power load data from the National Electricity Market (Australia) is used to analyze the characteristics and regulations of electricity (the average value of every eight hours). Then, considering the inverse of Euclidean distance as the weight, this paper proposes a novel short-term load forecasting model based on the weighted k-nearest neighbor algorithm to receive higher satisfied accuracy. In addition, the forecasting errors are compared with the back-propagation neural network model and the autoregressive moving average model. The comparison results demonstrate that the proposed forecasting model could reflect variation trend and has good fitting ability in short-term load forecasting.
Neural Network-Based Model Reference Adaptive System for Torque Ripple Reduction in Sensorless Poly Phase Induction Motor Drive
Authors: Usha S. , Subramani C. , Padmanaban Sanjeevikumar
Published: March 2019

This paper proposes the modified, extended Kalman filter, neural network-based model reference adaptive system and the modified observer technique to estimate the speed of a five-phase induction motor for sensorless drive. The proposed method is generated to achieve reduced speed deviation and reduced torque ripple efficiently. In inclusion, the result of speed performance and torque ripple under parameter variations were analysed and compared with the conventional direct synthesis method. The speed estimation of a five-phase motor in the four methods is analysed using MATLAB Simulink platform, and the optimum method is recognized using time domain analysis. It is observed that speed error is minimized by 60% and torque ripple is reduced by 75% in the proposed method. The hardware setup is carried out for the optimized method identified.
A Modified Self-Synchronized Synchronverter in Unbalanced Power Grids with Balanced Currents and Restrained Power Ripples
Authors: Wang Xiaohe , Chen Liang , Sun Dan , Zhang Li , Nian Heng
Published: March 2019

This paper proposes a modified self-synchronized synchronverter for unbalanced power grids. Small signal analysis of the conventional synchronverter shows that its stability margin around 50 Hz is very limited. Thus, power ripples will be caused at the frequency of 50 Hz. Filter- based current feeding loops are adopted in the conventional synchronverter in order to enhance its stability and eliminate power ripples. In addition, the characteristics of the conventional synchronverter in unbalanced power grids are analyzed, and an improved strategy using a resonant controller is proposed to restrain the current harmonics and power ripples. The parameter design is also studied for the proposed synchronverter. Experimental studies prove that the proposed strategy can achieve precise self-synchronization when the grid voltage is unbalanced, and the power-control performance is also improved significantly.
Optimal Sizing of Storage Elements for a Vehicle Based on Fuel Cells, Supercapacitors, and Batteries
Authors: Sampietro José Luis , Puig Vicenç , Costa-Castelló Ramon
Published: March 2019

To achieve a vehicle-efficient energy management system, an architecture composed of a PEM fuel cell as the main energy source and a hybrid storage system based on battery banks and supercapacitors is proposed. This paper introduces a methodology for the optimal component sizing aiming at minimizing the total cost, achieving a cheaper system that can achieve the requirements of the speed profiles. The chosen vehicle is an urban transport bus, which must meet the Buenos Aires Driving Cycle, and the Manhattan Driving Cycle. The combination of batteries and supercapacitors allows a better response to the vehicle’s power demand, since it combines the high energy density of the batteries with the high power density of the supercapacitors, allowing the best absorption of energy coming from braking. In this way, we address the rapid changes in power without reducing the global efficiency of the system. Optimum use of storage systems and fuel cell is analyzed through dynamic programming.
Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods
Authors: Karabiber Orhan Altuğ , Xydis George
Published: March 2019

In this paper day-ahead electricity price forecasting for the Denmark-West region is realized with a 24 h forecasting range. The forecasting is done for 212 days from the beginning of 2017 and past data from 2016 is used. For forecasting, Autoregressive Integrated Moving Average (ARIMA), Trigonometric Seasonal Box-Cox Transformation with ARMA residuals Trend and Seasonal Components (TBATS) and Artificial Neural Networks (ANN) methods are used and seasonal naïve forecast is utilized as a benchmark. Mean absolute error (MAE) and root mean squared error (RMSE) are used as accuracy criterions. ARIMA and ANN are utilized with external variables and variable analysis is realized in order to improve forecasting results. As a result of variable analysis, it was observed that excluding temperature from external variables helped improve forecasting results. In terms of mean error ARIMA yielded the best results while ANN had the lowest minimum error and standard deviation. TBATS performed better than ANN in terms of mean error. To further improve forecasting accuracy, the three forecasts were combined using simple averaging and ANN methods and they were both found to be beneficial, with simple averaging having better accuracy. Overall, this paper demonstrates a solid forecasting methodology, while showing actual forecasting results and improvements for different forecasting methods.
A Novel Energy Optimization Approach for Electrical Vehicles in a Smart City
Authors: Aymen Flah , Mahmoudi Chokri
Published: March 2019

Electric Vehicles (EVs) have emerged rapidly across the globe as a powerful eco-friendly initiative that if integrated well with an urban environment could be iconic for the host city’s commitment to sustainable mobility and be a key ingredient of the smart city concept. This paper examines ways that will help us to develop a better understanding of how EVs can achieve energy use optimization and be connected with a smart city. As a whole, the present study is based on an original idea that would be useful in informing policy-makers, automotive manufacturers and transport operators of how to improve and embrace better EV technologies in the context of smart cities. The proposed approach is based on vehicles’ and buildings’ communication to share some special information related to the vehicles’ status and to the road conditions. EVs can share their own information related to their energy consumption experience on a specific path. This information can be gathered in a gigantic database and used for managing the power inside these vehicles. In this field, this paper exposes a new approach to power management inside an electric vehicle based on two-way communication between vehicles and buildings. The principle of this method is established in two sections; the first one is related to vehicles’ classification and the second one is attached to the buildings’ recommendations, according to the car position. The classification problem is resolved using the support vector classification method. The recommendation phase is resolved using the artificial intelligence principle and a neural network was employed to give the best decision. The optimal decision will be calculated inside the building, according to its position and using the old vehicle’s data, and transferred to the coming vehicle, for optimizing its energy consumption method in the corresponding building zone. Different possibilities and situations in this approach were discussed. The proposed power management methodology was tested and validated using Simulink/Matlab tool. Results related to the battery state of charge and to the consumed energy were compared at the end of this work, to show the efficiency of this approach.
Design of Robust Total Site Heat Recovery Loops via Monte Carlo Simulation
Authors: Schlosser Florian , Peesel Ron-Hendrik , Meschede Henning , Philipp Matthias , Walmsley Timothy G. , Walmsley Michael R. W. , Atkins Martin J.
Published: March 2019

For increased total site heat integration, the optimal sizing and robust operation of a heat recovery loop (HRL) are prerequisites for economic efficiency. However, sizing based on one representative time series, not considering the variability of process streams due to their discontinuous operation, often leads to oversizing. The sensitive evaluation of the performance of an HRL by Monte Carlo (MC) simulation requires sufficient historical data and performance models. Stochastic time series are generated by distribution functions of measured data. With these inputs, one can then model and reliably assess the benefits of installing a new HRL. A key element of the HRL is a stratified heat storage tank. Validation tests of a stratified tank (ST) showed sufficient accuracy with acceptable simulation time for the variable layer height (VLH) multi-node (MN) modelling approach. The results of the MC simulation of the HRL system show only minor yield losses in terms of heat recovery rate (HRR) for smaller tanks. In this way, costs due to oversizing equipment can be reduced by better understanding the energy-capital trade-off.