bluuebunny/crossref_metadata_2025 huggingface dataset:
Synthesis of DHA/EPA Ethyl Esters via Lipase-Catalyzed Acidolysis Using Novozym® 435: A Kinetic Study
Authors: Kuo Chia-Hung , Huang Chun-Yung , Lee Chien-Liang , Kuo Wen-Cheng , Hsieh Shu-Ling , Shieh Chwen-Jen
Published: May 2020

DHA/EPA ethyl ester is mainly used in the treatment of arteriosclerosis and hyperlipidemia. In this study, DHA+EPA ethyl ester was synthesized via lipase-catalyzed acidolysis of ethyl acetate (EA) with DHA+EPA concentrate in n-hexane using Novozym® 435. The DHA+EPA concentrate (in free fatty acid form), contained 54.4% DHA and 16.8% EPA, was used as raw material. A central composite design combined with response surface methodology (RSM) was used to evaluate the relationship between substrate concentrations and initial rate of DHA+EPA ethyl ester production. The results indicated that the reaction followed the ordered mechanism and as such, the ordered mechanism model was used to estimate the maximum reaction rate (Vmax) and kinetic constants. The ordered mechanism model was also combined with the batch reaction equation to simulate and predict the conversion of DHA+EPA ethyl ester in lipase-catalyzed acidolysis. The integral equation showed a good predictive relationship between the simulated and experimental results. 88–94% conversion yields were obtained from 100–400 mM DHA+EPA concentrate at a constant enzyme activity of 200 U, substrate ratio of 1:1 (DHA+EPA: EA), and reaction time of 300 min.
Identification of Main Active Sites and the Role of NO2 on NOx Reduction with CH4 over In/BEA Catalyst: A Computational Study
Authors: Gao Erhao , Pan Hua , Wang Li , Shi Yao , Chen Jun
Published: May 2020

The main active sites and the catalytic process in selective catalytic reduction of NOx by CH4 (CH4-SCR) on In/BEA catalyst were investigated by density functional theory (DFT) using a periodic model. The [InO]+ and [InOH]2+ moieties were constructed in the channel of periodic BEA zeolite representing the Lewis and Brønsted acid sites. The electronic structures [InO]+ and [InOH]2+ were analyzed, and it was found that the [InO]+ group were the main active sites for CH4 activation and NO/NO2 adsorption in the CH4-SCR process. CH4 molecules could be activated on the O site of the [InO]+ group in In/BEA, which was resulted from the strong interactions between the C-p orbital of the CH4 molecule and the O-p orbital of the [InO]+ group. CH4 activation was the initial step in CH4-SCR on In/BEA catalyst. NO2 molecules were essential in the SCR process, and they could be produced by NO reacting with gaseous O2 or the O atom of the [InO]+ group. The presence of NO2 could facilitate the key intermediate nitromethane (CH3NO2) formation and lower the reaction barrier in the SCR process.
Solar Degradation of Sulfamethazine Using rGO/Bi Composite Photocatalysts
Authors: Fernández-Perales María , Rozalen Marisa , Sánchez-Polo Manuel , Rivera-Utrilla José , López-Ramón María Victoria , Álvarez Miguel A.
Published: May 2020

Heterogeneous photocatalysts for water decontamination were obtained by the optimized synthesis of bismuth-functionalized reduced graphene oxide (rGO/Bi) using the Hummer method and microwave treatment. Sulfamethazine (SMZ) was used as model pollutant to evaluate the photocatalytic efficacy. Photocatalysts were characterized by VP-SEM, HRTEM, XDR, XPS, RAMAN, and FTIR analyses, which confirmed the effective reduction of GO to rGO and the presence of bismuth as a crystalline phase of Bi2O3 polydispersed on the surface. Their performance was influenced by the rGO/Bi ratio, microwave temperature, and treatment time. The as-obtained 5%rGO/Bi composite had the highest photocatalytic activity for SMZ degradation under visible light irradiation (λ > 400 nm), achieving 100% degradation after only 2 h of treatment. The degradation yield decreased with higher percentages of rGO. Accordingly, the rGO/Bi catalysts efficiently removed SMZ, showing a high photocatalytic activity, and remained unchanged after three treatment cycles; furthermore, cytotoxicity tests demonstrated the nontoxicity of the aqueous medium after SMZ degradation. These findings support the potential value of these novel composites as photocatalysts to selectively remove pollutants in water treatment plants.
Trimesoyl Chloride-Melamine Copolymer-TiO2 Nanocomposites as High-Performance Visible-Light Photocatalysts for Volatile Organic Compound Degradation
Authors: Zhang Luqian , Wang Chen , Sun Jing , An Zhengkai
Published: May 2020

Benzene is a typical volatile organic compound (VOC) and is found widely in industrial waste gases. In this study, trimesoyl chloride-melamine copolymer (TMP)-TiO2 nanocomposites with excellent photocatalytic efficiency in visible-light degradation of gas-phase benzene were synthesized via an in situ hydrothermal synthesis. The optimal conditions for TMP-TiO2 nanocomposite synthesis were determined by orthogonal experiments. The structural, physiochemical, and optoelectronic properties of the samples were studied by various analytical techniques. Ultraviolet-visible diffuse reflectance spectroscopy and surface photovoltage spectra showed that the positions of the light-absorbance edges of the TMP-TiO2 nanocomposites were sharply red-shifted to the visible region relative to those of unmodified TiO2. The most efficient TMP-TiO2 nanocomposite was used for photocatalytic oxidative degradation of gas-phase benzene (initial concentration 230 mg m−3) under visible-light irradiation (380–800 nm); the degradation rate was 100% within 180 min. Under the same reaction conditions, the degradation rates of unmodified TiO2 (hydrothermally synthesized TiO2) and commercial material Degussa P25 were 19% and 23.6%, respectively. This is because the Ti–O–N and Ti–O–C bonds in TMP-modified TiO2 reduce the band gap of TMP-TiO2. The amide bonds in the TMP decrease the TiO2 nanoparticle size and thus increased the specific surface area. The conjugated structures in the TMP provide abundant active sites for trapping photogenerated electrons and promote the separation and transfer of photogenerated electrons and holes.
Metabolic Adaptation during nab-Paclitaxel Resistance in Pancreatic Cancer Cell Lines
Authors: Braun Lukas M. , Lagies Simon , Guenzle Jessica , Fichtner-Feigl Stefan , Wittel Uwe A. , Kammerer Bernd
Published: May 2020

Pancreatic ductal adenocarcinoma (PDAC) correlates with high mortality and is about to become one of the major reasons for cancer-related mortality in the next decades. One reason for that high mortality is the limited availability of effective chemotherapy as well as the intrinsic or acquired resistance against it. Here, we report the impact of nab-paclitaxel on the cellular metabolome of PDAC cell lines. After establishment of nab-paclitaxel resistant cell lines, comparison of parental and resistant PDAC cell lines by metabolomics and biochemical assessments revealed altered metabolism, enhanced viability and reduced apoptosis. The results unveiled that acute nab-paclitaxel treatment affected primary metabolism to a minor extent. However, acquisition of resistance led to altered metabolites in both cell lines tested. Specifically, aspartic acid and carbamoyl-aspartic acid were differentially abundant, which might indicate an increased de novo pyrimidine synthesis. This pathway has already shown a similar behavior in other cancerous entities and thus might serve in the future as vulnerable target fighting resistance acquisition occurring in common malignancies.
Direct Inhibition of the Allergic Effector Response by Raw Cow’s Milk—An Extensive In Vitro Assessment
Authors: Abbring Suzanne , Blokhuis Bart R. J. , Miltenburg Julie L. , Olmedo Kiri G. J. Romano , Garssen Johan , Redegeld Frank A. , van Esch Betty C. A. M.
Published: May 2020

The mechanisms underlying the allergy-protective effects of raw cow’s milk are poorly understood. The current focus is mainly on the modulation of T cell responses. In the present study, we investigated whether raw cow’s milk can also directly inhibit mast cells, the key effector cells in IgE-mediated allergic responses. Primary murine bone marrow-derived mast cells (BMMC) and peritoneal mast cells (PMC), were incubated with raw milk, heated raw milk, or shop milk, prior to IgE-mediated activation. The effects on mast cell activation and underlying signaling events were assessed. Raw milk was furthermore fractionated based on molecular size and obtained fractions were tested for their capacity to reduce IgE-mediated mast cell activation. Coincubation of BMMC and PMC with raw milk prior to activation reduced β-hexosaminidase release and IL-6 and IL-13 production, while heated raw milk or shop milk had no effect. The reduced mast cell activation coincided with a reduced intracellular calcium influx. In addition, SYK and ERK phosphorylation levels, both downstream signaling events of the FcεRI, were lower in raw milk-treated BMMC compared to control BMMC, although differences did not reach full significance. Raw milk-treated BMMC furthermore retained membrane-bound IgE expression after allergen stimulation. Raw milk fractionation showed that the heat-sensitive raw milk components responsible for the reduced mast cell activation are likely to have a molecular weight of > 37 kDa. The present study demonstrates that raw cow’s milk can also directly affect mast cell activation. These results extend the current knowledge on mechanisms via which raw cow’s milk prevents allergic diseases, which is crucial for the development of new, microbiologically safe, nutritional strategies to reduce allergic diseases.
Membrane-Associated Heat Shock Proteins in Oncology: From Basic Research to New Theranostic Targets
Authors: Shevtsov Maxim , Balogi Zsolt , Khachatryan William , Gao Huile , Vígh László , Multhoff Gabriele
Published: May 2020

Heat shock proteins (HSPs) constitute a large family of conserved proteins acting as molecular chaperones that play a key role in intracellular protein homeostasis, regulation of apoptosis, and protection from various stress factors (including hypoxia, thermal stress, oxidative stress). Apart from their intracellular localization, members of different HSP families such as small HSPs, HSP40, HSP60, HSP70 and HSP90 have been found to be localized on the plasma membrane of malignantly transformed cells. In the current article, the role of membrane-associated molecular chaperones in normal and tumor cells is comprehensively reviewed with implications of these proteins as plausible targets for cancer therapy and diagnostics.
A Multiparametric Assay Platform for Simultaneous In Vivo Assessment of Pronephric Morphology, Renal Function and Heart Rate in Larval Zebrafish
Authors: Steenbergen Petrus J. , Heigwer Jana , Pandey Gunjan , Tönshoff Burkhard , Gehrig Jochen , Westhoff Jens H.
Published: May 2020

Automated high-throughput workflows allow for chemical toxicity testing and drug discovery in zebrafish disease models. Due to its conserved structural and functional properties, the zebrafish pronephros offers a unique model to study renal development and disease at larger scale. Ideally, scoring of pronephric phenotypes includes morphological and functional assessments within the same larva. However, to efficiently upscale such assays, refinement of existing methods is required. Here, we describe the development of a multiparametric in vivo screening pipeline for parallel assessment of pronephric morphology, kidney function and heart rate within the same larva on a single imaging platform. To this end, we developed a novel 3D-printed orientation tool enabling multiple consistent orientations of larvae in agarose-filled microplates. Dorsal pronephros imaging was followed by assessing renal clearance and heart rates upon fluorescein isothiocyanate (FITC)-inulin microinjection using automated time-lapse imaging of laterally positioned larvae. The pipeline was benchmarked using a set of drugs known to induce developmental nephrotoxicity in humans and zebrafish. Drug-induced reductions in renal clearance and heart rate alterations were detected even in larvae exhibiting minor pronephric phenotypes. In conclusion, the developed workflow enables rapid and semi-automated in vivo assessment of multiple morphological and functional parameters.
Land Use Change and Its Impacts on Land Surface Temperature in Galle City, Sri Lanka
Authors: Dissanayake DMSLB
Published: May 2020

This study investigated the spatiotemporal changes of land use land cover (LULC) and its impact on land surface temperature (LST) in the Galle Municipal Council area (GMCA), Sri Lanka. The same was achieved by employing the multi-temporal satellite data and geo-spatial techniques between 1996 and 2019. The post-classification change detection technique was employed to determine the temporal changes of LULC, and its results were utilized to assess the LST variation over the LULC changes. The results revealed that the area had undergone a drastic LULC transformation. It experienced 38% increase in the built-up area, while vegetation and non-built-up area declined by 26% and 12%, respectively. Rapid urban growth has had a significant effect on the LST, and the built-up area had the highest mean LST of 22.7 °C, 23.2 °C, and 26.3 °C for 1996, 2009, and 2019, correspondingly. The mean LST of the GMCA was 19.2 °C in 1996, 20.1 °C in 2009, and 22.4 °C in 2019. The land area with a temperature above 24 °C increased by 9% and 12% in 2009 and 2019, respectively. The highest LST variation (5.5 °C) was observed from newly added built-up area, which was also transferred from vegetation land. Meanwhile, the lowest mean LST difference was observed from newly added vegetation land. The results show that the mean annual LST increased by 3.2 °C in the last 22 years in GMCA. This study identified significant challenges for urban planners and respective administrative bodies to mitigate and control the negative effect of LST for the long livability of Galle City.
Erratum: Thickness and Structure of Adsorbed Water Layer and Effects on Adhesion and Friction at Nanoasperity Contact. Colloids Interfaces 2019, 3, 55.
Authors:   Colloids and Interfaces Editorial Office
Published: May 2020

The Editorial Office of Colloids and Interfaces would like to make the following correction to the paper by Xiao, C [...]
Role of Potassium Substitution in the Magnetic Properties and Magnetocaloric Effect in La0.8−xKxBa0.05Sr0.15MnO3 (0 ≤ x ≤ 0.20)
Authors: Razaq Dhawud Sabilur , Kurniawan Budhy , Munazat Dicky Rezky , Watanabe Kazumitsu , Tanaka Hidekazu
Published: May 2020

The magnetic and magnetocaloric effects of potassium-substituted La0.8−xKxBa0.05Sr0.15MnO3 (0 ≤ x ≤ 0.20) manganite were explored. The samples in polycrystalline form were synthesized by the sol–gel method, with a final sintering temperature of 1100 °C. Powder X-ray diffraction (XRD) patterns refined by Rietveld refinement show that all samples crystallized in rhombohedral structure with R-3c space group. The unit cell volume of the samples decreases with increasing potassium concentration. In addition, small changes in average bond length and bond angle are also observed in the samples. Scanning electron microscope (SEM) images reveal that the largest average grain size was observed for x = 0.10. Field-cooled (FC) magnetization measurements show that the Curie temperature ( T C ) of the samples increases from 320 K for x = 0 to 360 K for x = 0.2. The largest magnetocaloric (MCE) effect, which is represented by maximum magnetic entropy change (− Δ S M ,   M A X ), reaches its greatest value for the x = 0.10 sample. The monotonous increase in T C suggests that TC is mainly governed by the ferromagnetic coupling between Mn ions induced by the changes on average bond length and bond angle. The obtained − Δ S M ,   M A X value suggests that MCE property is more sensitive to Zener theory of double exchange, which is strongly related to the Mn3+/Mn4+ ratio of the samples.
(Ti,Cr)C-Based Cermets with Varied Nicr Binder Content via Elemental SHS for Perspective Cutting Tools
Authors: Vorotilo Stepan , Kiryukhantsev-Korneev Philipp V. , Seplyarskii Boris S. , Kochetkov Roman A. , Abzalov Nail I. , Kovalev Ivan D. , Lisina Tatyana G. , Zaitsev Alexander A.
Published: May 2020

The effects of granulation of reactive mixtures Ti-Cr-C and Ti-Cr-C-Ni on the combustion temperature and velocity, as well as phase composition and mechanical properties (crushing ability) of combustion products, were studied. Granulation was associated with a 1.5-fold increase in combustion velocity, caused by a nearly 10-fold increase in gas permeability. Secondary reactions between TiC, Cr7C3, and molten Ni led to the formation of (Ti,Cr)C FCC solid solution and Ni2.88Cr1.12 intermetallics. After the combustion of Ti-Cr-C-Ni mixtures, prolonged fluorescence was registered, suggesting the exothermic nature of secondary phase formation reactions. The introduction of the nickel binder decreased the content of Cr in the solid solution (Ti,Cr)C owing to the formation of the Ni2.88Cr1.12 phase. To prevent the Cr depletion from the carbide solid solution, Ni-20%Cr binder was added to the granulated 80%(Ti + C)/20%(3Cr + 2C) mixture. Combustion of granulated mixture yielded brittle porous sinter cake, which was easy to crush and mill, whereas the combustion products from the powdered mixtures were more ductile and harder to crush.
Data from Experimental Analysis of the Performance and Load Cycling of a Polymer Electrolyte Membrane Fuel Cell
Authors: Ramírez-Cruzado Andrea , Ramírez-Peña Blanca , Vélez-García Rosario , Iranzo Alfredo , Guerra José
Published: May 2020

Fuel cells are electrochemical devices that convert the chemical energy stored in fuels (hydrogen for polymer electrolyte membrane (PEM) fuel cells) directly into electricity with high efficiency. Fuel cells are already commercially used in different applications, and significant research efforts are being carried out to further improve their performance and durability and to reduce costs. Experimental testing of fuel cells is a fundamental research activity used to assess all the issues indicated above. The current work presents original data corresponding to the experimental analysis of the performance of a 50 cm2 PEM fuel cell, including experimental results from a load cycling dedicated test. The experimental data were acquired using a dedicated test bench following the harmonized testing protocols defined by the Joint Research Centre (JRC) of the European Commission for automotive applications. With the presented dataset, we aim to provide a transparent collection of experimental data from PEM fuel cell testing that can contribute to enhanced reusability for further research.
Electronic Health Information Systems to Improve Disease Diagnosis and Management at Point-of-Care in Low and Middle Income Countries: A Narrative Review
Authors: Khubone Thokozani , Tlou Boikhutso , Mashamba-Thompson Tivani Phosa
Published: May 2020

The purpose of an electronic health information system (EHIS) is to support health care workers in providing health care services to an individual client and to enable data exchange among service providers. The demand to explore the use of EHIS for diagnosis and management of communicable and non-communicable diseases has increased dramatically due to the volume of patient data and the need to retain patients in care. In addition, the advent of Coronavirus disease 2019 (COVID-19) pandemic in high disease burdened low and middle income countries (LMICs) has increased the need for robust EHIS to enable efficient surveillance of the pandemic. EHIS has potential to enable efficient delivery of disease diagnostics services at point-of-care (POC) and reduce medical errors. This review provides an overview of literature on EHIS’s with a focus on describing the key components of EHIS and presenting evidence on enablers and barriers to implementation of EHISs in LMICs. With guidance from the presented evidence, we proposed EHIS key stakeholders’ roles and responsibilities to ensure efficient utility of EHIS for disease diagnosis and management at POC in LMICs.
Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image
Authors: Ali Aqib , Qadri Salman , Khan Mashwani Wali , Kumam Wiyada , Kumam Poom , Naeem Samreen , Goktas Atila , Jamal Farrukh , Chesneau Christophe , Anam Sania , Sulaiman Muhammad
Published: May 2020

The object of this study was to demonstrate the ability of machine learning (ML) methods for the segmentation and classification of diabetic retinopathy (DR). Two-dimensional (2D) retinal fundus (RF) images were used. The datasets of DR—that is, the mild, moderate, non-proliferative, proliferative, and normal human eye ones—were acquired from 500 patients at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. Five hundred RF datasets (sized 256 × 256) for each DR stage and a total of 2500 (500 × 5) datasets of the five DR stages were acquired. This research introduces the novel clustering-based automated region growing framework. For texture analysis, four types of features—histogram (H), wavelet (W), co-occurrence matrix (COM) and run-length matrix (RLM)—were extracted, and various ML classifiers were employed, achieving 77.67%, 80%, 89.87%, and 96.33% classification accuracies, respectively. To improve classification accuracy, a fused hybrid-feature dataset was generated by applying the data fusion approach. From each image, 245 pieces of hybrid feature data (H, W, COM, and RLM) were observed, while 13 optimized features were selected after applying four different feature selection techniques, namely Fisher, correlation-based feature selection, mutual information, and probability of error plus average correlation. Five ML classifiers named sequential minimal optimization (SMO), logistic (Lg), multi-layer perceptron (MLP), logistic model tree (LMT), and simple logistic (SLg) were deployed on selected optimized features (using 10-fold cross-validation), and they showed considerably high classification accuracies of 98.53%, 99%, 99.66%, 99.73%, and 99.73%, respectively.
Taylor’s Law in Innovation Processes
Authors: Tria Francesca , Crimaldi Irene , Aletti Giacomo , Servedio Vito D. P.
Published: May 2020

Taylor’s law quantifies the scaling properties of the fluctuations of the number of innovations occurring in open systems. Urn-based modeling schemes have already proven to be effective in modeling this complex behaviour. Here, we present analytical estimations of Taylor’s law exponents in such models, by leveraging on their representation in terms of triangular urn models. We also highlight the correspondence of these models with Poisson–Dirichlet processes and demonstrate how a non-trivial Taylor’s law exponent is a kind of universal feature in systems related to human activities. We base this result on the analysis of four collections of data generated by human activity: (i) written language (from a Gutenberg corpus); (ii) an online music website (Last.fm); (iii) Twitter hashtags; (iv) an online collaborative tagging system (Del.icio.us). While Taylor’s law observed in the last two datasets agrees with the plain model predictions, we need to introduce a generalization to fully characterize the behaviour of the first two datasets, where temporal correlations are possibly more relevant. We suggest that Taylor’s law is a fundamental complement to Zipf’s and Heaps’ laws in unveiling the complex dynamical processes underlying the evolution of systems featuring innovation.
Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai
Authors: Ali Haidery Saqib , Ullah Hidayat , Khan Naimat Ullah , Fatima Kanwal , Rizvi Sanam Shahla , Kwon Se Jin
Published: May 2020

In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures and symmetries in user activities as well as density estimations. In the current study, different density estimation techniques are utilized to analyze the check-in frequency of users in more detail from location-based social network dataset acquired from Sina-Weibo, also referred as Weibo, over a specific period in 10 different districts of Shanghai, China. The aim of this study is to analyze the density of users in Shanghai city from geolocation data of Weibo as well as to compare their density through univariate and bivariate density estimation techniques; i.e., point density and kernel density estimation (KDE) respectively. The main findings of the study include the following: (i) characteristics of users’ spatial behavior, the center of activity based on their check-ins, (ii) the feasibility of check-in data to explain the relationship between users and social media, and (iii) the presentation of evident results for regulatory or managing authorities for urban planning. The current study shows that the point density and kernel density estimation. KDE methods provide useful insights for modeling spatial patterns using geo-spatial dataset. Finally, we can conclude that, by utilizing the KDE technique, we can examine the check-in behavior in more detail for an individual as well as broader patterns in the population as a whole for the development of smart city. The purpose of this article is to figure out the denser places so that the authorities can divide the mobility of people from the same routes or at least they can control the situation from any further inconvenience.
Exploration of FPGA-Based Hardware Designs for QR Decomposition for Solving Stiff ODE Numerical Methods Using the HARP Hybrid Architecture
Authors: Alberto Oliveira de Souza Junior Carlos , Bispo João , Cardoso João M. P. , Diniz Pedro C. , Marques Eduardo
Published: May 2020

In this article, we focus on the acceleration of a chemical reaction simulation that relies on a system of stiff ordinary differential equation (ODEs) targeting heterogeneous computing systems with CPUs and field-programmable gate arrays (FPGAs). Specifically, we target an essential kernel of the coupled chemistry aerosol-tracer transport model to the Brazilian developments on the regional atmospheric modeling system (CCATT-BRAMS). We focus on a linear solve step using the QR factorization based on the modified Gram-Schmidt method as the basis of the ODE solver in this application. We target Intel hardware accelerator research program (HARP) architecture with the OpenCL programming environment for these early experiments. Our design exploration reveals a hardware design that is up to 4 times faster than the original iterative Jacobi method used in this solver. Still, even with hardware support, the overall performance of our QR-based hardware is lower than its original software version.
Impact of the Partitioning Method on Multidimensional Adaptive-Chemistry Simulations
Authors: D’Alessio Giuseppe , Cuoci Alberto , Aversano Gianmarco , Bracconi Mauro , Stagni Alessandro , Parente Alessandro
Published: May 2020

The large number of species included in the detailed kinetic mechanisms represents a serious challenge for numerical simulations of reactive flows, as it can lead to large CPU times, even for relatively simple systems. One possible solution to mitigate the computational cost of detailed numerical simulations, without sacrificing their accuracy, is to adopt a Sample-Partitioning Adaptive Reduced Chemistry (SPARC) approach. The first step of the aforementioned approach is the thermochemical space partitioning for the generation of locally reduced mechanisms, but this task is often challenging because of the high-dimensionality, as well as the high non-linearity associated to reacting systems. Moreover, the importance of this step in the overall approach is not negligible, as it has effects on the mechanisms’ level of chemical reduction and, consequently, on the accuracy and the computational speed-up of the adaptive simulation. In this work, two different clustering algorithms for the partitioning of the thermochemical space were evaluated by means of an adaptive CFD simulation of a 2D unsteady laminar flame of a nitrogen-diluted methane stream in air. The first one is a hybrid approach based on the coupling between the Self-Organizing Maps with K-Means (SKM), and the second one is the Local Principal Component Analysis (LPCA). Comparable results in terms of mechanism reduction (i.e., the mean number of species in the reduced mechanisms) and simulation accuracy were obtained for both the tested methods, but LPCA showed superior performances in terms of reduced mechanisms uniformity and speed-up of the adaptive simulation. Moreover, the local algorithm showed a lower sensitivity to the training dataset size in terms of the required CPU-time for convergence, thus also being optimal, with respect to SKM, for massive dataset clustering tasks.
Bioelectrosynthetic Conversion of CO2 Using Different Redox Mediators: Electron and Carbon Balances in a Bioelectrochemical System
Authors: Li Shuwei , Song Young Eun , Baek Jiyun , Im Hyeon Sung , Sakuntala Mutyala , Kim Minsoo , Park Chulhwan , Min Booki , Kim Jung Rae
Published: May 2020

Microbial electrosynthesis (MES) systems can convert CO2 to acetate and other value-added chemicals using electricity as the reducing power. Several electrochemically active redox mediators can enhance interfacial electron transport between bacteria and the electrode in MES systems. In this study, different redox mediators, such as neutral red (NR), 2-hydroxy-1,4-naphthoquinone (HNQ), and hydroquinone (HQ), were compared to facilitate an MES-based CO2 reduction reaction on the cathode. The mediators, NR and HNQ, improved acetate production from CO2 (165 mM and 161 mM, respectively) compared to the control (without a mediator = 149 mM), whereas HQ showed lower acetate production (115 mM). On the other hand, when mediators were used, the electron and carbon recovery efficiency decreased because of the presence of bioelectrochemical reduction pathways other than acetate production. Cyclic voltammetry of an MES with such mediators revealed CO2 reduction to acetate on the cathode surface. These results suggest that the addition of mediators to MES can improve CO2 conversion to acetate with further optimization in an operating strategy of electrosynthesis processes.
Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits
Authors: von Platten Jenny , Sandels Claes , Jörgensson Kajsa , Karlsson Viktor , Mangold Mikael , Mjörnell Kristina
Published: May 2020

Building databases are important assets when estimating and planning for national energy savings from energy retrofitting. However, databases often lack information on building characteristics needed to determine the feasibility of specific energy conservation measures. In this paper, machine learning methods are used to enrich the Swedish database of Energy Performance Certificates with building characteristics relevant for a chosen set of energy retrofitting packages. The study is limited to the Swedish multifamily building stock constructed between 1945 and 1975, as these buildings are facing refurbishment needs that advantageously can be combined with energy retrofitting. In total, 514 ocular observations were conducted in Google Street View of two building characteristics that were needed to determine the feasibility of the chosen energy retrofitting packages: (i) building type and (ii) suitability for additional façade insulation. Results showed that these building characteristics could be predicted with an accuracy of 88.9% and 72.5% respectively. It could be concluded that machine learning methods show promising potential to enrich building databases with building characteristics relevant for energy retrofitting, which in turn can improve estimations of national energy savings potential.
Operational Profile Based Optimization Method for Maritime Diesel Engines
Authors: Nguyen Khac Hoang , Zenger Kai , Storm Xiaoguo , Hyvönen Jari
Published: May 2020

This paper presents an approach to a new engine calibration method that takes the engine’s operational profile into account. This method has two main steps: modeling and optimization. The Design of Experiments method is first conducted to model the engine’s responses such as Brake Specific Fuel Consumption (BSFC) and Nitrogen Oxide ( N O x ) emissions as the functions of fuel injection timing, common rail pressure and charged air pressure. These response surface models are then used to minimize the fuel consumption during a year, according to a typical load profile of a ferry, and to fulfill the N O x limits set by International Maritime Organization (IMO) regulations, Tier II, test cycle E2. The Sequential Quadratic Programming algorithm is used to solve this minimization problem. The results showed that the fuel consumption can be effectively reduced with the flexibility to trade it off with the N O x emissions while still fulfilling the IMO regulations. In general, this method can decrease the manual calibration effort and improve the engine’s performance with a tailored setting for individual operational profiles.
ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling
Authors: Bokde Neeraj Dhanraj , Yaseen Zaher Mundher , Andersen Gorm Bruun
Published: May 2020

This paper introduces an R package ForecastTB that can be used to compare the accuracy of different forecasting methods as related to the characteristics of a time series dataset. The ForecastTB is a plug-and-play structured module, and several forecasting methods can be included with simple instructions. The proposed test-bench is not limited to the default forecasting and error metric functions, and users are able to append, remove, or choose the desired methods as per requirements. Besides, several plotting functions and statistical performance metrics are provided to visualize the comparative performance and accuracy of different forecasting methods. Furthermore, this paper presents real application examples with natural time series datasets (i.e., wind speed and solar radiation) to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset. Modeling results indicated the applicability and robustness of the proposed R package ForecastTB for time series forecasting.
Voltage Fed Control of Distributed Power Generation Inverters with Inherent Service to Grid Stability
Authors: Klaes Norbert , Goldschmidt Nico , Fortmann Jens
Published: May 2020

In many countries the percentage of power electronic interfaced power sources (PEIPS), especially renewable energies like wind power and photovoltaic (PV), has increased significantly during the last decade.Retaining system stability with a declining number of conventional synchronous generators is a new challenge that starts to be addressed by Grid Operators. The existing control schemes used in distributed energy generation inverters generally do not provide significant services to grid stability. This paper focuses on a control scheme that is in many ways similar to the control of conventional power plants, but avoids a higher rating of the inverters which is often required by control approaches emulating the response of a synchronous generator. The control parameters of the proposed scheme are derived analytically and their main dependencies from major system parameters are discussed. An add-on to achieve fault ride through capability for both balanced and unbalanced faults for voltage controlled inverters is presented. Model validation results in a laboratory setup show very good correlation and have proven practicability of the theory as well as fault ride through and islanding capability.
Modeling the Total Cost of Ownership of an Electric Car Using a Residential Photovoltaic Generator and a Battery Storage Unit—An Italian Case Study
Authors: Scorrano Mariangela , Danielis Romeo , Pastore Stefano , Lughi Vanni , Massi Pavan Alessandro
Published: May 2020

As electric vehicles gain acceptance, an increasing number of households consider the possibility of buying the bundle including an electric car, a photovoltaic system, and a battery storage unit. Apart from the attractive environmental benefits, a relevant uncertainty concerns the economic convenience of such a choice. Since many variables play a role, we set up a total cost of ownership model to evaluate whether, and under which conditions, the bundle is cost-competitive relative to buying an electric car only (and charging it from the electrical grid) or a conventional combustion engine car. By combining, for the first time, such an economic model with an energy model and a driving profile model, we find that the degree of electricity self-production used to charge the electric car might be very high, varying from 90% to 62%, depending on the annual distance traveled. The cost of such electricity varies widely and can be lower than the grid electricity price when fiscal incentives are available and for long annual distances traveled. A smart charging practice based on both economic factors and weather forecast can greatly enhance self-sufficiency, i.e., independence from the electrical grid. We estimate that, given the current Italian financial incentives, 10,000 km/year are needed to make the electric car cost-competitive with respect to an equivalent petrol-fueled one. Such threshold increases to more than 25,000 km/year if financial incentives are removed.
A Stepped-Segmentation Method for the High-Speed Theoretical Elevator Car Air Pressure Curve Adjustment
Authors: Qiu Lemiao , Zhou Huifang , Wang Zili , Lou Wenqian , Zhang Shuyou , Zhang Lichun
Published: May 2020

As the demand for high-speed elevators grows, the requirements of elevator performance have also increased. Most of these are single variables that do not consider the comprehensive impact of multiple variables on performance, especially comfort. To overcome this problem, a stepped segmentation method for a theoretical high-speed elevator car air pressure curve (THEC-APC) adjustment is proposed that could actively help to select a suitable theoretical elevator car air pressure adjustment curve. By utilizing the proposed Particle Swarm Optimization (PSO) algorithm, the theoretical elevator car air pressure curve is optimized for multiple performances (including passenger comfort, energy consumption, and aerodynamic characteristics). In addition, the THEC-APC is smoothed by the Bezier curve for the variable destination floor. To verify the proposed method, the KLK2 (Canny Elevator Co., Ltd., 2015, Suzhou) high-speed elevator design process is applied. The numerical experiment results show that the proposed method can improve the accuracy and search efficiency of the optimal solution. Meanwhile, the proposed method helps to promote further air pressure compensation design for high-speed elevators.
Biodiesel Production by Lipase-Catalyzed in Situ Transesterification of Rapeseed Oil Containing a High Free Fatty Acid Content with Ethanol in Diesel Fuel Media
Authors: Santaraite Migle , Sendzikiene Egle , Makareviciene Violeta , Kazancev Kiril
Published: May 2020

In this study, low-quality rapeseed was used as a raw material for biodiesel fuel production. The application of such seeds with an enzyme catalyst is a green approach to producing renewable biodiesel fuel. During the in situ transesterification process, mineral diesel was selected as an extraction solvent for the simultaneous extraction and transesterification of rapeseed oil (RO). This allowed, at the end of the process, for the production of a mixture of mineral diesel and biodiesel fuel. Energy is saved using this process, as the need to extract the oil separately is eliminated and extraction and transesterification take place together in the in situ process. In this study, 11 different lipases were analyzed from which to select the most effective biocatalyst according to the chosen experimental conditions. The most suitable lipase for in situ transesterification was Lipozyme TL IM (Thermomyces lanuginosus). The impact of the temperature and duration of the reaction was investigated along with the concentration of the lipase. A ethanol-to-oil molar ratio of 5:1 was chosen. The optimal reaction conditions were as follows: a reaction duration of 7 h, a reaction temperature of 30 °C and a lipase concentration of 5% (based on oil weight). Under these conditions, 99.92% of oil was extracted from the rapeseed. The degree of oil transesterification acquired was 99.89%. A mineral diesel and rapeseed oil ethyl ester blend of 9:1 (w/w) was produced.
Probing Synergies between Lignin-Rich and Cellulose Compounds for Gasification
Authors: Taylor Martin J. , Michopoulos Apostolos K. , Zabaniotou Anastasia A. , Skoulou Vasiliki
Published: May 2020

The fixed-bed gasification of lignin-rich and -deficient mixtures was carried out to probe the synergistic effects between two model compounds, Lignin Pink (LP) rich in Na and Cellulose Microcrystalline (CM). Reaction conditions utilized the most commonly used air ratios in current wood gasifiers at 750 and 850 °C. It was found that by increasing the lignin content in the mixture, there was a selectivity change from solid to gas products, contrary to a similar study previously carried out for pyrolysis. This change in product mix was promoted by the catalytic effect of Na edge recession deposits on the surface of the char. As a result, the water gas shift reaction was enhanced at 850 °C for the LP48CM52 mixture across all air ratios. This was evidenced by a strong correlation between the produced H2 and COx. Meanwhile, by lowering the lignin content in the mixtures, the reactivity of cellulose microcrystalline was found to generate more char at higher temperatures, similar to lignin mixtures when undergoing pyrolysis.
Analyzing the Criteria of Efficient Carbon Capture and Separation Technologies for Sustainable Clean Energy Usage
Authors: Liu Haibing , Yüksel Serhat , Dinçer Hasan
Published: May 2020

This study focuses on carbon capture and distribution technology, which is a new approach to the solution of this problem. In order to use this technology more effectively, six significant criteria are defined by considering the essentials of the international Loss Control Institute and the supported literature. Moreover, the analytic network process (ANP) is applied for measuring the relative importance of each factor. The findings demonstrate that organizational factor has the greatest importance, whereas market factor is the weakest element. In addition, the education of the personnel is the most important criterion for low-cost industrial carbon dioxide capture and separation technologies. In this context, it is seen that companies need competent personnel in order to reduce the costs of these products. There are two types of strategies that companies can develop to achieve this goal. Firstly, it would be appropriate for companies to provide their staff with the necessary training on carbon capture and storage technologies. The second most important strategy is for the new personnel to be employed in the company. When choosing new employees, it is necessary to measure whether they have sufficient knowledge about this technology. These strategies will contribute to lower costs when developing products for carbon capture and storage technology.
Syngas Production, Clean-Up and Wastewater Management in a Demo-Scale Fixed-Bed Updraft Biomass Gasification Unit
Authors: Calì Gabriele , Deiana Paolo , Bassano Claudia , Meloni Simone , Maggio Enrico , Mascia Michele , Pettinau Alberto
Published: May 2020

This paper presents the experimental development at demonstration scale of an integrated gasification system fed with wood chips. The unit is based on a fixed-bed, updraft and air-blown gasifier—with a nominal capacity of 5 MWth—equipped with a wet scrubber for syngas clean-up and an integrated chemical and physical wastewater management system. Gasification performance, syngas composition and temperature profile are presented for the optimal operating conditions and with reference to two kinds of biomass used as primary fuels, i.e., stone pine and eucalyptus from local forests (combined heat and power generation from this kind of fuel represents a good opportunity to exploit distributed generation systems that can be part of a new energy paradigm in the framework of the circular economy). The gasification unit is characterised by a high efficiency (about 79–80%) and an operation stability during each test. Particular attention has been paid to the optimisation of an integrated double stage wastewater management system—which includes an oil skimmer and an activated carbon adsorption filter—designed to minimise both liquid residues and water make-up. The possibility to recycle part of the separated oil and used activated carbon to the gasifier has been also evaluated.
Carbon Dioxide Emissions and Their Driving Forces of Land Use Change Based on Economic Contributive Coefficient (ECC) and Ecological Support Coefficient (ESC) in the Lower Yellow River Region (1995–2018)
Authors: Rong Tianqi , Zhang Pengyan , Jing Wenlong , Zhang Yu , Li Yanyan , Yang Dan , Yang Jiaxin , Chang Hao , Ge Linna
Published: May 2020

Land use change is the second largest source of greenhouse gas emissions after fossil combustion, which can hurt ecological environment severely. Intensive study on land use carbon emissions is of great significance to alleviate environmental pressure, formulate carbon emission reduction policy, and protect ecological development. The lower Yellow River area is an important area of economic development, grain cultivation, and agricultural production in China. Land use change has significant economic, environmental, and ecological impacts in this region. Deep study of land used carbon emissions and its influencing factors in the lower Yellow River area is not only of great significance to the environmental improvement in the Yellow River basin, but also can provide references for the research of other basins. Based on this, this paper studies the land use carbon emissions of 20 cities in the lower Yellow River area from 1995 to 2018. The results showed that from 1995 to 2018, the land use change was characterized by the decrease of the ecological land and the increase of the built-up land significantly. The overall carbon emission of the lower Yellow River area is increasing, and the built-up land is the main factor that leads to the increase of carbon emission, which can be also proven by the analysis of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model. The economic contributive coefficient (ECC) and ecological support coefficient (ESC) of carbon emission in the lower Yellow River area show a trend of high in Zhengzhou, Jinan, and Zibo and low in Zhoukou, Shangqiu, and Heze, and there was no significant changes during the study period, which indicates that each city did not achieve the coordinated development of the ecological economy. Finally, analysis results of the STIRPAT model indicated that the area of built-up land had the greatest impact on land use carbon emissions, followed by tertiary industry, whereas per capita gross domestic product (GDP) had the smallest impact. For every 1% increase in the area of built-up land, carbon emissions increased by 1.024%. By contrast, for every 1% increase in the contribution of tertiary industry to the GDP and per capita GDP, carbon emissions decreased by 0.051% and 0.034%, respectively. According to the study, there are still many problems in the coordinated development of economy and ecology in the lower Yellow River area. The lower Yellow River area should control the expansion of built-up land, afforestation, development of technology, reduction of carbon emissions, and promotion of the high-quality development of the Yellow River Basin.