bluuebunny/crossref_metadata_2025 huggingface dataset:
Seismic Behavior of RC Beam Column Joints with 600 MPa High Strength Steel Bars
Authors: Feng Jian , Wang Shuo , Meloni Marco , Zhang Qian , Yang Jingwen , Cai Jianguo
Published: July 2020

This paper presents an experimental investigation of the seismic performance of interior beam–column joints with beams reinforced with Grade 600MPa longitudinal steel bars. Six full-scale reinforcement concrete (RC) interior joints are designed with different axial compression ratios and longitudinal reinforcement ratios, which are tested under reversed cyclic loading. Failure modes, hysteretic curves, skeleton curves, energy dissipation capacity, and the ductility of joints are investigated systematically. Moreover, the effect of the different axial compression ratios and longitudinal reinforcement ratios on the seismic behavior of the joints are deeply studied. Comparisons performed between specimens demonstrate that among the beam–column joints with 600 MPa high strength steel bars, specimens with high reinforcement ratios have better energy dissipation capacity, slower stiffness degradation, and lower ductility. Moreover, with the increase of the axial compression ratios, the energy dissipation capacity and ductility become weaker. The test results show the favorable seismic properties of beam–column joints equipped with 600 MPa high strength steel bars, which can be regarded as the research basis of the popularization and application of 600 MPa high strength steel bars in reinforcement concrete frame structures.
Attenuated Total Reflection for Terahertz Modulation, Sensing, Spectroscopy and Imaging Applications: A Review
Authors: Huang Yuxin , Singh Ranjan , Xie Lijuan , Ying Yibin
Published: July 2020

Terahertz (THz) technique has become one of the most promising analytical methods and has been applied in many fields. Attenuated total reflection (ATR) technique applied in THz spectroscopy and imaging has been proven to be superior in functionalities such as modulation, sensing, analyzing, and imaging. Here, we first provide a concise introduction to the principle of ATR, discuss the factors that impact the ATR system, and demonstrate recent advances on THz wave modulation and THz surface plasmon sensing based on the THz-ATR system. Then, applications on THz-ATR spectroscopy and imaging are reviewed. Towards the later part, the advantages and limitations of THz-ATR are summarized, and prospects of modulation, surface plasmon sensing, spectroscopy and imaging are discussed.
Optimization of a Six-Step Pressure Swing Adsorption Process for Biogas Separation on a Commercial Scale
Authors: Kottititum Bundit , Srinophakun Thongchai , Phongsai Niwat , Phung Quoc Tri
Published: July 2020

Pressure swing adsorption (PSA) appears to be an effective technology for biogas upgrading under different operating conditions with low greenhouse gas emissions. This study presents the simulation of biomethane adsorption with the adsorption bed filled with a carbon molecular sieve (CMS). A six dual-bed six-step PSA process was studied which produced a high purity of biomethane. The design of the adsorption bed was followed by the real process of which the biomethane capacity was more than 5000 Nm3/h. For the adsorbent, a CMS-3K was used, and a biomethane gas with a minimum 92% purity was produced at 6.5 bar adsorption pressure. To understand the adsorption characteristics of the CH4 and CO2 gases, the Langmuir isotherm model was used to determine the isotherm of a mixed gas containing 55% CH4 and 45% CO2. Furthermore, the experimental data from the work of Cavenati et al. were used to investigate the kinetic parameter and mass transfer coefficient. The mass transfer coefficients of two species were determined to be 0.0008 s−1 and 0.018 s−1 at 306 K for CH4 and CO2, respectively. The PSA process was then simulated with a cyclic steady state until the relative tolerance was 0.0005, which was then used to predict the CH4 and CO2 mole fraction along the adsorption bed length at a steady state. Moreover, the optimal conditions were analyzed using Aspen Adsorption to simulate various key operating parameters, such as flowrate, adsorption pressure and adsorption time. The results show a good agreement between the simulated results and the real operating data obtained from the company REBiofuel. Finally, the sensitivity analysis for the major parameters was presented. The optimal conditions were found to be an adsorption pressure of 6 bar, an adsorption time of 250 s and a purity of up to 97.92% with a flowrate reducing to 2000 Nm3/h. This study can serve as a commercial approach to reduce operating costs.
Development of Wireless Power Transmission System for Transfer Cart with Shortened Track
Authors: Jin Jae Sik , Jung Sunghun , Kim Han Joo
Published: July 2020

In this study, a wireless power transmission (WPT) system for high power was developed to supply the wirelessly powered transfer cart for a clean environment (such as liquid crystal display (LCD), semiconductor, and flat panel display (FPD) device industries) to improve the cleanliness of related industrial production lines and save energy. The power transmission method of WPT and the core design were optimized, and a shortened track was fabricated to enable WPT via short power lines for diverse applications in a small space-constrained workshop. In realizing the shortened Litz wire system, the amount of heat generated increased due to the increased resistance in the system, and efforts were made to improve the thermal performance. A simple approach was also proposed to estimate the skin depth caused by the skin effects in a cable made up of multiple strands of multiple wires, validated through thermal analysis by using ANSYS software in terms of heat generation by an electric field. Structure designs were implemented to improve the heat transfer performance, and the experimental results of WPT systems at a power level of 21.54 kW demonstrate that the power transfer distance of WPT was above 15 mm with a charging efficiency above 83.24%.
Elasticity Solutions for In-Plane Free Vibration of FG-GPLRC Circular Arches with Various End Conditions
Authors: Liu Dongying , Sun Jing , Lan Linhua
Published: July 2020

In-plane free vibration of functionally graded graphene platelets reinforced nanocomposites (FG-GPLRCs) circular arches is investigated by using the two-dimensional theory of elasticity. The graphene platelets (GPLs) are dispersed along the thickness direction non-uniformly, and the material properties of the nanocomposites are evaluated by the modified Halpin-Tsai multi-scaled model and the rule of mixtures. A state-space method combined with differential quadrature technique is employed to derive the governing equation for in-plane free vibration of FG-GPLRCs circular arch, the semi-analytical solutions are obtained for various end conditions. An exact solution of FG-GPLRCs circular arch with simply-supported ends is also presented as a benchmark to valid the present numerical method. Numerical examples are performed to study the effects of GPL distribution patterns, weight fraction and dimensions, geometric parameters and boundary conditions of the circular arch on the natural frequency in details.
Metamorphosis of the Architectural Space of Goetheanum
Authors: Kiuntsli Romana , Stepanyuk Andriy , Besaha Iryna , Sobczak-Piąstka Justyna
Published: July 2020

In the beginning of the XX century, political, economic, and demographic revolutions contributed to the emergence of extraordinary people. In architecture, they were Frank Lloyd Wright, Antonio Gaudí, Frank Owen Gary, Le Corbusier, Hugo Hering, Alvar Aalto, Hans Sharun, Walter Burley Griffin, and Marion Mahony Griffin. Each of them was given a lot of attention in the media resources and their creativity was researched in different fields of knowledge. However, Rudolf Steiner’s work remains controversial to this day. Although many of the architects mentioned above enthusiastically commented on Steiner’s architectural works, there was always ambiguity in the perception of this mystic architect. Such a careful attitude to the work of the architect is due primarily to his worldview, his extraordinary approach to art and architecture in particular, because it is in architecture that Steiner was able to implement the basic tenets of anthroposophy, which he founded. The purpose of this study is to determine the content of the spatial structure of Steiner’s architecture, which makes it unique in the history of architectural heritage. The authors offer the scientific community the first article in a series of articles on the anthroposophical architecture of Rudolf Steiner and the philosophical concept that influenced the formation of this architecture.
Interlaboratory Empirical Reproducibility Study Based on a GD&T Benchmark
Authors: Aidibe Ali , Tahan Souheil Antoine , Kamali Nejad Mojtaba
Published: July 2020

The ASME Y14.5 geometric dimensioning and tolerancing (GD&T) and ISO-GPS (geometrical product specifications) standards define tolerances that can be added to components to achieve the necessary functionality and performance. The zone that each feature must lie within is defined in each tolerance. Measurement processes, including planning, programming, data collection (with contact or without contact), and data processing, check the compliance of the part with these specifications (tolerances). Over the last two decades, many works have been realized by the metrology community to investigate the accuracy, the measuring methods, and, specifically, the measurement errors of fixed and portable coordinate measuring machines (CMMs). A review of the literature showed the progression of CMMs in terms of accuracy and repeatability. However, discrepancies were observed between measurements using different CMMs or operators. This paper proposed a GD&T-based benchmark for the evaluation of the performance of different CMM operators in computer-aided inspection (CAI), considering different criteria related to the dimensional and geometrical features. An artifact was designed using basic geometries (cylinder and plane) and free-form surfaces. The results obtained from the interlaboratory comparison study showed significant performance variability for complex GD&T, such as in the composite profile and localization. This, in turn, emphasized the importance of GD&T training and certification in order to ensure a uniform understanding among different operators, combined with a fully automated inspection code generator for GD&T purposes.
Vertical Correlation and Array Gain Analysis for Vertical Line Array in Deep Water
Authors: Liang Yan , Meng Zhou , Chen Yu , Zhou Zemin , Chen Mo
Published: July 2020

Array gain (AG) is significant in evaluating the detection performance of the vertical line array, which is directly determined by the correlation of signal and noise, respectively. In this paper, we analyze the vertical correlation for a 16-element vertical line array experimented in the deep ocean in 2016. The ray interference theory is utilized to interpret the mechanism of the vertical correlation of the sound field in different zones. In the direct-arrival zone, the direct rays and once-surface-reflected rays are two dominated components, whose arrival time difference for each element are nearly the same, and the vertical correlation is high. In the shadow zone, the sound field is mainly dominated by bottom-reflected rays and the vertical correlation decreases due to different grazing angles and arrival times of each ray. Different from the previous assumption of noise independence, the effect of noise correlation on the AG is analyzed through the measured marine environmental noise. Results indicate that the noise correlation coefficients in two zones are low but not 0. In the direct-arrival zone, AG is about 10 dB, very close to the ideal value of 10 log M . AG even exceeds it when NG is negative. Moreover, AG in the direct-arrival zone is higher than the one in the shadow zone.
Best Practices of Convolutional Neural Networks for Question Classification
Authors: Pota Marco , Esposito Massimo , De Pietro Giuseppe , Fujita Hamido
Published: July 2020

Question Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding text representation, architectural characteristics, and learning hyperparameters) and some of their interactions, and for finding the most appropriate strategies for QC. Results show the influence of CNN settings on performance. Optimal settings were found depending on language. Tests on different data validated the optimization performed, and confirmed the transferability of the best settings. Comparisons to configurations suggested by previous works highlight the best classification accuracy by those optimized here. These findings can suggest the best choices to configure a CNN for QC.
The Transformation by Catalysis of Prebiotic Chemical Systems to Useful Biochemicals: A Perspective Based on IR Spectroscopy of the Primary Chemicals II. Catalysis and the Building of RNA
Authors: Larsson Ragnar , Malek Abdul , Odenbrand Ingemar
Published: July 2020

Having found that carbonyl sulfide (COS), works well as a catalyst in the transformation of amino acids to polypeptides, we have now tested COS as a catalyst also for the formation of substances that might be thought of as partners in the building of RNA. The model used was selective energy transfer (SET). This model implies that a certain number of vibrational quanta are donated from the catalyst system and a corresponding number of quanta of the reactant accept the energy thus transferred. In this way, we found that carbonyl sulfide, COS, was a perfect catalyst for combining, first, five molecules of formaldehyde to form one molecule of ribose, and next, five molecules of hydrogen cyanide, HCN, to form one molecule of adenine, one of the nucleobases of RNA. However, beyond this, we found that COS was a perfect catalyst for precisely all reactions, needed to build the RNA, ribonucleic acid.
Research on the Scope of Spectral Width Parameter of Frequency Domain Methods in Random Fatigue
Authors: Xu Jie , Zhang Yaolei , Han Qinghua , Li Jia , Lacidogna Giuseppe
Published: July 2020

In the current fatigue life calculation theory, the most commonly used method is the frequency domain method. However, most of the frequency domain fatigue life prediction models do not indicate the scope of the application of the spectral width parameter. Different frequency domain methods have strict applicability to the spectral width parameter, and improper model selection will lead significant error. Therefore, it is particularly important to determine the scope of application of the spectral width parameter for different frequency-domain methods. This paper firstly introduces the current frequency domain methods, then simulates the analogue spectrum and selects three materials for comparison in the different frequency-domain methods. By analyzing and comparing the results of random fatigue life and relative error results, the application of different frequency-domain methods is obtained, and random vibration simulation verification is carried out with the practical engineering example, which can provide a reference for the selection of life prediction models.
Optimization of ER8 and 42CrMo4 Steel Rail Wheel for Road–Rail Vehicles
Authors: Lisowski Filip , Lisowski Edward
Published: July 2020

Railway track maintenance services aim to shorten the time of removing failures on the railways. One of the most important element that shorten the repair time is the quick access to the failure site with an appropriate equipment. The use of road-rail vehicles is becoming increasingly important in this field. In this type of constructions, it is possible to use proven road vehicles such as self-propelled machines or trucks running on wheels with tires. Equipping these vehicles with a parallel rail drive system allows for quick access to the failure site using both roads and railways. Steel rail wheels of road-rail vehicles are designed for specific applications. Since the total weight of vehicle is a crucial parameter for roadworthiness, the effort is made to minimize the mass of rail wheels. The wheel under consideration is mounted directly on the hydraulic motor. This method of assembly is structurally convenient, as no shafts or intermediate couplings are required. On the other hand, it results in strict requirements for the wheel geometry and can cause significant stress concentration. Therefore, the problem of wheel geometry optimization is discussed. Consideration is given to the use of ER8 steel for railway application and 42CrMo4 high-strength steel. Finite element analysis within Ansys software and various optimization tools and methods, such as random tool, subproblem approximation method and first-order method are applied. The obtained results allow to minimize the rail wheel mass with respect to the used material. Moreover, computational demands and methods leading to the best results are compared.
Blockchain-Based Data-Preserving AI Learning Environment Model for AI Cybersecurity Systems in IoT Service Environments
Authors: Kim Jinsu , Park Namje
Published: July 2020

Artificial intelligence (AI) has a limitation in that it is only in the passive cognition area, so its operating process is not transparent; therefore, the technology relies on learning data. Since raw data for AI learning are processed and inspected manually to assure high quality for sophisticated AI learning, human errors are inevitable, and damaged and incomplete data and differences from the original data may lead to unexpected outputs of AI learning for which processed data are used. In this context, this research examines cases where AI learning data were inaccurate, in terms of cybersecurity, and the need for learning data management before machine learning through analysis of cybersecurity attack techniques, and we propose the direction of establishing a data-preserving AI system, which is a blockchain-based learning data environment model to verify the integrity of learning data. The data-preserving AI learning environment model is expected to prevent cyberattacks and data deterioration that may occur when data are provided and utilized in an open network for the processing and collection of raw data.
Recent Advancement of Molecular Structure and Biomaterial Function of Chitosan from Marine Organisms for Pharmaceutical and Nutraceutical Application
Authors: Li Bailei , Elango Jeevithan , Wu Wenhui
Published: July 2020

Chitosan is an innate cationic biological polysaccharide polymer, naturally obtained from chitin deacetylation, that possesses broad-spectrum properties such as antibacterial, biodegradability, biocompatibility, non-toxic, non-immunogenicity, and so on. Chitosan can be easily modified owing to its molecular chain that contains abundant active amino and hydroxyl groups, through various modifications. Not only does it possess excellent properties but it also greatly accelerates its solubility and endows it with additional special properties. It can be developed into bioactive materials with innovative properties, functions, and multiple uses, especially in the biomedical fields. In this paper, the unique properties and the relationship between the molecular structure of chitosan and its derivatives are emphasized, an overview of various excellent biomedical properties of chitosan and its current progress in the pharmaceutical and nutraceutical field have prospected, to provide the theoretical basis for better development and utilization of new biomedical materials of chitosan and its derivatives.
UV-Index Climatology for Europe Based on Satellite Data
Authors: Vitt Ronja , Laschewski Gudrun , Bais Alkiviadis , Diémoz Henri , Fountoulakis Ilias , Siani Anna-Maria , Matzarakis Andreas
Published: July 2020

The UV-Index (UVI) is aimed at the prevention of skin cancer as well as other negative implications of ultraviolet radiation exposure. In order to support health related applications, assessments and planning that rely on long term data in high spatial resolution and as there exist only limited ground-based measurements, satellite products from reliable atmospheric monitoring services are used as sustainable data sources to create a climatology of the UVI at the local noon. In this study, the (all-sky) UVI as well as the hypothetically clear-sky UVI were analysed for the European region from 30° North to 65° North and from 25° West to 35° East in a spatial resolution of 0.05° for the time period 1983 to 2015. Maps of the monthly mean UVI provide an overview of the distribution of UVI for Europe as well as the spatial and temporal differences and regional variability at local solar noon. Additionally, eight selected locations provide insight into the effects of latitude and altitude on UVI in Europe. Monthly boxplots for each location provide information about regional differences in the variability of UVI, showing maximum variability in Northern and Central Europe in summer, where in Southern Europe this basically occurs in spring. The frequency of the World Health Organization exposure categories moderate, high and very high UVI is provided based on ten-day means for each month. The maximum difference between mean values per decade of 2006–2015 compared to 1983–1992 ranges from −1.2 to +1.2 for UVI and from −0.4 to +0.6 for UVI c l e a r − s k y . All locations, except the Northern European site, show an increase of UVI during spring and early summer months. A statistically significant increase in the annual mean all-sky UVI has been found for four sites, which ranges from +1.2% to +3.6% per decade. The latest eleven-year period of the UVI climatology (2005–2015) has been validated with UVI measured in five sites. The sites that are located north of the Alps show an underestimation of the UVI, likely due to the cloud modification. In the south, the UVI climatology provides values that are on average overestimated, possibly related to the use of climatological aerosol information. For the site within the Alps, a switch between underestimation and overestimation during the course of the year has been found. 7% to 9% of the UVI values of the climatology differ from the measured UVI by more than one unit.
Fast Warming Has Accelerated Snow Cover Loss during Spring and Summer across the Northern Hemisphere over the Past 52 Years (1967–2018)
Authors: Wu Xuejiao , Shen Yongping , Zhang Wei , Long Yinping
Published: July 2020

With snow cover changing worldwide in several worrisome ways, it is imperative to determine both the variability in snow cover in greater detail and its relationship with ongoing climate change. Here, we used the satellite-based snow cover extent (SCE) dataset of National Oceanic and Atmospheric Administration (NOAA) to detect SCE variability and its linkages to climate over the 1967–2018 periods across the Northern Hemisphere (NH). Interannually, the time series of SCE across the NH reveal a substantial decline in both spring and summer (−0.54 and −0.71 million km2/decade, respectively), and this decreasing trend corresponded with rising spring and summer temperatures over high-latitude NH regions. Among the four seasons, the temperature rise over the NH was the highest in winter (0.39 °C/decade, p < 0.01). More precipitation in winter was closely related to an increase of winter SCE in mid-latitude areas of NH. Summer precipitation over the NH increased at a significant rate (1.1 mm/decade, p < 0.01), which likely contribute to the accelerated reduction of summer’s SCE across the NH. However, seasonal sensitivity of SCE to temperature changes differed between the Eurasian and North American continents. Thus, this study provides a better understanding of seasonal SCE variability and climatic changes that occurred at regional and hemispheric spatial scales in the past 52 years.
Bioreactor Parameters for Microcarrier-Based Human MSC Expansion under Xeno-Free Conditions in a Vertical-Wheel System
Authors: Lembong Josephine , Kirian Robert , Takacs Joseph D. , Olsen Timothy R. , Lock Lye Theng , Rowley Jon A. , Ahsan Tabassum
Published: July 2020

Human mesenchymal stem/stromal cells (hMSCs) have been investigated and proven to be a well-tolerated, safe therapy for a variety of indications, as shown by over 900 registered hMSC-based clinical trials. To meet the commercial demand for clinical manufacturing of hMSCs, production requires a scale that can achieve a lot size of ~100B cells, which requires innovative manufacturing technologies such as 3D bioreactors. A robust suspension bioreactor process that can be scaled-up to the relevant scale is therefore crucial. In this study, we developed a fed-batch, microcarrier-based bioreactor process, which enhances media productivity and drives a cost-effective and less labor-intensive hMSC expansion process. We determined parameter settings for various stages of the culture: inoculation, bioreactor culture, and harvest. Addition of a bioreactor feed, using a fed-batch approach, was necessary to replenish the mitogenic factors that were depleted from the media within the first 3 days of culture. Our study resulted in an optimized hMSC culture protocol that consistently achieved hMSC densities between 2 × 105–6 × 105 cells/mL within 5 days with no media exchange, maintaining the final cell population doubling level (PDL) at 16–20. Using multiple hMSC donors, we showed that this process was robust and yielded hMSCs that maintained expansion, phenotypic characteristic, and functional properties. The developed process in a vertical-wheel suspension bioreactor can be scaled to the levels needed to meet commercial demand of hMSCs.
Role of B Cell Lymphoma 2 in the Regulation of Liver Fibrosis in miR-122 Knockout Mice
Authors: Teng Kun-Yu , Barajas Juan M. , Hu Peng , Jacob Samson T. , Ghoshal Kalpana
Published: July 2020

MicroRNA-122 (miR-122) has been identified as a marker of various liver injuries, including hepatitis- virus-infection-, alcoholic-, and non-alcoholic steatohepatitis (NASH)-induced liver fibrosis. Here, we report that the extracellular miR-122 from hepatic cells can be delivered to hepatic stellate cells (HSCs) to modulate their proliferation and gene expression. Our published Argonaute crosslinking immunoprecipitation (Ago-CLIP) data identified several pro-fibrotic genes, including Ctgf, as miR-122 targets in mice livers. However, treating Ctgf as a therapeutic target failed to rescue the fibrosis developed in the miR-122 knockout livers. Alternatively, we compared the published datasets of human cirrhotic livers and miR-122 KO livers, which revealed upregulation of BCL2, suggesting its potential role in regulating fibrosis. Notably, ectopic miR-122 expression inhibited BCL2 expression in human HSC (LX-2) cells). Publicly available ChIP-seq data in human hepatocellular cancer (HepG2) cells and mice livers suggested miR-122 could regulate BCL2 expression indirectly through c-MYC, which was confirmed by siRNA-mediated depletion of c-MYC in Hepatocellular Carcinoma (HCC) cell lines. Importantly, Venetoclax, a potent BCL2 inhibitor approved for the treatment of leukemia, showed promising anti-fibrotic effects in miR-122 knockout mice. Collectively, our data demonstrate that miR-122 suppresses liver fibrosis and implicates anti-fibrotic potential of Venetoclax.
Secreted Factors from Keloid Keratinocytes Modulate Collagen Deposition by Fibroblasts from Normal and Fibrotic Tissue: A Pilot Study
Authors: Alghamdi Mansour A. , AL-Eitan Laith N. , Stevenson Andrew , Chaudhari Nutan , Hortin Nicole , Wallace Hilary J. , Danielsen Patricia L. , Manzur Mitali , Wood Fiona M. , Fear Mark W.
Published: July 2020

Interactions between keratinocytes and fibroblasts in the skin layers are crucial in normal tissue development, wound healing, and scarring. This study has investigated the role of keloid keratinocytes in regulating collagen production by primary fibroblasts in vitro. Keloid cells were obtained from removed patients’ tissue whereas normal skin cells were discarded tissue obtained from elective surgery procedures. Fibroblasts and keratinocytes were isolated, cultured, and a transwell co-culture system were used to investigate the effect of keratinocytes on collagen production using a ‘scar-in-a-jar’ model. Keloid fibroblasts produced significantly more collagen than normal skin fibroblasts in monoculture at the RNA, secreted protein, and stable fibrillar protein level. When keloid keratinocytes were added to normal skin fibroblasts, expression of collagen was significantly upregulated in most samples, but when added to keloid fibroblasts, collagen I production was significantly reduced. Interestingly, keloid keratinocytes appear to decrease collagen production by keloid fibroblasts. This suggests that signaling in both keratinocytes and fibroblasts is disrupted in keloid pathology.
Modelling Autonomous Agents’ Decisions in Learning to Cross a Cellular Automaton-Based Highway via Artificial Neural Networks
Authors: Xie Shengkun , Lawniczak Anna T. , Hao Junlin
Published: July 2020

A lot of effort has been devoted to mathematical modelling and simulation of complex systems for a better understanding of their dynamics and control. Modelling and analysis of computer simulations outcomes are also important aspects of studying the behaviour of complex systems. It often involves the use of both traditional and modern statistical approaches, including multiple linear regression, generalized linear model and non-linear regression models such as artificial neural networks. In this work, we first conduct a simulation study of the agents’ decisions learning to cross a cellular automaton based highway and then, we model the simulation data using artificial neural networks. Our research shows that artificial neural networks are capable of capturing the functional relationships between input and output variables of our simulation experiments, and they outperform the classical modelling approaches. The variable importance measure techniques can consistently identify the most dominant factors that affect the response variables, which help us to better understand how the decision-making by the autonomous agents is affected by the input factors. The significance of this work is in extending the investigations of complex systems from mathematical modelling and computer simulations to the analysis and modelling of the data obtained from the simulations using advanced statistical models.
Non-Hermitian Floquet Phases with Even-Integer Topological Invariants in a Periodically Quenched Two-Leg Ladder
Authors: Zhou Longwen
Published: July 2020

Periodically driven non-Hermitian systems could possess exotic nonequilibrium phases with unique topological, dynamical, and transport properties. In this work, we introduce an experimentally realizable two-leg ladder model subjecting to both time-periodic quenches and non-Hermitian effects, which belongs to an extended CII symmetry class. Due to the interplay between drivings and nonreciprocity, rich non-Hermitian Floquet topological phases emerge in the system, with each of them characterized by a pair of even-integer topological invariants ( w 0 , w π ) ∈ 2 Z × 2 Z . Under the open boundary condition, these invariants further predict the number of zero- and π -quasienergy modes localized around the edges of the system. We finally construct a generalized version of the mean chiral displacement, which could be employed as a dynamical probe to the topological invariants of non-Hermitian Floquet phases in the CII symmetry class. Our work thus introduces a new type of non-Hermitian Floquet topological matter, and further reveals the richness of topology and dynamics in driven open systems.
Area-Efficient Differential Fault Tolerance Encoding for Finite State Machines
Authors: Park Jiwoon , Yoo Hoyoung
Published: July 2020

A differential fault tolerance encoding is presented for finite state machines (FSMs) to improve their area efficiency. As the manufacturing technology for semiconductors continues to scale down, the probability of the occurrence of unexpected faults in integrated circuits has been increasing. Because an FSM controls an entire digital circuit, the faults in FSMs should be carefully addressed. Whereas the previous encoding applies a fault tolerance scheme to all the states in an FSM, the proposed encoding applies a fault tolerance scheme to only specific states depending on their importance. Compared with the previous complete fault tolerance encoding, the proposed encoding provides a comparable failure probability with a small hardware by applying the fault tolerance scheme differently to each state. The proposed method improves the area efficiency by 36.1%, 43.8%, 49.2%, and 74.6% compared with that by the non-fault tolerance, previous hardware redundancy, information redundancy, and time redundancy methods, respectively. Consequently, the proposed method can provide a flexible solution by applying the fault tolerance differently depending on the importance of the states.
The Philippines’ and Singapore’s Journeys towards Liberalised Electricity Supply Industries—Takeaways for Malaysia
Authors: Aris Hazleen , Mohd Zawawi Iskandar Shah , Jørgensen Bo Nørregaard
Published: July 2020

Malaysia is in the process of liberalising its electricity supply industry (ESI) further, with the second reform series announced in September 2018. If everything goes as planned, Malaysia would be the third country in the Association of Southeast Asia Nations (ASEAN) to have a fully liberalised ESI after the Philippines and Singapore. A number of initiatives have been in the pipeline to be executed and a lot more will be planned. At this juncture, it is important for Malaysia to look for the best practices and lessons that can be learnt from the experience of other countries that have successfully liberalised their ESIs. Being in the same region, it is believed that there is a lot that Malaysia can learn from the Philippines and Singapore. This paper therefore presents and deliberates on the chronological development of the countries’ progressive journeys in liberalising their ESIs. The aim is to discern the good practices, the challenges as well as the lessons learnt from these transformations. Analysis is being made and discussed from the following four perspectives; legislative framework, implementation phases, market components and impact on renewable energy penetration. Findings from this study would provide useful insight for Malaysia in determining the course of actions to be taken to reform its ESI. Beyond Malaysia, the findings can also serve as the reference for the other ASEAN countries in moving towards liberalising their ESIs.
Bio-Inspired Rotor Design Characterization of a Horizontal Axis Wind Turbine
Authors: Gaitan-Aroca J. , Sierra Fabio , Castellanos Contreras Jose Ulises
Published: July 2020

In this paper, the performance of a biomimetic wind rotor design inspired by Petrea Volubilis seed is presented. Experimentation for this rotor is configured as a horizontal axis wind turbine (HAWT) and numerical analysis is done in order to obtain performance curves with the open-source computational fluid dynamics (CFD) software OpenFoam®. Numerical analysis and experimental results are compared for power Coefficient (Cp) and thrust coefficient (CT). The biomimetic rotor analysis is also compared with experimental results exposed by Castañeda et al. (2011), who were the first to develop those experimentations with this new rotor design. Computational fluid dynamics simulations were performed using an incompressible large Edyy simulation (LES) turbulence models with a localized sub-grid scale (SGS) dynamic one-equation eddy-viscosity. A dynamic mesh based on an arbitrary mesh interface (AMI) was used to simulate rotation and to evaluate flow around rotor blades in order to accurately capture the flow field behavior and to obtain global variables that allow to determine the power potential of this wind rotor turbine. This study will show the potential of this new rotor design for wind power generation.
Halide Pb-Free Double–Perovskites: Ternary vs. Quaternary Stoichiometry
Authors: Palummo Maurizia , Varsano Daniele , Berríos Eduardo , Yamashita Koichi , Giorgi Giacomo
Published: July 2020

In view of their applicability in optoelectronics, we review here the relevant structural, electronic, and optical features of the inorganic Pb-free halide perovskite class. In particular, after discussing the reasons that have motivated their introduction in opposition to their more widely investigated organic-inorganic counterparts, we highlight milestones already achieved in their synthesis and characterization and show how the use of ab initio ground and excited state methods is relevant in predicting their properties and in disclosing yet unsolved issues which characterize both ternary and quaternary stoichiometry double-perovskites.
Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network
Authors: Huynh Anh Ngoc-Lan , Deo Ravinesh C. , An-Vo Duc-Anh , Ali Mumtaz , Raj Nawin , Abdulla Shahab
Published: July 2020

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).
Design Optimization and Coupled Dynamics Analysis of an Offshore Wind Turbine with a Single Swivel Connected Tether
Authors: Chen Jieyan , Li Chengxi
Published: July 2020

The increased interest in renewable wind energy has stimulated many offshore wind turbine concepts. This paper presents a design optimization and a coupled dynamics analysis of a platform with a single tether anchored to the seabed supported for a 5 MW baseline wind turbine. The design is based on a concept named SWAY. We conduct a parametric optimization process that accounts for important design considerations in the static and dynamic view, such as the stability, natural frequency, performance requirements, and cost feasibility. Through these optimization processes, we obtain and present the optimized model. We then establish the fully coupled aero-hydro-servo-elastic model by the time-domain simulation tool FAST (Fatigue, Aerodynamics, Structures, and Turbulence) with the hydrodynamic coefficients from an indoor program HydroGen. We conduct extensive time-domain simulations with various wind and wave conditions to explore the effects of wind speed and wave significant height on the dynamic performance of the optimized SWAY model in various water depths. The swivel connection between the platform and tether is the most special design for the SWAY model. Thus, we compare the performance of models with different tether connection designs, based on the platform motions, nacelle velocity, nacelle accelerations, resonant behaviors, and the damping of the coupled systems. The results of these comparisons demonstrate the advantage of the optimized SWAY model with the swivel connection. From these analyses, we prove that the optimized SWAY model is a good candidate for deep water deployment.
Testing the Capability of Low-Cost Tools and Artificial Intelligence Techniques to Automatically Detect Operations Done by a Small-Sized Manually Driven Bandsaw
Authors: Cheţa Marius , Marcu Marina Viorela , Iordache Eugen , Borz Stelian Alexandru
Published: July 2020

Research Highlights: A low-cost experimental system was developed to enable the production monitoring of small-scale wood processing facilities by the means of sensor-collected data and the implementation of artificial intelligence (AI) techniques, which provided accurate results for the most important work operations. Background and Objectives: The manufacturing of wood-based products by small-scale family-held business is commonly affected by a lack of monitoring data that, on the one hand, may prevent the decision-making process and, on the other hand, may lead to less technical efficiency that could result in business failure. Long-term performance of such manufacturing facilities is limited because data collection and analysis require significant resources, thus preventing the approaches that could be pursued for competitivity improvement. Materials and Methods: An external sensor system composed of two dataloggers—a triaxial accelerometer and a sound pressure level meter—was used in combination with a video camera to provide the input signals and meta-documentation for the training and testing of an artificial neural network (ANN) to check the accuracy of automatic classification of the time spent in operations. The study was based on a sample of ca. 90 k observations collected at a frequency of 1 Hz. Results: The approach provided promising results in both the training (ca. 20 k) and testing (ca. 60 k) datasets, with global classification accuracies of ca. 85%. However, the events characterizing the effective sawing, which requires electrical power, were even better recognized, reaching a classification accuracy of 98%. Conclusions: The system requires low-cost devices and freely available software that could enable data feeding on local computers by their direct connection to the devices. As such, it could collect, analyze and plot production data that could be used for maintaining the competitiveness of traditional technologies.
Effect of Day or Night and Cumulative Shift Time on the Frequency of Tree Damage during CTL Harvesting in Various Stand Conditions
Authors: Bembenek Mariusz , Tsioras Petros A. , Karaszewski Zbigniew , Zawieja Bogna , Bakinowska Ewa , Mederski Piotr S.
Published: July 2020

Thinning is one of the most important tools of forest management, although thinning operations require the use of machines which ultimately cause damage to the remaining stand. The level of damage largely depends on the human factor, and a tired, less focused operator will create more injuries in the forest. With this in mind, the objectives of this research were to find out whether the probability of tree damage caused by an operator is also affected by: (1) the part of the day (dawn/day/dusk/night), and (2) the cumulative shift time. The research was carried out in pure pine stands of different ages, density and thinning intensities. Sample plots were selected that had an increasing number of trees per hectare and growing thinning intensities were applied. The same Komatsu 931.1 harvester was used for the thinning operations in each stand. In all the age classes combined, 5.41% of the remaining trees were wounded. There was a significant influence of the part of the day on the percentage of damaged trees, which was positively correlated with the cumulative shift time. Stand conditions, such as age class and stand density, as well as thinning characteristics—thinning intensity, number of harvested trees and productivity—have different effects on the distribution of damage intensity and on probability. The results may improve the planning of operators’ work shifts in forests of various ages and densities, allowing harvester productivity to be maintained while at the same time inflicting the lowest possible level of damage.
Transition from Diffusion to Wave Propagation in Fractional Jeffreys-Type Heat Conduction Equation
Authors: Bazhlekova Emilia , Bazhlekov Ivan
Published: July 2020

The heat conduction equation with a fractional Jeffreys-type constitutive law is studied. Depending on the value of a characteristic parameter, two fundamentally different types of behavior are established: diffusion regime and propagation regime. In the first case, the considered equation is a generalized diffusion equation, while in the second it is a generalized wave equation. The corresponding memory kernels are expressed in both cases in terms of Mittag–Leffler functions. Explicit representations for the one-dimensional fundamental solution and the mean squared displacement are provided and analyzed analytically and numerically. The one-dimensional fundamental solution is shown to be a spatial probability density function evolving in time, which is unimodal in the diffusion regime and bimodal in the propagation regime. The multi-dimensional fundamental solutions are probability densities only in the diffusion case, while in the propagation case they can have negative values. In addition, two different types of subordination principles are formulated for the two regimes. The Bernstein functions technique is extensively employed in the theoretical proofs.
Behavior of a Large Diameter Bored Pile in Drained and Undrained Conditions: Comparative Analysis
Authors: Al-Atroush Mohamed Ezzat , Hefny Ashraf , Zaghloul Yasser , Sorour Tamer
Published: July 2020

Despite the difficulties in obtaining the ultimate capacity of the large diameter bored piles (LDBP) using the in situ loading test, this method is the most recommended by several codes and design standards. However, several settlement-based approaches, alongside the conventional capacity-based design approach for LDBP, are proposed in the event of the impossibility of performing a pile-loading test during the design phase. With that in mind, natural clays usually involve some degree of over consolidation; there is considerable debate among the various approaches on how to represent the behavior of the overconsolidated (OC)stiff clay and its design parameters, whether drained or undrained, in the pile-load test problems. In this paper, field measurements of axial loaded to failure LDBP load test installed in OC stiff clay (Alzey Bridge Case Study, Germany) have been used to assess the quality of two numerical models established to simulate the pile behavior in both drained and undrained conditions. After calibration, the load transfer mechanism of the LDBP in both drained and undrained conditions has been explored. Results of the numerical analyses showed the main differences between the soil pile interaction in both drained and undrained conditions. Also, field measurements have been used to assess the ultimate pile capacity estimated using different methods.