bluuebunny/crossref_metadata_2025 huggingface dataset:
The Interplay between Atmospheric Conditions and Grape Berry Quality Parameters in Portugal
Authors: Costa Cátia , Graça António , Fontes Natacha , Teixeira Marta , Gerós Hernâni , Santos João A.
Published: July 2020

The atmospheric conditions are a strong modulator of grape berry composition, but further research is required to better understand this relationship, which is particularly pertinent under the context of climate change. The present study assesses the relationship between interannual variability in atmospheric conditions (mean, maximum and minimum air temperatures and precipitation totals) on grape berry quality attributes in three main Portuguese wine regions—Douro, Dão and Alentejo—and targets two major varieties growing in Portugal (cv. Touriga Nacional and cv. Aragonez/Tempranillo). Berry weight, titratable acidity (TA), pH, potential alcohol (PA), anthocyanins and total phenols index (TPI) data, collected two to three weeks after the end of the veraison until technological maturity, since 1999 in Douro, 2004 in Alentejo and 2008 in Dão, were selected. Meteorological data were obtained from both automatic weather stations and a climatic database defined at a very-high-resolution grid (<1 km) (PTHRES). The influence of daily mean, maximum and minimum air temperatures (November–October) and precipitation totals (April to June and July to September) on the above-mentioned berry quality parameters were first explored to identify the months/periods more influential to grape berry composition. Different statistical approaches were subsequently carried out to explore in greater detail these relationships. At technological maturity, temperature was negatively correlated to berry weight, titratable acidity, anthocyanins and TPI, but was positively correlated to pH and potential alcohol. Moreover, lowest levels of berry weight and TA (and highest levels of pH) were more frequent in warmer regions, while the opposite was seen in the cooler regions. PA, TPI and anthocyanins at maturity did not show a clear trend across regions. In addition, the maturation parameters of each site were grouped into two clusters—years where the maturation parameter is higher (cluster 1) and years where it is lower (cluster 2)—and significant differences in monthly mean temperatures between clusters were found. Overall, temperatures at veraison and maturation periods (June–August) were more influential in determining grape berry composition at harvest. The influence of precipitation was dependent on location and variety. The results also suggested that berry composition in Alentejo is more sensitive to atmospheric variability, while Aragonez seems more resilient than Touriga Nacional. These outcomes are based on a systematized and unprecedentedly large grape berry quality database in Portugal and provided the grounds for the development of grape quality forecast models, either to be used operationally in each vintage or for assessing potential modifications in berry composition in response to changing climates.
Application of Machine Learning for Fault Classification and Location in a Radial Distribution Grid
Authors: Mamuya Yordanos Dametw , Lee Yih-Der , Shen Jing-Wen , Shafiullah Md , Kuo Cheng-Chien
Published: July 2020

Fault location with the highest possible accuracy has a significant role in expediting the restoration process, after being exposed to any kind of fault in power distribution grids. This paper provides fault detection, classification, and location methods using machine learning tools and advanced signal processing for a radial distribution grid. The three-phase current signals, one cycle before and one cycle after the inception of the fault are measured at the sending end of the grid. A discrete wavelet transform (DWT) is employed to extract useful features from the three-phase current signal. Standard statistical techniques are then applied onto DWT coefficients to extract the useful features. Among many features, mean, standard deviation (SD), energy, skewness, kurtosis, and entropy are evaluated and fed into the artificial neural network (ANN), Multilayer perceptron (MLP), and extreme learning machine (ELM), to identify the fault type and its location. During the training process, all types of faults with variations in the loading and fault resistance are considered. The performance of the proposed fault locating methods is evaluated in terms of root mean absolute percentage error (MAPE), root mean squared error (RMSE), Willmott’s index of agreement (WIA), coefficient of determination ( R 2 ), and Nash-Sutcliffe model efficiency coefficient (NSEC). The time it takes for training and testing are also considered. The proposed method that discrete wavelet transforms with machine learning is a very accurate and reliable method for fault classifying and locating in both a balanced and unbalanced radial system. 100% fault detection accuracy is achieved for all types of faults. Except for the slight confusion of three line to ground (3LG) and three line (3L) faults, 100% classification accuracy is also achieved. The performance measures show that both MLP and ELM are very accurate and comparative in locating faults. The method can be further applied for meshed networks with multiple distributed generators. Renewable generations in the form of distributed generation units can also be studied.
Digital Holographic Interferometry without Phase Unwrapping by a Convolutional Neural Network for Concentration Measurements in Liquid Samples
Authors: Guerrero-Mendez Carlos , Saucedo-Anaya Tonatiuh , Moreno Ivan , Araiza-Esquivel Ma. , Olvera-Olvera Carlos , Lopez-Betancur Daniela
Published: July 2020

Convolutional neural networks (CNNs) and digital holographic interferometry (DHI) can be combined to improve the calculation efficiency and to simplify the procedures of many DHI applications. In DHI, for the measurements of concentration differences between liquid samples, two or more holograms are compared to find the difference phases among them, and then to estimate the concentration values. However, liquid samples with high concentration difference values are difficult to calculate using common phase unwrapping methods as they have high spatial frequencies. In this research, a new method to skip the phase unwrapping process in DHI, based on CNNs, is proposed. For this, images acquired by Guerrero-Mendez et al. (Metrology and Measurement Systems 24, 19–26, 2017) were used to train the CNN, and a multiple linear regression algorithm was fitted to estimate the concentration values for liquid samples. In addition, new images were recorded to evaluate the performance of the proposed method. The proposed method reached an accuracy of 0.0731%, and a precision of ±0.0645. The data demonstrated a high repeatability of 0.9986, with an operational range from 0.25 gL−1 to 1.5 gL−1. The proposed method was performed with liquid samples in a cylindrical glass.
Evaluation of WRF-Chem Predictions for Dust Deposition in Southwestern Iran
Authors: Foroushani Mansour A. , Opp Christian , Groll Michael , Nikfal Amirhossein
Published: July 2020

The relationships between monthly recorded ground deposition rates (GDRs) and the spatiotemporal characteristics of dust concentrations in southwest Iran were investigated. A simulation by the Weather Research and Forecasting Model coupled with the Chemistry modeling system (WRF-Chem) was conducted for dust deposition during 2014–2015. The monthly dust deposition values observed at 10 different gauge sites (G01–G10) were mapped to show the seasonal and spatial variations in dust episodes at each location. An analysis of the dust deposition samples, however, confirmed that the region along the deposition sites is exposed to the highest monthly dust load, which has a mean value of 2.4 mg cm−2. In addition, the study area is subjected to seasonally varying deposition, which follows the trend: spring > summer > winter > fall. The modeling results further demonstrate that the increase in dust emissions is followed by a windward convergence over the region (particularly in the spring and summer). Based on the maximum likelihood classification of land use land cover, the modeling results are consistent with observation data at gauge sites for three scenarios [S.I, S.II, and S.III]. The WRF model, in contrast with the corresponding observation data, reveals that the rate factor decreases from the southern [S.III—G08, G09, and G10] through [S.II—G04, G05, G06, and G07] to the northern points [S.I—G01, G02, and G03]. A narrower gap between the modeling results and GDRs is indicated if there is an increase in the number of dust particles moving to lower altitudes or an increase in the dust resident time at high altitudes. The quality of the model forecast is altered by the deposition rate and is sensitive to land surface properties and interactions among land and climate patterns.
Impairments of Synaptic Plasticity Induction Threshold and Network Oscillatory Activity in the Hippocampus Underlie Memory Deficits in a Non-Transgenic Mouse Model of Amyloidosis
Authors: Mayordomo-Cava Jennifer , Iborra-Lázaro Guillermo , Djebari Souhail , Temprano-Carazo Sara , Sánchez-Rodríguez Irene , Jeremic Danko , Gruart Agnès , Delgado-García José María , Jiménez-Díaz Lydia , Navarro-López Juan D.
Published: July 2020

In early Alzheimer disease (AD) models synaptic failures and upstreaming aberrant patterns of network synchronous activity result in hippocampal-dependent memory deficits. In such initial stage, soluble forms of Amyloid-β (Aβ) peptides have been shown to play a causal role. Among different Aβ species, Aβ25–35 has been identified as the biologically active fragment, as induces major neuropathological signs related to early AD stages. Consequently, it has been extensively used to acutely explore the pathophysiological events related with neuronal dysfunction induced by soluble Aβ forms. However, the synaptic mechanisms underlying its toxic effects on hippocampal-dependent memory remain unresolved. Here, in an in vivo model of amyloidosis generated by intracerebroventricular injections of Aβ25–35 we studied the synaptic dysfunction mechanisms underlying hippocampal cognitive deficits. At the synaptic level, long-term potentiation (LTP) of synaptic excitation and inhibition was induced in CA1 region by high frequency simulation (HFS) applied to Schaffer collaterals. Aβ25–35 was found to alter metaplastic mechanisms of plasticity, facilitating long-term depression (LTD) of both types of LTP. In addition, aberrant synchronization of hippocampal network activity was found while at the behavioral level, deficits in hippocampal-dependent habituation and recognition memories emerged. Together, our results provide a substrate for synaptic disruption mechanism underlying hippocampal cognitive deficits present in Aβ25–35 amyloidosis model.
Using Coarse-Grained Simulations to Characterize the Mechanisms of Protein–Protein Association
Authors: Dhusia Kalyani , Su Zhaoqian , Wu Yinghao
Published: July 2020

The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates.
The Constitutive Lack of α7 Nicotinic Receptor Leads to Metabolic Disorders in Mouse
Authors: Gausserès Blandine , Liu Junjun , Foppen Ewout , Tourrel-Cuzin Cécile , Rodriguez Sanchez-Archidona Ana , Delangre Etienne , Cruciani-Guglielmacci Céline , Pons Stéphanie , Maskos Uwe , Thorens Bernard , Magnan Christophe , Movassat Jamileh , Maouche Kamel
Published: July 2020

Objective: Type 2 diabetes (T2D) occurs by deterioration in pancreatic β-cell function and/or progressive loss of pancreatic β-cell mass under the context of insulin resistance. α7 nicotinic acetylcholine receptor (nAChR) may contribute to insulin sensitivity but its role in the pathogenesis of T2D remains undefined. We investigated whether the systemic lack of α7 nAChR was sufficient to impair glucose homeostasis. Methods: We used an α7 nAChR knock-out (α7−/−) mouse model fed a standard chow diet. The effects of the lack of α7 nAChR on islet mass, insulin secretion, glucose and insulin tolerance, body composition, and food behaviour were assessed in vivo and ex vivo experiments. Results: Young α7−/− mice display a chronic mild high glycemia combined with an impaired glucose tolerance and a marked deficit in β-cell mass. In addition to these metabolic disorders, old mice developed adipose tissue inflammation, elevated plasma free fatty acid concentrations and presented glycolytic muscle insulin resistance in old mice. Finally, α7−/− mice, fed a chow diet, exhibited a late-onset excessive gain in body weight through increased fat mass associated with higher food intake. Conclusion: Our work highlights the important role of α7 nAChR in glucose homeostasis. The constitutive lack of α7 nAChR suggests a novel pathway influencing the pathogenesis of T2D.
Isoprostanoid Profiling of Marine Microalgae
Authors: Vigor Claire , Oger Camille , Reversat Guillaume , Rocher Amandine , Zhou Bingqing , Linares-Maurizi Amandyne , Guy Alexandre , Bultel-Poncé Valérie , Galano Jean-Marie , Vercauteren Joseph , Durand Thierry , Potin Philippe , Tonon Thierry , Leblanc Catherine
Published: July 2020

Algae result from a complex evolutionary history that shapes their metabolic network. For example, these organisms can synthesize different polyunsaturated fatty acids, such as those found in land plants and oily fish. Due to the presence of numerous double-bonds, such molecules can be oxidized nonenzymatically, and this results in the biosynthesis of high-value bioactive metabolites named isoprostanoids. So far, there have been only a few studies reporting isoprostanoid productions in algae. To fill this gap, the current investigation aimed at profiling isoprostanoids by liquid chromatography -mass spectrometry/mass spectrometry (LC-MS/MS) in four marine microalgae. A good correlation was observed between the most abundant polyunsaturated fatty acids (PUFAs) produced by the investigated microalgal species and their isoprostanoid profiles. No significant variations in the content of oxidized derivatives were observed for Rhodomonas salina and Chaetoceros gracilis under copper stress, whereas increases in the production of C18-, C20- and C22-derived isoprostanoids were monitored in Tisochrysis lutea and Phaeodactylum tricornutum. In the presence of hydrogen peroxide, no significant changes were observed for C. gracilis and for T. lutea, while variations were monitored for the other two algae. This study paves the way to further studying the physiological roles of isoprostanoids in marine microalgae and exploring these organisms as bioresources for isoprostanoid production.
Structural Elucidation of Irish Ale Bioactive Polar Lipids with Antithrombotic Properties
Authors: Tsoupras Alexandros , Lordan Ronan , O'Keefe Eoin , Shiels Katie , Saha Sushanta Kumar , Zabetakis Ioannis
Published: July 2020

The structures of bioactive polar lipids (PLs) of Irish ale with potent antithrombotic and cardioprotective properties were elucidated. Ale PL was fractionated by preparative thin layer chromatography (TLC) into subclasses, and their antithrombotic effect was assessed against human platelet aggregation induced by the pro-inflammatory mediator, platelet-activating factor (PAF). The fatty acid content and the overall structures of ale PL were elucidated by liquid chromatography mass spectrometry (LC-MS). Phosphatidylcholines (PC) and molecules of the sphingomyelin (SM) family exhibited the strongest anti-PAF effects, followed by phosphatidylethanolamines (PE). PC contained higher amounts of omega-3 polyunsaturated fatty acids (n-3 PUFA) and thus the lowest n-6/n-3 ratio. Bioactive diacyl and alkyl-acyl PC and PE molecules bearing n-3 PUFA at their sn-2 position, especially docosahexaenoic acid (DHA) and α-linolenic acid (ALA) but mostly oleic acid (OA), were identified in both PC and PE subclasses. Eicosapentaenoic acid (EPA) was present only in bioactive PC molecules and not in PE, explaining the lower anti-PAF effects of PE. Bioactive sphingolipid and glycolipid molecules with reported anti-inflammatory and anti-tumour properties, such as specific ceramides and glucosylcerebrosides with sphingosine, phytosphingosine and dihydrosphingosine bases but also specific monogalactodiglycerides and SM species bearing ALA at their sn-2 position, were identified in the SM subclass, providing a rational for its strong bioactivities against the PAF pathway. Further studies are required on the health benefits of bioactive PL from beer and brewery by-products.
Cerebrospinal Fluid Biomarkers in Parkinson’s Disease: A Critical Overview of the Literature and Meta-Analyses
Authors: Katayama Takayuki , Sawada Jun , Takahashi Kae , Yahara Osamu
Published: July 2020

Parkinson’s disease (PD) is a common neurodegenerative disorder; however, well-established biochemical markers have not yet been identified. This review article covers several candidate cerebrospinal fluid (CSF) biomarkers for PD based on the recent literature and meta-analysis data. The decrease of α-synuclein in PD is supported by meta-analyses with modest reproducibility, and a decrease of amyloid β42 is seen as a prognostic marker for cognitive decline. Tau, phosphorylated tau (p-tau), and neurofilament light chains have been used to discriminate PD from other neurodegenerative disorders. This article also describes more hopeful biochemical markers, such as neurotransmitters, oxidative stress markers, and other candidate biomarkers.
Luxembourg Fund Data Repository
Authors: Skoura Angeliki , Presber Julian , Schiltz Jang
Published: July 2020

In this paper, we introduce the Luxembourg Fund Data Repository, a novel database of investment funds available for academic research that was created at the Department of Finance of the University of Luxembourg. The database contains the population of Undertakings for Collective Investment in Transferable Securities funds domiciled in Luxembourg from the starting month of their existence (March 1988) to October 2016. The fund characteristics are organized in a comprehensive database architecture encompassing static and dynamic data over the entire life of the funds. The characteristics include fund identifiers, official name, status information, management company and other service providers, daily and monthly performance time-series, portfolio holdings, classification of investment objective, fees, dividends, and cash flows. The database was constructed after collecting and assembling complementary historical information from three data providers. Importantly, funds no longer in existence due to liquidation or mergers are included in the database, preventing survivorship bias. The database has been constructed to serve as a research dataset of high accuracy due to the maximization of population coverage, the maximization of historical coverage, and validation by using information acquired from the supervisory authority of the financial sector of Luxembourg. License currently available to researchers of the Department of Finance of the University of Luxembourg. Future plans for extending accessibility to the global academic community.
Knowledge in Transition in Healthcare
Authors: Sousa Maria José , Dal Mas Francesca , Garcia-Perez Alexeis , Cobianchi Lorenzo
Published: July 2020

Organizations are challenged by the need to transform Dynamic Knowledge, embedded in each worker, into Static Knowledge, rooted in factual documental information. However, innovation and knowledge creation seem to be facilitated by the personal knowledge and life experiences of people, which appear to be dynamic. The tensions between Dynamic and Static Knowledge in facilitating the transfer and sharing of knowledge arise as compelling research as well as practical topic for organizations. Our paper aims to investigate such tensions by employing a case study. We decided to deepen such dynamics in the healthcare field, given its importance for business and society. In more detail, we analyzed one Emergency Room (ER) department through a series of interviews. Our findings highlight the importance of the right balance between Static and Dynamic Knowledge. On the one hand, the healthcare organization recognized the need to incorporate knowledge into practical and tangible instruments. On the other hand, the flows of Dynamic Knowledge must be fostered through a culture of knowledge translation and sharing, and the development of soft skills.
Two-Stage Checkpoint Based Security Monitoring and Fault Recovery Architecture for Embedded Processor
Authors: Wang Xiang , Zhao Zongmin , Xu Dongdong , Zhang Zhun , Hao Qiang , Liu Mengchen , Si Yu
Published: July 2020

Nowadays, the secure program execution of embedded processor has attracted considerable research attention, since more and more code tampering attacks and transient faults are seriously affecting the security of embedded processors. The program monitoring and fault recovery strategies are not only closely related to the security of embedded devices, but also directly affect the performance of the processor. This paper presents a security monitoring and fault recovery architecture for run-time program execution, which takes regular backup copies of the two-stage checkpoint. In this framework, the integrity check technology based on the basic block (BB) is utilized to monitor the program execution in real-time, while the rollback operation is taken once the integrity check is failed. In addition, a Monitoring Cache (M-Cache) is built to buffer the reference data for integrity checking. Moreover, a recovery strategy mainly for three tampered positions (registers in processor, instructions in Cache, and codes in memory) is provided to ensure the smooth running of the embedded system. Finally, the open RISC processor is adopted to implement and verify the presented security architecture, which has been proved to be effective for program detection in the execution of tamper attack and quick recovery of the running environment as well as code.
A Reconfigurable Polarization—Frequency Supershape Patch Antenna with Enhanced Bandwidth
Authors: Koutinos Anastasios , Xanthopoulou Georgia , Kyriacou George , Chryssomallis Michael
Published: July 2020

In this article a reconfigurable antenna for WLAN/WiMAX applications is presented. A super-shape radiator of an ellipsis shape is used to achieve wider intrinsic bandwidth compared to the classical rectangular patch antenna, while the dimensions remain comparable. The proposed antenna is fed at two points exciting both horizontal and vertical polarization but in different operating frequencies. To achieve wider bandwidth, as a whole but also for each polarization, the symmetrical feeding points for each excitation are also employed with a proper feeding network. PIN diodes are also used in the feeding network to provide the option of narrower bandwidth. The antenna substrate is Rogers RO4003C with dielectric constant εr = 3.55 and dissipation losses tanδ = 0.0027 with height h = 1.524 mm. The antenna operates in the range of 2.3 GHz to 2.55 GHz but, using the proposed procedure, it can be designed for different frequency ranges.
Improvement of Radio Frequency Identification Security Using New Hybrid Advanced Encryption Standard Substitution Box by Chaotic Maps
Authors: El Batouty Amira S. , Farag Hania H. , Mokhtar Amr A. , El-Badawy El-Sayed A. , Aly Moustafa H.
Published: July 2020

Radio Frequency Identification (RFID) technology is widely utilized by businesses, organizations and wireless communication systems. RFID technology is secured using different ways of data encryption, e.g., Advanced Encryption Standard (AES). The Substitution Box (S-Box) is the core of AES. In this paper, a new algorithm is proposed to generate a modified S-Box with new keys, specifically a key and plaintext-dependent S-Box using an improved RC4 encryption algorithm with Logistic Chaotic Maps (LCM). The strength of the proposed S-Box is tested throughout the paper, and compared against the state-of-the-art S-Box implementations, namely, the static S-Box, dynamic S-box, KSA and PRGA S-Box, and RC4 S-Boxes with Henon chaotic maps. The comparison between the state-of-the-art S-Boxes and the proposed S-Box demonstrates that the use of the Logistic Chaotic Map increases the security of the S-Box and makes the differential and linear cryptography more sturdy. In particular, using the strict avalanche test, we demonstrate that the proposed S-Box improves the security by achieving a cipher text bit-flip ratio of 0.4765, which is closer to 0.5 (where half the bits are flipped), while maintaining a minimum elapsed time of 19 milliseconds for encryption and decryption.
Deep-Learning-Based Frame Format Detection for IEEE 802.11 Wireless Local Area Networks
Authors: Kim Minjae , Zhang Zhongfeng , Kim Daejin , Choi Seungwon
Published: July 2020

Backward compatibility is one of the key issues for radio equipment that supports IEEE 802.11, which is a typical communication protocol for wireless local area networks (WLANs). For achieving successful packet decoding with backward compatibility, frame format detection is the core precondition. In this study, we present a novel, deep-learning-based frame format detection method for IEEE 802.11 WLANs. Considering that the detection performance of conventional methods is mainly degraded because of poor performance in symbol synchronization and/or channel estimation in environments with a low signal-to-noise ratio, we propose a novel detection method based on a deep learning network to replace conventional detection procedures. The proposed deep-learning network method achieves robust detection directly from the received (Rx) data. Through extensive computer simulations performed in multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), we confirmed that the proposed method exhibits significantly higher frame format detection performance than that of the conventional method.
Single-Switch Bipolar Output DC-DC Converter for Photovoltaic Application
Authors: Litrán Salvador P. , Durán Eladio , Semião Jorge , Barroso Rafael S.
Published: July 2020

Bipolar DC grids have become an adequate solution for high-power microgrids. This is mainly due to the fact that this configuration has a greater power transmission capacity. In bipolar DC grids, any distributed generation system can be connected through DC-DC converters, which must have a monopolar input and a bipolar output. In this paper, a DC-DC converter based on the combination of single-ended primary-inductor converter (SEPIC) and Ćuk converters is proposed, to connect a photovoltaic (PV) system to a bipolar DC grid. This topology has, as main advantages, a reduced number of components and a high efficiency. Furthermore, it can contribute to regulate/balance voltage in bipolar DC grids. To control the proposed converter, any of the techniques described in the literature and applied to converters of a single input and single output can be used. An experimental prototype of a DC-DC converter with bipolar output based on the combination of SEPIC and Ćuk converters was developed. On the other hand, a perturb and observe method (P and O) has been applied to control the converter and has allowed maximum power point tracking (MPPT). The combined converter was connected in island mode and in parallel with a bipolar DC microgrid. The obtained results have allowed to verify the behavior of the combined converter with the applied strategy.
Proactive Forensics in IoT: Privacy-Aware Log-Preservation Architecture in Fog-Enabled-Cloud Using Holochain and Containerization Technologies
Authors: Janjua Kanwal , Shah Munam Ali , Almogren Ahmad , Khattak Hasan Ali , Maple Carsten , Din Ikram Ud
Published: July 2020

Collecting and preserving the smart environment logs connected to cloud storage is challenging due to the black-box nature and the multi-tenant cloud models which can pervade log secrecy and privacy. The existing work for log secrecy and confidentiality depends on cloud-assisted models, but these models are prone to multi-stakeholder collusion problems. This study proposes ’PLAF,’ a holistic and automated architecture for proactive forensics in the Internet of Things (IoT) that considers the security and privacy-aware distributed edge node log preservation by tackling the multi-stakeholder issue in a fog enabled cloud. We have developed a test-bed to implement the specification, as mentioned earlier, by incorporating many state-of-the-art technologies in one place. We used Holochain to preserve log integrity, provenance, log verifiability, trust admissibility, and ownership non-repudiation. We introduced the privacy preservation automation of log probing via non-malicious command and control botnets in the container environment. For continuous and robust integration of IoT microservices, we used docker containerization technology. For secure storage and session establishment for logs validation, Paillier Homomorphic Encryption, and SSL with Curve25519 is used respectively. We performed the security and performance analysis of the proposed PLAF architecture and showed that, in stress conditions, the automatic log harvesting running in containers gives a 95% confidence interval. Moreover, we show that log preservation via Holochain can be performed on ARM-Based architectures such as Raspberry Pi in a very less amount of time when compared with RSA and blockchain.
Inertia and Droop Frequency Control Strategy of Doubly-Fed Induction Generator Based on Rotor Kinetic Energy and Supercapacitor
Authors: Yan Xiangwu , Sun Xuewei
Published: July 2020

The large-scale application of wind power eases the shortage of conventional energy, but it also brings great hidden danger to the stability and security of the power grid because wind power has no ability for frequency regulation. When doubly-fed induction generator (DFIG) based wind turbines use rotor kinetic energy to participate in frequency regulation, it can effectively respond to frequency fluctuation, but has the problems of secondary frequency drop and output power loss. Furthermore, it cannot provide long-term power support. To solve these problems, a coordinated frequency control strategy based on rotor kinetic energy and supercapacitor was proposed in this paper. In order to ensure the DFIG provides fast and long-term power support, a supercapacitor was used to realize the droop characteristic, and rotor kinetic energy was used to realize the inertia characteristic like synchronous generator (SG). Additionally, the supercapacitor is also controlled to compensate for the power dip of the DFIG when rotor kinetic energy exits inertia support to avoid secondary frequency drop. Additionally, a new tracking curve of DFIG rotor speed and output power was adopted to reduce the power loss during rotor speed recovery.
Advance Care Planning (ACP) vs. Advance Serious Illness Preparations and Planning (ASIPP)
Authors: Heyland Daren K.
Published: July 2020

COVID-19 has highlighted the reality of an impending serious illness for many, particularly for older persons. Those faced with severe COVID-19 infection or other serious illness will be faced with decisions regarding admission to intensive care and use of mechanical ventilation. Past research has documented substantial medical errors regarding the use or non-use of life-sustaining treatments in older persons. While some experts advocate that advance care planning may be a solution to the problem, I argue that the prevailing understanding and current practice of advance care planning perpetuates the problem and results in patients not receiving optimal patient-centered care. Much of the problem centers on the framing of advance care planning around end of life care, the lack of use of decision support tools, and inadequate language that does not support shared decision-making. I posit that a new approach and new terminology is needed. Advance Serious Illness Preparations and Planning (ASIPP) consists of discrete steps using evidence-based tools to prepare people for future clinical decision-making in the context of shared decision-making and informed consent. Existing tools to support this approach have been developed and validated. Further dissemination of these tools is warranted.
A Machine Learning Approach to Delineating Neighborhoods from Geocoded Appraisal Data
Authors: Ali Rao , Graves Josh , Wu Stanley , Lee Jenny , Linstead Erik
Published: July 2020

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information that can be extracted from property appraisals. This paper demonstrates the potential of using only the distance between subjects and their comparable properties, identified in an appraisal, to delineate neighborhoods that are composed of properties with similar prices and features. Using spatial filters, we first identify regions with the most appraisal activity, and through the application of a spatial clustering algorithm, generate neighborhoods composed of properties sharing similar characteristics. Through an application of bootstrapped linear regression, we find that delineating neighborhoods using geolocation of subjects and comparable properties explains more variation in a property’s features, such as valuation, square footage, and price per square foot, than ZIP codes or Census tracts. We also discuss the ability of the neighborhoods to grow and shrink over the years, due to shifts in each housing submarket.
Optimal Design of a Ljungström Turbine for ORC Power Plants: From a 2D model to a 3D CFD Validation
Authors: Coronetta Umberto , Sciubba Enrico
Published: July 2020

In the last few years, waste-energy recovery systems based on the Organic Rankine Cycle (ORC) have gained increased attention in the global energy market as a versatile and sustainable technology for thermo-electric energy conversion from low-to-medium temperature sources, up to 350 °C. For a long time, water has been the only working fluid commercially adopted in powerplants: axial and, for smaller machines, radial inflow turbines have been the preferred expanders since their gulp capacity matches the ρ-T curve of water steam. The density of most organic compounds displays extremely large variations during the expansion (and the volume flow rate correspondingly increases along the machine channels), so that Radial Outflow Turbines (ROTs) have been recently considered instead of traditional solutions. This work proposes a two-dimensional inviscid model for the stage optimization of a counter-rotating ROT, known as the Ljungström turbine. The study starts by considering five different working fluids that satisfy both the gulp requirements of the turbine and the hot source characteristics. On the basis of a limited number of geometric assumptions and for a fixed set of operating conditions, different kinematic parameters are optimized to obtain the most efficient cascade configuration. Moreover, as shown in the conclusions, the most efficient blade profile leads to higher friction losses, making further investigation regarding the best configuration necessary.
Improving Smart Cities Safety Using Sound Events Detection Based on Deep Neural Network Algorithms
Authors: Ciaburro Giuseppe , Iannace Gino
Published: July 2020

In recent years, security in urban areas has gradually assumed a central position, focusing increasing attention on citizens, institutions and political forces. Security problems have a different nature—to name a few, we can think of the problems deriving from citizens’ mobility, then move on to microcrime, and end up with the ever-present risk of terrorism. Equipping a smart city with an infrastructure of sensors capable of alerting security managers about a possible risk becomes crucial for the safety of citizens. The use of unmanned aerial vehicles (UAVs) to manage citizens’ needs is now widespread, to highlight the possible risks to public safety. These risks were then increased using these devices to carry out terrorist attacks in various places around the world. Detecting the presence of drones is not a simple procedure given the small size and the presence of only rotating parts. This study presents the results of studies carried out on the detection of the presence of UAVs in outdoor/indoor urban sound environments. For the detection of UAVs, sensors capable of measuring the sound emitted by UAVs and algorithms based on deep neural networks capable of identifying their spectral signature that were used. The results obtained suggest the adoption of this methodology for improving the safety of smart cities.
Influence of Dimple Height on Turbulent Heat Transfer of Fin Array with Alternate Convex/Concave Dimples
Authors: Wu Horng-Wen , Chen Tang-Hong , Kelana Nugroho-Putra , Huang De-An
Published: July 2020

This study analyzes transient turbulent modeling of three-dimensional multiple dimpled fin array using large eddy simulation (LES). The Navier–Stokes equations as well as the energy equation were constructed by the finite volume method and then discretized to form algebraic equations, which were solved by semi-implicit method for pressure-linked equation (SIMPLE). The solutions of temperature and velocity were obtained by iterating computation until it converged within each step. This simulation places nine fins on the bottom surface of a channel and changes the height of the dimple (0.4, 0.8, and 1.2 mm) with three different levels of Reynolds number (Re) (3500, 5000, and 6500) to investigate the temperature and flow field without gravity in forced convection. The results indicate that the dimpled fin array can generate vortices between the convex/concave dimples and the fin base and increase the influences of the height of the dimple on the flow field around the fin array. The averaged time-mean of the Nusselt number (Nu) for the dimple height of 0.8 mm is higher than that of the no-dimple case up to 14.4%, while the averaged time-mean Nu for the dimple height of 1.2 mm is lower than that of the no-dimple case up to 11.6%.
Differences in Balance Ability and Motor Control between Dancers and Non-Dancers with Varying Foot Positions
Authors: Harmon Brooke V. , Reed Andrea N. , Rogers Rebecca R. , Marshall Mallory R. , Pederson Joseph A. , Williams Tyler D. , Ballmann Christopher G.
Published: July 2020

The purpose of this study was to investigate balance and motor control in dancers and non-dancers with different foot positions. Physically active female dancers (n = 11) and non-dancers (n = 9) randomly completed two balance tests in a single visit: 1) Y-balance test (YBT), and 2) motor control test (MCT). Each test was completed with two different foot positions: 1) first ballet position in which heels were touching and feet were externally rotated to 140 degrees, and 2) sixth ballet position in which heels were spaced 10 cm apart and forward parallel. For the YBT, participants completed three attempts at anterior, posteromedial, and posterolateral reaches, which were averaged and standardized to limb length for a composite score. For the MCT, participants completed a multi-directional target test on a Biosway balance system, and accuracy and time to completion were analyzed. Findings revealed no differences in YBT score (p = 0.255), MCT score (p = 0.383), or MCT time (p = 0.306) between groups in the sixth position. However, dancers displayed better YBT scores (p = 0.036), MCT scores (p = 0.020), and faster MCT times (p = 0.009) in the first position. Results suggest that superior balance and motor control in dancers may be limited to less innate dance-specific foot positions.
The Evidence for Geary’s Theory on the Role of Mitochondrial Functioning in Human Intelligence Is Not Entirely Convincing
Authors: Schubert Anna-Lena , Hagemann Dirk
Published: July 2020

Geary (2018, 2019) suggested that heritable and environmentally caused differences in mitochondrial functioning affect the integrity and efficiency of neurons and supporting glia cells and may thus contribute to individual differences in higher-order cognitive functioning and physical health. In our comment, we want to pose three questions aimed at different aspects of Geary’s theory that critically evaluate his theory in the light of evidence from neurocognitive, cognitive enhancement, and behavioral genetics research. We question (1) if Geary’s theory explains why certain cognitive processes show a stronger age-related decline than others; (2) if intervention studies in healthy younger adults support the claim that variation in mitochondrial functioning underlies variation in human intelligence; and (3) if predictions arising from the matrilineal heredity of mitochondrial DNA are supported by behavioral genetics research. We come to the conclusion that there are likely many more biological and social factors contributing to variation in human intelligence than mitochondrial functioning.
Integrating Real-Time Monitoring Data in Risk Assessment for Crane Related Offshore Operations
Authors: Ancione Giuseppa , Paltrinieri Nicola , Milazzo Maria Francesca
Published: July 2020

The oil and gas sector is one of the most dangerous and stringent workplaces, due to the hazardousness of materials involved as well as the critical tasks that workers have to perform. Cranes are widely used in this sector for several activities. A wrong load lifting or handling often is due to a limited visibility of working area and could bring to severe accidental scenarios, for this reason safety of these operations becomes of paramount importance. The use of safety devices, that provide an augmented vision to the crane-operator, is essential to avoid potential accidents, moreover risk analysis could benefit from the acquisition of real time information about the process. This work aims to extrapolate and adapt dynamic risk assessment concepts for crane-related operations of a typical oil and gas industry by means of the support of safety devices. To achieve this objective, a set of risk indicators, reporting continuous information about the operations that are carried out, will be defined; successively, a technique of aggregation of these indicators will also be applied with the aim to update the frequency of critical events by a proper Risk Metric Reduction Factor that accounts for the effect of the use of safety barriers.
Field Measurements of a High-Energy Headland Deflection Rip Current: Tidal Modulation, Very Low Frequency Pulsation and Vertical Structure
Authors: Sous Damien , Castelle Bruno , Mouragues Arthur , Bonneton Philippe
Published: July 2020

Headland rips, sometimes referred to as boundary rips, are rip currents flowing against natural or artificial obstructions extending seaward from the beach, such as headland or groynes. They can be driven either by the deflection of the longshore current against the obstacle or by alongshore variation in breaking wave height due to wave shadowing in the lee of the obstacle. The driving mechanism therefore essentially depends on the angle of wave incidence with respect to the natural or artificial obstruction. We analyze 42 days of velocity profile measurements against a natural headland at the high-energy meso-macrotidal beach of Anglet, southwest France. Measurements were collected in 6.5–10.5-m depth as tide elevation varied, during the autumn–winter period with offshore significant wave height and period ranging 0.9–6 m and 8–16 s, respectively, and the angle of wave incidence ranging from −20 ∘ to 20 ∘ . Here we analyze deflection rip configurations, corresponding to approximately 24 days of measurements, for which the current meter was alternatively located in the rip neck, rip head or away from the rip as wave and tide conditions changed. Deflection rips were associated with large offshore-directed velocities (up to 0.6 m/s depth-averaged velocities) and tide modulation for low- to moderate-energy waves. The vertical profile of deflection rips was found to vary from depth-uniform in the rip neck to strongly depth-varying further offshore in the rip head with maximum velocities near the surface. Very low frequency motions of the rip were dramatic, ranging 10–60 min with a dominant peak period of approximately 40 min, i.e., with longer periods than commonly reported. The strong offshore-directed velocities measured well beyond the surf zone edge provide new insight into deflection rips as a dominant mechanism for water and sediment exchanges between embayed (or structurally-controlled) beaches and the inner-shelf and/or the adjacent embayments.
A Hybrid Wind Load Estimation Method for Container Ship Based on Computational Fluid Dynamics and Neural Networks
Authors: Prpić-Oršić Jasna , Valčić Marko , Čarija Zoran
Published: July 2020

The estimation of wind loads on ships and other marine objects represents a continuous challenge because of its implication for various aspects of exposed structure exploitation. An extended method for estimating the wind loads on container ships is presented. The method uses the Generalized Regression Neural Network (GRNN), which is trained with Elliptic Fourier Descriptors (EFD) of sets of frontal and lateral closed contours as inputs. Wind load coefficients (Cx, Cy, CN), used as outputs for network training, are derived from 3D steady RANS CFD analysis. This approach is very suitable for assessing wind loads on container ships wherever there is a wind load database for a various container configuration. In this way, the cheaper and faster calculation can bridge the gap for the container configurations for which calculations or experiments have not already been made. The results obtained by trained GRNN are in line with available experimental measurements of the wind loads on various container configuration on the deck of a 9000+ TEU container ship obtained through a series of wind tunnel tests, as well as with performed CFD simulation for the same conditions.