bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial and Temporal Distribution and Habitat Selection of Native Yellowstone Cutthroat Trout and Nonnative Utah Chub
Authors: McCarrick Darcy K. , Dillon Jeffrey C. , High Brett , Quist Michael C.
Published: April 2022

Henrys Lake, Idaho, is a renowned trophy trout fishery that faces an uncertain future following the establishment of Utah Chub (UTC) Gila atraria. Utah Chub were first documented in the lake in 1993 and have become abundant over the past two decades. Little is known about the ecology of UTC, but they typically have negative effects on salmonids in systems where they have been introduced. We sought to fill knowledge gaps in UTC ecology and provide insight on potential interactions with Yellowstone Cutthroat Trout (YCT) Oncorhynchus clarkii bouvieri. Ninety-four YCT and 95 UTC were radio-tagged in spring 2019 and 2020 to better understand potential interactions between YCT and UTC in Henrys Lake. Fish were located via mobile tracking and fixed receivers from June to December 2019 and 2020. In June of both years, YCT and UTC were concentrated in nearshore habitats. As water temperatures increased, UTC were documented in deeper water (mean ± SD = 3.6 ± 1.4 m) and YCT became more concentrated in areas with cold water (e.g., mouths of tributaries, in-lake springs). In July and August, large congregations of UTC were observed. Yellowstone Cutthroat Trout were detected in tributaries from June to August, but no UTC were detected in the tributaries. By late fall (November–December), YCT were located along the shoreline and UTC were detected in the middle of the lake. Both YCT and UTC were observed in areas with dense vegetation. Macrophytes likely provided a food source for UTC and cover from predators for both species. Locations of YCT were negatively related to warm water temperatures, whereas UTC were positively associated with warm water temperatures. Results from this research fill knowledge gaps in UTC and YCT interactions as well as provide valuable insight on the ecology of UTC and adfluvial Cutthroat Trout populations. Furthermore, distribution patterns and habitat selectivity of YCT and UTC in Henrys Lake can be used to inform management decisions for fishery improvement and YCT conservation.
General analytical solutions for one‐dimensional nonlinear consolidation of saturated clay under non‐isothermal distribution condition
Authors: Li Jiangshan , Jiang Wenhao , Ge Shangqi , Chen Xiaohui , Chen Xin , Liu Lei
Published: April 2022

The fluctuation of temperature leads to the changes of physical‐mechanical properties of clayey soils. In some practical projects such as landfills, the compacted clay liner is usually subjected to a non‐isothermal distribution state. For one‐dimensional nonlinear consolidation process of saturated clay under non‐isothermal distribution condition, the general analytical solutions considering time‐dependent loading are derived for the first time, where the methods of algebraic transformation and separation variable are used. Moreover, two forms of boundary conditions are included according to engineering practice. Referring to the proposed general analytical solutions, the expressions for the analytical solutions under instantaneous loading pattern and single‐stage linear loading pattern are developed. Besides, the correctness of the presented analytical solutions is validated by comparing with the existing analytical solutions and finite difference solutions. Based on the proposed analytical solutions, the influence of temperature gradient, final loading and loading time on the consolidation behaviors is analyzed. It is found that the increase in temperature gradient accelerates the consolidation rate, and the average volume compressibility coefficient decreases by 65.4% when final loading increases from 50 to 500 kPa. In conclusion, the analytical solutions proposed in this study are more comprehensive and can be applied in different engineering cases.
Study of the optical, thermal, morphological and mechanical characteristics of a laser weldable fiber reinforced polymer
Authors: Silva Luiz R. R. , Marques Eduardo A. S. , Carbas Ricardo J. C. , Akhavan‐Safar Alireza , da Silva Lucas F. M.
Published: April 2022

In recent years, varied industrial users such as the automotive and electronic manufacturers, have greatly expanded the application of technical polymers, exploring the lower weight, improved moldability of complex parts and good mechanical performance that these materials can provide, especially when reinforced with fibers. Among these materials, polybutylene terephthalate reinforced with 30% glass fiber, designated as PBT GF30, is highly interesting, since it combines excellent mechanical properties with watertightness, allowing PBT GF30 to be used in the encapsulation of sensitive electronic elements. However, to maintain access, PBT GF30 products always require the use of joints, which are often created using laser welding. This work presents a detailed characterization of the thermal, optical, mechanical, and morphological characteristics of PBT‐GF30 suitable for laser welding, supported by a numerical analysis. This process allowed to determine the influence of thermal energy on the behavior of the material and thus fully understand the mechanical response of the material after laser welding. Testing of the PBT GF30 in the as received state confirmed a fusion temperature of 225°C and large energy absorption for wavelengths around 1000 nm. Exposure to large temperatures leads to increased fiber exposure and matrix degradation, with a deleterious effect on the mechanical properties. When treated at the highest temperature under consideration, the material exhibited a reduction of 66% in its ultimate tensile stress, 67% in the yield at failure, 45% in the Young's modulus and 44% in the stress intensity factor. Hardness was less affected by the temperature increase. From the numerical model for the tensile and the compact tension tests, it was possible to find a good degree of convergence. This material was generally found suitable for laser welding processes and the determined useful to support the modeling the behavior of laser welded joints.
Psychological impact of the COVID‐19 pandemic on hospital workers in Kobe: A cross‐sectional survey
Authors: Fukushima Haruko , Imai Hissei , Miyakoshi Chisato , Miyai Hiroyuki , Otani Kyohei , Aoyama Shinsuke , Matsuishi Kunitaka
Published: April 2022

Aim

Many health‐care workers exposed to coronavirus disease 2019 (COVID‐19) are psychologically distressed. This study aimed to investigate the psychological impact of the COVID‐19 pandemic on hospital workers under the emergency declaration in Japan.

Methods

This cross‐sectional, survey‐based study collected sociodemographic data and responses to 19 stress‐related questions and the Impact of Event Scale‐Revised (IES‐R), which measures post‐traumatic stress disorder (PTSD) symptoms, from all 3217 staff members at Kobe City Medical Center General Hospital from April 16, 2020 to June 8, 2020. Exploratory factor analysis was applied to the 19 stress‐related questions. Multiple regression models were used to evaluate the association of personal characteristics with each score of the four factors and the IES‐R.

Results

We received 951 valid responses; 640 of these were by females, and 311 were by respondents aged in their 20s. Nurses accounted for the largest percentage of the job category. Women, those aged in their 30s–50s, nurses, and frontline workers had a high risk of experiencing stress. The prevalence of stress (IES‐R ≥ 25) was 16.7%. The psychological impact was significantly greater for those aged in their 30s–50s and those who were not medical doctors.

Conclusions

This is the first study to examine the stress of hospital workers, as measured by the IES‐R, under the emergency declaration in Japan. It showed that women, those aged in their 30s–50s, nurses, and frontline workers have a high risk of experiencing stress. Health and medical institutions should pay particular attention to the physical and psychological health of these staff members.
Genetic diagnosis of common fetal renal abnormalities detected on prenatal ultrasound
Authors: Liu Ling , Li Juan , Li Ying , Li Haiyu , Yang Bo , Fan Hui , Wang Jing , Gu Yanting , Yu Hui , Bai Maohuan , Yu Tantan , Cui Shihong , Cheng Guomei , Ren Chenchen
Published: April 2022

Objectives

This retrospective study aimed to investigate the correlations between phenotypes of fetal renal abnormalities on prenatal ultrasound and genetic aetiologies detected using chromosomal microarray analysis (CMA) and whole‐exome sequencing (WES).

Methods

Fetuses with renal abnormalities were subjected to CMA and were further analysed by WES when CMA‐negative. The detection rates for chromosomal abnormalities and monogenic variants among different types of isolated renal abnormalities and those with extrarenal abnormalities (non‐isolated cases) were determined and compared.

Results

CMA detected chromosomal abnormalities in 78 of 577 fetuses (13.52%). WES detected monogenic variants in 31 of 160 fetuses (19.38%) that had non‐diagnostic CMA results. In cases of isolated hyperechogenic kidney, polycystic kidney disease, and multicystic dysplastic kidney, the detection rates of copy number variants (CNVs) by CMA and monogenic variants by WES were not significantly different (p > 0.05). However, monogenic variants were more frequently detected than CNVs when kidney abnormalities were accompanied by reduced amniotic fluid (p < 0.05). Other renal abnormalities identified on prenatal ultrasound had different detection rates.

Conclusions

Our findings contribute to the overall knowledge of genetic variants associated with prenatally identified renal anomalies and may aid in decision making regarding prenatal genetic testing options for affected pregnancies.
Effect of intumescent flame retardants on non‐isothermal crystallization behavior of high‐density polyethylene
Authors: Mysiukiewicz Olga , Sałasińska Kamila , Barczewski Mateusz , Celiński Maciej , Skórczewska Katarzyna
Published: April 2022

In this study, the influence of the addition of intumescent flame retardants (IFRs) on the non‐isothermal crystallization of high‐density polyethylene (HDPE) was studied. The investigations were carried out for polyethylene compositions containing two different IFRs, ammonium polyphosphate (APP) and a mixture of copper phosphate and melamine phosphate (CUMP). Both IFRs have been incorporated with a constant amount of 20 wt%. Non‐isothermal kinetic parameters were calculated from differential scanning calorimetry (DSC) analysis using the Jeziorny and Mo methods. The research demonstrates the appropriateness of using the latter approach to evaluate the HDPE‐based crystallization process changes. The results of the obtained kinetic parameters describing HDPE crystallization were related to the activation energy assessed by the Friedman method, microscopic analysis performed using polarized light microscopy, and wide‐angle X‐ray scattering (WAXS). Despite the increase in the activation energy (EX) of the flame retarded polyethylene series, the degree of crystallinity was comparable to that of unmodified HDPE due to a limited dependence effect of EX on relative crystallinity. Investigations made using polarized light microscopy showed an increase in the density of spherulite impingement for HDPE/IFRs. Moreover, despite introducing crystalline organic compounds, polyethylene did not reveal any polymorphic changes according to WAXS.
Kinetic Predictions Concerning the Long‐Term Stability of TKX‐50 and Other Common Explosives Using the NETZSCH Kinetics Neo Software
Authors: Harter Alexander G. , Klapötke Thomas M. , Lechner Jasmin T. , Stierstorfer Jörg
Published: April 2022

Explosives are used in both military and civilian applications all over the world. Sufficient longevity and good thermal stability are therefore essential for safe handling and safe storage of energetic materials. In this work, five well‐known compounds, TKX‐50, RDX, HMX, CL‐20 and PETN, were investigated by means of different kinetic models, in order to make predictions about their long‐term stability. For this purpose, the compounds were synthesized according to literature‐known procedures and thermogravimetric (TG) measurements were performed. The TG plots were analyzed using the Ozawa‐Flynn‐Wall, Friedman and ASTM E698 kinetic models with the NETZSCH Kinetics Neo software and the activation energy and isothermal long‐term stability were determined. Moreover, various climatic predictions of different countries were made.
Membranome 3.0: Database of single‐pass membrane proteins with AlphaFold models
Authors: Lomize Andrei L. , Schnitzer Kevin A. , Todd Spencer C. , Cherepanov Stanislav , Outeiral Carlos , Deane Charlotte M. , Pogozheva Irina D.
Published: April 2022

The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.
Pulse‐field gradient nuclear magnetic resonance of protein translational diffusion from native to non‐native states
Authors: Whitehead Richard D. , Teschke Carolyn M. , Alexandrescu Andrei T.
Published: April 2022

Hydrodynamic radii (Rh‐values) calculated from diffusion coefficients measured by pulse‐field‐gradient nuclear magnetic resonance are compared for folded and unfolded proteins. For native globular proteins, the Rh‐values increase as a power of 0.35 with molecular size, close to the scaling factor of 0.33 predicted from polymer theory. Unfolded proteins were studied under four sets of conditions: in the absence of denaturants, in the presence of 6 M urea, in 95% dimethyl sulfoxide (DMSO), and in 40% hexafluoroisopropanol (HFIP). Scaling factors under all four unfolding conditions are similar (0.49–0.53) approaching the theoretical value of 0.60 for a fully unfolded random coil. Persistence lengths are also similar, except smaller in 95% DMSO, suggesting that the polypeptides are more disordered on a local scale with this solvent. Three of the proteins in our unfolded set have an asymmetric sequence‐distribution of charged residues. While these proteins behave normally in water and 6 M urea, they give atypically low Rh‐values in 40% HFIP and 95% DMSO suggesting they are forming electrostatic hairpins, favored by their asymmetric sequence charge distribution and the low dielectric constants of DMSO and HFIP. While diffusion‐ordered NMR spectroscopy can separate small molecules, we show a number of factors combine to make protein‐sized molecules much more difficult to resolve in mixtures. Finally, we look at the temperature dependence of apparent diffusion coefficients. Small molecules show a linear temperature response, while large proteins show abnormally large apparent diffusion coefficients at high temperatures due to convection, suggesting diffusion reference standards are only useful near 25°C.
93Nb NMR Study of (K, Na)NbO3‐Doped SiO2–Na2O–Al2O3 Glasses
Authors: Otsuka Takahito , Cicconi Maria Rita , Dobesh David , Schroeder Barbara , Hayakawa Tomokatsu
Published: April 2022

Aluminosilicate glasses containing octahedral NbO6 units are developed for future applications as optical materials. Soda‐aluminosilicate glasses doped with Nb‐based alkaline perovskite crystals (K0.5Na0.5NbO3 [KNN]) at different doping levels (2–30 mol%) are studied by 93Nb nuclear magnetic resonance (NMR) spectroscopy. The Al2O3 concentration in the starting soda‐aluminosilicate glass matrix is varied from 5 to 17 mol% to investigate the influence of the composition of local Nb structures with 4–5, 6, and 7–8 fold‐oxygen coordination on the optical properties of the glasses. The chemical shifts are analyzed by comparison with the 93Nb NMR data of reference crystals, including KNN, to determine the feasibility of producing more NbO6 octahedra in the aluminosilicate glasses, where these octahedra are expected to promote the development of optically active Nb‐glass‐ceramics. The data show that both glass polymerization and KNN content influence the Nb local environment. Therefore, an appropriate amount of Al2O3 combined with an adequate doping level of the KNN perovskite is required to achieve the desired optical features. Contrary to expectations, a higher KNN doping level is achieved with the starting depolymerized soda‐aluminosilicate glass containing 5 mol% Al2O3, leading to perovskite‐like structures of NbO6 octahedra in the resultant glass system.
Nonmonotonic Magnetic Field Dependence of Remnant Ferroelectric Polarization in Reduced Graphene Oxide–BiFeO3 Nanocomposite
Authors: Chatterjee Tania , Mukherjee Arnab , Pal Prabir , Kaushik Somdatta , Siruguri Vasudeva , Mandal Subhankar , Hazra Satyajit , Bhattacharjee Swarupananda , Ghosh Chandan Kumar , Bhattacharya Dipten
Published: April 2022

The thin‐film heterostructures or nanocomposites exhibit vastly different properties from those observed in bulk systems. Herein, in a nanocomposite of reduced graphene oxide (RGO) and BiFeO3 (BFO), the remnant ferroelectric polarization is found to follow nonmonotonic magnetic field dependence at room temperature as the applied magnetic field is swept across 0–20 kOe on a pristine sample. Bulk BiFeO3, in contrast, exhibits monotonic suppression of polarization under magnetic field. The remanent ferroelectric polarization, in the present case, is determined, primarily, from powder neutron diffraction patterns recorded under 0–20 kOe field. The nanosized (≈20 nm) particles of BFO are anchored onto the graphene sheets of RGO via FeC bonds with concomitant rise in covalency in the FeO bonds. Field‐dependent competition between positive and negative magnetoelectric coupling arising from magnetostriction due to, respectively, interface and bulk magnetization appears to have given rise to the observed nonmonotonic field dependence of polarization. The emergence of FeC bonds and consequent change in the magnetic and electronic structure of the interface region have influenced coupling between ferroelectric and magnetic properties remarkably and thus creates a new way of tuning the magnetoelectric properties via reconstruction of interfaces in nanocomposites or heterostructures of graphene/single‐phase‐multiferroic systems.
Sarcoidosis following SARS‐CoV‐2 infection: Cause or consequence?
Authors: Palones Esther , Pajares Virginia , López Laura , Castillo Diego , Torrego Alfons
Published: April 2022

The COVID‐19 pandemic has been a worldwide medical challenge. Despite rapid advancements, many questions regarding SARS‐CoV‐2 interaction with other pathologies and long‐term consequences remained unanswered. Sarcoidosis is a multi‐systemic granulomatous disease that develops in genetically predisposed individuals following their exposure to an environmental trigger. We present the case of a patient who was diagnosed with sarcoidosis following a SARS‐CoV‐2 infection.
Biomarker based biosensors: An opportunity for diagnosis of COVID‐19
Authors: Khaksarinejad Reza , Arabpour Zohreh , RezaKhani Leila , Parvizpour Farzad , Rasmi Yousef
Published: April 2022

Early diagnosis and treatment of diseases are crucial research areas of human health. For early diagnosis, one method that has proven efficient is the detection of biomarkers which can provide real‐time and accurate biological information. Most biomarker detection is currently carried out at localised dedicated laboratories using large and automated analysers, increasing waiting time and costs. Smaller, faster, and cheaper devices could potentially replace these time‐consuming laboratory analyses and make analytical results available as point‐of‐care diagnostics. Innovative biosensor‐based strategies could allow biomarkers to be tested reliably in a decentralised setting. Early diagnosis of COVID‐19 patients has a key role in order to use quarantine and treatment strategies in a timely manner. Raised levels of several biomarkers in COVID‐19 patients are associated with respiratory infections or dysfunction of various organs. Through clinical studies of COVID‐19 patient biomarkers such as ferritin, Interleukins, albumin and …are found to reveals significant differences in their excretion ranges from healthy patients and patients with SARS‐CoV‐2, in addition to the development of biomarkers based biosensor such as stated biomarkers can be used and to investigate more specific biomarkers further proteomic analysis can be performed. This review presents several biomarker alterations in COVID‐19 patients such as salivary, circulatory, coagulation, cardiovascular, renal, liver, C‐reactive protein (CRP), immunological and inflammatory biomarkers. Also, biomarker sensors based on electrochemical, optical, and lateral flow characteristics which have potential applications for SARS‐COV‐2 in the recent COVID‐19 pandemic, will be discussed.
Effects of grazing, rotation, and tillage on surface runoff in a heavy textured Uruguayan soil
Authors: Barreto Patricia , Ernst Oswaldo , Pérez Bidegain Mario , Perdomo Carlos
Published: April 2022

Adopting no‐till (NT) after conventional tillage (CT) could affect surface runoff, but this effect may also depend on the rotation type, such as continuous grain crop (CC) or crop–pasture rotation (CP). The objective of this study was to assess the effects of soil‐tillage practices and rotation type on runoff in a long‐term field (LTF) trial established in 1993 on a Mollisol. This experiment was carried from 2012 to 2017 and included three tillage rotations (NT–CP, NT–CC, and CT–CP) and a natural pasture (NP) as a control, with three replications. Runoff was monitored under natural rainfall in microplots. The pasture of the CP treatment was rotationally grazed with dairy cows at high stocking rates before this experiment. We hypothesized that soil compaction by animal trampling during grazing would increase surface runoff regardless of tillage type. We measured the number of events and runoff depth in all treatments, grain yield, soil bulk density (BD), and infiltration rate (IR) in NT–CP and NT–CC. The results showed a statistically significant increase in average runoff (107 mm yr–1) for all rotations relative to NP (68 mm yr–1) and a significant difference for the mean of both CP rotations (119 mm yr–1) compared with NT–CC (83 mm yr–1). During the crop phase (excluding the pasture), the two NT rotations showed a similar trend, but with higher BD (1.23 g cm–3) and lower IR (27.2 mm h–1) for NT–CP. We concluded that animal trampling increased surface runoff regardless of tillage, but this effect would need to be confirmed at other sites.
Understanding transgender persons' careers to advance sustainable development: The case of Trans for Career Thailand
Authors: Fontana Enrico , Siriwichai Prapassorn
Published: April 2022

Careers are fundamental in progressing gender and income equality as key objectives of sustainable development. However, transgender persons often face unique challenges that prevent them from advancing in the world of work. This article elaborates upon the case of “Trans for Career Thailand,” an advocacy group established during the COVID‐19 pandemic with the purpose of offering career and employment opportunities to all transgender persons. Through primary and secondary data, the findings elucidate how “Trans for Career Thailand” uses its social media presence to buildcareer knowledge, a community of career professionalsand, finally,a trans career movement. This article shines a spotlight on the careers of transgender persons as a point of departure for advancing gender and income equality. Hence, it contributes to career development literature while offering important recommendations for sustainable development, employment policies, and advocacy groups.
Startup acquisitions, relocation, and employee entrepreneurship
Authors: Kim J. Daniel
Published: April 2022

Research Summary

This study examines the impact of startup acquisition on the acquired employees' propensity for entrepreneurship. As a new owner, the acquirer may not prioritize acquired employees' ideas to be commercialized internally, thereby increasing the odds that they are pursued externally. Leveraging employee‐employer matched data from U.S. Census on high‐tech startup acquisitions and their workforce in 1990–2011, I find that startup acquisitions substantially increase the rate of employee entrepreneurship both within and outside the target firm's industry. Post‐acquisition entrepreneurship is disproportionally concentrated among individuals with high human capital. Moreover, this effect is amplified when the target startup is relocated to the acquirer's location, and largely muted when left in its original location. Overall, this study highlights startup acquisitions as an important organizational precursor to employee entrepreneurship.

Managerial Summary

The past few decades have shown a significant increase in established firms acquiring startups. Although startup acquisitions can be a new source of technological innovation, talent, and market power, their long‐term outcomes likely depend on the ability to retain the acquired personnel. Using comprehensive data in the United States in 1990–2011, this study shows that startup acquisitions push many acquired employees to leave and launch their own ventures, some of which can be new competitors. This effect is disproportionately driven by the departures of founders and other top earners, who are generally the most valuable individuals to retain after the acquisition. Acquirers can mitigate these effects by allowing the acquired startup to remain in its original location rather than relocating it inside their existing establishments.
Numerical Simulation for Two‐Phase Flow, Heat Transfer, and Inclusion Transport in Bloom Mold Considering the Effects of Argon Gas Injection and Mold Electromagnetic Stirring
Authors: Xuan Mingtao , Chen Min
Published: April 2022

A mathematical model based on the Eulerian–Eulerian–Lagrangian method is developed to investigate the two‐phase flow, temperature distribution, and inclusion transport, considering the effect of argon gas injection (AGI) and mold electromagnetic stirring (M‐EMS) in the bloom mold. The magnetic resistance force is adopted to account for the effect of the magnetic field on bubbles. It is demonstrated in the results that when AGI is applied, plumes form near the submerged entry nozzle (SEN) and increase the velocity in the upper part of the mold. While M‐EMS is applied, the swirl flow significantly increases the overall flow velocity and superheat dissipation. In addition, AGI and M‐EMS increase the fluctuation and temperature of the free surface, lower the peak heat flow, and promote inclusions removal. When AGI is applied with M‐EMS, the swirl flow and plumes in the upper part of the mold are weakened due to the interaction between them, and the level fluctuation is lower than that of applying AGI or M‐EMS separately. The heat flow and free surface temperature are more uniform, and the inclusion removal rate further increases. Meanwhile, M‐EMS drives bubble columns to the location between the two ports of SEN and gathers bubbles around the SEN.
Monotonic Tensile and Cyclic Deformation Behaviors of Carbide‐Free Bainitic Steel Subjected to Austempering and Tempering After Air Cooling
Authors: Zhou Qian , Qian Lihe , Meng Jiangying , Qin Feng , Zhao Leijie
Published: April 2022

The monotonic tensile and tension–compression cyclic deformation behaviors of carbide‐free bainitic steel with two microstructures obtained by isothermal treatment (ISO) and air cooling followed by tempering (ACT) are investigated. Tensile results demonstrate that the ISO sample exhibits a lower yield strength but longer elongation due to more and bigger blocky retained austenite than that of the ACT sample, which can lead to a higher plastic deformation capacity for the former sample. Fatigue results indicate that the ACT sample shows a higher cyclic hardening capacity but lower cyclic softening capacity at lower strain amplitudes compared to the ISO sample, whereas an opposite phenomenon can be found at higher strain amplitudes. At given total strain amplitudes, the fatigue life of the ISO sample is shorter, which is mainly attributed to its lower yield strength and more brittle martensite. In comparison with the ACT sample, the ISO sample exhibits similar or slightly longer fatigue life at lower plastic strain amplitudes but shows shorter fatigue life at higher plastic strain amplitudes. Accordingly, a bilinear Coffin––Manson relationship is revealed for the ISO sample, which is closely associated with significantly more transformation at higher strain amplitudes compared to the lower strain amplitudes.
Shear performance of vertical joints with different lapping splices for precast concrete frame–shear wall structures
Authors: Li Yongquan , Tong Jiaxin , Yang Yuanzhang , Sun Bochao , Zhao Weijian
Published: April 2022

Precast concrete structures have been widely applied in the construction industry worldwide. However, the current construction method for the reinforced concrete frame–shear wall structure (FSWS) is semi‐precast because the shear wall is commonly cast‐in‐place. This paper proposes a full precast FSWS and investigates three new vertical joints with different lap splices that can facilitate on‐site construction. Push‐off tests are conducted to investigate the shear performance on the aspect of cracking and failure patterns, the evolution of deformation, stiffness reduction, and energy dissipation. A finite element model with delicate consideration of the contact is developed and validated against experimental data. A further parametric study is conducted. This study shows that the lap splice with a more uniformly distributed reinforcement layout yields the crack distribution more uniformly and increases the ductility of the joints. The proposed three vertical joints exhibit an improvement in ductility compared to the monolithic one. Furthermore, the shear keys change the failure mechanism of the precast joints into the multi‐struts failure mechanism thus increasing the shear strength. The parametric study shows that the decreasing of the splice lap length could increase the shear capacity of the vertical joints but decreases the ductility. However, the ductility reduction depends on the variation of lap splices.
Water insecurity in the Global North: A review of experiences in U.S. colonias communities along the Mexico border
Authors: Wutich Amber , Jepson Wendy , Velasco Carmen , Roque Anais , Gu Zhining , Hanemann Michael , Hossain Mohammed Jobayer , Landes Laura , Larson Rhett , Li Wen Wen , Morales‐Pate Olga , Patwoary Nargish , Porter Sarah , Tsai Yu‐shiou , Zheng Madeleine , Westerhoff Paul
Published: April 2022

Since the late 1970s, the term “colonias” (in English) has described low‐income, peri‐urban, and rural subdivisions north of the U.S.‐Mexico border. These communities are in arid and semi‐arid regions—now in a megadrought—and tend to have limited basic infrastructure, including community water service and sanitation. Recent scholarship has demonstrated how colonias residents experience unjust and inequitable dynamics that produce water insecurity in the Global North. In this review, we explain why U.S. colonias are an important example for theorizing water insecurity in the United States and beyond in the Global North. Tracing the history of water infrastructure development in U.S. colonias, we show how colonias are legally and socially defined by water insecurity. We draw on the published literature to discuss key factors that produce water insecurity in U.S. colonias: political exclusion, municipal underbounding, and failures in water quality monitoring. We show that water insecurity had led to negative outcomes—including poor water access, risks to physical health, and mental ill‐health—in U.S. colonias. We present four possible approaches to improving water security in U.S. colonias: (1) soft paths & social infrastructure for water delivery, (2) decentralized water treatment approaches, such as point‐of‐use, point‐of‐entry, and fit‐for‐purpose systems; (3) informality, including infrastructural, economic, and socio‐cultural innovations; and (4) political, policy, and law innovations and reforms. At the same time, we reflect seriously on how water security can be ethically achieved in partnership and aligning with the visions of U.S. colonias residents themselves.

This article is categorized under:

Human Water > Water Governance

Engineering Water > Water, Health, and Sanitation
Statistical inference for stochastic differential equations
Authors: Craigmile Peter , Herbei Radu , Liu Ge , Schneider Grant
Published: April 2022

Many scientific fields have experienced growth in the use of stochastic differential equations (SDEs), also known as diffusion processes, to model scientific phenomena over time. SDEs can simultaneously capture the known deterministic dynamics of underlying variables of interest (e.g., ocean flow, chemical and physical characteristics of a body of water, presence, absence, and spread of a disease), while enabling a modeler to capture the unknown random dynamics in a stochastic setting. We focus on reviewing a wide range of statistical inference methods for likelihood‐based frequentist and Bayesian parametric inference based on discretely‐sampled diffusions. Exact parametric inference is not usually possible because the transition density is not available in closed form. Thus, we review the literature on approximate numerical methods (e.g., Euler, Milstein, local linearization, and Aït‐Sahalia) and simulation‐based approaches (e.g., data augmentation and exact sampling) that are used to carry out parametric statistical inference on SDE processes. We close with a brief discussion of other methods of inference for SDEs and more complex SDE processes such as spatio‐temporal SDEs.

This article is categorized under:

Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data

Algorithms and Computational Methods > Maximum Likelihood Methods

Statistical Models > Fitting Models
In search of a definition and frameworks for engineering leadership development
Authors: Komarek Rebecca L. D.
Published: April 2022

Despite an elusive definition of engineering leadership, educators and researchers forge ahead to identify disparate and complementary theories which frame engineering leadership.
Case Study #6: The University of Texas at El Paso
Authors: Joslyn Cole
Published: April 2022

Out in the west Texas town of El Paso … is found the first ABET accredited engineering leadership degree program in the USA.
Technologies and Applications for Big Data Value
Authors: Curry Edward , Auer Sören , Berre Arne J. , Metzger Andreas , Perez Maria S. , Zillner Sonja
Published: April 2022

The continuous and significant growth of data, together with improved access to data and the availability of powerful computing infrastructure, has led to intensified activities around Big Data Value (BDV) and data-driven Artificial Intelligence (AI). Powerful data techniques and tools allow collecting, storing, analysing, processing and visualising vast amounts of data, enabling data-driven disruptive innovation within our work, business, life, industry and society.

The adoption of big data technology within industrial sectors facilitates organisations to gain a competitive advantage. Driving adoption is a two-sided coin. On one side, organisations need to master the technology necessary to extract value from big data. On the other side, they need to use the insights extracted to drive their digital transformation with new applications and processes that deliver real value. This book has been structured to help you understand both sides of this coin and bring together technologies and applications for Big Data Value.

This chapter defines the notion of big data value, introduces the Big Data Value Public-Private Partnership (PPP) and gives some background on the Big Data Value Association (BDVA)—the private side of the PPP. It then moves on to structure the contributions of the book in terms of three key lenses: the BDV Reference Model, the Big Data and AI Pipeline, and the AI, Data and Robotics Framework.
3D Human Big Data Exchange Between the Healthcare and Garment Sectors
Authors: Durá Gil Juan V. , Remon Alfredo , Rodriguez Iván Martínez , Pariente-Lobo Tomas , Salmeron-Majadas Sergio , Perrone Antonio , Ciuhu-Pijlman Calina , Znamenskiy Dmitry , Karavaev Konstantin , Codina Javier Ordono , Boura Laura , Silva Luísa , Redon Josep , Real Jose , Cipresso Pietro
Published: April 2022

3D personal data is a type of data that contains useful information for product design, online sale services, medical research and patient follow-up.

Currently, hospitals store and grow massive collections of 3D data that are not accessible by researchers, professionals or companies. About 2.7 petabytes a year are stored in the EU26.

In parallel to the advances made in the healthcare sector, a new, low-cost 3D body-surface scanning technology has been developed for the goods consumer sector, namely, apparel, animation and art. It is estimated that currently one person is scanned every 15 min in the USA and Europe. And increasing.

The 3D data of the healthcare sector can be used by designers and manufacturers of the consumer goods sector. At the same time, although 3D body-surface scanners have been developed primarily for the garment industry, 3D scanners’ low cost, non-invasive character and ease of use make them appealing for widespread clinical applications and large-scale epidemiological surveys.

However, companies and professionals of the consumer goods sector cannot easily access the 3D data of the healthcare sector. And vice versa. Even exchanging information between data owners in the same sector is a big problem today. It is necessary to overcome problems related to data privacy and the processing of huge 3D datasets.

To break these silos and foster the exchange of data between the two sectors, the BodyPass project has developed: (1) processes to harmonize 3D databases; (2) tools able to aggregate 3D data from different huge datasets; (3) tools for exchanging data and to assure anonymization and data protection (based on blockchain technology and distributed query engines); (4) services and visualization tools adapted to the necessities of the healthcare sector and the garment sector.

These developments have been applied in practical cases by hospitals and companies of in the garment sector.
Data-Driven Artificial Intelligence and Predictive Analytics for the Maintenance of Industrial Machinery with Hybrid and Cognitive Digital Twins
Authors: Unal Perin , Albayrak Özlem , Jomâa Moez , Berre Arne J.
Published: April 2022

This chapter presents a Digital Twin Pipeline Framework of the COGNITWIN project that supports Hybrid and Cognitive Digital Twins, through four Big Data and AI pipeline steps adapted for Digital Twins. The pipeline steps are Data Acquisition, Data Representation, AI/Machine learning, and Visualisation and Control. Big Data and AI Technology selections of the Digital Twin system are related to the different technology areas in the BDV Reference Model. A Hybrid Digital Twin is defined as a combination of a data-driven Digital Twin with First-order Physical models. The chapter illustrates the use of a Hybrid Digital Twin approach by describing an application example of Spiral Welded Steel Industrial Machinery maintenance, with a focus on the Digital Twin support for Predictive Maintenance. A further extension is in progress to support Cognitive Digital Twins includes support for learning, understanding, and planning, including the use of domain and human knowledge. By using digital, hybrid, and cognitive twins, the project’s presented pilot aims to reduce energy consumption and average duration of machine downtimes. Data-driven artificial intelligence methods and predictive analytics models that are deployed in the Digital Twin pipeline have been detailed with a focus on decreasing the machinery’s unplanned downtime. We conclude that the presented pipeline can be used for similar cases in the process industry.
Model-Based Engineering and Semantic Interoperability for Trusted Digital Twins Big Data Connection Across the Product Lifecycle
Authors: Lázaro Oscar , Alonso Jesús , Holom Roxana-Maria , Rafetseder Katharina , Kritzinger Stefanie , Ubis Fernando , Fritz Gerald , Wiesinger Alois , Sehrschön Harald , Nguyen Jimmy , Luniewski Tomasz , Zietak Wojciech , Clavel Jerome , Perez Roberto , Hildebrand Marlene , Kiritsis Dimitris , Garious Hugues-Arthur , de la Maza Silvia , Ventura-Traveset Antonio , Hierro Juanjo , Boege Gernot , Ahle Ulrich
Published: April 2022

With the rising complexity of modern products and a trend from single products to Systems of Systems (SoS) where the produced system consists of multiple subsystems and the integration of multiple domains is a mandatory step, new approaches for development are demanded. This chapter explores how Model-Based Systems Engineering (MBSE) can benefit from big data technologies to implement smarter engineering processes. The chapter presents the Boost 4.0 Testbed that demonstrates how digital twin continuity and digital thread can be realized from service engineering, production, product performance, to behavior monitoring. The Boost 4.0 testbed demonstrates the technical feasibility of an interconnected operation of digital twin design, ZDM subtractive manufacturing, IoT product monitoring, and spare part 3D printing services. It shows how the IDSA reference model for data sovereignty, blockchain technologies, and FIWARE open-source technology can be jointly used for breaking silos, providing a seamless and controlled exchange of data across digital twins based on open international standards (ProStep, QIF), allowing companies to dramatically improve cost, quality, timeliness, and business results.
A Data Science Pipeline for Big Linked Earth Observation Data
Authors: Koubarakis Manolis , Bereta Konstantina , Bilidas Dimitris , Pantazi Despina-Athanasia , Stamoulis George
Published: April 2022

The science of Earth observation uses satellites and other sensors to monitor our planet, e.g., for mitigating the effects of climate change. Earth observation data collected by satellites is a paradigmatic case of big data. Due to programs such as Copernicus in Europe and Landsat in the United States, Earth observation data is open and free today. Users that want to develop an application using this data typically search within the relevant archives, discover the needed data, process it to extract information and knowledge and integrate this information and knowledge into their applications. In this chapter, we argue that if Earth observation data, information and knowledge are published on the Web using the linked data paradigm, then the data discovery, the information and knowledge discovery, the data integration and the development of applications become much easier. To demonstrate this, we present a data science pipeline that starts with data in a satellite archive and ends up with a complete application using this data. We show how to support the various stages of the data science pipeline using software that has been developed in various FP7 and Horizon 2020 projects. As a concrete example, our initial data comes from the Sentinel-2, Sentinel-3 and Sentinel-5P satellite archives, and they are used in developing the Green City use case.
Supporting Semantic Data Enrichment at Scale
Authors: Ciavotta Michele , Cutrona Vincenzo , De Paoli Flavio , Nikolov Nikolay , Palmonari Matteo , Roman Dumitru
Published: April 2022

Data enrichment is a critical task in the data preparation process in which a dataset is extended with additional information from various sources to perform analyses or add meaningful context. Facilitating the enrichment process design for data workers and supporting its execution on large datasets are only supported to a limited extent by existing solutions. Harnessing semantics at scale can be a crucial factor in effectively addressing this challenge. This chapter presents a comprehensive approach covering both design- and run-time aspects of tabular data enrichment and discusses our experience in making this process scalable. We illustrate how data enrichment steps of a Big Data pipeline can be implemented via tabular transformations exploiting semantic table annotation methods and discuss techniques devised to support the enactment of the resulting process on large tabular datasets. Furthermore, we present results from experimental evaluations in which we tested the scalability and run-time efficiency of the proposed cloud-based approach, enriching massive datasets with promising performance.
Processing Big Data in Motion: Core Components and System Architectures with Applications to the Maritime Domain
Authors: Giatrakos Nikos , Deligiannakis Antonios , Bereta Konstantina , Vodas Marios , Zissis Dimitris , Alevizos Elias , Akasiadis Charilaos , Artikis Alexander
Published: April 2022

Rapidly extracting business value out of Big Data that stream in corporate data centres requires continuous analysis of massive, high-speed data while they are still in motion. So challenging a goal entails that analytics should be performed in memory with a single pass over these data. In this chapter, we outline the challenges of Big streaming Data analysis for deriving real-time, online answers to application inquiries. We review approaches, architectures and systems designed to address these challenges and report on our own progress within the scope of the EU H2020 project INFORE. We showcase INFORE into a real-world use case from the maritime domain and further discuss future research and development directions.
Big Data and AI Pipeline Framework: Technology Analysis from a Benchmarking Perspective
Authors: Berre Arne J. , Tsalgatidou Aphrodite , Francalanci Chiara , Ivanov Todor , Pariente-Lobo Tomas , Ruiz-Saiz Ricardo , Novalija Inna , Grobelnik Marko
Published: April 2022

Big Data and AI Pipeline patterns provide a good foundation for the analysis and selection of technical architectures for Big Data and AI systems. Experiences from many projects in the Big Data PPP program has shown that a number of projects use similar architectural patterns with variations only in the choice of various technology components in the same pattern. The project DataBench has developed a Big Data and AI Pipeline Framework, which is used for the description of pipeline steps in Big Data and AI projects, and supports the classification of benchmarks. This includes the four pipeline steps of Data Acquisition/Collection and Storage, Data Preparation and Curation, Data Analytics with AI/Machine Learning, and Action and Interaction, including Data Visualization and User Interaction as well as API Access. It has also created a toolbox which supports the identification and use of existing benchmarks according to these steps in addition to all of the different technical areas and different data types in the BDV Reference Model. An observatory, which is a tool, accessed via the toolbox, for observing the popularity, importance and the visibility of topic terms related to Artificial Intelligence and Big Data technologies has also been developed and is described in this chapter.