85000 research outputs found
Sort by
Use of passive sampling and high-resolution mass spectrometry for screening emerging pesticides of concern within surface waters
No full textThis study addresses the challenges for environmental monitoring of the increasing number of pesticides used. A novel approach for regional monitoring is proposed, utilizing local pesticide registration data, non-target aquatic organism toxicity, and non-routine pesticides. A suspect screening method, combining passive sampling and high-resolution mass spectrometry was developed. In Greater Melbourne, Australia, 181 priority pesticides were investigated across 32 waterway sites with diverse land uses. Liquid chromatography and quadrupole-time-of-flight mass spectrometry were employed for pesticide detection in a data-independent acquisition mode. Of the 181 pesticides, 21 were tentatively detected at 22 sites, with 5 confirmed using certified reference materials. Notably, newly emerging pesticides not previously identified in Australian waterways were detected. Confirming priority pesticides before routine screening is vital for monitoring program efficiency. The study demonstrates the efficacy of combining regional screening and broad-field sampling with suspect screening using high-resolution mass spectrometry. This approach enhances understanding of emerging pesticide levels, aiding in prioritizing compounds for routine screening programs, thus providing a comprehensive strategy for updating pesticide monitoring in specific regions
Mechanical properties, corrosion behavior, and cytotoxicity of biodegradable Zn/Mg multilayered composites prepared by accumulative roll bonding process
No full textZinc (Zn), magnesium (Mg), and their respective alloys have attracted great attention as biodegradable bone-implant materials due to their excellent biocompatibility and biodegradability. However, the poor mechanical strength of Zn alloys and the rapid degradation rate of Mg alloys limit their clinical application. The manufacture of Zn and Mg bimetals may be a promising way to improve their mechanical and degradation properties. Here we report on Zn/Mg multilayered composites prepared via an accumulative roll bonding (ARB) process. With an increase in the number of ARB cycles, the thicknesses of the Zn layer and the Mg layer were reduced, while a large number of heterogeneous interfaces were introduced into the Zn/Mg multilayered composites. The composite samples after 14 ARB cycles showed the highest yield strength of 411±3 MPa and highest ultimate tensile strength of 501±3 MPa among all the ARB processed samples, significantly higher than those of the Zn/Zn and Mg/Mg multilayered samples. The Zn and Mg layers remained continuous in the Zn/Mg composite samples after annealing at 150 °C for 10 min, resulting in a decrease in yield strength from 411±3 MPa to 349±3 MPa but an increase in elongation from 8±1% to 28±1%. The degradation rate of the Zn/Mg multilayered composite samples in Hanks’ solution was ranged from 127±18 µm/y to 6±1 µm/y. The Zn/Mg multilayered composites showed over 100% cell viability with their 25% and 12.5% extracts in relation to MG-63 cells after culturing for 3 d, indicating excellent cytocompatibility. Statement of significance: This work reports a biodegradable Zn/Mg multilayered composite prepared by accumulative roll bonding (ARB) process. The yield and ultimate tensile strength of the Zn/Mg multilayered composites were improved due to grain refinement and the introduction of a large number of heterogeneous interfaces. The composite samples after 14 ARB cycles showed the highest yield strength of 411±3 MPa and highest ultimate tensile s
Unraveling quantum computing system architectures: An extensive survey of cutting-edge paradigms
No full textContext: The convergence of physics and computer science in the realm of quantum computing systems has sparked a profound revolution within the computer industry. However, despite such promise, the existing focus on quantum software systems primarily centers on the generation of quantum source code, inadvertently overlooking the pivotal role of the overall software architecture. Objectives: In order to provide comprehensive guidance to researchers and practitioners engaged in quantum software development, employing an architecture-centered development model, an extensive literature review was conducted pertaining to existing research on quantum software architecture. The analysis encompasses a detailed examination of the characteristics exhibited by these studies and the identification of prospective challenges that lie ahead in the field of quantum software architecture. Methods: We have closely examined instances of quantum software engineering, quantum modeling languages, quantum design patterns, and quantum communication security to gain insights into the distinctive attributes associated with various software architecture approaches. Results: Our findings underscore the critical significance of prioritizing software architecture in the development of robust and efficient quantum software systems. Through the synthesis of these multifaceted aspects, both researchers and practitioners can devise quantum software solutions that are inherently architecture-centric. Conclusion: The software architecture of quantum computing systems plays a pivotal role in determining their ultimate success and usability. Given the ongoing advancements in quantum computing technology, the migration of traditional software architecture development methods to the domain of quantum software development holds significant importance
Machine learning approaches to predict compressive strength of fly ash-based geopolymer concrete: A comprehensive review
No full textGeopolymer concrete is a sustainable replacement to the Ordinary Portland Cement (OPC) concrete as it mitigates some of the associated problems of OPC manufacturing such as greenhouse gas emission and natural resource depletion. There has been significant recent research in the design of fly ash-based geopolymer concrete using advanced machine learning techniques which can address some of the problems with classical mix design approaches. However, practical application of geopolymer concrete is limited due to lack of standard mix design procedure. This comprehensive review summarizes the current literature on machine learning methodologies to predict the compressive strength of fly ash-based geopolymer concrete. Firstly, the input parameters used for the machine learning model development are categorized based on feature selection or feature extraction. Secondly, available machine learning approaches are categorized based on analysis methods namely, nonlinear regression, ensemble learning, and evolutionary programming. The effect of hyperparameters on the individual model performance, and model comparison based on the prediction performance are also discussed to identify potentially more suitable model type and hyper parameter ranges. Further, the paper discusses the input variable’s sensitivity towards the model performance which provides guidance towards future model developments. Overall, this paper will provide an understanding of the current state of machine learning approaches to predict the compressive strength of geopolymer concrete and the gaps in research for the development of models and achieving the required performance. Hence, the summarized knowledge will be highly beneficial to design prospective research towards sustainable cement-free concrete using fly ash
Robustness of Principal Component Analysis with Spearman’s Rank Matrix
No full textThis paper is concerned with robust principal component analysis (PCA) based on spatial sign and spatial rank vectors. The most common PC approach is based on the eigenvectors of the sample covariance matrix; however, this approach is known to be sensitive to outliers. Several robust alternatives based on spatial sign and spatial rank vectors have been discussed including Kendall’s tau or Marden’s rank and Spearman’s rank matrices. Our aims in this paper are to investigate properties of PCA based on Spearman’s rank matrix from theoretical and practical view points and to compare the performance of PCA based on these robust alternatives. A concentration inequality for an estimator of Spearman’s rank matrix is derived, which reveals consistency of the estimator. The influence functions for eigenvalues and eigenvectors of Spearman’s rank matrix are examined which establish the asymptotics for PCA based on Spearman’s rank matrix. Monte Carlo simulations and application to a real dataset show that the performance of PCA using Spearman’s rank matrix is comparable to or sometime better than that of Kendall’s tau and Marden’s rank matrices
Controlled generation of high-frequency liquid metal microdroplets
No full textLiquid metal (LM) microdroplets with high conductivity and fluidity have drawn extensive attention in the field of electronics, biomedicine, and catalysis. The properties of LM droplets, e.g., melting points, electrical actuation, and stability, are affected by their sizes. Continuous generation of LM microdroplets with precisely controlled size will benefit their applications, which however, remains an outstanding challenge. In this study, we have designed a scalable high frequency droplet generator for the liquid metal (EGaIn) that works on the principle of manipulating the electrohydrodynamic behaviour of EGaIn. By applying electric potentials < − 0.75 V vs Ag/ AgCl, EGaIn droplets with uniform sizes can be rapidly and continuously generated. The electric potential effectively tunes the thickness of the oxide layer on the EGaIn surface which in turn modulates the surface tension of the EGaIn and induces the generation of consistent sized EGaIn droplets. The size of the EGaIn microdroplets can be varied between 40 and 200 µm, depending on the electric potential applied and the diameter of the exit orifice. Droplet generation using this method requires relatively low energies, i.e., 2.77 × 10-7 kWh, and is coupled with high frequency droplet generation ∼ 60 droplet/min. The scalability of this droplet generation methodology can be further increased via design of an array of exit orifice. With the presence of surfactants, these EGaIn droplets can remain well-dispersed without coalescing. This considerably widens their capability for further applications
Development of Low-Calorie Food Products with Resistant Starch-Rich Sources.–a Review
No full textA significant percentage of the world population suffers from non-communicable diseases (NCDs) such as diabetes, cardiovascular diseases, cancers, and obesity due to unhealthy food habits. There is an association between the ingestion of carbohydrate-dense food products and diabetes and obesity. Resistant (RS) starch is chemically tolerable to the digestion process in the human gut. RS has several health benefits such as hypoglycemic effects, hypocholesterolemic effects, acting as a prebiotic, prevention of colonic cancers. Most of the inherent characteristics of RS such as high gelatinization temperature, favorable color, prebiotic properties, and good extrusion qualities make it suitable to use as a functional ingredient. Incorporating RS into food products is one of the strategies food scientists implement to lower the Glycemic Index (GI) and Glycemic Load (GL) of the food products. When carefully scrutinizing the plant-based bio-sphere, many potential food sources are enriched in resistant starches. Different processing techniques can be used to alter RS characteristics, such as granule morphologies, crystalline patterns, changes in the organizational groups, and increase the amount of RS. Therefore, this review focused on resistant starch sources, their health benefits, the effect of processing techniques on resistant starch, potential applications in the dynamic food industry, and future trends
Supporting university students’ learning across time and space: a from-scratch, personalised and mobile-friendly approach
No full textThe purpose of this study is to evaluate the use and effectiveness of a bespoke mobile learning resource, Pocket Tutor. This resource responds to a number of teaching and learning challenges within the tertiary education context. These include those related to the number and type of learning activities that can be offered, class pacing, subject-specific content considerations and the availability and quality of off-the-shelf learning resources. Educators have to potentially contend with all of these amidst mounting institutional constraints and external pressures. Yet, a supplemental, from-scratch online learning resource can help mitigate some of these challenges. Design/methodology/approachThis study presents the successes and challenges of introducing a mobile learning resource, Pocket Tutor, to bolster autonomous learning in a supported university learning environment. Pocket Tutor was designed and developed in 2019 and integrated in 2020 and 2021 into a multimedia design class offered at a large university in the Asia-Pacific. The resource's effectiveness is measured against common technology acceptance factors - including self-efficacy, enthusiasm and enjoyment in relation to contextual purpose and class learning outcomes - through a multi-pronged approach consisting of a class-wide survey, developed specifically for this purpose and analysis of usage data. Deeper context was also provided through a small pool of follow-up interviews. FindingsEvidence from this study's data suggests that a bespoke, mobile-learning resource can provide greater consistency, more relevance, more flexibility for when and where students learn and more efficiency with limited opportunities for synchronous interaction. At the same time, a bespoke mobile-learning resource represents a significant investment of skill and time to develop and maintain. Originality/valueThis study responds to calls from scholars who argue that more research (especially that is qualitative and discipline-specific) is needed to investigate students' willingness to use learning apps on their mobile devices. This study pairs such research about student willingness with actual usage data and student reflections to more concretely address the role of mobile learning resources in higher education contexts. This study also, importantly, does not just assess perceptions and attitudes about mobile learning resources in the abstract but assesses attitudes and usage patterns for specific generic and bespoke mobile learning resources available for students in a specific university class (thereby providing discipline-specific insights). This study also provides a unique contribution by including multiple years of data and, thus, offers a longitudinal view on how mobile-learning resources are perceived and used in a particular higher education context
The first record of a Konservat-Lagerstätten in which early post-settlement stages of fossil archaeobalanids (Cirripedia: Balanomorpha) are preserved
No full textA diverse sessile barnacle fauna from a Miocene shallow-water deposit at Dolnja Stara vas in Slovenia is described. It includes the first descriptions of early post settlement juveniles of Actinobalanus sloveniensis attached to mangrove leaves. These represent three distinct growth phases, the earliest being interpreted as being less than 24 h post settlement, the others being 1 to 2 days post settlement. An assessment of their taphonomy is provided. Associated adult balanomorphs are attached to a variety of organic substrates, including mangrove leaves and branches, fragments of the conifers ?Taxodioxylon, Carapoxylon, pine cones, molluscs, and cetacean bones. The barnacles include A. sloveniensis, Amphibalanus venustus, and Perforatus perforatus—many with opercula retained within the shells. A. venustus retains some of the original shell color. This is the second record of barnacle–plant associations from the Central Paratethys from Kamnik and Trbovlje. The paleoecology and paleogeography of the site are discussed
A hybrid ISM and fuzzy MICMAC approach to modelling risk analysis of imported fresh food supply chain
No full textPurpose
The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.
Design/methodology/approach
A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.
Findings
The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.
Research limitations/implications
The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.
Originality/value
This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts