bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nitrated monoaromatic hydrocarbons (nitrophenols, nitrocatechols, nitrosalicylic acids) in ambient air: levels, mass size distributions and inhalation bioaccessibility
Authors: Kitanovski Zoran , Hovorka Jan , Kuta Jan , Leoni Cecilia , Prokeš Roman , Sáňka Ondřej , Shahpoury Pourya , Lammel Gerhard 
Published: June 2020

AbstractNitrated monoaromatic hydrocarbons (NMAHs) are ubiquitous in the environment and an important part of atmospheric humic-like substances (HULIS) and brown carbon. They are ecotoxic and with underresearched toxic potential for humans. NMAHs were determined in size-segregated ambient particulate matter collected at two urban sites in central Europe, Ostrava and Kladno, Czech Republic. The average sums of 12 NMAHs (Σ12NMAH) measured in winter PM10 samples from Ostrava and Kladno were 102 and 93 ng m−3, respectively, and 8.8 ng m−3 in summer PM10 samples from Ostrava. The concentrations in winter corresponded to 6.3–7.3% and 2.6–3.1% of HULIS-C and water-soluble organic carbon (WSOC), respectively. Nitrocatechols represented 67–93%, 61–73% and 28–96% of NMAHs in PM10 samples collected in winter and summer at Ostrava and in winter at Kladno, respectively. The mass size distribution of the targeted substance classes peaked in the submicrometre size fractions (PM1), often in the PM0.5 size fraction especially in summer. The bioaccessible fraction of NMAHs was determined by leaching PM3 samples in two simulated lung fluids, Gamble’s solution and artificial lysosomal fluid (ALF). More than half of NMAH mass is found bioaccessible, almost complete for nitrosalicylic acids. The bioaccessible fraction was generally higher when using ALF (mimics the chemical environment created by macrophage activity, pH 4.5) than Gamble’s solution (pH 7.4). Bioaccessibility may be negligible for lipophilic substances (i.e. log KOW > 4.5).
A neuro-inspired general framework for the evolution of stochastic dynamical systems: Cellular automata, random Boolean networks and echo state networks towards criticality
Authors: Pontes-Filho Sidney , Lind Pedro , Yazidi Anis , Zhang Jianhua , Hammer Hugo , Mello Gustavo B. M. , Sandvig Ioanna , Tufte Gunnar , Nichele Stefano 
Published: June 2020

AbstractAlthough deep learning has recently increased in popularity, it suffers from various problems including high computational complexity, energy greedy computation, and lack of scalability, to mention a few. In this paper, we investigate an alternative brain-inspired method for data analysis that circumvents the deep learning drawbacks by taking the actual dynamical behavior of biological neural networks into account. For this purpose, we develop a general framework for dynamical systems that can evolve and model a variety of substrates that possess computational capacity. Therefore, dynamical systems can be exploited in the reservoir computing paradigm, i.e., an untrained recurrent nonlinear network with a trained linear readout layer. Moreover, our general framework, called EvoDynamic, is based on an optimized deep neural network library. Hence, generalization and performance can be balanced. The EvoDynamic framework contains three kinds of dynamical systems already implemented, namely cellular automata, random Boolean networks, and echo state networks. The evolution of such systems towards a dynamical behavior, called criticality, is investigated because systems with such behavior may be better suited to do useful computation. The implemented dynamical systems are stochastic and their evolution with genetic algorithm mutates their update rules or network initialization. The obtained results are promising and demonstrate that criticality is achieved. In addition to the presented results, our framework can also be utilized to evolve the dynamical systems connectivity, update and learning rules to improve the quality of the reservoir used for solving computational tasks and physical substrate modeling.
Machine learning based customer sentiment analysis for recommending shoppers, shops based on customers’ review
Authors: Yi Shanshan , Liu Xiaofang 
Published: June 2020

AbstractBig data analytics plays a major role in various industries using computing applications such as E-commerce and real-time shopping. Big data are used for promoting products and provide better connectivity between retailers and shoppers. Nowadays, people always use online promotions to know about best shops for buying better products. This shopping experience and opinion about the shopper’s shop can be observed by the customer-experience shared across social media platforms. A new customer when searching a shop needs information about manufacturing date (MRD) and manufacturing price (MRP), offers, quality, and suggestions which can only be provided by the previous customer experience. The MRP and MRD are already available in the product cover or label. Several approaches have been used for predicting the product details but not providing accurate information. This paper is motivated towards applying Machine Learning algorithms for learning, analysing and classifying the product information and the shop information based on the customer experience. The product data with customer reviews is collected from benchmark Unified computing system (UCS) which is a server for data based computer product lined up for evaluating hardware, support to visualization, software management. From the results and comparison, it has been found that machine learning algorithms outperform than other approaches. The proposed HRS system has higher values of MAPE which is 96% and accuracy is nearly 98% when compared to other existing techniques. Mean absolute error of proposed HRS system is nearly 0.6 which states that the performance of the system is significantly effective.
Monitoring Pneumatic Actuators’ Behavior Using Real-World Data Set
Authors: Kovacs Tibor , Ko Andrea 
Published: June 2020

AbstractDeveloping a big data signal processing method is to monitor the behavior of a common component: a pneumatic actuator. The method is aimed at supporting condition-based maintenance activities: monitoring signals over an extended period, and identifying, classifying different machine states that may indicate abnormal behavior. Furthermore, preparing a balanced data set for training supervised machine learning models that represent the component’s all identified conditions. Peak detection, garbage removal and down-sampling by interpolation were applied for signal preprocessing. Undersampling the over-represented signals, Ward’s hierarchical clustering with multivariate Euclidean distance calculation and Kohonen self-organizing map (KSOM) methods were used for identifying and grouping similar signal patterns. The study demonstrated that the behavior of equipment displaying complex signals could be monitored with the method described. Both hierarchical clustering and KSOM are suitable methods for identifying and clustering signals of different machine states that may be overlooked if screened by humans. Using the proposed methods, signals could be screened thoroughly and over a long period of time that is critical when failures or abnormal behavior is rare. Visual display of the identified clusters over time could help analyzing the deterioration of machine conditions. The clustered signals could be used to create a balanced set of training data for developing supervised machine learning models to automatically identify previously recognized machine conditions that indicate abnormal behavior.
Performance and Security Evaluation of S-Box Using Current-Pass Optimized Symmetric Pass Gate Adiabatic Logic
Authors: Koyasu Hiroki , Takahashi Yasuhiro 
Published: June 2020

AbstractSecure adiabatic logics are identified as the optimal solution for cryptographic modules. We previously proposed an adiabatic logic called Current-Pass Optimized Symmetric Pass Gate Adiabatic Logic (CPO-SPGAL). The proposed CPO-SPGAL realizes a flat current waveform by considering the current path compared with conventional adiabatic logics. In this paper, to confirm more details about countermeasure against power analysis attacks, we compare S-box circuits based on the conventional and proposed adiabatic logics which are implemented using 0.18 $$\upmu $$
μ
m standard CMOS. From the SPICE simulation for correlation power analysis (CPA), 409,600 power consumption traces are obtained, and the hamming distance/weight are calculated. The simulation results show that the proposed S-box is more resistant to CPA attacks than the existing adiabatic S-boxes.
U.S. Consumer Demand for Traditional and Greek Yogurt Attributes, Including Livestock Management Attributes
Authors: Bir Courtney , Delgado Michael S. , Widmar Nicole Olynk 
Published: June 2020

AbstractConsumer willingness to pay (WTP) for yogurt attributes was evaluated using a survey targeted to be nationally representative within the United States. A novel approach was used to allow for self-selection into the choice experiment for commonly purchased types of yogurt, either Greek or traditional, based on what consumers purchase. They were willing to pay a positive amount for requiring pasture access and not permitting dehorning/disbudding (which references the removal of horns or horn buds) for both traditional and Greek yogurt. Respondents had positive WTP for Greek yogurt labeled free of high-fructose corn syrup and a higher WTP for low-fat yogurt when compared to nonfat for both yogurt types.
Antarctic subglacial drilling rig: Part IV. Electrical and electronic control system
Authors: Zhang Nan , Talalay Pavel , Liu Jingbiao , Fan Xiaopeng , Kong Qingpeng , Yu Haibin , Liu Yunchen , Liu Benkun , Gong Da , Li Xingchen , Wu Wei , Hong Jialin , Sysoev Mikhail 
Published: June 2020

AbstractIn many cases, the efficiency and safety of a drilling project depend on the reliability of the electrical and electronic control system, as the process progresses without visual access of the operator. The electrical and electronic system provides and regulates the power supply for the drill, collects and monitors the drill data during the whole operating process, and sends and receives the control instructions and feedback signals. The entire system is composed of the surface, borehole and software subsystems. The surface subsystem serves for operating the drilling process, transmitting the drilling and environmental data, and supplying power for the drill motor and downhole control system. The borehole subsystem is generally intended for borehole data acquisition, drill motor control, power regulation and communication. The software subsystem is designed for human–computer interaction, data processing and storage, and programming of signal acquisition and transmission of data. The control system of Antarctic subglacial drilling rig was tested during the 2018–2019 summer season near Zhongshan Station, East Antarctica, in the course of drilling to the bedrock at a depth of 198 m. It exhibited a steady and efficient performance without significant system failures.
Effect of various decontamination procedures on disposable N95 mask integrity and SARS-CoV-2 infectivity
Authors: Smith Jeffrey S. , Hanseler Haley , Welle John , Rattray Rogan , Campbell Mary , Brotherton Tacy , Moudgil Tarsem , Pack Thomas F. , Wegmann Keith , Jensen Shawn , Jin Justin , Bifulco Carlo B. , Prahl Scott A. , Fox Bernard A. , Stucky Nicholas L. 
Published: June 2020

Abstract

Introduction:
The COVID-19 pandemic has created a high demand on personal protective equipment, including disposable N95 masks. Given the need for mask reuse, we tested the feasibility of vaporized hydrogen peroxide (VHP), ultraviolet light (UV), and ethanol decontamination strategies on N95 mask integrity and the ability to remove the infectious potential of SARS-CoV-2.

Methods:
Disposable N95 masks, including medical grade (1860, 1870+) and industrial grade (8511) masks, were treated by VHP, UV, and ethanol decontamination. Mask degradation was tested using a quantitative respirator fit testing. Pooled clinical samples of SARS-CoV-2 were applied to mask samples, treated, and then either sent immediately for real-time reverse transcriptase–polymerase chain reaction (RT-PCR) or incubated with Vero E6 cells to assess for virucidal effect.

Results:
Both ethanol and UV decontamination showed functional degradation to different degrees while VHP treatment showed no significant change after two treatments. We also report a single SARS-CoV-2 virucidal experiment using Vero E6 cell infection in which only ethanol treatment eliminated detectable SARS-CoV-2 RNA.

Conclusions:
We hope our data will guide further research for evidenced-based decisions for disposable N95 mask reuse and help protect caregivers from SARS-CoV-2 and other pathogens.
PATTERN BASED SYSTEMS ENGINEERING – APPLICATION OF SOLUTION PATTERNS IN THE DESIGN OF INTELLIGENT TECHNICAL SYSTEMS
Authors: Anacker H. , Dumitrescu R. , Kharatyan A. , Lipsmeier A. 
Published: June 2020

AbstractFor the development of intelligent technical systems, Systems Engineering and Solution Patterns are the guarantee for success. In order to avoid cost-intensive iterations, the documentation and reuse of solution knowledge is addressed during the systems design. Using an interdisciplinary specification technique, a uniform structuring of Solution Patterns as well as the composition in a multidimensional knowledge space takes place. This is the basis of an associated systematics for a solution pattern-based system design of mechatronic systems, which is validated by two cooperating DeltaRobots.
DATA ANALYSIS METHOD SUPPORTING CAUSE AND EFFECT STUDIES IN PRODUCT-SERVICE SYSTEM DEVELOPMENT
Authors: Wall J. , Aeddula O. K. , Larsson T. 
Published: June 2020

AbstractA data analysis method aiming to support cause and effect analysis in design exploration studies is presented. The method clusters and aggregates effects of multiple design variables based on the structural hierarchy of the evaluated system. The resulting dataset is intended as input to a visualization construct based on colour-coding CAD models. The proposed method is exemplified in a case study showing that the predictive capability of the created, clustered, dataset is comparable to the original, unmodified, one.
A FRAMING OF DESIGN AS PATHWAYS BETWEEN PHYSICAL, VIRTUAL AND COGNITIVE MODELS
Authors: Jones D. E. , Snider C. , Hicks B. 
Published: June 2020

AbstractDuring engineering design, designers employ three types of model: physical, virtual and cognitive. The role and contribution of each is documented in literature albeit fragmented in nature. Consequentially, a gap in understanding exists in terms of how these models and the transitions between them impact the designer and design process. This paper begins to address this through a characterisation of each model class and an appraisal of the transitional pathways including their alignment to seminal design frameworks.
DEFICITS IN THE APPLICATION OF ALUMINUM FOAM SANDWICH: AN INDUSTRIAL PERSPECTIVE
Authors: Hommel P. , Roth D. , Binz H. 
Published: June 2020

AbstractAluminum foam sandwich (AFS) is an innovative sandwich material for designing lighter products and has many advantages such as high stiffness and high mechanical-energy absorption capacity. Although AFS is ready for series production, the number of use cases is low. A survey was carried out in order to identify the obstacles in the application of aluminum foam sandwich. This paper presents the results of the survey, derives the demand for a support method for designing with aluminum foam sandwich and shows various support options to simplify the application of the material.
FOSTERING PROTOTYPING MINDSETS IN NOVICE DESIGNERS WITH THE PROTOTYPING PLANNER
Authors: Hansen C. A. , Jensen L. S. , Özkil A. G. , Martins Pacheco N. M. 
Published: June 2020

AbstractPrototyping is an essential activity in product development, but novice designers lack awareness and purpose when they prototype. To foster prototyping mindsets in novice designers, we introduce a prototyping support tool that structures prototyping activities. This paper outlines the Prototyping Planner's development, evolution, and evaluation by 125 novice designers. The majority of novice designers’ experienced that the Prototyping Planner helped them create purposeful prototypes and evaluate results from prototyping.
DEVELOPING A TOOL TO SUPPORT DECISIONS IN SUSTAINABILITY-RELATED TRADE-OFF SITUATIONS: UNDERSTANDING NEEDS AND CRITERIA
Authors: Kravchenko M. , Pigosso D. C. A. , McAloone T. C. 
Published: June 2020

AbstractEarly integration of sustainability considerations into decision making is seen as a key enabler for companies to understand the potential implications of their decisions on the triple bottom line aspects. Lack of the tools to support decisions when trade-off between sustainability aspects occur, however, may lead to uninformed decision-making and undesired outcomes. By consolidating the learnings from empirical work together with literature recommendations, we propose key criteria to be considered when developing decision support tools to manage sustainability-related trade-off situations.
VISUALISING SYSTEMS: MAPPING SYSTEM FEATURES AND INTERACTIVE INFORMATION VISUALISATIONS IN DESIGN
Authors: Idrissov A. , Škec S. , Maier A. M. 
Published: June 2020

AbstractInteractive computer-supported information visualisations are being increasingly used in design. However, while there are frameworks that discuss how traditional representations, such as sketches, CAD models and static diagrams support design tasks, no such mapping exists for interactive visualisations of product-related information. As novel contributions, this paper reviews the design literature for the use of information visualisations. Moreover, using systems theory and Gestalt principles, insights on the applicability of such information visualisations for various design tasks are given.
RAPID CREATION OF VEHICLE LINE-UPS BY EIGENSPACE PROJECTIONS FOR STYLE TRANSFER
Authors: Friedrich T. , Schmitt S. , Menzel S. 
Published: June 2020

AbstractIn product development, an automated generation of shape variations enables a rapid assessment of potentially appealing design directions. We present a framework for computing a product line-up of automotive body shapes based on spectral methods for mesh processing. We calculate the eigenspace projections of 3D vehicle meshes and identify the relevant style as well as content components based on the eigenvalues. The style of a model is then transferred to arbitrary prototype content car shapes, which allows for a rapid portfolio generation of various car types with minimal user interaction.
DESIGN FOR TEACHER TRAINING – TO FACILITATE STUDENT TEAMS WORKING WITH REAL-LIFE SUSTAINABILITY CHALLENGES
Authors: Nilsson S. C. , Norell Bergendahl M. 
Published: June 2020

AbstractThe aim of the study is to contribute to the knowledge on how to develop students’ skills and capabilities required when addressing complex societal challenges in practice. In this paper we are investigating the design and implementation of a teacher training module focusing on improving teacher's ability to facilitate students’ teams learning and collaboration skills. The feedback and learning from the design and implementation of the module at universities in Botswana, Kenya and Sweden is presented and discussed in this paper.
EVALUATION OF TOPOLOGY OPTIMIZATION AND GENERATIVE DESIGN TOOLS AS SUPPORT FOR CONCEPTUAL DESIGN
Authors: Vlah D. , Žavbi R. , Vukašinović N. 
Published: June 2020

AbstractNowadays, a large number of different tools that support early phases of design are available to engineers. In the past decade a specialized set of CAD-based tools were developed, that support the ideation process by generating different design alternatives according to the criteria given by the designer. Two types of tools are discussed in this paper: topology optimization and generative design tools. To investigate to what extent these tools are suitable for use in early design phases and what are the main differences between them, a study was conducted on an industrial case.
RELIABLE IDENTIFICATION AND SELECTION OF CRITICAL INNOVATION DESIGN TASKS FOR PROCESS INTENSIFICATION IN PROCESS ENGINEERING
Authors: Livotov P. , Mas'udah  , Chandra Sekaran A. P. 
Published: June 2020

AbstractSustainable design of equipment for process intensification requires a comprehensive and correct identification of relevant stakeholder requirements, design problems and tasks crucial for innovation success. Combining the principles of the Quality Function Deployment with the Importance-Satisfaction Analysis and Contradiction Analysis of requirements gives an opportunity to define a proper process innovation strategy more reliably and to develop an optimal process intensification technology with less secondary engineering and ecological problems.
PRELIMINARY DESIGN AND EVALUATION OF STRATEGIC DASHBOARDS THROUGH THE TECHNOLOGY ACCEPTANCE MODEL
Authors: Vasnier J.-M. , Maranzana N. , Messaadia M. , Aoussat A. 
Published: June 2020

AbstractIn the age of the fourth industrial revolution, the competition between enterprises is fierce to operate efficiently and hold on to their customers. Due to lack of time and methodology, many leaders struggle to establish optimized strategies for their businesses. To do so, the key processes need to be measured using dashboards that proactively help decisions making, facilitate the strategy execution and keep the employees focused. The purpose of this article is to identify the design features of a risk analysis dashboard and their effects on the perceived ease of use and perceived usefulness.
FUNCTIONALLY GRADED ADDITIVE MANUFACTURING: A TEACHING CASE STUDY OF INEX-ADAM
Authors: Kabir I. R. , Loh G. H. , Pei E. 
Published: June 2020

AbstractThe multidisciplinary nature and lack of comprehensive ‘materials-product-manufacturing’ knowledge of Functionally Graded Additive Manufacturing (FGAM) require training to support the future Additive Manufacturing experts. INEX-ADAM, an EU funded project is building a transnational platform to promote FGAM. Brunel University London conducted two-day workshop on FGAM at the University of Zagreb in Croatia with academics and industry professionals. The workshop will strengthen the research capabilities to harness the potential of the FGAM and mitigate the constraints to industrial applications.
MANAGEMENT OF DESIGN ITERATIONS ON COUPLED PARAMETERS IN DESIGN TEAMWORK USING MULTIPLE DOMAIN MATRIX AND COLOURED PETRI NETS
Authors: Juranić J. , Pavković N. , Jurinić D. 
Published: June 2020

AbstractA new way of structuring and interpretation of multiple domain matrix is proposed as the basis for categorisation of design parameter relations complexity. Depending on the kind and the degree of coupling of the parameters, the developed methodology activates the appropriate coloured Petri net (CPN) models for semi-automatic support of communication between the members of the design team. The proposed extension of MDM combined with CPN is a novel approach to predicting and managing communication patterns necessary during teamwork coordination on critical interfaces between product components.
SUPPORTING ENGINEERING STUDENTS IN ANALYSING SOCIAL SUSTAINABILITY OF A PRODUCT: LESSONS LEARNED
Authors: Lagun Mesquita P. , Missimer M. 
Published: June 2020

AbstractThough many engineering schools are integrating sustainability in the curriculum, most are still struggling, especially with social sustainability. The aim with this study was to test a process created to help students work systematically with these issues. Results show that students in the study did indeed benefit from structured process support as their general knowledge level of social sustainability issues as well as their understanding of how to integrate them in a product assessment was very low. Reflections from teaching staff on how to further develop such a process are also shared.