bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influence of Saccharomyces boulardii inoculation on post‐harvest processing methods on the coffee quality
Authors: Lim GiGi Chin Wen , Hou Chih‐Yao , Hsieh Lu‐Sheng , Wu Shinn‐Dar , Ciou Jhih‐Ying 
Published: November 2022

AbstractCoffee is an economically important plant and has been a famous non‐alcoholic beverage globally. This study aimed to investigate the coffee quality of Saccharomyces boulardii (S. boulardii) inoculated to different post‐harvest processed coffee beans (dry method and semi‐dry method). The result showed that the pH value decreased along with the processing period. Moisture content had the final content below 12%. Water activity decreased along with the processing period, reaching around 0.40 at the end of processing. Color parameters resulted in an increased browning index after roasting. The organic acid showed an increasing trend in each acid after roasting, especially for glycolic acid and citric acid. The phenolic acid degraded after roasting each sample. The caffeine content increased after roasting each sample. The D and S groups had higher total sugar content than the yeast‐inoculated group before roasting, while roasting the S group had the highest total sugar content. Total phenolic content resulted in the highest content in group D while roasted in group SY. For antioxidant capacity, the SY had the lowest IC50 value (2.85 g/ml) in unroasted beans. For the sensory evaluation, each of the samples presented a different flavor after roasting.Novelty impact statementThe coffee quality including physicochemical, chemical analysis, and sensory evaluation of Saccharomyces boulardii inoculated to different post‐harvest processed coffee beans (dry method and semi‐dry method) are determined. The result of the physicochemical analysis did not show significant changes; however, for chemical analysis, there were significant changes in semi‐dry processed beans.
Recent advances and future perspectives in early‐maturing cotton research
Authors: Zhao Hang , Chen Yanli , Liu Ji , Wang Zhi , Li Fuguang , Ge Xiaoyang 
Published: November 2022

SummaryCotton's fundamental requirements for long periods of growth and specific seasonal temperatures limit the global arable areas that can be utilized to cultivate cotton. This constraint can be alleviated by breeding for early‐maturing varieties. By delaying the sowing dates without impacting the boll‐opening time, early‐maturing varieties not only mitigate the yield losses brought on by unfavorable weathers in early spring and late autumn but also help reducing the competition between cotton and other crops for arable land, thereby optimizing the cropping system. This review presents studies and breeding efforts for early‐maturing cotton, which efficiently pyramid early maturity, high‐quality, multiresistance traits, and suitable plant architecture by leveraging pleiotropic genes. Attempts are also made to summarize our current understanding of the molecular mechanisms underlying early maturation, which involves many pathways such as epigenetic, circadian clock, and hormone signaling pathways. Moreover, new avenues and effective measures are proposed for fine‐scale breeding of early‐maturing crops to ensure the healthy development of the agricultural industry.
Age‐related differences in reasoning about the acceptability of eating animals
Authors: McGuire Luke , Fry Emma , Palmer Sally , Faber Nadira S. 
Published: November 2022

AbstractChildren in the western world often are socialized as meat‐eaters, while caring much about animals. Yet we know little about how children think about eating animals and animal products, which will have important consequences for global meat consumption. Participants (n = 479, 80% White British, 57% female; children n = 119, Mage = 10.03, SD = 0.72; young adults n = 181, Mage = 19.09, SD = 0.85; adults n = 179, Mage = 40.97, SD = 8.18) reasoned about the acceptability of eating animals and animal products. Using the framework of social domain theory, we found that that children focused on moral concerns (p < .001, ηp2 = .01), whereas adults referenced conventions about the natural and necessary components of eating meat. Participants across age groups reported eating animal products (e.g., milk) to be acceptable because animals were not harmed. Together these results indicate that attempts to reduce meat consumption ought to be tailored in a domain‐specific manner to age groups.
Cardiac sodium channel complexes and arrhythmia: structural and functional roles of the β1 and β3 subunits
Authors: Salvage Samantha C. , Jeevaratnam Kamalan , Huang Christopher L.‐H. , Jackson Antony P. 
Published: November 2022

AbstractIn cardiac myocytes, the voltage‐gated sodium channel NaV1.5 opens in response to membrane depolarisation and initiates the action potential. The NaV1.5 channel is typically associated with regulatory β‐subunits that modify gating and trafficking behaviour. These β‐subunits contain a single extracellular immunoglobulin (Ig) domain, a single transmembrane α‐helix and an intracellular region. Here we focus on the role of the β1 and β3 subunits in regulating NaV1.5. We catalogue β1 and β3 domain specific mutations that have been associated with inherited cardiac arrhythmia, including Brugada syndrome, long QT syndrome, atrial fibrillation and sudden death. We discuss how new structural insights into these proteins raises new questions about physiological function.
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Surrogate Models and Time Series for Flow Prediction on the Red River Dam Network
Authors: Alizadeh Reza , Allen Janet K. , Mistree Farrokh 
Published: November 2022

Abstract
Surrogate models have been used to replace computationally expensive analysis models in engineering design problems. However, time-dependent variables and historical data are usually ignored in the surrogate modeling process. For instance, in a dam network design, using hydraulic simulations to estimate the water flow is computationally expensive, and the data is in the form of time series. So, we need time-dependent surrogate models to replace these simulations and manage this computational complexity. In this paper, we describe surrogate models to predict the amount of water flow into a reservoir. The challenge is that the flow is a time-dependent variable, and we need to incorporate time series into surrogate models. Thus, there are three contributions: (1) using surrogate modeling to predict flow for dam network design, (2) incorporating time series analysis in surrogate models for water network design, (3) using an ensemble of surrogates to increase the accuracy of prediction. We also demonstrate how to integrate surrogate models and machine learning with time series analysis for more accurate and faster prediction. Due to the availability of data, we use the Buffalo Reservoir in the Red River Basin as an example. Based on the time series data for flow, evaporation, precipitation, and maximum and minimum temperature, three surrogate models are used to examine the impact of integrating time series into surrogate models. These are multivariate autoregressive integrated moving average (MARIMA), a classic time series analysis method; artificial neural network (ANN), and random forest (RF) methods, two machine learning surrogate models. We use seven different time lags as features within an RF model, as an ensemble of surrogate models, and predict the flow for seven-time steps ahead. We successfully incorporate the time series data and particularly the concept of the time lag within surrogate models. We show that RF as the ensemble of surrogates provides more accurate predictions than the other two surrogate models. Although this method has been demonstrated for the Red River Basin, it could also be applied to designing anything in which time-dependent flow is an issue, for example, in biomedical applications, the management of manufacturing processes and product sales as well as any products in which fluid flow is an issue.
Evaluating the Potential for a Novel Irrigation System Controller to Be Adopted by Medium-Scale Contract Farmers in East Africa
Authors: Van de Zande Georgia D. , Sheline Carolyn , Winter Amos G. 
Published: November 2022

Abstract
Food insecurity in East Africa (EA) is exacerbated by the effects of climate change. Water scarcity and high energy costs make it difficult for farmers to increase their yields to levels needed to feed growing populations. One way for countries to sustainably increase food production is to increase the adoption of water- and energy-efficient irrigation technology. Solar-powered drip irrigation systems are such a technology. These systems provide many benefits to farmers who have been able to install them on their fields, but their high costs is one barrier that currently limits its adoption among the majority of EA farmers. In this work, a concept for a novel irrigation controller is introduced and assessed for its viability in the EA market. The proposed controller determines complex irrigation schedules based on local weather predictions and specifics of a given farm. It does this while optimizing for the lifetime performance of the irrigation system, something that existing controllers do not do and something that could lower the lifetime costs of solar-powered drip irrigation systems. The proposed controller communicates this complex schedule to farmers in a way that allows them to continue to use low-cost, manually-operated valves on their fields. This concept is evaluated through storyboard-based interviews with EA farmers and key market stakeholders. The results provide initial positive feedback from these lead users, suggesting that this controller could be a viable product in EA. If adopted at scale, this technology could help lower the barrier to adopting water- and energy-efficient irrigation, ultimately having a positive, sustainable impact on food security in EA.
Design and Analysis of a Piezo-Driven Precision Positioning Stage Suitable for XBPM
Authors: Yang Xuefeng , Cui Xu , Zhang Mengchen , Zhuang Jian 
Published: November 2022

Abstract
X-ray beam position monitor (XBPM) is an indispensable device in the fourth-generation synchrotron light sources. However, the vacuum micro-positioning stage suitable for key components in advanced light sources has received scant attention. Aiming at the demand of high positioning accuracy and simple position adjustment of XBPM, an XZ stacked piezo-driven precision positioning stage is proposed in this paper. The stage configuration is decoupled, which can realize independent translations in X and Z directions. The overall scheme is non-closed and the selection of material and actuator in special occasion is focused, which is suitable for general and ultra-high vacuum system. In addition, the static and dynamic characteristics of the precision positioning stage are verified with finite element analysis (FEA), and the prototype test system is built. The experimental results indicate that the workspace under closed-loop feedback control is 51.39 μm × 35.07 μm, which is almost consistent with the simulation results. Furthermore, the output displacement linearity, maximum output coupling error rate and actual resolution of the stage are assessed. The impact of environmental fluctuation on actual resolution is also quantitatively evaluated. According to the simulated and test results, the designed stage achieves the expected performances and can meet the demands of micronano positioning accuracy and motion decoupling of XBPM.
Additive Manufacturing Strategy for Ultra-Lightweight High Value-Added Components
Authors: Catar Louis , Tabiai Ilyass , St-Onge David 
Published: November 2022

Abstract
Additive manufacturing and its growth in advanced industrial fields are changing many process paradigms for the development of highly customizable components. It condenses the traditional approaches to produce assemblies that are more and more integrated at different levels: functionality (strength, lightness), productivity (flexibility, customization) and development (passage from the digital part to the final part).
This work presents a methodology to help select a combination of possibilities (materials, process, etc.). Derived from the double diamond method, this methodology aims to propose a rationalization of the choices available to designers of high value-added products with strict and mutually intertwined requirements. The whole methodology is then illustrated with the design of an indoor aerostat drone which illustrates the implementation of some of the key elements of the proposed process.
Evaluation and Modification of Kinetic Gas Collision Theory as Applied to Encounter Rate Dynamics for Multi-Robot Groups and Robot Swarms
Authors: Schroeder Adam , Rooney Mark , Lipscomb Glenn 
Published: November 2022

Abstract
Robots encountering other robots in a group can be beneficial, e.g., to exchange information, or detrimental, e.g., obstructing one another from operating. Kinetic gas theory (KGT) provides a statistical mechanical analysis of the collision rate between molecules. KGT has been applied to model robot encounter rates but there has been some inconsistency in how it has been applied to robot groups. There is a nine order of magnitude difference in size between a typical robot and molecule, so it is not a surprise that some adjustments may need to be made when considering robots instead of molecules. This work develops a model in detail by applying KGT, articulates limitations of applying this theory to robots, highlights inconsistencies in how it has been previously applied to robots, and suggests modifications to the model. A simple numerical study is also shown to validate the model and highlight the effect of differences in the implementation.
The most important gap that this research has identified is the need to collect data on the magnitude and direction distribution of robots’ velocities. Robots move and behave differently than gas molecules, whose velocity magnitude follow a Boltzmann distribution. A second major result is the connection of the KGT-based model developed in this work and previous research on robot encounter rate which independently arrived at the same relationship between robot size, number of robots, and encounter rate.
Accommodating Irregular Panel Profiles and Thicknesses in LET Joint Arrays
Authors: Smith Samuel P. , Magleby Spencer P. , Howell Larry L. 
Published: November 2022

Abstract
Origami-inspired mechanisms can produce compact, easily-manufacturable designs. Compliant mechanisms have been used to create surrogate folds in many kinds of engineering materials by allowing large deflections without yielding. LET arrays are best used as surrogate folds when the regions being folded on each other have parallel sides, keeping the LET joint segment geometry constant. This consistency allows for homogeneous stresses/strains throughout the joint and constant curvature along the joint is achieved. However, when panel sides are irregular, optimal LET design is not necessarily obvious. This paper presents an exploration into characterizing folding motions of LET joints to achieve arbitrary curvatures of the folded region. Curvatures are obtained through variations in joint segment geometry whether length, width, thickness, or a combined approach. Testing of several specimens is conducted and the results are consistent with the analysis.
Automatic Shape Modification for Self-Supporting Structures in Additive Manufacturing
Authors: Chen Jiangce , Patterson Matt , Mirzendehdel Amir M. , Behandish Morad 
Published: November 2022

Abstract
Additive manufacturing (AM) is a class of advanced manufacturing technologies that facilitates fabrication of geometrically complex, high-performance functional parts. AM processes often produce a “near-net” shape comprising the intended design and sacrificial support structures to be removed in postprocessing. The amount of extra material in the near-net shape directly increases fabrication cost and clean-up time. This paper presents a shape modification method for AM parts to minimize the need for sacrificial material in the near-net shape. Given an existing triangulated boundary representation of the part, we formulate the problem as a generic shape optimization that minimizes the changes made to the original geometry by locally modifying vertex coordinates while both maintaining the manifold nature of the representation and satisfying the overhang angle constraints, when possible. Specifically, we augment a pseudo-energy regularization term to our primary objective function for self-supporting to ensure that the optimized shape is valid and suitable for AM. We propose two mesh regularization methods, using virtual springs and sphere packing, and provide differentiable expressions of the objective function and constraints to enable efficient gradient-based optimization. We show that the sphere packing is more robust and retains the quality of the mesh during optimization. Finally, we demonstrate the effectiveness and efficiency of our method in modifying realistic models in 3D.
Triggered: Using Human-AI Dialogue for Problem Understanding in Collaborative Design
Authors: Arzberger Anne , van der Burg Vera , Chandrasegaran Senthil , Lloyd Peter 
Published: November 2022

Abstract
Creative conversation among designers and stakeholders in a design project enables new ideas to naturally originate and evolve. Language allows for the exchange of values, priorities, and past experience whilst keeping solution forms usefully ambiguous. Yet there is a danger that only the language of people directly involved in the design process gets to be heard, limiting how inclusively the problems are interpreted, which in turn can impede how complex design problems are addressed. Recent advances in artificial intelligence (AI) have shown the exclusionary spaces that are often inhabited by designers, engineers, and developers of new artefacts and technologies. On the other hand, text data used to train language models for machine learning applications have the potential to highlight societal biases in ways that designers can utilise. In this paper, we report the results of an exploratory study using AI text generation to synthesize and narrate opinions and experiences that may be unfamiliar to designers. Three pairs of designers were given a complex socio-technical problem to solve. Of these, two pairs interacted with an AI text generator during the task, while one pair acted as a baseline condition. Analysing the conversational exchanges between the designers and the designers & AI, we observe how the use of AI leads to prompting nuanced interpretations of problems and ideas, opening up the objective problem and design lenses and interpretations. Finally, we discuss how the designers (re)assign different roles to the AI to suit their creative purposes.
Patent Mining to Understand Functional Evolution of Engineered Products
Authors: Song Hyeonik , Selva Daniel , McAdams Daniel A. 
Published: November 2022

Abstract
This work presents a patent mining methodology to qualitatively examine functional evolution of engineered products. Using a topic modeling method called Nonnegative Matrix Factorization, topics of nutcracker- and drone-related patents of different time frames are automatically generated to understand how the two products have functionally evolved over time. The topic modeling results demonstrate that nutcracker’s nut cracking mechanism has changed from an impact method to a rotating method for facilitating mass production. Drone system has evolved from a mechanical system with flight capability to an electromechanical system widening its application in package delivery and videography. The topic modeling results provide different design insights that could assist designers during the early product development process. In addition to the topic modeling approach, an analysis with patent citations is performed to gain additional insights about the functional evolution of the engineered products. The patent citation analysis suggests that the topic modeling approach provides a reasonable illustration of how products have evolved over time, but the algorithm requires improvement as some functional aspects of a product can be obscured by topics that simply characterize the technology. The objective of the study is to provide a basis for automated modeling of patents to understand the functional evolution of engineered products.
A Parallel Multi-Constraint Topology Optimization Solver
Authors: Diaz Adrian , Morgan Nathaniel , Bernardin John 
Published: November 2022

Abstract
This paper presents an open source parallel Topology Optimization (TO) code capable of optimizing mechanical designs subject to multiple inertial constraints. The code utilizes Open-MPI and Kokkos to enable fine-grained parallelism in every major computational segment of a TO code: global equation assembly, global equation solution, and the non-linear optimization of the design. Most of the the Finite Element (FE) infrastructure for the TO code is implemented on the Fierro open source code base; which also leverages the ELEMENTS (grants FE basis functions) and MATAR (grants efficient multidimensional sparse matrix storage) libraries. Essential Numerical Algorithms such as a parallel multi-grid solver for the global equilibrium equations and non-linear optimization come from the MueLu and ROL packages (both found in the Trilinos library) respectively. It is found that the Fierro TO algorithm is capable of providing minimum compliance solutions in multi-constraint problems involving mass, several moment of inertia targets, and constraints related to load bearing regions; where the relative error in the satisfaction of all constraints seen in this work does not exceed 3%. Additionally, this work demonstrates the use of a piece-wise continuous interpolation of material density; which avoids the implementation of filters to avoid well-known mesh dependent issues such as checker-boarding.
Techniques for Designing Stable Flat-Foldable Wood Furniture
Authors: Parkinson Bethany , Ames Daniel , Andrews David , Morgan David , Bateman Terri , Howell Larry L. , Magleby Spencer 
Published: November 2022

Abstract
In this paper we develop techniques for designing stable flat-foldable wooden furniture. Thickness-accommodation techniques used to enable folding of origami from thick materials are shown to also be applicable to wood. Specifically, wood may be used in (and is stable in) the unique load-bearing conditions of vertical lamina emergent torsional (LET) joints and arrays and angled-from-vertical modified origami tessellations, which utilize membrane joints to accommodate thickness. We present and analyze data for LET arrays of various heights in compression. In addition to being unique in load-bearing conditions, the presented structures are unique in that they are collapsible and deployable and fold from their deployed state to a completely flat state. The viability of using LET arrays as load-supporting joints — both in vertical and horizontal orientations — is also discussed. We present a design for a deployable stool and chair which gain their movement from carefully-placed LET joints and may be manufactured with a CNC mill from a single sheet of plywood. A design for a deployable chair based on an origami tessellation is also presented, which consists of six wooden panels cut on a CNC mill, and has a comparatively small surface area in its flat-folded state and may thus be more easily transported.