bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Mathematical Model for Cloud-Based Scheduling Using Heavy Traffic Limit Theorem in Queuing Process
Authors: Rashidifar Rasoul , Chen F. Frank , Bouzary Hamed , Shahin Mohammad 
Published: October 2022

AbstractCloud manufacturing (CMfg) is a service-oriented manufacturing paradigm that distributes resources in an on-demand business model. In the cloud manufacturing environment, scheduling is considered as an effective tool for satisfying customer requirements which has attracted attention from researchers. In this case, quality of service (QoS) in the scheduling plays a vital role in assessing the impacts of the distributed resources in operation on the performance of scheduling functions. In this paper, a queuing system is employed to model the scheduling problem with multiple servers and then scheduling in cloud manufacturing is classified based on various QoS requirements. Moreover, a set of heavy traffic limit theorems is introduced as a new approach to solving this scheduling problem in which different heavy traffic limits are provided for each of QoS-based scheduling classes. Finally, the number of operational resources in the scheduling is determined by considering the results obtained in the numerical analysis of the heavy traffic limit with different queue disciplines. The results show that different numbers of active machines in various QoS requirements classes play a vital role in that the required QoS metrics such as the expected waiting time and the expected completion time which are critical performance indicators of the cloud’s service are intimately related.
Cost-Minimal Selection of Material Supply Strategies in Matrix Production Systems
Authors: Ranke Daniel , Bauernhansl Thomas 
Published: October 2022

AbstractCompanies are facing changing market demands, high variance, and volatile quantities. Resilient production systems are needed to meet these challenges. The matrix production is such a system. It offers degrees of freedom in terms of operation sequence flexibility and work distribution flexibility through redundantly used resources. For the material supply this is a challenge in planning. The material must be supplied in a cost-efficient manner and without shortages.To increase planning quality, a method for selecting the least expensive material supply strategy is developed. Depending on consumption, constraints of space, and supply framework conditions, different strategies are advantageous for each material. The developed method requires three steps.First, required data for step 2 and step 3 is collected. In step 2, standardized process blocks combine to describe a company-specific material supply strategy. The approach is company-independent and added by cost functions to the process blocks. Through the cost functions applied to the process blocks the costs of a supply strategy is achieved. As material can be supplied in alternative ways, multiple expected costs for supplying arise. As only one supply strategy needs to be selected, step 3 is necessary. It uses the branch-and-cut algorithm on the mathematical description of the logistic selection problem to find the cost-minimal configuration of supply strategies. As the problem is in the context of matrix production, several conditions and requirements need to be included in the selection process.The result is the assignment of a material supply strategy to each material while minimizing the costs.
Feasibility Analysis of Safety Training in Human-Robot Collaboration Scenario: Virtual Reality Use Case
Authors: Dianatfar Morteza , Heshmatisafa Saeid , Latokartano Jyrki , Lanz Minna 
Published: October 2022

AbstractDesign and modification of human-robot collaboration workspace requires analysis of the safety of systems. Generally, the safety analysis process of a system commences with conducting a risk assessment. There exists a number international standards for design robotics work cells and collaborative shared workspaces. These guidelines expound on principles and measures to identify hazards and reduce risks. Measures of risk reductions include eliminating hazards by design, safeguarding, and providing supplementary protective measures such as user training. This study analyzed the technical feasibility and industrial readiness of Virtual Reality (VR) technology for safety training in manufacturing sector. The test case of a VR-based safety training application is defined in the human-robot collaboration pilot-line of diesel engines. The Analytic Hierarchy Process method was utilized for conducting a quantitative analysis of the survey with ten experts. The participants performed the importance rating with respect to two hierarchy level criteria. Regarding the evaluation of safety training methods in a human-robot collaboration environment, two alternatives of traditional and Virtual Reality -based training are compared. The results indicates that the VR-based training is valued over the traditional method, with a scored proportion of approximately 65 percent over 35 percent.
The Impact of Learning Factories on Teaching Lean Principles in an Assembly Environment
Authors: Monetti Fabio Marco , Boffa Eleonora , de Giorgio Andrea , Maffei Antonio 
Published: October 2022

AbstractLearning factories are realistic manufacturing environments built for education; many universities have recently introduced learning factories in engineering programs to tackle real industrial problems; however, statistical studies on its effectiveness are still scarce. This paper presents a statistical study on the impact of learning factories on the students’ learning process, when teaching the lean manufacturing concepts in an assembly environment. The analysis is carried out through the Lean Manufacturing Lab at KTH, a learning factory supporting the traditional educational activities. In the lab, the students assemble a product on an assembly line; during three rounds, they identify problems on the line, apply the appropriate lean tools to overcome the problems, and try to achieve a higher productivity. The study is based on the analysis of the times recorded during the sessions of the lab. A questionnaire submitted to the students after the course evaluates the level of knowledge of lean production principles that the students achieved. The results are twofold: the improvement of the assembly times through the implementation of the lean tools and the positive effect of a hands-on experience on the students’ understanding of the lean principles, highlighted by the answers to the questionnaire. The main contributions are that applying the lean tools on an assembly line improves the productivity even with inexperienced operators, implementing a learning factory is effective in enhancing the learning process, and, lastly, that a first-hand experience applying the lean tools in a real assembly environment is an added value to the students’ education.
Generation of Synthetic AI Training Data for Robotic Grasp-Candidate Identification and Evaluation in Intralogistics Bin-Picking Scenarios
Authors: Holst Dirk , Schoepflin Daniel , Schüppstuhl Thorsten 
Published: October 2022

AbstractRobotic bin picking remains a main challenge for the wide enablement of industrial robotic tasks. While AI-enabled picking approaches are encouraging they repeatedly face the problem of data availability. The scope of this paper is to present a method that combines analytical grasp research with the field of synthetic data creation to generate individual training data for use-cases in intralogistics transportation scenarios. Special attention is given to systematic grasp finding for new objects and unknown geometries in transportation bins and to match the generated data to a real two-finger parallel gripper. The presented approach includes a grasping simulation in Pybullet to investigate the general tangibility of objects under uncertainty and combines these findings with a previously reported virtual scene generator in Blender, which generates AI-images of fully packed transport boxes, including depth maps and necessary annotations. This paper, therefore, contributes a synthesizing and cross-topic approach that combines different facets of bin-picking research such as geometric analysis, determination of tangibility of objects, grasping under uncertainty, finding grasps in dynamic and restricted bin-environments, and automation of synthetic data generation. The approach is utilized to generate synthetic grasp training data and to train a grasp-generating convolutional neural network (GG-CNN) and demonstrated on real-world objects.
Objectifying Machine Setup and Parameter Selection in Expert Knowledge Dependent Industries Using Invertible Neural Networks
Authors: Müller Kai , Posada-Moreno Andrés , Pelzer Lukas , Gries Thomas 
Published: October 2022

AbstractThe textile industry is one of the oldest and largest industries in the world. The fields of application for textile products are diverse. Although the technologies for manufacturing textiles are extensively researched, the industry is still highly dependent on expert knowledge. To date, manual process- and machine adjustments and quality control are the norms rather than the exception. Heat setting is used in the process chain to dissolve or selectively introduce tensions from the weaving or knitting process and to prepare the products for digital printing. The correct setting of the machine depends on a large number of different materials-, processes- & environmental parameters. For each product, the machine has to be set up again by an experienced textile engineer. To ease the training for new workers and shorten the machine setting process, this study aims to use machine learning to facilitate and objectify the setting of the heat-setting process. Machine parameters are generated using an invertible neural network (INN) based on pre-defined target parameters. The results can be used to identify trends in machine settings and respond accordingly. Thus, a reduction of machine setting time could be realized.
Augmented Virtuality Input Demonstration Refinement Improving Hybrid Manipulation Learning for Bin Picking
Authors: Blank Andreas , Zikeli Lukas , Reitelshöfer Sebastian , Karlidag Engin , Franke Jörg 
Published: October 2022

AbstractBeyond conventional automated tasks, autonomous robot capabilities aside human cognitive skills are gaining importance in industrial applications. Although machine learning is a major enabler of autonomous robots, system adaptation remains challenging and time-consuming. The objective of this research work is to propose and evaluate an augmented virtuality-based input demonstration refinement method improving hybrid manipulation learning for industrial bin picking. To this end, deep reinforcement and imitation learning are combined to shorten required adaptation timespans to new components and changing scenarios. The method covers initial learning and dataset tuning during ramp-up as well as fault intervention and dataset refinement. For evaluation standard industrial components and systems serve within a real-world experimental bin picking setup utilizing an articulated robot. As part of the quantitative evaluation, the method is benchmarked against conventional learning methods. As a result, required annotation efforts for successful object grasping are reduced. Thereby, final grasping success rates are increased. Implementation samples are available on: https://github.com/FAU-FAPS/hybrid_manipulationlearning_unity3dros
3D Printing of Hydrogel-Based Seed Planter for In-Space Seed Nursery
Authors: Huang Yanhua , Yu Li , Jiang Liangkui , Shi Xiaolei , Qin Hantang 
Published: October 2022

AbstractInterest in manufacturing parts using 3D printing became popular across academic and industrial sectors because of its improved reliability and accessibility. With the necessity of self-sustentation, growing plant in space is one of the most popular topics. Carboxymethyl cellulose (CMC) is one of the best candidates for sprouting substrate with 3D printing fabrication as it is non-toxic, biodegradable, and suitable for extrusion-based 3D printing. Soybeans were placed into the designed and printed CMC gel with different orientations. Without visible light, soybeans with hilum facing side had the highest water absorption average comparing those facing up or down. Hydrogel weight dominated the water absorption efficiency. These findings signified that bean orientation affects the sprouting process. This study demonstrates the substrate geometry and seed orientation impacts on germination of soybeans, proposed guidelines for optimizing the sprouting process for high-level edible plants and promoting innovated in-space seed nursery approach.
Assessment of Reconfigurable Vibrating Screen Technology for the Mining Industries
Authors: Ramatsetse Boitumelo , Mpofu Khumbulani , Daniyan Ilesanmi , Makinde Olasumbo 
Published: October 2022

AbstractVibrating screens are very vital in the mineral processing industries for the beneficiation (separation) of mineral particles into different sizes. The breaking down of vibrating screens due to unforeseen contingencies have reduced the productivity of these machines thereby reducing the competitiveness, availability and reliability of these machines for the set production target made by the company. Also since human wants are insatiable, fluctuation in the mineral concentrates demands has been an inevitable scenario, thus, reducing the efficiency of the mineral beneficiation industries. During peak mineral concentrates demand, most of these industries do not have an option than to purchase another beneficiation screen in order to meet up with the continuously increasing production demand. A solution called the Reconfigurable Vibrating Screen (RVS) that can cover the gaps created by machine breakdown, and ensure that the variations in quantity of mineral concentrates needed by customers are met. In this paper, a state of configurations achieved by RVS as compared to the existing conventional vibrating screens was made. In addition to this, a market assessment of the proposed RVS and other existing screening technologies was performed. The index parameters used for this analysis are capacity, reliability, efficiency, versatility and cost. From the comparative analysis, it was observed that there are high advantages for using RVS for beneficiation operations in the mineral beneficiation in place of existing vibrating screens.
Ausgangslage
Authors: Lorenz Sandro 
Published: October 2022

ZusammenfassungDas anästhesiologische Prämedikationsgespräch beinhaltet drei grundlegende Kernprozesse, nämlich die anästhesiologische Prämedikation mit Anamnese, präoperativen Anordnungen, Medikation und der Auswahl des geeigneten Narkoseverfahrens; die Aufklärung und Einwilligung des Patienten im Sinne des informed consents und aller rechtlich vorgeschriebenen Teilaspekte und den Aufbau einer tragfähigen Arzt-Patienten-Beziehung.
Präoperative Angst
Authors: Lorenz Sandro 
Published: October 2022

ZusammenfassungIn diesem Kapitel soll Angst, im Rahmen von operativen Eingriffen und im Speziellen im Vorfeld einer derartigen Prozedur besprochen werden. Dabei sollen sowohl die Definitionen, die Genese, die Auswirkungen und die Messung detailliert betrachtet werden.
Qualität und Qualitätsmanagement im stationären Sektor
Authors: Lorenz Sandro 
Published: October 2022

ZusammenfassungUm Qualität und Qualitätsmanagement im Krankenhausbereich adäquat beleuchten zu können ist ein Exkurs in die politischen und gesundheitsökonomischen Strukturen notwendig. Die Verhältnisse im Gesundheitsbereich und insbesondere im stationären Bereich haben sich in den letzten 20 Jahren drastisch verändert [371].
Leptogenesis in SO(10) models with A4 modular symmetry
Authors: Ding Gui-Jun , King Stephen F. , Lu Jun-Nan , Qu Bu-Yao 
Published: October 2022

Abstract
We study the prediction for leptogenesis in two renormalizable supersymmetric SO(10) × A4 modular models in which the neutrino mass is dominantly generated by the type I seesaw mechanism. The evolution of the lepton asymmetries are described in terms of the three-flavored density matrix equations for three heavy Majorana neutrinos, where both vanishing initial condition and thermal initial condition of the right-handed neutrinos are considered. We also present an analytical approximation based on the Boltzmann equations. We find regions of parameter space compatible with the measured fermion masses and mixing parameters as well as the baryon asymmetry of the Universe. The predictions for the light neutrino masses, the effective mass in neutrinoless double beta decay and the leptonic CP violation phases are discussed.
Celestial holography on Kerr-Schild backgrounds
Authors: Gonzo Riccardo , McLoughlin Tristan , Puhm Andrea 
Published: October 2022

Abstract
We explore the celestial holography proposal for non-trivial asymptotically flat backgrounds including the Coulomb field of a static and spinning point charge, their gravitational counterparts described by the Schwarzschild and Kerr metrics, as well as the Aichelburg-Sexl shockwave and spinning shockwave geometries and their electromagnetic cousins. We compute celestial two-point amplitudes on these Kerr-Schild type backgrounds which have the desirable feature, due to the presence of an external source, that they are non-vanishing for general operator positions and are not constrained by the kinematic delta functions of flat space celestial CFT correlators. Of particular interest is the case of shockwave backgrounds where the two-point scattering amplitude of massless scalars can be interpreted as a standard CFT three-point correlator between two massless asymptotic states and a conformal primary shockwave operator. We furthermore show that the boundary on-shell action for general backgrounds becomes the generating functional for tree-level correlation functions in celestial CFT. Finally, we derive (conformal) Faddeev-Kulish dressings for particle-like backgrounds which remove all infrared divergent terms in the two-point functions to all orders in perturbation theory.
Lumbo-pelvino-azetabuläres Alignment – Grundlagen und klinische Konsequenzen
Authors: Heimkes Bernhard , Berger Nina , Frimberger Vincent 
Published: October 2022

ZusammenfassungDie Wirbelsäulenform wie auch die pelvine Ante‑/Retroversion eines Individuums werden durch seine angeborene, genetisch fixierte lumbosakrale Angulation bestimmt. Diese kann wenig aufwendig in der seitlichen Stehaufnahme des Patienten vermessen werden. In der Wirbelsäulenchirurgie existiert zum Thema eine große Anzahl von Originalarbeiten, in der Hüftchirurgie wurde die individuelle Beckenversion und ihre Konsequenzen für die azetabuläre Orientierung der Hüftpfanne bisher weniger beachtet.Im vorliegenden Review werden bisherige Kenntnisse zum Zusammenhang zwischen lumbosakraler Angulation und pelviner Ante‑/Retroversion dargestellt. Es lassen sich hierbei vier anatomisch definierbare Beckentypen unterscheiden, wovon drei als fakultativ pathogen angesehen werden müssen. Klinische Konsequenzen ergeben sich für die Krankheitsbilder der Spondylolisthesis, des nichtspezifischen Kreuzschmerzes, der azetabulären Retroversion, der kongenitalen Hüftdysplasie sowie für die Pfannenpositionierung in der Hüftendoprothetik.
Abdominelle Weichgewebstumoren
Authors: Wardelmann Eva , Kuntze Anna , Trautmann Marcel , Hartmann Wolfgang 
Published: October 2022

ZusammenfassungGastrointestinale Stromatumoren sind mit einer Inzidenz von 10–15 Fällen pro 1 Mio. Einwohner in Deutschland die häufigsten mesenchymalen Tumoren im Abdominalbereich. Ihre eindeutige Identifikation und Charakterisierung ist für betroffene Personen prognostisch und therapeutisch von großer Bedeutung. Ebenso wichtig ist aber auch die klare Abgrenzung anderer mesenchymaler Neoplasien, zu denen leiomyomatöse, neurogene, adipozytäre und fibroblastäre Tumoren gehören. Zudem wächst die Zahl der translokationspositiven Entitäten, die sich nur mit entsprechenden molekularen Methoden eindeutig diagnostizieren lassen. Ziel dieses Beitrags ist es, für deren sichere Identifikation praktische Hinweise zu geben. Eine mögliche Referenzpathologie kann die Diagnosefindung unterstützen.
Fast and Stable Approximation of Analytic Functions from Equispaced Samples via Polynomial Frames
Authors: Adcock Ben , Shadrin Alexei 
Published: October 2022

AbstractWe consider approximating analytic functions on the interval $$[-1,1]$$

[
-
1
,
1
]

from their values at a set of $$m+1$$

m
+
1

equispaced nodes. A result of Platte, Trefethen & Kuijlaars states that fast and stable approximation from equispaced samples is generally impossible. In particular, any method that converges exponentially fast must also be exponentially ill-conditioned. We prove a positive counterpart to this ‘impossibility’ theorem. Our ‘possibility’ theorem shows that there is a well-conditioned method that provides exponential decay of the error down to a finite, but user-controlled tolerance $$\epsilon > 0$$

ϵ
>
0

, which in practice can be chosen close to machine epsilon. The method is known as polynomial frame approximation or polynomial extensions. It uses algebraic polynomials of degree n on an extended interval $$[-\gamma ,\gamma ]$$

[
-
γ
,
γ
]

, $$\gamma > 1$$

γ
>
1

, to construct an approximation on $$[-1,1]$$

[
-
1
,
1
]

via a SVD-regularized least-squares fit. A key step in the proof of our main theorem is a new result on the maximal behaviour of a polynomial of degree n on $$[-1,1]$$

[
-
1
,
1
]

that is simultaneously bounded by one at a set of $$m+1$$

m
+
1

equispaced nodes in $$[-1,1]$$

[
-
1
,
1
]

and $$1/\epsilon $$

1
/
ϵ

on the extended interval $$[-\gamma ,\gamma ]$$

[
-
γ
,
γ
]

. We show that linear oversampling, i.e. $$m = c n \log (1/\epsilon ) / \sqrt{\gamma ^2-1}$$

m
=
c
n
log

(
1
/
ϵ
)

/

γ
2

-
1

, is sufficient for uniform boundedness of any such polynomial on $$[-1,1]$$

[
-
1
,
1
]

. This result aside, we also prove an extended impossibility theorem, which shows that such a possibility theorem (and consequently the method of polynomial frame approximation) is essentially optimal.
Density-impact functions for invasive house mouse (Mus musculus) effects on indigenous lizards and invertebrates
Authors: Norbury Grant , Wilson Deborah J. , Clarke Dean , Hayman Ella , Smith James , Howard Simon 
Published: October 2022

AbstractHouse mice are among the most widely distributed mammals in the world, and adversely affect a wide range of indigenous biota. Suppressing mouse populations, however, is difficult and expensive. Cost-effective suppression requires knowing how low to reduce mouse numbers to achieve biodiversity outcomes, but these targets are usually unknown or not based on evidence. We derived density-impact functions (DIFs) for mice and small indigenous fauna in a tussock grass/shrubland ecosystem. We related two indices of mouse abundance to five indices of indigenous lizard and invertebrate abundance measured inside and outside mammal-resistant fences. Eight of 22 DIFs were significantly non-linear, with positive responses of skinks (Oligosoma maccanni,O. polychroma) and ground wētā (Hemiandrusspp.) only where mice were not detected or scarce (< 5% footprint tunnel tracking rate or printing rate based on footprint density). Kōrero geckos (Woodworthiaspp.) were rarely detected where mice were present. A further 9 DIFs were not differentiated from null models, but patterns were consistent with impacts at 5% mouse abundance. This study suggests that unless mouse control programmes commit to very low abundances, they risk little return for effort. Impact studies of invasive house mice are largely restricted to island ecosystems. Studies need to be extended to other ecosystems and species to confirm the universality or otherwise of these highly non-linear DIFs.