Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the performance evaluation and preference of design thinking methods in interdisciplinary online course

Under the COVID-19 epidemic, faced with the problem of ensuring the quality of teaching online, Design Thinking, as a design teaching and evaluation tool for interdisciplinary collaborative courses, has attracted much attention. This research purpose is to explore the learning performance and using preference of four popular design thinking methods in interdisciplinary online courses. This study took an intensive online course for graduate students jointly held by two departments, information engineering and industrial design, as the case study. It developed curriculum based on Brainstorming, Crazy 8, User Journey Mapping and Storyboarding. Quantitative evaluation and Evaluation Grid Method were ad opted to compare participants’ performance and preference of these four design thinking methods. The results revealed that, compared with Crazy 8, Brainstorming which has the characteristics of open communication and out-of-convention ideas may be the reason why industrial design students are more prominent in Flexibility and Elaboration. By contrast, compared with User Journey Mapping, Storyboarding performed better in Originality and Elaboration than others. This study provides an educational scientific reference of design thinking methods and expects to help educators improve the design curriculum in the future.
The stock market valuation of intangible expenditures: An empirical examination of US Net Firms

We examine whether stock market fully value intangible assets, especially R&D and Advertising expenses in a specific sample of firms: US net firms along eight years (1996 until 2003). We find that the market value-to-book increases when the return on equity is positive, but also rises as return on equity becomes more negative. We argue that the negative pricing is due the collision of large expenditures in R&D and Advertising that are subject to conservatism accounting practice. Our results are robust when compared with a sample of recent contemporaneous IPOs of US net firms. We are able to conclude that: i) investors look beyond aggregate earnings, ii) that investors value certain components of losses (R&D and Advertising expenditures) positively, iii) capital markets seem to give strong value to firms undertaking such investments, iv) and that the presence of growing R&D and Advertising expenditures, implies that the persistence of losses may have become a weaker indicator of likelihood of liquidation, v) when faced financial distressed, the mergers an acquisitions is the main strategy to exercise the growth option and maximize the value of the firm and vi) consequently analyse loss firms homogenous can lead incorrect specifications.
Subject-Based Organization of the Information Space in Multi-database Networks

1 I n t r o d u c t i o n The dramat ic growth in global interconnectivity has placed vast amounts of da ta within easy reach. At the same time it has made on-demand access to widelydistr ibuted data a natural expectation for a variety of users. A limiting factor however, is the difficulty in providing coherent access and correlation of da ta tha t originate from diverse widely-distributed da ta sources. This is an involved process not only due to the sheer volume of information available, but also because of heterogeneity in naming conventions, meanings and modes of da ta usage. Differences in data descriptions, abstract ion levels, and precise meanings of terms being used in disparate da ta sources do not yield well at all to automation. These problems are compounded by differences in user perceptions and interpretations, and variations tha t may occur at autonomous database sites over time. Users are thus presented with the problem of gaining adequate knowledge of a potentially huge, complex dynamic system, in order to access and combine information in
Effects of Standardized Diabetes Education in Clinical Nurses

The purpose of this study was to exam effects of Standardized Diabetes Education for clinical nurses on the level of diabetes knowledge, the level of persuasion of knowledge and the level of practical application of knowledge for nursing care of patients with diabetes mellitus. This study was a one group quasi-experimental study. Study participants were 115 clinical nurses who 3 general hospitals in Korea from August 3 to September 30, 2013. Data were collected using structured questionnaires including the Knowledge on the DM and NPQ tool by Brett. The data were analyzed using Cronbach's α, means, standard deviations, and t-test with the SPSS windows 19.0 program. The result of implementing the standardized education of diabetes showed that the level of diabetes knowledge (t=2.19, p=.032) was significantly increased. And the level of practical application of diabetes knowledge (t=2.98, p=.004) based on the standardized education of diabetes showed a statistically significant difference between before education and after education
Construction of Defect Detection System for Image Data Using Machine Learning and Image Processing

: Recent technological advances in manufacturing allow for high-speed mass production and more manufacturers to utilize online inspection machines, such as cameras that detect defective products, to enhance their production ability. However, online inspection machines are still in the development stage and might cause misclassifications, which can result in unnecessary investment or potential loss in manufacturers’ reliability. The objective of this study is to build a more precise defect detection system to discriminate between non-defective products and defective products. Previous studies utilized filtering methods such as feature extraction in relation to a filtering window before classification to enhance precision. Our study adopted a different approach in classification process to further increase its precision. We designed a preprocessing process and a feature extracting process based on a variable importance from Random Forest, and a classification process composed of our newly developed ensemble classifiers. As a result, we succeeded in constructing a defect detection system that performs classification with over 90% accuracy, which is more precise than the previous system. Making the most of AdaBoost’s algorithm to overcome the difficulty in the classification resulted in an improvement in the system’s precision.
Vehicle Detection Method Based on Improved HOG-LBP

Aiming at the limitations of histogram of oriented gradient (HOG) feature descriptors, such as redundant information, easy to cause false detections and so on, in order to further improve the detection accuracy rate and speed, this paper proposes a vehicle detection method based on improved hog. A-HOG features are introduced and combined with LBP features to realize vehicle detection. Experiments show that our method has more efficient performance in accuracy.
Bioenergetic defects in G2435R-RYR1 knock-in mice associated with malignant hyperthermia

Mutations in the skeletal muscle ryanodine receptor gene (RYR1) can cause susceptibility to malignant hyperthermia (MH), a potentially lethal genetic condition triggered by volatile anesthetics. MH is associated with hypermetabolism which has directed research interest into oxidative phosphorylation (OXPHOS) and muscle bioenergetics. The most common cause of MH in the United Kingdom is the c.7300G>A RYR1 variant, which is present in ~16% of MH families. Our study focuses on the MH susceptible G2435R-RYR1 knock-in mouse model, which is the murine equivalent of the human c.7300G>A genotype. We investigated bioenergetics in this mouse model using a combination of transcriptomics, protein expression and functional analysis using adult muscle fibers. RNA sequencing data showed reduced expression of genes associated with mitochondria and fatty acid oxidation in RYR1 mutants when compared to wild-type (WT). Mitochondrial function was assessed by measuring oxygen consumption rates in permeabilized muscle fibers. Comparisons between WT and homozygous G2435R-RYR1 mitochondria showed a significant increase in complex I-facilitated OXPHOS in mutant muscle. Furthermore, a gene-dose specific increase in reactive oxygen species production was seen in G2435R-RYR1 muscle fibers. Collectively these findings provide evidence of metabolic defects in G2435R-RYR1 knock-in mouse muscle under basal conditions. Differences in metabolic profile could be the result of differential gene expression in metabolic pathways, in conjunction with mitochondrial damage accumulated from chronic exposure to increased oxidative stress.
Machine Learning for Process Automation of Agricultural Machines in Field Applications

Modern machines for agricultural ﬁeld applications, such as tractors with different implements, oﬀer a vast variety of static as well as dynamic conﬁguration parameters that must be managed by the operator during ﬁeld operation. To operate these machines eﬃciently and achieve the desired results, experience is required. In order to facilitate the operation of these complex machines for less experienced users, the capabilities of machine learning are being researched to control the machine and hence increase its level of automation, easing the cognitive load for the operator. The use of model-free reinforcement learning (RL) enables us to ﬁnd the optimal parameter conﬁguration for a given machine state, with respect to a operator deﬁned performance metric. The use of RL enables us to be learn directly from measurement data without requiring a detailed model of this complex system. An initialization of the system is learned from behavior that has previously been recorded (Imitation Learning), based on this initialization the system improves itself by exploring untried actions in the real world. The evaluation is conducted on a real tractor during ﬁeld operation.
Sustainable Land Planning in Case of Decentralized Land Authorities

: The main focus of this paper is to analyze the effect of local public ﬁnance on spatial land use through economic models and empirical evidence from Israel. The theoretic models extends the Alonso-Mill-Muth model by incorporating local public ﬁnance. The ﬁrst ﬁnding is that steady population growth provides a channel for land capitalization through the mechanism of long term land property right. This implies a possible conﬂict of interest if ownership of land leasing revenue and the ad valorem property tax are not consistent. The empirical section examines one of the implications derived from the models highlighting a possible inconsistency between central and local governments due to land ownership centralization. This causes local tax revenue inequality among Israeli municipalities. Statistical evidence shows that cities with a larger share business land use can generate more tax without assistance from the central government, and are therefore more ﬁscal independent. Fiscal status has a signiﬁcant effect on the planning time of residential construction. Municipalities with higher local tax revenues have shorter planning time(higher probability of acceptance) conditional on the plan’s size and other features.
Piecing the learning analytics puzzle: a consolidated model of a field of research and practice

ABSTRACT The field of learning analytics was founded with the goal to harness vast amounts of data about learning collected by the extensive use of technology. After the early formation, the field has now entered the next phase of maturation with a growing community who has an evident impact on research, practice, policy, and decision-making. Although learning analytics is a bricolage field borrowing from many related other disciplines, there is still no systematised model that shows how these different disciplines are pieced together. Existing models and frameworks of learning analytics are valuable in identifying elements and processes of learning analytics, but they insufficiently elaborate on the links with foundational disciplines. With this in mind, this paper proposes a consolidated model of the field of research and practice that is composed of three mutually connected dimensions – theory, design, and data science. The paper defines why and how each of the three dimensions along with their mutual relations is critical for research and practice of learning analytics. Finally, the paper stresses the importance of multi-perspective approaches to learning analytics based on its three core dimensions for a healthy development of the field and a sustainable impact on research and practice.
Nonlinear Vibration Analysis of a Dielectric Elastomer Based Microbeam Resonator

Dynamic characteristics of a dielectric elastomer based micro beam resonator are investigated by taking into consideration of squeeze-film damping, large deformation and electrical voltage. The analysis shows that the resonant frequency of the resonator can be tuned through changing applied electrical voltage. It is observed that the natural frequency of the resonator increases with the increase of the vibration amplitude. In addition, the ambient pressure can significantly alter the resonant frequency of the resonator. The analysis is envisaged to provide qualitative predictions and guidelines for design and application of DE-based micro resonators.
A data-driven methodology for separately quantifying the effects of tool wear of upper and lower tool on the quality of cut surfaces in shear cutting processes

Shear cutting processes are characterised by physical parameters that change in time and space because of influencing process effects. Those parameters partwise can be measured and recorded by sensors and assigned to causal process effects through detailed evaluation based on analytical models. If the analytical model can classify the causal process effects in detail, the quality of the processed sheet metal component can be monitored on the basis of knowledge about tool wear influences and so a permanent part quality only can be ensured by a preventive tool change. In detail, the quality of the cut surface of a shear cut component to be assessed in this process is influenced by both the tool wear effect of the upper and lower tool, whereby both in fact show a significant influence on the different dimensional and surface characteristics of the cut surface. Analytical models for such analysis often are not available, or, if available, it may deliver only proximate results that needs improvement for the respective process effects. Previous procedures for such prediction purpose are frequently based on large and complex neural networks that require as well as process a huge amount of data. In this regard, a novel evaluation method has shown great potential for data-driven quantification of tool wear effects and looks capable of far outperforming previous process evaluation methods in its performance under different process conditions. In previous studies, specific upper and/or lower tool wear was not considered in a separate manner and a detailed inspection of the finished cut surface there for could not be carried out. This paper investigates how a data-driven approach can be used to evaluate specific wear effects of upper and lower tools in a differentiated manner, and in which degree of detail the main quality characteristics of the cutting surface in a shearing process thus can be assessed.
Analysis of Distributed-Ledger-Technology for the Exchange of Design, Production and Simulation Data in Roll Forming

Digitalization in the metal forming industry needs to be improved to achieve the goals set by Industrie 4.0. Distributed-Ledger-Technology (DLT) has been identified as a promising foundation for tackling the underlying problem of consistent information exchange. DLT-based solutions have already been developed, but none explicitly covers the roll forming use-case. This paper presents a Hyperledger-Fabric-based blockchain network to fill this research gap. This network is specifically designed for the roll forming industry, while still aiming to meet general information exchange requirements. The roll forming use-case is divided into the material supply chain and a design/simulation workflow. Participants and parameters in these two data transfer chains have been validated with the help of industry experts. Running the conceptualized network on an on-premise server has allowed for a detailed evaluation. Feedback provided by experts of the roll forming industry shows the potential of the presented network. Furthermore, the presented solution covers requirements often neglected by existing approaches like large data handling, compatibility to existing interfaces, secure communication, and access rights definition. In summary, this paper provides a pioneering implementation and evaluation of a DLT-based solution for the roll forming industry and, therefore, a foundation for the next steps towards Industrie 4.0.
Adaptive Structures – Soft Mechanical Approach

Since their earliest conceptualization, structures with reconfigurable characteristics contributed to the emergence of an architecture, able to respond and adjust itself to shifting environmental conditions, or time dependent users’ needs. In this respect, the development of tensegrity and scissor-like structures to obtain adaptive capabilities, is primarily based on articulated joints and embedded mechanical actua- tors, following a hard mechanical approach. Although these systems are usually designed to use a small number of components to achieve maximum shape adjustments, their implementation often causes an increase of unsustainable processes with regard to the number and characteristics of the actuators used and mechanisms complexity, as well as energy-inefficient processes, in terms of both construction and kinetic operation. An alternative soft approach for adaptive structures is proposed in the current paper through an implementation of hybrid cable bending-active members, while the latter replaces multiple local hinges through reversible elastic bending deformations. Through the cables own length modifi- cation, these are responsible for the structure deformability and sufficient prestressing of the primary elastic members. Such pliable structures increase the level of design complexity due to the inherent elastic properties of the materials used and their nonlinear structural behaviour during transforma- tion. In demonstrating this, a series of single, coupled and coupled-interconnected cable bending-active system configurations are investigated. Results obtained describe the stress distribution between the structural components during the systems’ form-finding process and load-bearing behaviour.
Integrated analysis of inventory management and transportation systems for the single-period problem

In a two-stage supply chain system, vendor managed inventory policies (VMI) is an integrating decisions between a supplier and his customers.The supplier assumes the responsibility of maintaining inventory at its customers and ensuring that they will not run out of stock at any moment.The objective of this paper is to optimize the inventory and the transportation costs of the customers for a two-stage supply chain. The problem is to determine the delivery quantities as well as the times and routes to the customers, while ensuring feasibility regardless of the realized demands.This paper considers the single-period stochastic inventory routing problem (SP-SIRP) system. The problem formulated as a linear mixed-integer stochastic program for the solutions of the SP-SIRP problem. An illustrative example for the SP-SIRP is presented to explain the proposed model. The finding shows that the vehicle capacity is optimized efficiently with the average capacity utilization for all tours is 100%.
Modelling of an SBR WWTP to Enhance the Performance under Hydraulic Shock Load Using STOAT Software

: Modelling is a tool used to simulate the performance of any type of WWTP (wastewater treatment plant) which empowers the user to optimize the response of works by changing influent loads and operational conditions with minimum effort and low cost. STOAT (sewage treatment operational analysis over time) software is used to simulate the performance of wastewater treatment plants dynamically. In this paper, a model was built by STOAT software for the Hannoville WWTP allowing to analyze and study the enquiries in a shorter period of time associated with laboratory analysis. Additionally, the model can be used to estimate the response of the system to a diversity of problems. The hydraulic shock load was tested for the entire WWTP consequently allowing the application of strategies that guarantee a better performance by presenting the analysis for the entire plant. Through the aid of STOAT software, a model was built for the whole plant with daily sewage volume entering the treatment plant in a year. The study showed whether the plant can accept a higher flow than that it regularly receives or not. This proved to be successful and the plant has the possibility to accept double the hydraulic shock load, meanwhile, the variations of resulting data were acceptable when compared to the Egyptian environmental requirements.
Detection of activity and position of speakers in real-world environments by using Deep Neural Networks and Acoustic Data Augmentation

The task of Speaker LOCalization (SLOC) has been the focus of numerous works in the research ﬁeld, where SLOC is performed on pure speech data, requiring the presence of an Oracle Voice Activity Detection (VAD) algorithm. Nevertheless, this perfect working condition is not satisﬁed in a real world scenario, where employed VADs do commit errors. This work addresses this issue with an extensive analysis focusing on the relationship between several data-driven VAD and SLOC models, ﬁnally proposing a reliable framework for VAD and SLOC. The eﬀectiveness of the approach here discussed is assessed against a multi-room scenario, which is close to a real world environment. Furthermore, up to the authors’ best knowledge, only one contribution proposes a unique framework for VAD and SLOC acting in this addressed scenario; however this solution does not rely on data-driven approaches. This work comes as an extension of the authors’ previous research addressing the VAD and SLOC tasks, by proposing numerous advancements to the original neural network architectures. In details, four diﬀerent models based on convolutional neural networks (CNNs) are here tested, in order to easily high-light the advantages of the introduced novelties. In addition, two diﬀerent CNN models go under study for SLOC. Furthermore, training of data-driven models is here improved through a speciﬁc data augmentation technique. During this procedure, the room impulse responses (RIRs) of two virtual rooms are generated from the knowledge of the room size, reverberation time and microphones and sources placement. Finally, the only other framework for simultaneous detection and localization in a multi-room scenario is here taken into account to fairly compare the proposed method.