Miracle-dz/UniFAR-FTUs huggingface dataset:
Big Data in the Metal Processing Value Chain: A Systematic Digitalization Approach under Special Consideration of Standardization and SMEs

Within the rise of the fourth industrial revolution, the role of Big Data became increasingly important for a successful digital transformation in the manufacturing environment. The acquisition, analysis, and utilization of this key technology can be defined as a driver for decision-making support, process and operation optimization, and therefore increase the efficiency and effectiveness of a complete manufacturing site. Furthermore, if corresponding interfaces within the supply chain can be connected within a reasonable effort, this technology can boost the competitive advantage of all stakeholders involved. These developments face some barriers: especially SMEs have to be able to be connected to typically more evolved IT systems of their bigger counterparts. To support SMEs with the development of such a system, this paper provides an innovative approach for the digitalization of the value chain of an aluminum component, from casting to the end-of-life recycling, by especially taking into account the RAMI 4.0 model as fundament for a standardized development to ensure compatibility within the complete production value chain. Furthermore, the key role of Big Data within digitalized value chains consisting of SMEs is analytically highlighted, demonstrating the importance of associated technologies in the future of metal processing and in general, manufacturing.
Robust Authentication Protocol for Dynamic Charging System of Electric Vehicles

Electric vehicles have emerged as the future of transport worldwide with the rising need for greener and energy-efficient transportation solutions. This remarkable shift delegated a wide variety of protocols for reliable, scalable and secure communications in the electric vehicle transportation system. With this vision in mind, security researchers have proposed diverse authentication protocols vital for the electric vehicle charging system to protect the information exchanged between the various principals involved. Several existing authentication techniques, on the other hand, are either vulnerable to significant security threats or are limited to static charging. This article proposes a robust authentication mechanism for charging electric vehicles while driving, a scenario that is rapidly approaching in the future. The proposed protocol makes use of secure and lightweight primitives such as elliptic curves and hash functions. The proposed protocol's security is being analyzed to demonstrate that it is capable of withstanding relevant attacks. Additionally, the protocol achieves its objectives at a cost that is practically conceivable in terms of transmission and computing.
Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction

SUMMARY Code smells can be detected using tools such as a static analyzer that detects code smells based on source code metrics. Developers perform refactoring activities based on the result of such detection tools to improve source code quality. However, such an approach can be considered as reactive refactoring , i.e., developers react to code smells after they occur. This means that developers ﬁrst suﬀer the eﬀects of low-quality source code before they start solving code smells. In this study, we focus on proactiverefactoring , i.e., refactoringsourcecodebeforeitbecomessmelly. This approach would allow developers to maintain source code quality without having to suﬀer the impact of code smells. To support the proactive refactoring process, we propose a technique to detect decaying modules , which are non-smelly modules that are about to become smelly. We present empirical studies on open source projects with the aim of studying the characteristics of decaying modules. Additionally, to facilitate developers in the refactoring planning process, we perform a study on using a machine learning technique to predict decaying modules and report a factor that contributes most to the performance of the model under consideration.
Neural networks and multivariate SPC

Recent developments in the instrumentation of plants has led to multivariate statistical process control (MSPC) techniques becoming increasingly popular for process monitoring in the chemical industry over the last few years. This paper examines one such algorithm, the partial least squares (PLS), and shows how the basic principles of this linear technique can be extended into the nonlinear domain via the application of radial basis function (RBF) neural networks. Results showing the successful application of these methods to fault detection in a validated model of an industrial overheads condenser and reflux drum plant are also given.
Energy-efficient charge-recovery switching scheme for dual-capacitive arrays SAR ADC

Proposed is an energy-efficient charge-recovery switching scheme for a dual-capacitive arrays successive approximation register (DCA-SAR) analogue-to-digital converter (ADC). With the use of the charge-recovery technique in a fully differential DCA-SAR architecture, the proposed switching scheme achieves 98.7 and 46.9% reduction in switching energy and area, respectively, while maintaining the switching energy constant for different output codes.
Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron

Rolling element bearings are critical mechanical components in rotating machinery and fault detection in the early stages of damage is important to prevent their malfunctioning and failure. Vibration monitoring is the most widely used and cost-effective monitoring technique to detect, locate and distinguish faults in rolling element bearings. This paper purposes single hidden layer architecture for fault diagnosis of rolling element bearings. The particular of this proposed architecture is its ability to generalize for solving both basic classification and fault identification. The network uses the features of time-domain vibration signals with normal and defective bearings. The Multi Layer Perceptron (MLP) was trained and tested with a set of experimental data obtained from previous experiments developed by FEG, CWRU and RANDALL laboratories. The results show the effectiveness of the MLP to diagnose the machine condition for the various data used.
Safeguard measures and fresh produce trade: The case of U.S. blueberry imports

Safeguard measures are used to limit excessive import growth and protect domestic producers from unfair import competition. The global safeguard investigation for blueberries highlights these concerns and raises questions about the relationship between imports, prices, and the well-being of U.S. producers. Although the U.S. International Trade Commission (USITC) ruled that imports have not caused serious injury to U.S. blueberry producers, it was important to further examine this issue. In this study, we employ an inverse demand model and dynamic Vector Autoregressive (VAR) procedure linking source-specific fresh blueberry imports from countries like Mexico and Chile to U.S. blueberry prices. Our results mostly support the USITC ruling. Results indicate that declines in U.S. prices are small when compared to the level of import growth. Impulse response functions indicate that import price shocks are not long lasting.
Optimal bidding strategy of electricity retailers using robust optimisation approach considering time-of-use rate demand response programs under market price uncertainties

In restructured electricity markets, the electricity retailers try to obtain the consumers' electricity demand at the minimum cost of different resources such as self-generating facilities, bilateral contracts and pool market purchases. Hence, more attention should be paid to the demand response programs (DRPs) which aim to electricity procurement cost reduction. Owing to the uncertain nature of pool prices and the price fluctuation in the pool markets, the uncertainty modelling is inevitable for retailers. In this study, a robust optimisation approach is proposed for decision making of electricity retailers. Meanwhile, considering the effect of DRP on total procurement cost, an optimal bidding strategy is proposed of electricity retailers with the time-based model of DRP in the electricity market. For this purpose, a collection of robust mixed-integer linear programming (RMILP) problem should be solved in the proposed method. Rather than using the forecasted prices as inputs, the upper and lower limits of pool prices are considered for the uncertainty modelling. The range of pool prices is sequentially partitioned into successive nested subintervals, which permits formulating the RMILP problems. The results of these problems give sufficient data to obtain an optimal bidding strategy for electricity retailers considering DRP. Detailed analysis is performed to delineate the proposed method.
Potential to Implement an Industrial Symbiosis Network in Rural Portugal

Industrial Symbiosis (IS) is a tool that may facilitate coordination of the factors necessary to improve resource use efficiency in regional levels. This thesis studies the potential of developing an IS network in rural Portugal, that could support the competitiveness of the local companies, whilst also improving their own and the regions environmental profile. The research is based on a hybrid of action research and case study research. It analyses the special case of the municipality of Reguengos de Monsaraz and the factors crucial for the development of a IS network at the regional level. These factors are linked to domains such as technical, informational, economic, political/ organisational and institutional. The research identified and worked with eleven organisations in the region: five private, three public/ private joint ventures, two government bodies and one non-governmental organization The following crucial factors for the implementation of an IS network were shown to exists in Reguengos de Monsaraz: Engagement of the various stakeholders, identification of their own inter-organisational connections, the willingness of the local administration in taking part or supporting such a project, the availability of economic gains for stakeholders, the available economic aid from national or European funds, and the degree of awareness various companies had regarding the resources available, their quantities and needs of each other. With all these factors identified, the research concentrated on setting the foundation that can support further developments leading to the emergence of an IS network. Rather than direct exchanges between companies, the research is focused on the creation of central infrastructures for the revalorisation of waste streams into valuable products. The research project also provides partial analysis related to certain technologies that could enable synergistic inter-organizational relations.
Management of Grade II Furcation Defect in Mandibular Molars with Allograft and Alloplastic Bone Graft: A Clinico- Radiographic Study

Aim: The present study was carried out to compare clinically and radiographically the efficacy of regenerative potential of Demineralizedfreeze-dried bone allograft (DFDBA) and Bioactive glass putty (Novabone© dental putty) in mandibular grade II furcation defects. Methods: In 34 Patients, 60 mandibular grade II furcation defects were treated using DFDBA and Bioactive glass putty. 30 furcations were treated using DFDBA, while bioactive glass putty was used to treat remaining 30 furcation defect. Clinical parameters evaluated were Plaque index (PI), Gingival index (GI), Probing pocket depth (PPD), Relative vertical attachment level (RVAL), Relative horizontal attachment level (RHAL) at baseline, 3 months and 6 months. Radiographic parameters recorded were linear measurement of defect depth and bone density in gray scale at baseline and 6 months. Results: Both the group showed significant reduction in mean (P≤0.05) GI, PI, PPD, RVAL and RHAL at 6 months. Group I showed greater reduction in PPD. Radiographic evaluation showed significant (P≤0.05) reduction in defect depth and increase in bone density in both groups. Conclusions: Bioactive glass putty showed comparable regeneration to that of DFDBA in the treatment of mandibular grade II furcation defect. Additional putty consistency of bioactive glass makes it easier and more convenient to use.
The response of the Amazon ecosystem to the photosynthetically active radiation fields: integrating impacts of biomass burning aerosol and clouds in the NASA GEOS Earth system model

Abstract. The Amazon experiences fires every year, and the resulting biomass burning aerosols, together with cloud particles, influence the penetration of sunlight through the atmosphere, increasing the ratio of diffuse to direct photosynthetically active radiation (PAR) reaching the vegetation canopy and thereby potentially increasing ecosystem productivity. In this study, we use the NASA Goddard Earth Observing System (GEOS) model with coupled aerosol, cloud, radiation, and ecosystem modules to investigate the impact of Amazon biomass burning aerosols on ecosystem productivity, as well as the role of the Amazon's clouds in tempering this impact. The study focuses on a 7-year period (2010–2016) during which the Amazon experienced a variety of dynamic environments (e.g., La Niña, normal years, and El Niño). The direct radiative impact of biomass burning aerosols on ecosystem productivity – called here the aerosol diffuse radiation fertilization effect – is found to increase Amazonian gross primary production (GPP) by 2.6 % via a 3.8 % increase in diffuse PAR (DFPAR) despite a 5.4 % decrease in direct PAR (DRPAR) on multiyear average during burning seasons. On a monthly basis, this increase in GPP can be as large as 9.9 % (occurring in August 2010). Consequently, the net primary production (NPP) in the Amazon is increased by 1.5 %, or ∼92 Tg C yr−1 – equivalent to ∼37 % of the average carbon lost due to Amazon fires over the 7 years considered. Clouds, however, strongly regulate the effectiveness of the aerosol diffuse radiation fertilization effect. The efficiency of this fertilization effect is the highest in cloud-free conditions and linearly decreases with increasing cloud amount until the cloud fraction reaches ∼0.8, at which point the aerosol-influenced light changes from being a stimulator to an inhibitor of plant growth. Nevertheless, interannual changes in the overall strength of the aerosol diffuse radiation fertilization effect are primarily controlled by the large interannual changes in biomass burning aerosols rather than by changes in cloudiness during the studied period.
Development of Higher Order Thinking Skill in Junior High School: Studies on Social Studies Teachers in Pekalongan City

The objective of this research is to explain the ability of teachers to develop high-level questions (HOTS) and its relationship with students' critical thinking skills. The research informants were social studies teachers in Pekalongan City. The conclusions of the study are developing higher order thinking skills has an impact on students' skills in critical thinking. Learning development in this way has a dependence on teacher factors. The teacher is the main factor that can improve critical thinking skills.
Research SoftReliability:An InterdisciplinaryApproach withaUser-SystemFocus

A recent trend in technological innovation is towards the developmentof increasingly multifunctional and complex products to be used within rich socio-cultural contexts such asthe high-endoffice,the digitalhome,andprofessionalor personalhealthcare. One important consequence of the development of strongly innovative products is a growing market uncertainty regarding ‘if’, ‘how’, and ‘when’ users can and will adopt such products. Often, it is not even clear to what extent these products are understood and interacted with in the intended manner. The mentioned problems have already become an evident concern in the field, where there is a significant rise in the numbers of seemingly sound products being complained about, signaling a lack of soft reliability. In this paper, we position soft reliability as a growing and critical industrial problem, whose solution requires new academic expertise from various disciplines. We illustrate potential root causes for soft reliability problems, such as discrepancy between the perceptions of users and designers. We discuss the necessary approach to effectively capture subjective feedback data from actual users, e.g. when they contact call centers. Furthermore, we present a novel observation and analysis approach that enables insight into actual product usage, and outline opportunities for combining such objective data with the subjective feedback provided by users. Copyright © 2008 John Wiley & Sons, Ltd.
PREPARING TEACHERS OF TODAY FOR THE LEARNERS OF TOMORROW

The trend towards globalization and the knowledge economy pose numerous challenges as well as opportunities for AsiaPacific countries. Expanding trade and increasing mobility of goods and services pressure economies to restructure, retraining those made redundant in declining industries and to upgrade the skills of those employed in new industries. Also, the dynamic global flow of information creates a demand for higher-level cognitive skills and continuous learning, resulting in the phase-out of skills that the graduates currently acquire in Technical and Vocational Education and Training (TVET) system. More complex skills are needed to effectively cope with the accelerating technological change. The emergent concern on climatic change and sustainable development emphasizes the vital role of TVET towards sustainability is becoming more evident. Specifically, it aims to prepare our next generation for accepting responsibility through Education for Sustainable Development (ESD). It cited that the resiliency of education and training systems, as well as the TVET teachers in meeting these challenges is beneficial for the region's development for it will enable new opportunities for the TVET teachers in the new era. In this light, this paper deals with such changes in preparing teachers for learners of TVET system in the near future.
High accuracy multisensor road vehicle state estimation

This paper describes the theoretical development and evaluation of a multisensor navigation system for high speed road vehicles. The particular contributions of the theoretical work are in the designing the nonlinear process model that is able to cope with vehicle slip using multisensor data from inertial sensors, odometry and the D-GPS. The algorithm was evaluated using a vehicle dynamics simulator built to allow the simulation of a wide variety of driving scenarios. The simulation results indicate that the scheme is able to significantly reduce the errors in vehicle position and orientation estimates. Besides, the results also show that the scheme allows slip angle estimation (which is of importance in itself), and accelerometer bias estimation.
Does Insurgent Selective Punishment Deter Collaboration? Evidence from the Drone War in Pakistan

Scholars of civil wars have long argued that non-state actors can use selective punishment to reduce collaboration with state adversaries. However, there is little systematic evidence confirming this claim, nor investigation into the mechanisms at play. In this paper, we provide such evidence from the drone war in Pakistan. Militants in Pakistan’s tribal areas engaged in a brutal counterespionage campaign with the aim of reducing collaboration with the United States. Our analysis combines a novel dataset of collaborator killings with data on drone strike outcomes. We find that strikes killed half as many militant leaders and fighters following collaborator killings and that this suppressive effect likely works by deterring spying in the future. Beyond providing an empirical confirmation of the selective punishment hypothesis, our paper suggests an unacknowledged vulnerability of the drone program to reprisals against local allies and collaborators that limits its effectiveness as a long-term tool of counterterrorism.
Qualitative Study Protocol: Augmented Reality Technology to Deliver Asthma Inhaler Technique Training for Children and Adolescents With Asthma

Asthma is a chronic inflammatory condition of the airways with a heterogenous symptom profile. When symptoms are poorly controlled and frequent, asthma sufferers are impacted regularly, with limitations on physical activities and sleep disturbances, significantly impairing quality of life. Asthma is highly prevalent and the leading cause of disease burden in young people. Those aged 0–14 contribute to over half of the asthma hospitalisations within Australia. Asthma education and self-management remains a key component of care; however, challenges remain in the paediatric population with difficulties of engagement. Augmented reality (AR) may provide a novel and effective solution with its ability to superimpose virtual objects into a real-world setting. Using a smartphone or tablet to deliver AR makes this modality accessible to much of the population. AR is a growing field in technology and has already established uses in education and training. The ability to increase motivation, enhance enjoyment and encourage faster concept understanding in the educational setting is encouraging and supports our proposal that AR technology can provide a generation appropriate education delivery modality for young people with asthma. To ensure successful implementation of an AR asthma educational resource on a large scale, the usability, acceptability, barriers and enablers of its use must be investigated. Using an iterative co-design process, an asthma resource utilising AR to deliver education on inhaler technique will be created. Qualitative research will be undertaken using semi-structured interviews with moderator guides to obtain mixed-method data on the AR resource. Participants will be key members of the asthma community including children and adolescents with asthma (8–17 years old), caregivers of children and adolescents with asthma, and health professionals. Understanding the usability, acceptability, barriers and enablers of the AR resource will enable us to improve our alpha version and test an optimal version in a planned feasibility study.
Few-shot Learning for Semantic Segmentation of Seismic Data

Summary Seismic interpretation i.e. the process of identifying objects of interest in the subsurface using seismic data, can be effectively represented as a semantic segmentation task in machine learning. Contrary to many semantic segmentation tasks, seismic data present a major challenge with the amount of quality labeled data available. This stems from two factors: first, seismic data can be labeled almost exclusively by trained experts, and second, due to the subsurface’s heterogeneity, labels created for one location are not always transferable to another dataset. We embrace this data shortage and reformulate the seismic segmentation problem from a fully supervised approach to a few-shot learning task. Our approach builds upon an existing few-shot learning method, which we adapt to the specific requirements of a multi-class subsurface segmentation problem. Furthermore, we address the image-patching locality problem by injecting a global view of the labels into the network during training and inference. Finally, we provide a computationally efficient post-processing approach that can be used with other existing seismic segmentation methods. The proposed few-shot learning approach for semantic seismic segmentation outperforms a supervised UNet baseline implementation in terms of qualitative and quantitative results while being trained on a total of 10 inlines of each survey