Miracle-dz/UniFAR-FTUs huggingface dataset:
Results from using an Automl Tool for Error Analysis in Manufacturing

: Machine learning (ML) is increasingly used by various user groups to analyze product errors with data recorded during production. Quality engineers and production engineers as well as data scientists are the main users of ML in this area. Finding a product error is not a trivial task due to the complexity of today’s production processes. Products have often many features to check and they are tested in various stages in the production line. ML is a promising technology to analyze production errors. However, a key challenge for applying ML in quality management is the usability of ML tools and the incorporation of domain knowledge for non-experts. In this paper, we show results from using our AutoML tool for manufacturing. This tool makes the use of domain knowledge in combination with ML easy to use for non-experts. We present findings obtained with this approach along with five sample cases with different products and production lines. Within these cases, we discuss the occurred error origins that were found and show the benefit of a supporting AutoML tool.
Dry and Wet Lubrication Analysis for Multi-Material Hip Assembly

Hip joint repair/replacement is one of the most thriving orthopedic surgical procedures in the human body. The group of patients undergoing hip replacement considerably includes young and physically active persons with varying movements thus requiring longer product life and ease of maintenance. Perfect lubrication in hip assembly ensures a low wear rate and better product life. The present work focuses on dry and wet lubrication analysis of complete implant assembly instead of an individual part. The assembly consists of a stem, head, liner and cup, each made of different materials like a ceramic femoral head mounted over a metallic femoral stem with a polyethylene liner and a metallic acetabular cup. In this work, eight metal-materials are considered for stem/cup, three ceramic materials for the head and two polyethylene materials for the liner. The combinations of these materials are evaluated for various mechanical parameters. Dry (µ = 0.13) and wet (µ = 0.05) lubricating conditions between the liner and femoral head have been considered and their effects on the head, liner and cup have been evaluated for the optimization of Hip joint design. Fifty percent of re-surgery cases arise because of excessive wear out resulting in aseptic loosening of the femoral head and liner interface. Femoral head of size 28 mm diameter with 2 mm thick liner and 3 mm thick acetabular cup are modeled and are analyzed for axial pay load of 2.3 kN. The maximum von mises stress and total deformation for various material combinations of implant assembly have been compared to select the most suitable one for the arthroplasty implantation. The combination of CoCrMo – Ceramics – HXLPE – CoCrMo demonstrates minimum stress and deformation for all three parts i.e. femoral head, liner and acetabular cup under present loading and boundary conditions. ZTA is emerged as the preferred ceramic material for femoral head having a higher compressive strength.
A whole school approach to building relationships, promoting positive behaviour and reducing teacher stress in a secondary school

ABSTRACT This exploratory study considered the impact of a whole school programme to enhance relationships, promote positive behaviour, and reduce teacher stress in a secondary school. The programme was facilitated by the school’s educational psychologist (EP) drawing on psychological theory and practice across a range of paradigms (ecosystemic, cognitive, attribution, attachment, developmental, and relational). Some key aims of the programme were to change teacher attributions around challenging behaviour and to promote a whole school ethos to further embed relational approaches. The qualitative data suggest that the programme was supportive for teachers, increased their self-efficacy and reduced feelings of stress. Further longitudinal research is needed to compare programmes like the one described here with matched controls, ideally including observational measures of teachers and students. Challenges in progressing organisational change in secondary schools are discussed. EPs are well positioned to support school communities through systems consultation and the application of psychological problem solving frameworks.
Towards an Approach for Improving Exploratory Testing Tour Assignment based on Testers' Profile

: This work presents an empirical study on the relationship between the testers’ proﬁle and their efﬁciency and preference in the application of tours with tourist metaphor for exploratory software testing. For this purpose, we developed and applied a questionnaire based model to gather as much as possible information about the knowledge, expertise and education level from a group of testers. The results indicated that, in fact, the testers’ proﬁle have impact on the application of tours used in the tourist metaphor: there are differences between the tours preferred by different levels of education and most of testers tend to choose those tours based on what they believed to have the shortest execution time. This work raises a valuable discussion about a humanized process of assigning test tasks in order to improve the efﬁciency of software testing.
Two-Dimensional Material-Based Electrochemical Sensors/Biosensors for Food Safety and Biomolecular Detection

Two-dimensional materials (2DMs) exhibited great potential for applications in materials science, energy storage, environmental science, biomedicine, sensors/biosensors, and others due to their unique physical, chemical, and biological properties. In this review, we present recent advances in the fabrication of 2DM-based electrochemical sensors and biosensors for applications in food safety and biomolecular detection that are related to human health. For this aim, firstly, we introduced the bottom-up and top-down synthesis methods of various 2DMs, such as graphene, transition metal oxides, transition metal dichalcogenides, MXenes, and several other graphene-like materials, and then we demonstrated the structure and surface chemistry of these 2DMs, which play a crucial role in the functionalization of 2DMs and subsequent composition with other nanoscale building blocks such as nanoparticles, biomolecules, and polymers. Then, the 2DM-based electrochemical sensors/biosensors for the detection of nitrite, heavy metal ions, antibiotics, and pesticides in foods and drinks are introduced. Meanwhile, the 2DM-based sensors for the determination and monitoring of key small molecules that are related to diseases and human health are presented and commented on. We believe that this review will be helpful for promoting 2DMs to construct novel electronic sensors and nanodevices for food safety and health monitoring.
Reinforcement learning for robots through efficient simulator sampling

Reinforcement learning (RL) has great potential in robotic systems as a tool for developing policies and controllers in novel situations. However, the cost of realworld samples remains prohibitive as most RL algorithms require a large number of samples before learning near-optimal or even useful policies. Simulators are one way to decrease the number of required real-world samples, but imperfect models make deciding when and how to trust samples from a simulator difficult. Two frameworks are presented for efficient RL through the use of simulators. The first framework considers scenarios where multiple simulators of a target task are available, each with varying levels of fidelity. It is designed to limit the number of samples used in each successively higher-fidelity/cost simulator by allowing a learning agent to choose to run trajectories at the lowest level simulator that will still provide it with useful information. Theoretical proofs of this framework’s sample complexity are given and empirical results are demonstrated on a robotic car with multiple simulators. The second framework focuses on problems represented with continuous states and actions, as are common in many robotics domains. Using probabilistic model-based policy search algorithms and principles of optimal control, this second framework uses data from simulators as prior information for the real-world learning. The framework is tested on a propeller-driven inverted pendulum and on a drifting robotic car. These novel frameworks enable RL algorithms to find near-optimal policies in physical robot domains with fewer expensive real-world samples than previous transfer approaches or learning without simulators. Thesis Supervisor: Jonathan P. How Title: R. C. Maclaurin Professor of Aeronautics and Astonautics
An Experimental Study on Usability and User Experience Evaluation Techniques in Mobile Applications

: Usability and User Experience (UX) are two quality attributes of Human-Computer Interaction (HCI) relevant to the software development process. Thus, to verify the quality of a system, researchers and developers investigate this area, resulting in different Usability and UX evaluation techniques to improve the quality of applications. However, most of them verify only one of these criteria, being necessary, in many cases, to use more than one technique to evaluate an application in both aspects. Therefore, this research aims to present an experimental study to compare the efﬁciency, effectiveness, and acceptance of two inspection techniques, Userbility and UX-Tips, which jointly evaluate the Usability and UX of mobile applications. In this way, 99 volunteer participants used the techniques to identify defects in two mobile applications. After the evaluation, the participants answered an acceptance questionnaire about the techniques used. The quantitative comparison results show that the techniques have no signiﬁcant difference regarding efﬁciency and effectiveness. However, in terms of participant acceptance, Userbility achieved higher rates of usefulness and future usage intentions, while UX-Tips achieved better rates related to ease of use.
Desarrollo de una versión breve del Coping Strategies Inventory

Cuando se percibe que una demanda externa o interna agota los recursos individuales, se utilizan ciertas estrategias para afrontar el estrés que conlleva. Una de las estrategias que más se asocia con determinadas condiciones patológicas es la represión emocional. A pesar de ello, no existen en lengua española instrumentos breves que evalúen dicha estrategia de afrontamiento del estrés. Debido a eso, el objetivo del presente estudio consistió en el desarrollo de una versión breve del Coping Strategies Inventory que contiene solamente tres ítems por cada factor. La muestra argentina (N = 762) se recogió por Internet y se dividió en dos partes. La primera se utilizó para realizar un análisis factorial exploratorio, el cual mostró la existencia de ocho dimensiones. Para todos los análisis posteriores, se utilizó la segunda parte de la muestra. Los resultados del análisis factorial confirmatorio no solo evidenciaron unas propiedades psicométricas adecuadas, sino también la idoneidad para utilizar el instrumento con una estructura factorial de primer orden y no de orden superior. Con el fin de comprobar la validez convergente, se encontraron correlaciones significativas con todos los rasgos de la personalidad. Se concluye que el desarrollo de una versión breve del Coping Strategies Inventory se realizó de forma exitosa, debido a que todos los índices psicométricos alcanzaron unos valores iguales o incluso superiores a la versión original con mayor cantidad de ítems.
FAIR Principles and Big Data: A Software Reference Architecture for Open Science

: Open Science pursues the assurance of free availability and usability of every digital outcome originated from scientiﬁc research, such as scientiﬁc publications, data, and methodologies. It motivated the emergence of the FAIR Principles, which introduce a set of requirements that contemporary data sharing repositories must adopt to provide ﬁndability, accessibility, interoperability, and reusability. However, implementing a FAIR-compliant repository has become a core problem due to two main factors. First, there is a signiﬁcant complexity related to fulﬁlling the requirements since they demand the management of research data and metadata. Second, the repository must be designed to support the inherent big data complexity of volume, variety, and velocity. In this paper, we propose a novel FAIR-compliant software reference architecture to store, process, and query massive volumes of scientiﬁc data and metadata. We also introduce a generic metadata warehouse model to handle the repository metadata and support analytical query processing, providing different perspectives of data insights. We show the applicability of the architecture through a case study in the context of a real-world dataset of COVID-19 Brazilian patients, detailing different types of queries and highlighting their importance to big data analytics.
Dual Coating of Chitosan and Albumin Negates the Protein Corona-Induced Reduced Vascular Adhesion of Targeted PLGA Microparticles in Human Blood

Vascular-targeted carriers (VTCs) have the potential to localize therapeutics and imaging agents to inflamed, diseased sites. Poly (lactic-co-glycolic acid) (PLGA) is a negatively charged copolymer commonly used to construct VTCs due to its biodegradability and FDA approval. Unfortunately, PLGA VTCs experienced reduced adhesion to inflamed endothelium in the presence of human plasma proteins. In this study, PLGA microparticles were coated with chitosan (CS), human serum albumin (HSA), or both (HSA-CS) to improve adhesion. The binding of sialyl Lewis A (a ligand for E-selectin)-targeted PLGA, HSA-PLGA, CSPLGA, and HSA-CSPLGA to activated endothelial cells was evaluated in red blood cells in buffer or plasma flow conditions. PLGA VTCs with HSA-only coating showed improvement and experienced 35–52% adhesion in plasma compared to plasma-free buffer conditions across all shear rates. PLGA VTCs with dual coating—CS and HSA—maintained 80% of their adhesion after exposure to plasma at low and intermediate shears and ≈50% at high shear. Notably, the protein corona characterization showed increases at the 75 and 150 kDa band intensities for HSA-PLGA and HSA-CSPLGA, which could correlate to histidine-rich glycoprotein and immunoglobulin G. The changes in protein corona on HSA-coated particles seem to positively influence particle binding, emphasizing the importance of understanding plasma protein–particle interactions.
Authenticity, Involvement, and Nostalgia in Heritage Hotels in the Era of Digital Technology: A Moderated Meditation Model

Heritage hotels attract a large number of foreign and domestic tourists by offering luxurious experiences while also providing an opportunity to learn about the history, art, culture, tastes, traditions, and sentiments present in various eras in a region’s history. The literature on heritage lodging buildings is still sparse, limited, and lacking in well-established empirical evidence. The purpose of this study is to examine the relationships between perceived authenticity, visitor involvement, and nostalgia in heritage hotels, with self-congruity serving as a mediator and digital technology as a moderator in the tested relationships. A total of 278 guests of heritage sites participated in this study and the data were analysed by a structural equation modelling and smart PLS program. The empirical results provide evidence that perceived authenticity and visitor involvement positively impact heritage hotel nostalgia experience, this relationship was strengthened by self-congruity as a mediator and was dampened by digital technology as a moderator. Additionally, the theoretical and practical implications regarding the application of these findings to the tourism and hospitality industries are discussed.
Did the 2013 Government Shutdown Severely Damage the U.S. Economy

Scott Ross Baker is an Assistant Professor of Finance and Donald P. Jacobs Scholar at Northwestern University. His research is concentrated in empirical finance and macroeconomics. He is currently engaged in a variety of research projects regarding household leverage, the effects of policy uncertainty on financial markets and growth, as well as the effects of unemployment benefits on job search intensity. Recent work examines the impact of household leverage and credit constraints in driving sensitivity to both income and asset shocks during the Great Recession. Scott joined the Finance Department at Northwestern’s Kellogg School of Management in July 2014.
Continuous-Time Stereo-Inertial Odometry

The emerging paradigm of Continuous-Time Simultaneous Localization And Mapping (CTSLAM) has become a competitive alternative to conventional discrete-time approaches in recent times and holds the additional promise of fusing multi-modal sensor setups in a truly generic manner, rendering its importance to robotic navigation and manipulation seminal. In this spirit, this work expands upon continuous-time concepts, evaluates their suitability in common stereo and stereo-inertial online configurations and provides an extensible, generic, robust and modular open-source implementation to the community. The presented experimental analysis records the performance of our approach in these setups against the state-of-the-art in discrete-time Simultaneous Localization And Mapping (SLAM) on established datasets, achieving competitive results, and provides a direct comparison between online discrete- and continuous-time approaches for the first time. Targeting the absence of open-sourced, continuous-time pipelines and their associated, oftentimes prohibitive, initial developmental overhead, our implementation is made public.
Adaptive Multiservice Resource Allocation Algorithm With Wireless Network Virtualization

Wireless network virtualization (WNV) is emerging as a new paradigm to provide high speed communications and it is identified as one of the key enabling technologies to bring fifth-generation (5G) networks into fruition. With network slicing, physical networks are partitioned into multiple virtual networks to serve different types of service while satisfying their specific requirements. In this study, we present an adaptive spectrum control scheme for the WNV technology. Particularly, we develop a two-tier resource allocation algorithm to utilize a multi-service wireless network. In order to increase flexibility and independence among different network slices, our proposed approach is decoupled into two level bargaining games. These games are designed to solve the inter and intra slice resource allocation problems while ensuring traffic services with multiple requirements. Using four different bargaining solutions, we provide considerable benefits when network agents successfully cooperate and maintain an optimal performance balance. Simulation results and analyses are provided to reveal the effectiveness of our proposed approach compared with existing WNV based spectrum control protocols. Finally, we address some challenges and identify research areas for future studies.
Explainable AI for Cheating Detection and Churn Prediction in Online Games

Online gaming is a multibillion dollar industry that entertains a large, global population. Empowering online games with AI has made a great success, however, ignores the explainability of black-box model makes AI less responsible and hinders its further development. In this article, we introduce and discuss the audience and the concept of XAI (eXplainable AI) in online games. We propose a GXAI workflow, which combines the strong expressiveness of multiview data sources and the clear transparency of multiview black-box models. We present four specific classifiers and explainers in the character portrait view, the behavior sequence view, the client image view, and the social graph view. Experiments conducted on real-world datasets for game cheating detection and player churn prediction show the accuracy of classification and the rationality of explanation. We also discover and present numerous interesting and valuable findings from the individual, local, and global explanations. We implement and deploy three practical applications, including evidence and reason generation, model debugging and testing, and model compression and comparison in NetEase Games and have received quite positive reviews from user studies. More future work is in progress since this is the first work that introduces XAI in online games.
Transcriptome analysis of maize pollen grains under drought stress during flowering

ABSTRACT Drought stress is detrimental to male reproduction in maize (Zea mays L.), largely through reducing the quantity and quality of pollen grains. However, transcriptional response of maize pollen grains to drought stress has not been well documented. We compared pollen gene expression for a maize hybrid (ZhongDan909) under well-watered and drought-stress conditions, based on RNA-Seq validated by quantitative real-time PCR analysis. Expression of 6424 genes and 1302 transcripts was altered in pollen grains of maize subjected to 7 days of drought during flowering. Gene Ontology annotations showed 308 differentially expressed genes, annotated and classified into 50 primary functional categories. Kyoto Encyclopedia of Genes and Genomes analyses revealed 44 differentially expressed genes in nine metabolic pathways. In relation to carbohydrate metabolism pathways, there was downregulation of a polygalacturonase gene, which could reduce cell wall lysis in early pollen germination, and an increase in callose synthase transcripts along with reduced cellulase transcripts. These altered gene expressions responsible for cell wall integrity may inhibit the initiation of pollen tube growth. The onset of tube growth could be further impeded by observed changes in gene expression that potentially influence hormone metabolism (including downregulation of AUXIN RESPONSE FACTOR 18 and EIN3-BINDING F-BOX), reduce mitochondrial function, and alter protein translation. Genes with potential roles in adaptation were also altered in their transcript levels. These included genes encoding the upregulated transcription factor ZmNF-YC2, and the downregulated ZmbHLH13, a negative regulator of jasmonic acid responses. The upregulated flavin enzyme gene DIHYDROLIPOYL DEHYDROGENASE 1, associated with increased levels of reactive oxygen species, is of interest in relating redox homeostasis to stress adaptation. Overall, the analyses identified a suite of genes involved in the development of pollen grains and tubes and responsive to drought stress. The findings enhance understanding of the gene networks underlying compromised pollen viability under drought stress.
Recycling and Echelon Utilization of Used Lithium-Ion Batteries from Electric Vehicles in China

As one of the regions with the largest number of electric vehicles (EVs) in the world, China has experienced rapid growth in the penetration rate of EVs, which has led to the peak period of power battery retirement. One of the challenges that people need to solve is how to properly dispose of these retired lithium-ion batteries (LIBs). As a possible solution, echelon utilization is considered to be the most promising method in terms of energy-saving and environmental benefits. The rapid classification and reorganization of retired batteries is the difficulty in realizing the practical application of echelon utilization. Therefore, this paper focuses on the current status and challenges of the echelon utilization in China. Firstly, the current status, recycling modes, and standards are summarized comprehensively to analyze its situation. Secondly, the key technologies of the echelon utilization solutions are analyzed in detail. Guidance and feasible technical solutions are delivered for battery non-destructive disassembly, rapid classifying and recombination. Finally, the future development direction of echelon utilization is examined. The booming cloud computing and artificial intelligence technologies are regarded as advanced means to realize the management of the whole cycle of LIBs, which will be widely used in echelon utilization.