Miracle-dz/UniFAR-FTUs huggingface dataset:
The ‘Archpro Carnuntum’ Project – Integrated Archaeological Interpretation of Combined Prospection Data, Carnuntum (Austria)

The potential of large-scale, non-invasive prospection methods has been widely recognized in archaeology in recent years. Their outstanding possibilities for the exploration of urban centres have been realised early on and applied at selected sites. The ‘ArchPro Carnuntum’ project stands out for its extensive investigation of a Roman provincial capital by the combined application of a wide variety of survey methods (aerial archaeology, magnetometry, ground penetrating radar, extensive field survey) resulting in detailed information on the ancient infrastructure of the Roman metropolis. Within the project, it was not only possible to discover new settlement areas, but in some cases even to deduce their former purpose. As a result, the military administrative centre, newly built residential areas, and temporary military camps could be detected in the archaeological landscape of Carnuntum. This paper presents an overview of the results of this internationally unique prospection project.Az elmúlt évek során széles körben felismerték a régészet tudományterületén alkalmazható nagyszabású, nem invazív jellegű kutatási módszerekben rejlő potenciált. A módszer települési központok feltárásának területén alkalmazható lehetőségei már korán valósággá váltak, és alkalmazásra kerültek a kiválasztott helyszíneken. Az „ArchPro Carnuntum” projekt a római provinciális főváros átfogó vizsgálatával a legkülönfélébb felmérési módszerek (légi régészet, magnetometria, földradar, kiterjedt terepi felmérés) együttes alkalmazásával kiemelkedik ezek közül, és részletes információval szolgál a római város antik infrastruktúrájával kapcsolatban. A projekt során nem kizárólag új települési területek felfedezésére nyílt mód, hanem egyes esetekben következtetni lehetett a területek egykori funkciójára is. Mindezek eredményeként a carnuntumi régészeti területen kimutathatóvá vált a katonai igazgatási központ, valamint az új építésű lakónegyedek és az ideiglenes katonai táborhelyek is. Jelen tanulmány ezen nemzetközi szinten is egyedülálló kutatási projekt eredményeiről nyújt áttekintést.
A survey on analysis and detection of Android ransomware

Smart‐phones have become a necessity for users due to their abundance of services such as global positioning system, Wi‐Fi, voice/video calls, SMS, camera, and so forth. It contains personal information of users including photos, documents, messages, and videos. Android‐based smart‐phones enriched with many applications (commonly known as apps) fascinates users to use this ubiquitous technology up to a full extent. With open architecture and 73% of market share, Android is the most popular mobile operating system (OS) among developers. At the same time, the increasing popularity of Android OS woos attackers or cyber‐criminals to exploit its vulnerabilities. The attackers write malicious code to harm the device and grab users' sensitive information. For example, ransomware (a form of malware) demands ransom from victims to liberate the ceased material for illegal financial gain. The existing survey papers cover the analysis and detection of generic Android malware. The focus of this survey paper is to present an in‐depth threat scenario of Android ransomware. This article not only provides a comprehensive survey on analysis and detection methods for Android ransomware since its beginning (2015) till date (2020); but also presents observations and suggestions for researchers and practitioners to carry out further research.
Exploring the effects of using various designs of game-based materials on music learning

ABSTRACT This paper explores and sheds light on how various designs of game-based learning materials benefit music education. The developed games followed Gordon’s music skill learning sequence of discrimination, and aimed to teach listening and visual skills for the middle C octave. Two genres of game-based learning materials were proposed, one with most game design features and the other which excluded most game mechanisms but retained simple gamification features. A total of 34 elementary school students participated in the experiment and were separated into three groups to learn music with the game-based learning materials. The pros and cons of using various music game-based learning materials for learning performance and learning motivation are summarized. The findings suggest that teachers could adopt the simple version game as a course assisting tool to enable immediate game interaction with students in formal music classes, and the full version game could be provided to learners for after-class informal learning. Besides, the full version could be a trigger for advanced learners to challenge themselves, whereas the simple version would be good for novice learners to acquire the targeted music content more smoothly.
Assessing and Comparing Interpretability Techniques for Artificial Neural Networks Breast Cancer Classification

ABSTRACT Breast cancer is the most common type of cancer among women. Thankfully, early detection and treatment improvements helped decrease the number of deaths. Data Mining techniques have always assisted BC tasks whether it is screening, diagnosis, prognosis, treatment, monitoring, and/or management. Nowadays, the use of Data Mining is witnessing a new era. In fact, the main objective is no longer to replace humans but to enhance their capabilities, which is why Artificial Intelligence is now referred to as Intelligence Augmentation. In this context, interpretability is used to help domain experts learn new patterns and machine learning experts debug their models. This paper aims to investigate three black-boxes interpretation techniques: Feature Importance, Partial Dependence Plot, and LIME when applied to two types of feed-forward Artificial Neural Networks: Multilayer perceptrons, and Radial Basis Function Network, trained on the Wisconsin Original dataset for breast cancer diagnosis. Results showed that local LIME explanations were instance-level interpretations that came in line with the global interpretations of the other two techniques. Global/local interpretability techniques can thus be combined to define the trustworthiness of a black-box model.
Immunophenotypic Analysis of AIDS-Related Diffuse Large B-Cell Lymphoma and Clinical Implications in Patients

Diffuse large B-cell lymphoma (DLBCL) represents a clinically heterogeneous disease. Models based on immunohistochemistry predict clinical outcomes. However, whether these immunophenotypes could be applied as a predictive indicator in AIDS-Related DLBCL remains unclear. To explore predictive immunophenotypes and their clinical implications in AIDS-related DLBCL, we analyzed patient samples treated 2008–2019 with R-CHOP/CHOP-like and central nervous system (CNS) prevention treatment for DLBCL with HIV infection in Chongqing cancer hospital. c-MYC, CD4 count, and co-expression of BCL-2 and c-MYC presented the potential of being predictive markers in the prognosis of AIDS-related DLBCL, while absolute lymphocyte count did not show any effects in clinical outcomes. Surprisingly, we found that BCL-2 and the germinal center b-cell (GCB) subtype also could predict the prognosis of AIDS-related DLBCL, which against previous studies. Thus, the specific mechanism of BCL-2 and GCB subtype in AIDS-related DLBCL still requires to be explored.
Maximizing the Antioxidant Capacity of Padina pavonica by Choosing the Right Drying and Extraction Methods

Marine algae are becoming an interesting source of biologically active compounds with a promising application as nutraceuticals, functional food ingredients, and therapeutic agents. The effect of drying (freeze-drying, oven-drying, and shade-drying) and extraction methods (shaking at room temperature, shaking in an incubator at 60 °C, ultrasound-assisted extraction (UAE), and microwave-assisted extraction (MAE)) on the total phenolics content (TPC), total flavonoids content (TFC), and total tannins content (TTC), as well as antioxidant capacity of the water/ethanol extracts from Padina pavonica were investigated. The TPC, TFC, and TTC values of P. pavonica were in the range from 0.44 ± 0.03 to 4.32 ± 0.15 gallic acid equivalents in mg/g (mg GAE/g) dry algae, from 0.31 ± 0.01 to 2.87 ± 0.01 mg QE/g dry algae, and from 0.32 ± 0.02 to 10.41 ± 0.62 mg CE/g dry algae, respectively. The highest TPC was found in the freeze-dried sample in 50% ethanol, extracted by MAE (200 W, 60 °C, and 5 min). In all cases, freeze-dried samples extracted with ethanol (both 50% and 70%) had the higher antioxidant activity, while MAE as a green option reduces the extraction time without the loss of antioxidant activity in P. pavonica.
Automated fault detection for additive manufacturing using vibration sensors

ABSTRACT Online process control is a crucial task in modern production systems that use digital twin technology. The data acquisition from machines must provide reliable and on-the-fly data, reflecting the exact status of the ongoing process. This work presents an architecture to acquire data for an Additive Manufacturing (3D printer) process, using a set of consolidated Internet of Things (IoT) technologies to collect, verify and store these data in a trustful and secure way. The need for online monitoring and fault detection is addressed by the development of a classifier using Convolutional Neural Networks. This deep learning approach, using temporally aligned vibration data provided by the underlying architecture, allows raw data processing to detect patterns without signal pre-processing and without domain-specific knowledge for model building.
Glycosylated hemoglobin as an efficacious tool for early prediction of gestational diabetes mellitus

The incidence and prevalence of GDM is on the rise worldwide and, more so in developing countries including India. GDM is associated with maternal, fetal and neonatal morbidities. Current guidelines recommend GDM screening only at 24-28 weeks of gestation. Diagnosis of GDM in later half of pregnancy leads to fetal and maternal morbidities in spite of good glycemic control. This necessitates the use of a simpler, convenient, accurate, and reliable test, which can predict GDM in early gestation. Our article reviews the scope of using HbA1c for GDM and its efficacy in screening GDM.
Pulmonary nodule detection based on 3D feature pyramid network with incorporated squeeze‐and‐excitation‐attention mechanism

Lung cancer is mainly caused by malignant lung nodules. Early detection and diagnosis of lung nodules can diagnose the disease in time and significantly improve the survival rate of the patients. With the rapid development of deep learning networks in the field of medical aid diagnosis, many deep networks have been applied to lung nodule detection. Statistical distribution shows that most of the lung nodule radii are too small to be well detected. Therefore, 3D feature pyramid network (FPN) for single‐stage pulmonary nodule detection is proposed to solve this problem by combining the 3D characteristics of computed tomography (CT) image data. In addition, the squeeze‐and‐excitation (SE)‐attention module is added to improve detection performance. The validity of the network is verified on the public pulmonary nodule dataset LUNA16. The competition performance metric (CPM) value reaches 0.8934. Compared with other pulmonary nodule detection networks, the detection performance of this network improved by 2%.
Synthesis and characterisation of a novel Fe-based nanocomposite by mechanical alloying and spark plasma sintering

To improve low hardness and poor corrosion resistance of the Fe-based alloys, Fe–Ti–Al–B–Si–C–P–Cr novel alloy was prepared using mechanical alloying and spark plasma sintering (SPS). Two types of mills, including of high-energy planetary (HEP) and SPEX(S) ball mills (BM) were used to reach the solid solution. Mechanical alloying by the HEP route led to the formation of the Fe(α) phase after 60 h, while its milling after 15 h by high-energy SPEX mill led to the formation of the Fe(α) solid solution. After sintering of as-milled powders by SPS, the composite of Fe (α)/nanocrystalline was obtained. The micro-hardness of the HEP–SPS and the S–SPS samples obtained about 689.5 ± 0.15 and 2068.5 ± 0.18 HV0.3, respectively. The lowest corrosion current density and the highest polarization resistance were related to the HEP–SPS sample and was calculated to be 0.51 µA/cm2 and 41.668 KHz/cm2, respectively.
Global Oil Price and Innovation for Sustainability: The Impact of R&D Spending, Oil Price and Oil Price Volatility on GHG Emissions

Recently, sustainable economic growth has taken the front line of the global development agenda. The common dependency on fossil fuel energy, greenhouse gas (GHG) emissions and the continuous rising demands for energy have posed challenges that put the world in a climate change trap. This work empirically analyzes the effect of innovation, oil price, oil price volatility and economic growth on GHG emissions over the period of 1991–2015. The study compares the emission level between European Union countries (EU) (26), oil-producing countries (22), China and the United States of America (USA) using the Driscoll–Kraay model. The main empirical finding points to a positive effect of innovation on GHG emission reduction initiatives in oil-importing economies. Particularly, EU countries significantly minimized emissions due to innovation, followed by China and the USA. Contrarily, the effect of innovation increases GHG emission in oil-exporting economies. The results also indicate broader significant effects of oil price and oil price volatility on GHG emission. Interestingly, the effect of oil price on GHG emission is asymmetrical between oil-exporting and -importing economies. Oil price increases in oil-importing countries decrease GHG emission; contrarily, its effect increases emissions in oil-exporting countries. Thus, oil-exporting countries lack motivation to decrease emission levels due to oil price escalation. Unlike the oil price, oil price volatility comparably decreases GHG emissions in oil-exporting and -importing economies. Thus, one might be tempted to take oil price volatility and the future uncertainty of oil price as a virtuous instance rather than oil price increment. Thus, policymakers need to pay attention to market forces and policy measures to monitor GHG emissions due to economic activities. The results are also robust under the alternative econometrics estimation model of generalized method of moments (GMM)-Differenced.
Social outcomes from participating in the Youth Olympic Games: the role of the service environment

ABSTRACT Research question This study investigated social outcomes of interest regarding the Youth Olympic Games (YOG) and the International Olympic Committee's (IOC) from the perspective of the young athletes competing at the Buenos Aires 2018 YOG. In particular, the process of how the YOG's planned service environment contributes to young athletes’ learning is examined using a transformative service research lens and control value theory. Research methods An online post-Games survey measured the athletes’ perspective on (1) the service environment; (2) enjoyment; (3) learning; and (4) social outcomes of interest. A total of 330 anonymized responses were collected (boys = 130, girls = 200). Results and findings All service environment-related factors (i.e. service environment outside of sport, service environment in sport venue, and the Learn and Share program) significantly predicted the social outcomes of interest of the YOG through learning (Mediator1 ) and enjoyment (Mediator2 ) both individually and in-sequence. Implications For the IOC to create greater social outcomes of interest, athletes’ sense of enjoyment and learning must be fostered. Evidence demonstrates that, through athlete enjoyment and learning, the acquisition of social outcomes of interest increases, consequently bolstering the notions advanced in control value theory where positive emotions enhance one's learning. Hence, for the IOC, creating an environment that focuses on the sport competitions as well as on the education and cultural opportunities for athlete exchange is important.
MPC based active disturbance rejection control for automated steering control

This paper proposes a control strategy for the design of an automated steering control for an autonomous electric vehicle. The proposed Active Disturbance Rejection Control (ADRC) with Model Predective Control (MPC) is not only capable of alleviating the disturbance but also shows robustness against structured uncertainties which may arise due to models that represent the vehicle dynamics. Simulations have been carried out to assess the effectiveness of the proposed control strategy. Simulation results show that the proposed scheme is better in terms of tracking performance than MPC and ADRC. The steering control system, with the proposed strategy, can achieve faster response, higher tracking accuracy, and improved robustness performance in dealing with model uncertainties and external disturbances.
Organizing Multi-Enzyme Systems into Programmable Materials for Biocatalysis

Significant advances in enzyme discovery, protein and reaction engineering have transformed biocatalysis into a viable technology for the industrial scale manufacturing of chemicals. Multi-enzyme catalysis has emerged as a new frontier for the synthesis of complex chemicals. However, the in vitro operation of multiple enzymes simultaneously in one vessel poses challenges that require new strategies for increasing the operational performance of enzymatic cascade reactions. Chief among those strategies is enzyme co-immobilization. This review will explore how advances in synthetic biology and protein engineering have led to bioinspired co-localization strategies for the scaffolding and compartmentalization of enzymes. Emphasis will be placed on genetically encoded co-localization mechanisms as platforms for future autonomously self-organizing biocatalytic systems. Such genetically programmable systems could be produced by cell factories or emerging cell-free systems. Challenges and opportunities towards self-assembling, multifunctional biocatalytic materials will be discussed.
Robust Control of a PMSG-Based Wind Turbine Generator Using Lyapunov Function

This paper proposes a robust finite-time controller (FTC) for a permanent magnet synchronous generator (PMSG)-based wind turbine generator (WTG). An adaptive observer is used for the rotor angle, rotor speed, and turbine torque estimations of the PMSG, thus eliminating the use of anemometers. The robustness of the proposed FTC regarding parameter uncertainty and the external weak power grid is analyzed. The impacts of the power grid short-circuit ratio (SCR) at the point of common coupling (PCC) on the conventional proportional-integral (PI) controller and the proposed FTC are discussed. Case studies illustrate that the proposed observer-based FTC is able to estimate the mechanical variables accurately and provides robust control for WTGs with parameter uncertainty and weak power grids.
Local minima of dissonance functions

When the same sound is produced simultaneously with two different fundamental frequencies, auditory roughness is observed. If the first sound is fixed and the fundamental frequency of the second is varied continuously, auditory roughness also varies continuously. A vowel sound is distinguished by its spectral envelope – which is independent of the fundamental frequency. This is a motivation to define the metric space of timbres. Each timbre is associated with a dissonance function which has local minima at certain intervals of local consonance related to the timbre. This is related to the music-theoretical notion of consonant intervals and scales. For the subspace consisting of all timbres with an interval of local consonance at a chosen point β, the main theorem describes certain points on the boundary by the vanishing of one-sided derivatives of dissonance functions at β.
Nigerian English Usage in Literature: A Sociolinguistic Study of Wole Soyinka’s The Beatification of Area Boy

This paper examined the imbalances created by social situations and captured in the English language usage by the characters in Wole Soyinka’s The beatification of area boy. The play, set in the busy city of Lagos, is a theatrical typification of the Nigerian society that creates variety differentiation in language use. The sociolinguistic data for the analysis were extracted from the primary text. The findings indicate that, in the play, Soyinka succinctly displays characters as linguistic pointers to showcase the peculiarities in Nigerian English usage that differentiate the linguistic behaviours of Nigerians from other Englishes. The study reveals the categorisation of the ‘spoken’ varieties into Nigerian Pidgin, Incipient bilingual, Local colour variety and the Nigerian literary variety. These features which manifest at the phonological, semantic, lexical, syntactic and pragmatic levels altogether combine to represent the typical linguistic situation in a non-native speaker environment. The linguistic variations, when juxtaposed with sociolinguistic variables, explicitly express the domestic adaptations and modifications in English language usage suggestive of the playwright’s representation of the Nigerian multilingual society.
Multi-perspective structural analysis of supply chain networks

ABSTRACT Determining the structural positions and characteristics of multi-role sectors is critical for understanding supply chain networks. Thus, in this study, we developed an attribution analysis framework to assess the structure of sectors with multiple roles in a supply chain. Subsequently, we applied the framework in a case study, where the top-ranking Japanese sectors were identified for production-oriented, betweenness-oriented, and consumption-oriented carbon dioxide emission scores. Additionally, these attribution indicators were utilized to identify/visualize the structural positions of sectors. Using company-level data, we also evaluated the structural positions of Japanese companies in relation to their carbon disclosure project (CDP) reporting practices. The results demonstrate that a company's role in the supply chain is unlikely to be related to CDP reporting.
The effects of relevant instructor self-disclosure on student affect and cognitive learning: A live lecture experiment

ABSTRACT The purpose of this teaching experiment was to examine the causal effect of relevant (compared to irrelevant) instructor self-disclosure on student affect and cognitive learning. Undergraduate students (N = 288) were randomly assigned to a 19-minute classroom lecture with an instructor who taught the same lesson but self-disclosed either relevant or irrelevant information while teaching. Results indicated that relevant instructor self-disclosure increased student affect in students’ likelihood to enroll with the instructor again but did not influence students’ general affect toward the instructor. Findings also revealed a direct effect of self-disclosure relevance on students’ test scores; on average, students scored 8.42% higher on a short-term recall test compared to students in the irrelevant self-disclosure condition, controlling for lesson coherence. Practical implications are provided for how instructors might use relevant self-disclosures in their teaching.