Miracle-dz/UniFAR-FTUs huggingface dataset:
Female medical and nursing students’ knowledge, attitudes, and skills regarding breast self-examination in Oman: a comparison between pre- and post-training

Purpose Breast cancer is one of the most common cancers in women worldwide. Educational and awareness programs impact early practices of breast self-examination, resulting in the early detection of cancer and thereby decreasing mortality. The study aimed to assess the levels of knowledge and awareness of breast cancer and breast self-examination among medical and nursing students in Oman and to compare their knowledge, attitudes, and skills after a training program. Methods This quasi-experimental study was carried out for female 90 medical and 80 nursing students in Oman in November 2019. A pre-test questionnaire was given before the training program and a post-test questionnaire was administered after the training program. Students’ knowledge, attitude, and skills regarding breast cancer and breast self-examination were compared. Scores for skills of practicing breast self-examination were compared between lecture and activity group and lecture-only group. Results Pre-test and post-test data were collected from 170 female students. Significant improvements were observed in the post-test scores for students’ knowledge, attitude, and skills after the intervention (P<0.001). The mean scores for skills of practicing breast self-examination after the lecture and the activity were higher than those obtained after the lecture only (P=0.014 for medical students and P=0.016 for nursing students). Conclusion An educational training program on breast cancer and breast self-examination with an emphasis on skills can motivate participants to perform breast self-examination regularly, and may therefore help students to train other women to perform breast self-examination for the early detection of breast cancer.
Investigation of Relationship Between Trust in Automation and Human Personality Traits Among Air Traffic Controllers

The constant growth of air traffic will lead to greater complexity of the Air Traffic Controller (ATCo) tasks. The greater complexity is followed by increasing workload that will affect ATCo’s task performance. To cope with that it is necessary to develop a new generation of the Air Traffic Control and Air Traffic Management automation technology, both airborne and ground-based. The proper use of automated technologies should be ensured by the appropriate level of ATCo’s trust in automation. The research examines the relationship between ATCo’s level of trust in automation and its personality traits when one specific contemporary system is being used by two air traffic control centers. It was found that ATCo's trust in an automated system depends on their age and work experience, as well as on the personal traits that stand out for their openness to new things, both in technology and in everyday situations. The research participants understand how the system behaves by using all appropriate methods to provide the outcome, but they still rely on themselves in the ultimate decision making. They define system’s reliability as the most important system’s
Efficient, low-cost optical coupling mechanism for TiO2-SiO2 sol-gel derived slab waveguide surface grating coupler sensors

We present an optical signal coupling scheme for slab waveguide surface grating coupler sensors. The proposed solution is based on the use of polymer microlenses. In this work we analyze two types of compact polymer lenses: the aspheric plano-convex lenses and Fresnel lenses. The feasibility of the proposed scheme is demonstrated by the experimental investigation into the optical signal coupling to the test structure of TiO2-SiO2 slab waveguide surface grating coupler using both types of lenses.
Leverage Artificial Intelligence to Learn, Optimize, and Win (LAILOW) for the Marine Maintenance and Supply Complex System

A complex enterprise includes multiple subsystems and organizations. The U.S. Marine Corps (USMC) maintenance and supply chain is a complex enterprise and exemplifies a socio-technological infrastructures. It is imperative for the USMC to adopt more advanced data sciences including ML/AI techniques to the entire spectrum or end-to-end (E2E) logistic planning as a complex enterprise including maintenance, supply, transportation, health services, general engineering, and finance. In this paper, we first review an overall framework of leveraging artificial Intelligence to learn, optimize, and win (LAILOW) for a complex enterprise, and then show how a LAILOW framework is applied to the USMC maintenance and supply chain data as a use case. We also compare various machine learning (ML) algorithms such as supervised machine learning/predictive models and unsupervised machine learning algorithms such as lexical link analysis (LLA). The contribution of the paper is that LLA computes stable and sensitive components of a complex system with respective to a perturbation. LLA allows to discover and search for associations, predict probability of demand and fail rates, prepare spare parts, and improve operational availability and readiness.
Synthesis of Deceptive Cyberdefense with Temporal Logic Constraints

—The design of provably correct systems from temporal logic speciﬁcations employs reactive synthesis, which models the interaction between the system and its dynamic environment as a two-player zero-sum game with complete information. However, most situations encountered in cybersecurity applications are characterized by asymmetric incomplete information: the adversary may not know the exact network conﬁguration and the defender does not know the adversary’s capabilities or intention. In this context, we discuss the recent developments in hypergame theory and omega-regular games with incomplete information which show that the solution concepts of this class of games lead to deceptive strategies that are surely or almost-surely (with probability one) guaranteed to satisfy a given security speciﬁcation in Linear Temporal Logic (LTL).
Feature-Based Classiﬁcation of Networks: Supplementary Materials

In this study, we have used three data sets: call detail records (CDR), tumor gene expressions, and a variety of benchmark social network data. Below we describe the sources of these data sets and the processes used to construct networks from these data sets. Feature-based classification is a two-step procedure, regardless of the application. First, we identify and calculate a set of contextually important network features for each network as described below. Second, we split the data into training and testing sets and then feed the features into the classifier of choice. For a detailed schematic, see Fig. S1.
The eﬀects of translation between symbolic music formats: A case study with Humdrum, Lilypond, MEI, and MusicXML

After encoding or translation, symbolic music ﬁles are prompt to contain discrepancies that may lead to distinct interpretations in diﬀerent software. In this paper, we investigate the eﬀects of translating four collections of symbolic music ﬁles that have been distributed in diﬀerent formats: Humdrum (**kern), Lilypond, Music Encoding Initiative (MEI), and MusicXML. The corpus of symbolic music ﬁles used in our experiment corresponds to four collections of music scores from the common practice period of Western music obtained from well-established projects and websites. The sources we selected have made their scores publicly available and distributed them in diﬀerent symbolic music formats. We ﬁrstly present a survey of the possible ways that ﬁles can be translated between these formats, we then apply each translation path to all the symbolic music ﬁles in our corpus, and we ﬁnalize this paper by searching for the eﬀects and discrepancies that the translations add to the converted ﬁles. An initial analysis of our experiment shows that discrepancies were introduced—to a greater or lesser extent—in all of the translation paths that we tested. The discrepancies involve changes in the duration of the notes, articulations, and the oﬀset of a note in the translation with respect to the same note in the original ﬁle. Additionally, several ﬁles cannot be translated or parsed after the conversion. We present a brief summary of the circumstances related to these problems.
Exploiting the photoprotection capabilities of anthocyanin for human use

Leaves produce anthocyanin that provides effective photoprotective capabilities. Anthocyanin can intercept highenergy quanta to prevent important photo-labile molecules from degradation by intense light. These properties make anthocyanin a potential natural sunblock that could replace current commercial sunblock, which may contain hormone disrupting chemicals and carcinogens, and are also environmental pollutants. The objective of this study is to show the photoprotective capabilities of anthocyanin in plants. Results supported the hypothesis that a greater concentration of anthocyanin reflects greater photoprotective capabilities. By placing anthocyanin extracts on pig’s skin and leaving this set-up under the sun, this study also showed that extracted anthocyanin has better UV absorption capabilities compared to when using commercial spf30 sunblock. It can be concluded that anthocyanin has photoprotective properties and has potential to be incorporated into commercial sunblock.
The Value-Relevance of Revaluation Reserves in Brazil: an Empirical Investigation

This work intends to investigate the value relevance of revaluation reserves in Brazil. Brazilian companies’ law allows for such reserves as an alternative proxy for the measurement of the value of permanent assets. However, this instrument can be used to increase total assets and book value of equity in order to mislead investors. We use a crosssectional specification to test the value relevance of the revaluation reserve, in addition to the Ohlson (1995) specification, to explain securities’ prices. We results show that these reserves are not perceived to contain any value by investors. Coefficients are significantly negative showing the bad signal transmitted to investors by the revaluation. This result confirms previous research showing the great discretionary power present in the Brazilian corporate financial reporting strucuture. Future research in this are could be designed to investigate other aspects of the current Brazilian GAAP. Deferrement of currency devaluation expenses are an obvious candidate. Absence of leasing capitalization is another opportunity to further research.
Detecting Influential Communities in Twitter during Brazil Oil Field Auction in 2019

Critical issues in the Oil & Gas business have been discussed in social media, involving policymakers, oil and gas companies, industry individuals, and civic activists. As observed recently, such issues have a critical role in the economic debate and political polarization. In this context, this paper reports on a study for the detection of hidden communities and influential users on social networks through the analysis of Twitter posts during Brazil mega oil fields auction in 2019. We use a methodology focused on identifying relevant users for the target community and discovering the effects of interactions between interest groups based on the modeling of the oil and gas social network. Firstly, we applied a standard community detection algorithm called Louvain method. Next, we used the Herfindahl-Hirschman index to identify the most influential users in the network by measuring the distribution of users' influence in a community. As a result, it was possible to visualize the presence of clusters and specific topics of interest shared by the most influential communities. Moreover, it was also possible to distinguish the convergent and divergent opinions that permeated the pre-salt auction and the level of Twitter engagement of key players from the global energy market.
Hiatal Hernioplasty with a Novel Composite Mesh Design: Results of a Multicenter Study after Two- Years Follow-up

Background: Surgical treatment of paraesophageal hiatal hernia is still controversial, particularly in aspects related to surgical technique, being the main problem the high recurrence rate, between 20% and 30%. Methods: The aim of this study is to describe the surgical technique and results of laparoscopic repair of paraesophageal hernia using a new composite silicone/polypropylene mesh. This is a retrospective study of 61 patients who underwent laparoscopic repair of symptomatic large (6 cm or more in diameter) hiatal hernias in three public hospitals of the Valencia Community (Spain). The study analyzes postoperative mesh-related morbidity, functional outcomes and recurrence rate. Results: There were two conversions due to technical difficulties. The mean hospital stay was 3.2 days. Mean follow-up was 26 months. A recurrence rate of 11.7% was recorded. Complications, reoperations and quality of life are analyzed. Conclusion: This newly designed prosthetic mesh offers easy laparoscopic implantation with acceptable recurrence and complications. Additional studies are needed to determine the most appropriate material and shape to cover hiatal defects.
Adynamic Imbalanced Load Modulation Approach To Design And Implement An Energyefficient RF Power Amplifier

--------------------------------------------------------ABSTRACT---------------------------------------------------------------In this paper, a dynamic load modulation approach using an attenuator has been presented to design a concept of energy efficient class-AB RF power amplifier for modern wireless communication systems. The output power back off technique is to amplify the signal at the linear region to keep away from unwanted spurious. However, the use of dynamic load modulation approach causes excessive increase in PAE, drain efficiency and Pout.To improve the efficiency at wide range of output power and possess similar margin for signal with high crest factor, the dynamic load modulation technique with an offset line are introduced to operate over power amplifier load modulation technique. The dynamic load modulation technique is compared with balanced power amplifier technique to find a best candidate. A simulation results achieved from the dynamic load modulation power amplifier with 79% power added efficiency at 35dBm output power. The result has shown reasonable improve from dynamic load modulation over the balance power amplifier design.
Fabrication of coupled twin-shaped hollow hemispherical calcium molybdate via a facile ultrasound-assisted approach

Coupled twin-shaped hollow hemispherical calcium molybdate (CTHH-CaMoO4) microstructures were successfully synthesized via a facile ultrasound-assisted template-free route in the presence of absolute ethanol and ethylenediaminetetraacetic acid (EDTA). The oriented attachment and subsequent Ostwald ripening mechanism were responsible for the formation of this novel architecture.
Object Detection for UAV from Color and Depth Image

The work contains a review of different methods for object detection related to the MBZIRC 2020 competition. The objects are polystyrene blocks of a given size and color. The work demonstrates the design of algorithms for detection of cuboid objects from a color image using Color Segmentation, GrabCut methods, Closed Loop search and Neural Networks. Furthermore, it reviews methods of detection for depth image and geometrical verification of detected objects. Finally, both methods were used together for data fusion and its performance was reviewed. The data is obtained by Intel RealSense D435 camera.
Catalytic activity of enzymes immobilized on AlGaN/GaN solution gate field-effect transistors

Enzyme-modified field-effect transistors (EnFETs) were prepared by immobilization of penicillinase on AlGaN∕GaN solution gate field-effect transistors. The influence of the immobilization process on enzyme functionality was analyzed by comparing covalent immobilization and physisorption. Covalent immobilization by Schiff base formation on GaN surfaces modified with an aminopropyltriethoxysilane monolayer exhibits high reproducibility with respect to the enzyme/substrate affinity. Reductive amination of the Schiff base bonds to secondary amines significantly increases the stability of the enzyme layer. Electronic characterization of the EnFET response to penicillin G indicates that covalent immobilization leads to the formation of an enzyme (sub)monolayer.
Sliding Mode Control (SMC) with ANFIS based PWM Dual Inverter Based Grid Connected PV System: Modelling and Performance analysis By

This paper proposes a novel robust sliding mode (SM) management for a cascaded two-level electrical converter (CTLI)-based grid connected electrical phenomenon (PV) system. The modelling and style of the management theme for the CTLI-based grid connected PV system is developed to provide active power and reactive power with variable star irradiance. A vector controller is developed keeping the most power delivery of the PV in thought. Two different change schemes have been thought of two style SM controllers and studied beneath similar operative things. Rather than the referred area vector PWM technique, an easy PWM modulation technique is employed for the operation of the planned SM controller. The performance of the SM controller is improved by victimization an adaptive hysteresisband (HB) calculation. The controller performance is found to be satisfactory for each the schemes at considered load and star irradiance level variations in simulation setting. The laboratory paradigm, operated with the planned controller is found to be capable of implementing the management algorithmic program with success within the thought of scenario. Keywords—Photovoltaic (PV) system, multilevel inverter, Vector control, sliding mode control (SMC), distribution static compensator (Distribution-STATCOM
Latent Causation: An algorithm for pairs of correlated latent variables in Linear Non-Gaussian Structural Equation Modeling

This paper addresses the problem of inferring causation in a pair of linearly correlated continuous latent variables. We ﬁrst discuss the limitations of the Direction Dependance Analysis (DDA) approach and then introduce the Latent Causation (LC). Five variants (in terms of dependency statistic) of the LC algorithm are assessed with ROC curves, then we consider the case of a latent confounder (uniform or chi-square distributed). While the distribution and the correlations of the latent confounder inﬂuence the accuracy, experimental results show the robustness of the method using bootstrapped p-values. Implications and limits of the experimental results are then discussed together with future directions.
DIGITAL DATA: STEPPING-STONES FOR DIGITAL INNOVATION!

Digital data is argued to be a part of “the new oil” and a key driver for digital innovation, digitalization, business development, and the data-driven economy, but the term “digital data” is not well defined in the literature and numerous definitions exist. Without a more unified understanding of “digital data”, its assumed potential may be difficult to unlock. This study conducted a comprehensive review of the data construct in the information systems (IS) literature from a meso-perspective and gained insights from a qualitative case study of a platform as a service (PaaS) company. Building on these insights, we developed a conceptual model we labeled the RIQA framework to illustrate fundamental characteristics of “digital data” that were (1) considered to be important for future definitions of digital data, and (2) needed for the enablement of digital data. These insights also led to one of our main discoveries: the importance of data accessibility in enabling digital data to be effectively utilized, a consideration of which was lacking in the literature. We contributed to the IS literature by examining, in-depth, how studies have approached digital data and organizations have used it to deliver data services to clients. We also contributed by proposing a conceptual framework to facilitate a better understanding of what constitutes digital data.
Silicon surface cleaning by low dose argon‐ion bombardment for low‐temperature (750 °C) epitaxial silicon deposition. I. Process considerations

A low‐energy argon sputter process has been optimized to successfully remove native oxide from a silicon surface at elevated temperatures without introducing permanent damage. The process relies upon confining all sputtering events to the near‐surface region of the silicon and exploits the enhancement of sputter efficiencies observed for silicon and silicon dioxide above 600 °C. The procedure has been implemented as an in situ etch for low‐temperature (below 800 °C), very low‐pressure (1–10 mTorr), epitaxial silicon deposition in a high vacuum ambient. The reactor and conditions employed are presented along with measures of residual substrate damage as a function of processing conditions, and the process limitations are discussed. A companion paper describes the excellent structural quality of the resultant epitaxial films. The ion energies (100 eV) and fluxes (5×1013 cm−2 s−1) employed represent a significant departure from conventional sputter cleaning processes.
Efficient design 2k−1 binary to residue converter

In this paper, a binary to residue number system architecture based on the 2k-1 modulo set. For the integer modulo operation (X mod m), (p, 2) compressors are used, where m is restricted to the values 2k-1, for any value of k >; 1 and X is a 16 bit number. The novel 3-2, 4-2 and 5-2 compressors are illustrated for efficient design, which are used as the basic building blocks for the proposed binary to residue converter designs. The 3-2, 4-2 and 5-2 compressors are used in place of half adder and full adder to reduce the delay, power consumption as well as the area of the circuit. The 4-2 and 5-2 compressors cell can operate reliably in any tree structured parallel multiplier at very low supply voltages. The proposed converter can be implemented by fast and simple architecture and also required less hardware.
A symbol based analysis for multigrid methods for Block-Circulant and Block-Toeplitz Systems

In the literature, there exist several studies on symbol-based multigrid methods for the solution of linear systems having structured coefficient matrices. In particular, the convergence analysis for such methods has been obtained in an elegant form in the case of Toeplitz matrices generated by a scalar-valued function. In the block-Toeplitz setting, that is, in the case where the matrix entries are small generic matrices instead of scalars, some algorithms have already been proposed regarding specific applications and a first rigorous convergence analysis has been performed in [7]. However, with the existent symbol-based theoretical tools, it is still not possible to prove the convergence of many multigrid methods known in the literature. This paper aims to generalize the previous results giving more general sufficient conditions on the symbol of the grid transfer operators.In particular, we treat matrix-valued trigonometric polynomials which can be non-diagonalizable and singular at all points and we express the new conditions in terms of the eigenvectors associated with the ill-conditioned subspace. Moreover, we extend the analysis to the V-cycle method proving a linear convergence rate under stronger conditions, which resemble those given in the scalar case. In order to validate our theoretical findings, we present a classical block structured problem stemming from a FEM approximation of a second order differential problem. We focus on two multigrid strategies that use the geometric and the standard bisection grid transfer operators and we prove that both fall into the category of projectors satisfying the proposed conditions. In addition, using a tensor product argument, we provide a strategy to construct efficient V-cycle procedures in the block multilevel setting.