Miracle-dz/UniFAR-FTUs huggingface dataset:
Dependability Modeling of Software Systems with UML and DAM: A Guide for Real-Time Practitioners

The modeling of system non-functional properties is a broad field. Among these properties, dependability is an important one for real-time and embedded systems. On the other hand, UML offers the profiling mechanism to address specific modeling domains. In particular, the DAM (dependability analysis and modeling) profile provides a modeling framework for dependability in the model-driven paradigm. This work is for practitioners to understand the basics of dependability modeling, using DAM. In this sense, the paper digests the literature to understand the concept of the UML profile, the MARTE profile and to obtain a practical guide on dependability modeling using DAM. The modeling approach is illustrated through a case study taken from the literature.
A Web Application for Biomedical Text Mining of Scientific Literature Associated with Coronavirus-Related Syndromes: Coronavirus Finder

In this study, a web application was developed that comprises scientific literature associated with the Coronaviridae family, specifically for those viruses that are members of the Genus Betacoronavirus, responsible for emerging diseases with a great impact on human health: Middle East Respiratory Syndrome-Related Coronavirus (MERS-CoV) and Severe Acute Respiratory Syndrome-Related Coronavirus (SARS-CoV, SARS-CoV-2). The information compiled on this webserver aims to understand the basics of these viruses’ infection, and the nature of their pathogenesis, enabling the identification of molecular and cellular components that may function as potential targets on the design and development of successful treatments for the diseases associated with the Coronaviridae family. Some of the web application’s primary functions are searching for keywords within the scientific literature, natural language processing for the extraction of genes and words, the generation and visualization of gene networks associated with viral diseases derived from the analysis of latent semantic space, and cosine similarity measures. Interestingly, our gene association analysis reveals drug targets in understudies, and new targets suggested in the scientific literature to treat coronavirus.
Study on Irradiation Response of Nanocrystalline Phase in Sm-Doping Fluorapatite Glass-Ceramics under He Ion Irradiation

Two different of Sm-loading fluorapatite (Ca10−2xNaxSmx(PO4)6F2, x = 1 and 2) glass-ceramics were synthesized by a two-step melt sintering method. The samples were irradiated with 50 keV He+ ions with a fluence of 2.6 × 1016 ions/cm2 at 593 K. The irradiation induced microstructural evolution were characterized by grazing incidence X-ray diffraction and cross-sectional transmission electron microscopy. For the smaller Sm-doping samples, no phase transformation is observed. Meanwhile, in the lager Sm-doping samples, the irradiation induced the crystals into smaller nanocrystals. The mechanism of the transformation of the crystalline phase was also analyzed and discussed.
Rugged individualism, natural disasters, and inter-county variation in entrepreneurial activity

ABSTRACT Building on the negative to mixed effects of natural disasters on entrepreneurial activity, we posit that regional rugged individualism attenuates the decline in business activity following natural disasters. We find that greater property damage from natural disasters in the presence of greater rugged individualism does not affect the establishment entry rate. However, it lowers the decline in establishment exit rates and the job reallocation rate decline by small effect sizes (0.3%). The findings show that regional individualism in the face of natural disasters does not increase entrepreneurial activity but abates the decline in entrepreneurial activity.
HGG and LGG Brain Tumor Segmentation in Multi-Modal MRI Using Pretrained Convolutional Neural Networks of Amazon Sagemaker

Automatic brain tumor segmentation from multimodal MRI plays a significant role in assisting the diagnosis, treatment, and surgery of glioblastoma and lower glade glioma. In this article, we propose applying several deep learning techniques implemented in AWS SageMaker Framework. The different CNN architectures are adapted and fine-tuned for our purpose of brain tumor segmentation.The experiments are evaluated and analyzed in order to obtain the best parameters as possible for the models created. The selected architectures are trained on the publicly available BraTS 2017–2020 dataset. The segmentation distinguishes the background, healthy tissue, whole tumor, edema, enhanced tumor, and necrosis. Further, a random search for parameter optimization is presented to additionally improve the architectures obtained. Lastly, we also compute the detection results of the ensemble model created from the weighted average of the six models described. The goal of the ensemble is to improve the segmentation at the tumor tissue boundaries. Our results are compared to the BraTS 2020 competition and leaderboard and are among the first 25% considering the ranking of Dice scores.
Augmented Reality in Radiology for Education and Training—A Design Study

Education is an important component of every healthcare system. Patients need to be educated about their planned procedures; healthcare professionals need to be trained in their respective profession. Both patient education and the training of healthcare professionals are often completed in person, which requires resources and is bound to certain times and places. Virtual educational environments can potentially save human and monetary resources, increase learner engagement, and enable users to learn according to their own schedules. This design study describes proofs of concept for two augmented reality-enabled (AR) educational tools, utilizing a Microsoft HoloLens head-mounted display. In the first use case, we demonstrate an AR application which could be used to educate cancer patients about their radiotherapy treatment and potentially reduce patient anxiety. The second use case demonstrates an AR training environment, which could complement the practical training of undergraduate radiography students. Two prototypes—VIPER, for patient education, and ARTUR for the training of radiography students—were developed and tested for viability and usability, both based on individual user tests. Both patient and student education were evaluated as viable and usable additions to conventional educational methods, despite being limited in terms of accessibility, usability, and fidelity. Suitable hardware is becoming more accessible and capable, and higher-fidelity holograms, better utilization of real-world objects, and more intuitive input methods could increase user immersion and acceptance of the technology.
Overcoming barriers to the reuse of construction waste material in Australia: a review of the literature

Although much has been written on how to improve the management of construction waste and increase the use of recycled materials, little progress has been made to address the reuse of construction waste. Yet there is a consensus in the literature that waste reuse practices have a decisive role to play in improving reduction of waste, and that institutional barriers are the most problematic obstacles to implementing identified reuse strategies. This paper examines the literature from the last 10 years on the issues facing different stakeholders around reuse of construction waste in Australia, and the causes and effects of the institutional barriers encountered. Key texts from before this period are also referenced. The findings reveal that institutional impediments are related to problems outside of the construction industry, such as social, economic and political barriers to change. A number of constraints are identified: lack of interest and demand from clients; attitudes towards reuse practices; and training all of which act as disincentives to a proactive and sustainable application of construction waste reuse strategies. Above all, it is argued that legislation should be better implemented to ensure that all states in Australia are required to implement strategies to reuse waste construction materials.
Utility of Real-Time Three-Dimensional Echocardiography for the Assessment of Right Ventricular Morphology and Function in Large Animal Models

Real-time three-dimensional echocardiography (RT3DE) enables a noninvasive assessment of right ventricular (RV) morphology. However, no study has evaluated the relationship between RV function obtained by RT3DE and RV pressure-volume loops. This hypothesis-driven, experimental study aimed to assess the utility of RT3DE in the evaluation of RV morphology and function. Ten anesthetized beagle dogs sequentially underwent dobutamine infusion, acute infusion of lactated Ringer’s solution, and furosemide administration to alter RV contractility and loading conditions. RV pressure-volume loop-derived hemodynamic measurements and echocardiography, including two-dimensional speckle-tracking echocardiography and RT3DE, were performed in each study protocol. Bland–Altman analysis showed strong agreement in RV volume, ejection fraction, and stroke volume obtained by right heart catheterization and RT3DE. Multiple regression analyses revealed that the peak myocardial velocity of the lateral tricuspid annulus (RV s’) and global RV longitudinal strain rate were significantly associated with end-systolic elastance (adjusted r2 = 0.66, p < 0.001). RV s’, RV free wall longitudinal strain, and RT3DE-derived stroke volume/end-systolic RV volume ratio were associated with RV pressure-volume loops-derived end-systolic/arterial elastance ratio (adjusted r2 = 0.34, p < 0.001). RT3DE could detect the changes in catheterization-derived RV volume with a strong agreement and might be useful in estimating RV-pulmonary arterial coupling.
ANALISIS DATA KUALITATIF: MANUAL ATAU DENGAN APLIKASI?

Pesatnya perkembangan komputerisasi metode analisis data untuk penggunaan penyelidikan naturalistik dalam penelitian kualitatif, tujuan dari catatan penelitian ini adalah untuk membandingkan manual dan komputerisasi teknik analisis data. Dengan berbagai macan pertimbangan kelebihan, kekurangan, dan umum dimiliki asumsi penggunaan aplikasi dan secara manual terus berlanjut karena kelangkaan literatur yang membahas keuntungan dan kelemahan kedua metode tersebut. Dan pada dasarnya peneliti dapat menganalis data tanpa menggunakan aplikasi, peneliti senior lebih bebas bekerja tanpa aplikasi. Kemajuan teknologi menyebabkan penelitian tergantung pada teknologi, aplikasi analisis data kualitatif hanya efektif menganalisis data deskriptif. Penggunaan aplikasi hanya efektif pada pendekatan post-positivistik, pada dasarnya analisis data kualitatif lebih efektif dilakukan oleh peneliti sendiri selama ia di lapangan dan saat ia menulis laporan penelitian, kekuatan utama analisis data kualitatif adalah trianggulasi data dan trianggulasi hasil penelitian. Aplikasi tidak dapat menganalisis data kualitatif, faktanya bahwa kita telah melihat perkembangan analisis data kualitatif dengan bantuan aplikasi seharusnya tidak mengejutkan mengingat perkembangan yang meluas dan aksesibilitas aplikasi untuk analisis kualitatif data tetap menjadi masalah yang diperdebatkan dan belum digunakan secara universal (R. M. Lee dan Fielding, 1991; Morison dan Moir, 1998). Aplikasi dalam menganalisis data kualitatif dan aturan yang tepat tidak kompatibel dengan sifat data kualitatif yang tidak terstruktur dan ambigu sehingga dapat mendistorsi atau melemahkan data atau melumpuhkan kreativitas (Becker, 1993; Kelle dan Bird, 1995; Richards, 1998). Sifat penelitian kualitatif apabila ditinjau dari volume dan kompleksitas data yang tidak terstruktur dan cara di mana temuan dan teori muncul dari data juga membuat paket perangkat lunak, dikembangkan untuk mengelola dan menganalisis data tersebut, sulit untuk diaplikasikan dan digunakan secara memadai. (Kelle dan Bird, 1995).
Numerical simulation of vortex-induced vibration of a smooth circular cylinder at the subcritical regime

The present paper focuses on the simulation of vortex-induced vibration (VIV) of a rigid, smooth circular cylinder with elastic supports subject to a cross-flow at the subcritical regime of Reynolds number, 30,000<Re<80,000. The circular cylinder is allowed to move in one degree-of-freedom (DOF), heave. Unsteady Reynolds-averaged Navier-Stokes (URANS) equations are solved with Menter’s k — ω based Shear Stress Transport based Scale-Adaptive Simulation, SAS-SST, turbulence model, and two-equation transition transport γ — θ model. The transport equations are discretized using the Finite Volume Method (FVM). The numerical amplitude and frequency ratio is compared against the experiments conducted in the Marine Renewable Energy Laboratory (MRELab) at the University of Michigan. The angle in which the computed lift leads the displacement in VIV is compared against experimental results reported by Cornell-ONR Water Channel as well. The existence of the initial, upper, and lower VIV response branches is demonstrated. Wake vortex pattern mode has been studied in the different branches of VIV. The time records of the added mass force coefficient and the vortex force coefficient are obtained. Then, the time-averaged phase angle of the vortex and added mass force coefficients are compared against the experimental results. Lastly, the time records of the phase angle in different branches of VIV are shown and analyzed.
A Multi-Step Time-Series Clustering-Based Seq2Seq LSTM Learning for a Single Household Electricity Load Forecasting

The deep learning (DL) approaches in smart grid (SG) describes the possibility of shifting the energy industry into a modern era of reliable and sustainable energy networks. This paper proposes a time-series clustering framework with multi-step time-series sequence to sequence (Seq2Seq) long short-term memory (LSTM) load forecasting strategy for households. Specifically, we investigate a clustering-based Seq2Seq LSTM electricity load forecasting model to undertake an energy load forecasting problem, where information input to the model contains individual appliances and aggregate energy as historical data of households. The original dataset is preprocessed, and forwarded to a multi-step time-series learning model which reduces the training time and guarantees convergence for energy forecasting. Furthermore, simulation results show the accuracy performance of the proposed model by validation and testing cluster data, which shows a promising potential of the proposed predictive model.
Analysis in the Influencing Factors of Climate-Responsive Behaviors of Maize Growers: Evidence from China

Due to the natural production properties, agriculture has been adversely affected by global warming. As an important link between individual household farmers and modern agriculture, it is crucial to study the influence of agricultural productive services on farmers’ climate-responsive behaviors to promote sustainable development and improve agricultural production. In this paper, a questionnaire survey has been conducted among 374 maize farmers by using the combination of typical sampling and random sampling in Jilin Province of China. Moreover, the Poisson regression and the multi-variate Probit model have been used to analyze the effects of agricultural productive services on the choices of climate-responsive behaviors as well as the intensity of the behaviors. The results have shown that the switch to suitable varieties according to the frost-free period have been mostly common among maize growers in Jilin province. Agricultural productive services have a significant effect on the adoption intensity of climate- responsive behaviors, at the 1% level. Based on this conclusion, this paper proposes policy recommendations for establishing a sound agricultural social service system and strengthening the support for agricultural productive services. It has certain reference significance for avoiding climate risk and reducing agricultural pollution in regions with similar production characteristics worldwide.
H ∞ smooth switching distributed consensus controller for uncertain time-delay switched LPV multi-agent systems

This paper presents an approach to design H ∞ smooth switching distributed linear parameter-varying (LPV) controller for consensus of LPV multi-agent systems (MASs) with a large range of parameter variations, uncertainty, input disturbance, and time-varying delay. By dividing the region of variable parameters into subregions with overlaps, a H ∞ smooth switching distributed sliding mode LPV controller with a switched dynamic sliding surface is designed for each subregion and the controller in overlapping subregions is interpolated from two adjacent subregions. This control problem is formulated into parametric linear matrix inequality (LMI), which is solved by convex optimization algorithms. To illustrate the efficacy of the result, it is applied to consensus of practical vertical take-off and landing (VTOL) helicopter MAS.
Changes in Hematological and Biochemical Profiles in Ovariohysterectomized Bitches Using an Alfaxalone–Midazolam–Morphine–Sevoflurane Protocol

Simple Summary Laboratory tests are an integral part of the overall diagnostic procedures for both physiological and pathological conditions. Ovariohysterectomy (OHE) in bitches is an invasive procedure, producing moderate to severe pain. The response to surgical stress is characterized by a series of neuroendocrine and metabolic changes that can be represented on hematological and biochemical profiles. The objective of this study was to evaluate the physiological response to surgical stress, predict the presence of possible post-surgical complications, and where appropriate, establish the appropriate treatment based on the hematochemical changes during and after OHE in healthy bitches. OHE in this study induced transient changes in certain hematological and biochemical parameters. Knowledge of the hematochemical changes in response to stress and trauma induced by OHE in healthy dogs would allow to predict possible post-surgical complications. Abstract The aim of this study was to monitor hematochemical changes during and after OHE in bitches. Twenty-four females were anesthetized with alfaxalone, midazolam, morphine and sevoflurane. Blood samples were taken before anesthesia (T0), at 30 (T1), and 60 min (T2), at 3 (T3), 6 (T4), 12 (T5), and 24 h (T6), and at 3 (T7) and 7 days (T8) from the start of surgery. Red blood cells (RBC) and packed cell volume (PCV) decreased significantly from T1 to T5 and hemoglobin (HB) concentration from T4 to T6. Both the white blood cell (WBC) and neutrophil (NFS) count increased significantly from T3 to T6, monocyte (MON) from T2 to T5, and eosinophil (EOS) at T5. Platelet (PLT) and plateletcrit (PCT) significantly decreased at T5 and increased from T6 to T8; platelet distribution width (PDW) increased significantly from T3 to T6. Creatine kinase (CK) activity increased significantly from T5 to T7. Glucose (GLU) concentrations increased significantly at T2 and P from T2 to T3. TG levels decreased from T2 to T4 and blood urea nitrogen (BUN) levels from T1 to T7, subsequently increasing until T8. Changes possibly resulting from stress and surgical trauma, as well as hemodilution and splenic storage, are due to anesthesia and surgery. In healthy bitches, these changes tend to gradually stabilize after the ending of OHE. A post-operative follow-up is essential to detect possible post-operative complications.
One cycle controlled grid-tied differential boost inverter

A differential boost inverter (DBI) consists of two bidirectional boost converters and has a differential sinusoidal output voltage. This output voltage is obtained because each bidirectional boost converters generates a dc-biased sinusoidal voltage. Owing to this characteristic, this inverter topology requires a controller that can track a dc-biased sinusoidal voltage as the control reference. One cycle control (OCC) is a non-linear control approach that can meet the requirements and be easily implemented in a simple circuit with constant switching frequency. The controller must be capable of achieving a unity power factor (PF) because the inverter is designed to deliver maximum active power to the grid. Therefore, the sinusoidal reference signal of the OCC is formed by the scaled grid voltage and its derivation. Given this reference signal, the inverter output voltage represents the grid voltage with a small variation phase and amplitude. Furthermore, this strategy will guarantee that the output current and the grid voltage are in phase. An experimental prototype of DBI is implemented to verify that the control approach can achieve PF close to unity such that the maximum active power can be delivered to the grid with minimum current total harmonic distortion.
Design and Simulation of an Adaptive Dynamic Inversion Control System with Pseudo Control Compensation

Pseudo-Control Compensation is investigated in this paper, which has been used to resolve actuator saturation in adaptive control. The base control law is designed in dynamic inversion and neural networks are used to reconstruct inversion error. Pseudo-Control Compensation cancel the interaction between the actuator and the adaptive factor. Simulations of super-maneuvering flight control show that limitation of needing accurate mathematical model in dynamic inversion method can be released and actuator affects on adaptive factor is removed in this control structure, and that the robustness of the whole system is greatly improved.
Synchronization for Burst-Mode APSK

We derive bounds on the performance of data-aided joint estimators for timing offset, carrier phase offset, and carrier frequency offset for use in an APSK packet-based communication link. It is shown that the Cramer-Rao Bound (CRB) is a function of the training sequence, the signal-tonoise ratio (SNR), and the pulse shape. We also compute APSK training sequences of different lengths that minimize the CRB for each of the parameters.
Searchable encryption : A survey

ABSTRACT We explore the field of searchable encryption (SE) and present a comprehensive survey of relevant literature. Since the existing survey articles are primarily written for the security experts, we present the discussion to assist the general practitioners (not from security background) in identifying an appropriate SE scheme for their application of interest. We initiate with the brief overview of SE along with its application-oriented criteria. By analyzing various SE schemes, we derive five significant characteristics – key structure, search structure, search functionality, support to reader/writers, and reader’s capability. Based on these characteristics, we categorize the existing SE schemes and showcase the significant features offered by each scheme. We explore numerous schemes based on symmetric/asymmetric key structures, simple/inverted search structure, single/multi-keyword search functionality, single/multiple reader/writer support, and verification functionality owned by data reader. A most promising part of the survey is the comparative analysis of the existing schemes under specific category in terms of tables showing efficiency and security. We hope that this survey is indeed beneficial for the general practitioners to pick an appropriate SE scheme better suited to the selected application.