Miracle-dz/UniFAR-FTUs huggingface dataset:
An Interpolated Model Recovery Anti-Windup for a Canard-Guided Projectile Subject to Uncertainties

This paper presents an autopilot design for a dual-spin guided projectile subject to aerodynamic uncertainties and actuator saturations. The proposed design consists of a gain-scheduled baseline controller together with an interpolated dynamic anti-windup compensator based on model recovery. Evaluation of the closed-loop is done through IQC-based analysis at an operating point, and through Monte Carlo simulations. Both assessments show that the addition of an anti-windup compensator can drastically improve the behaviour of the system in degraded flight conditions.
WPU: A FPGA-based Scalable, Efficient and Software/Hardware Co-design Deep Neural Network Inference Acceleration Processor

Convolutional neural network (CNN) based algorithms are becoming dominant in a broad range of applications. Due to the high computation requirement, accelerator design for CNN has drawn many research interests in recent years. Besides GPU and ASIC, FPGA is also a major resource for CNN computation due to its re-programmability and flexibility for architecture designs [9]. In this paper, we propose a software-hardware combined CNN accelerator design for FPGAs. Our focus is on architecture design for computation efficiency per DSP and the adaptability for different sizes of FPGAs. We designed a scheme of network pruning algorithm which enables simpler sparse data selection logic in hardware. Our software-hardware combined solution achieves 4x computation speedup with the same number of DSPs. Full pipeline of data transferring and computing reduces the requirement of BRAM usage significantly. Our test on Yolo-v2 [8] network shows a DSP efficiency enhancement of 3x over RTX 2070 GPU and 58% over an existing FPGA design in [10], respectively.
Improving primary school teachers’ competence in teaching multi-literacy through RADEC-based training programs

The purpose of this research is to improve the competence of teachers in teaching multi-literacy through a training programme based on the Read–Answer–Discuss–Explain–Create (RADEC) learning model. This descriptive qualitative study was conducted in a private primary school in Bandung, West Java, Indonesia. The participants included were a teacher and 29 students (17 male and 12 female students, with an average age of 11 years). Documentations, observations and interviews were used as data collection. The data were analysed quantitatively and through the Rasch model. The results show that mentoring during the implementation of the RADEC model can increase teachers’ knowledge of multi-literacy learning and teachers’ skills in planning and implementing the RADEC model. It can be concluded that the RADEC learning model contributes to a positive change in student learning, promotes 21st-century skills and includes multi-literacy skills. Thus, trainers can use the RADEC learning model to enhance teachers’ ability in teaching multi-literacy. Keywords: Multi-literacy, RADEC model, teachers’ competence
Enterprise Microblogging at Siemens, Building Technologies Division: A Descriptive Case Study

Siemens is well known for ambitious efforts in knowledge management, providing a series of innovative tools and applications within the intranet. References@BT is such a web- based application aimed to support globally sharing knowledge, experiences and best-practices within the Building Technologies Division. As a reaction to the demand of employees, a new microblogging service, tightly integrated into References@BT, was implemented in March 2009. In this paper, we comprehensively describe motivation, experiences and advantages for the organization in providing an internal microblogging application. Because of the tight inte- gration, we also outline general facts of the knowledge management application.
A Novel Overvoltage Protection for Powerformer

For the generators operating in parallel, the common busbar may experience the increase of voltage when the excitation system of one generator is failed. All of the generators connected to this busbar will become isolated if their settings of overvoltage protection are identical. An appropriate solution must be proposed for the plant consisting of Powerformers since the Powerformers always operate in parallel. Based on the analysis of the relation between terminal voltage of a generator and the reactive power output, the disadvantage of the existent overvoltage protection scheme for generators working in parallel have been pointed out, and a novel overvoltage protection scheme is proposed. This protection can not only detect overvoltage events, but make it possible to identify which machine is the source of the overvoltage by virtue of the change of reactive power output. As a result, the selectivity of the protection is realized. Using a compensated factor to modify the operating time of the protection, the generator which has the most vital impact on the overvoltage of the busbar will be tripped at first. The effectiveness of this scheme is validated with the PSCAD/EMTDC simulation tests. It should be pointed out that the application of this protection scheme is not restricted to Powerformer.
Application of textlinguistic method in language teaching

Texts appear as the highest level units in language teaching. Textlinguistics researchers have put forward the thesis that the largest linguistic unit is the text, not the sentence. The main purpose of this research is to determine the effect of using textlinguistic analysis method and its ability to aid students in understanding the texts in language classes and improving and enriching their comprehension and expression skills. The research conducted an experiment and collected data with interviews. The sample of the research consisted of 30 students studying at the University and taking the text linguistics course. Concept Test of Textlinguistic Analysis Method was used in the study. As a result of the research, it was determined that the "Textlinguistic Analysis Method" increased the students' comprehension and expression success in Turkish classes compared to the traditional method and contributed to the increase of their participation in the class. Keywords: Language teaching; linguistics; Turkish text; textlinguistic analysis; stylistics
Removal of Mefenamic Acid from Aqueous Solution by Fenton Process: Optimization Using Response Surface Methodology with Central Composite Design

In the present study, the three main process parameters in the Fenton process for the removal of pharmaceutical compound Mefenamic acid from an aqueous solution were optimized using response surface methodology (RSM). Central composite design (CCD) was used for process optimization. The primary and secondary interaction effects of the selected parameters such as H2O2, Fe2+ and pH on the removal of mefenamic acid were examined. A mathematical model for the removal process based on the selected variables was developed. The interaction effect between the chosen parameters shows that the removal of mefenamic acid was enhanced in the acidic pH range at a high concentration of H2O2 and in a medium concentration level of the catalyst Fe2+. The removal efficiency of 81.24% was obtained for mefenamic acid at the optimized condition of variables such as 9.36 mM H2O2, 0.058 mM Fe2+and at a pH value of 2.1.
“I Expected More From You”: The Effect of Expectation-Disconfirmation on Employees’ Satisfaction With Supervisory Support

Are employees less satisfied with supervisor support when their expectations are disconfirmed? In this study, we examine this question for both predictive expectations (what will happen) and normative expectations (what should happen). Results from two preregistered experiments suggest that expectation-disconfirmation does not affect satisfaction with supervisor support. Instead, we find that expectation-disconfirmation as perceived by participants affects satisfaction with supervisor support. We conclude that even though supervisor support seems to be the most important predictor of satisfaction, perceived disconfirmation of expectations also influences employees’ satisfaction with supervisor support.
Machine Learning Based Emotion Level Assessment

With recent advancements of technology, identification of emotions of humans via facial recognition is done with the application of numerous methods including machine learning and deep learning. In this paper, machine learning techniques are applied for identifying different levels of emotions of individuals for unannotated video clips using Facial Action Coding System. In order to archive the above, first, two methods were experimented to obtain a labeled image data set to train classification models where in the first method, clustering of images was done using Action Units(AU) identified from literature and the emotion levels of the images were determined through the resulted clusters and images are labeled according to the cluster they belonged to. In the second method, the image set is analyzed explicitly to identify AUs contributing to emotions rather than relying on those identified in literature and then the clustering of image set was done using those identified AUs to label the images similar to the first method. The two labeled data sets were used to train classification models with Random Forest, Support Vector Machine and K-Nearest Neighbour algorithms separately.Classification models showed better accuracy with data set produced using the second method. An overall F1 score, accuracy, precision and recall of 87% was obtained for the best classification model which is developed using the Random Forest algorithm to identify levels of emotions. Identifying the AU combinations related to emotions and developing a classification model for identifying levels of emotions are the major contributions of this paper. The results of this research would be especially useful to identify levels of emotions of individuals who are having issues in verbal communication.
Lycopene alleviates aflatoxin B1 induced liver damage through inhibiting cytochrome 450 isozymes and improving detoxification and antioxidant systems in broiler chickens

Abstract The effects of lycopene on liver damage in Aflatoxin B1 (AFB1)-challenged broilers were investigated. A total of 240 Arbour Acres Broilers in five groups were fed basal diet (control), basal diet with 100 μg/kg AFB1 (AFB1), AFB1 diet with 100, 200, and 400 mg/kg lycopene (AL1, AL2, and AL3), respectively. Broilers in AFB1 group had higher hepatic activities of cytochrome P450 1A1 (CYP1A1) and cytochrome P450 2A6 (CYP2A6) than the control group (P < .05). The hepatic concentrations of AFB1-8,9-epoxide-DNA adduct (AFBO-DNA), reactive oxygen species (ROS), malondialdehyde (MDA), 4-hydroxynonenal (4-HNE), protein carbonyl (PC) and 8-hydroxydeoxyguanosine (8-OHdG) were higher in AFB1 group than the control group (P < .05). In lycopene supplementation groups, the activities of CYP1A1 and CYP2A6, and the concentrations of AFBO-DNA, ROS, MDA, and 8-OHdG were lower than AFB1 group (P < .05). The 4-HNE and PC concentrations in AL2 and AL3 groups were lower than AFB1 group (P < .05). Compared to the control group, broilers in the AFB1 group had reduced glutathione (GSH) concentration and decreased activities of glutathione-S-transferase (GST), glutamine-cysteine ligase (GCL), total superoxide dismutase (T-SOD), catalase (CAT), and glutathione peroxidase (GPx) (P < .05). Whereas the GSH concentration in AL2 group, the GST activity in AL1 and AL3 groups, the GCL and CAT activities in AL1 group, and GPx activity in AL1, AL2, and AL3 groups were higher than AFB1 group (P < .05). Therefore, lycopene alleviated AFB1-induced liver damage possibly through inhibiting cytochrome P450 isozymes and improving detoxification and antioxidant systems in broilers. HIGHLIGHTS Dietary lycopene alleviated liver damage induced by aflatoxin B1 in broilers. Lycopene inhibited crucial cytochrome 450 isozymes involved in aflatoxin B1 bioactivation. Lycopene increased detoxification and antioxidant ability in aflatoxin B1 exposed broilers.
Sliding Mode-Based Consensus in the Presence of Byzantine Agents*

A consensus protocol is designed for a connected network of cooperative (CO) and Byzantine (BZ) agents. An agent is considered CO if it plays by the rules, and BZ if it does not apply the same consensus protocol as the CO agents, sends different/misleading state values to its neighbors, or forges signatures of CO agents. Such a scenario can arise in networked multi-agent systems, for example, wireless sensor nodes, when some of the nodes become BZ as the result of an external attack. If the consensus protocol is not designed to handle BZ agents, then the CO agents may not reach a consensus. In this paper, agents with first-order continuous dynamics are considered; an extension to agents with higher-order dynamics is also presented. Results from sliding mode control (SMC) theory and the distributed computing systems literature, such as the use of signed messages that are exchanged between agents, are used to design CO agents' inputs that lead to consensus. A novel messaging algorithm is given to handle BZ agents that can forge signatures of CO agents to the messages originating from these agents. The use of SMC theory ensures that consensus is achieved by the CO agents within a finite-time interval. The protocol does not require the knowledge of the number of BZ agents or their locations in the network.
Development of a software simulator for small satellite swarm control

This article is devoted to creating a control code to simulate the complicated type of flight of a group of small spacecraft in formation, called swarms of spacecraft. The Swarm project with integrated Fem satellites on a silicon wafer (SWIFT) Jet Propulsion Laboratories is a new, a paradigm-changing definition of spacecraft technology that could provide swarms of fully capable femtosatellites to fly. These swarms have the capacity to be used as optical relays, distributed antennas, or for mass distribution probing applications, among others. In this article, a swarm is defined as a collection of hundreds to thousands of homogeneous spacecraft, and a femtosat is defined as a satellite weighing about 100 g. Because of their small size, femtosats have limited probing, actuation, and computational capabilities, requiring that the swarm guidance and control algorithms be both motor and computationally efficient.In the article for testing the SS swarm control system, a virtual software simulator is developed. This simulator is written in Matlab R2015b in C ++ programming language. The simulator, using flight parameters, creates a simulation and shows the reaction of the SS in different situations.The simulator checks the flight path of the SS according to the task algorithm. During testing of the SSA flight orientation, the accuracy of hitting the target as well as the reduction and elimination of collision with other objects during the flight path is calculated.This virtual software simulator is designed for virtual testing of any group of SS.The virtual software simulator is used in the field of space research activities by space industry specialists with higher education.
A Comprehensive Review of Computing Paradigms, enabling Computation Offloading and Task Execution in Vehicular Networks

Road safety, optimized traffic management, and passenger comfort have always been the primary goals of the vehicle networking research community. Advances in computer and communication technology have made the dream of modern intelligent vehicles a reality through the use of smart sensors, cameras, networking devices, and storage capabilities. Autonomous operation of modern intelligent vehicles requires massive computations where tasks are outsourced. The research community has proposed various computing paradigms: mobile cloud computing, vehicle cloud computing, multi-access edge computing, vehicle edge computing, vehicle fog computing, and voluntary computing-based VANET (VCBV) to move computational power close to the user and handle the delay-sensitive applications of modern intelligent vehicles. In this study, we have provided a comprehensive overview of all computing paradigms related to vehicular networks. We have presented the architectural details, similarities, differences, and key features of each computing paradigm. Finally, we concluded the study with open research challenges in vehicular networks.
Experimental Design and Control of a Smart Morphing Wing System using a Q-learning Framework

A novel control and testing platform for a smart morphing wing system is introduced to obtain optimal aerodynamic properties. This paper delves into the manufacturing process for said system, from the computer-aided modeling to the assembly, and its corresponding difficulties. The issues associated with the primary rendition of the model are addressed, as well as proposed solutions to these issues. Additionally, a novel formulation is introduced, which utilizes the 3D printed airfoil model for Computational Fluid Dynamics (CFD) analysis and reinforcement learning. In particular, image processing techniques and algorithms are employed to obtain an outline for the various morphed configurations, which are converted into airfoil coordinates and analyzed using a CFD tool, before being imported into a Q-learning algorithm.
Prediction of Cognitive Progression in Individuals with Mild Cognitive Impairment Using Radiomics as an Improvement of the ATN System: A Five-Year Follow-Up Study

Objective To improve the N biomarker in the amyloid/tau/neurodegeneration system by radiomics and study its value for predicting cognitive progression in individuals with mild cognitive impairment (MCI). Materials and Methods A group of 147 healthy controls (HCs) (72 male; mean age ± standard deviation, 73.7 ± 6.3 years), 197 patients with MCI (114 male; 72.2 ± 7.1 years), and 128 patients with Alzheimer’s disease (AD) (74 male; 73.7 ± 8.4 years) were included. Optimal A, T, and N biomarkers for discriminating HC and AD were selected using receiver operating characteristic (ROC) curve analysis. A radiomics model containing comprehensive information of the whole cerebral cortex and deep nuclei was established to create a new N biomarker. Cerebrospinal fluid (CSF) biomarkers were evaluated to determine the optimal A or T biomarkers. All MCI patients were followed up until AD conversion or for at least 60 months. The predictive value of A, T, and the radiomics-based N biomarker for cognitive progression of MCI to AD were analyzed using Kaplan-Meier estimates and the log-rank test. Results The radiomics-based N biomarker showed an ROC curve area of 0.998 for discriminating between AD and HC. CSF Aβ42 and p-tau proteins were identified as the optimal A and T biomarkers, respectively. For MCI patients on the Alzheimer’s continuum, isolated A+ was an indicator of cognitive stability, while abnormalities of T and N, separately or simultaneously, indicated a high risk of progression. For MCI patients with suspected non-Alzheimer’s disease pathophysiology, isolated T+ indicated cognitive stability, while the appearance of the radiomics-based N+ indicated a high risk of progression to AD. Conclusion We proposed a new radiomics-based improved N biomarker that could help identify patients with MCI who are at a higher risk for cognitive progression. In addition, we clarified the value of a single A/T/N biomarker for predicting the cognitive progression of MCI.
Analisis Perbandingan Nilai Akurasi Mekanisme Attention Bahdanau dan Luong pada Neural Machine Translation Bahasa Indonesia ke Bahasa Melayu Ketapang dengan Arsitektur Recurrent Neural Network

Di Indonesia, penerapan mesin penerjemah masih banyak dilakukan dengan berbasis statistik khususnya dalam eksperimen penerjemahan bahasa daerah. Dalam beberapa tahun terakhir, mesin penerjemah jaringan saraf tiruan telah mencapai kesuksesan yang luar biasa dan menjadi metode pilihan baru dalam praktik mesin penerjemah. pada penelitian ini menggunakan mekanisme attention dari Bahdanau dan Luong dalam bahasa Indonesia ke bahasa Melayu Ketapang dengan data korpus paralel sejumlah 5000 baris kalimat. Hasil pengujian berdasarkan metode penambahan secara konsisten dengan jumlah epoch didapatkan nilai skor BLEU yaitu pada attention Bahdanau menghasilkan akurasi 35,96% tanpa out-of-vocabulary (OOV) dengan menggunakan jumlah epoch 40, sedangkan pada attention Luong menghasilkan akurasi 26,19% tanpa OOV menggunakan jumlah 30 epoch. Hasil pengujian berdasarkan k-fold cross validation didapatkan nilai rata-rata akurasi tertinggi sebesar 40,25% tanpa OOV untuk attention Bahdanau dan 30,38% tanpa OOV untuk attention Luong, sedangkan pengujian manual oleh dua orang ahli bahasa memperoleh nilai akurasi sebesar 78,17% dan 72,53%.
Dynamic Alteration in Cerebral Total Hemoglobin Concentration Measured Using Portable Near-infrared Spectroscopy in Orthostatic Hypotension and Intolerance

A portable near infrared spectroscopy (NIRS) system was used to evaluate alterations in cerebral total hemoglobin concentration (HbT) in individuals with orthostatic hypotension (OH) and orthostatic intolerance (OI) symptoms. We enrolled 238 healthy participants (mean age, 47.9 years) and assessed the presence of OH (orthostatic blood pressure (BP) drop of systolic BP ≥ 20 mmHg or diastolic BP ≥ 10 mmHg within 3 minutes of supine-to-stand) and OI symptoms using the OH questionnaire. The participants were categorized into three groups based on the presence of OH and OI symptoms: the classic OH (OH-BP) group, symptom alone (OH-Sx) group, and control group. Random case-control matching sets (age, sex, hypertension, and diabetes mellitus) were constructed consisting of 16 OH-BP and 69 OH-Sx-control sets. We measured the time-derivative of HbT change in the prefrontal cortex during the squat-to-stand maneuver. There were no differences in demographics, baseline BP, and heart rate among the matched sets. Among the NIRS parameters, the peak-time of maximum slope variation was significantly longer in the OH-Sx and OH-BP groups than in the matched control groups during transition to the standing position after squatting. Our results suggested that OH and OI symptoms are associated with dynamic alteration in cerebral HbT.
Automated SiC MOSFET Power Module Switching Characterization Test Platform

To address the problems of large test error and low efficiency in switching characterization test of wide-bandgap (WBG) power semiconductor devices, an automated high accuracy switching performance test platform for SiC MOSFET power module is proposed. This paper focuses on optimizing the design of hardware and software which can efficiently, accurately and safely obtain switching characteristics parameters of SiC MOSFET power module by double pulse test (DPT), as well as short-circuit test and body diode reverse recovery test to acquire more comprehensive switching behaviors of the devices under test (DUT). Real-time test results can be achieved. The multi-process test is serial, and the test data can be compared and managed using a database system. The design principles are experimentally verified by testing the Cree 1.2kV SiC MOSFET power module.