Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Teaching Practicum in Malaysia in the Time of COVID-19 Pandemic

When the teachers’ training practicum was paralyzed during the COVID-19 pandemic, preservice teachers in Malaysia were required to adapt to the online practicum. This qualitative case study was conducted with 20 preservice teachers to investigate their online teaching practicum experiences. The study drew on the Engagement Theory and Disaster Management Cycle framework to further suggest teaching approaches that might be effective during a tragic situation. Data were collected from interviews and video observations, and analyzed thematically. The findings contribute to the understanding of how preservice teachers learn to modify their teaching practices during the challenging context. A number of themes related to positive experience were identified: (i) higher confidence level, (ii) improved interaction, (iii) engagement in active learning, and (iv) adapting to online syllabus. On the other hand, the negative experience revolves around the problem with Internet connection. It is hoped that the findings of this study will encourage preservice teachers to consider hybrid approaches and online teaching in the future.
Chanalyzer: A Computational Geometry Approach for the Analysis of Protein Channel Shape and Dynamics

Morphological analysis of protein channels is a key step for a thorough understanding of their biological function and mechanism. In this respect, molecular dynamics (MD) is a very powerful tool, enabling the description of relevant biological events at the atomic level, which might elude experimental observations, and pointing to the molecular determinants thereof. In this work, we present a computational geometry-based approach for the characterization of the shape and dynamics of biological ion channels or pores to be used in combination with MD trajectories. This technique relies on the earliest works of Edelsbrunner and on the NanoShaper software, which makes use of the alpha shape theory to build the solvent-excluded surface of a molecular system in an aqueous solution. In this framework, a channel can be simply defined as a cavity with two entrances on the opposite sides of a molecule. Morphological characterization, which includes identification of the main axis, the corresponding local radius, and the detailed description of the global shape of the cavity, is integrated with a physico-chemical description of the surface facing the pore lumen. Remarkably, the possible existence or temporary appearance of fenestrations from the channel interior towards the outer lipid matrix is also accounted for. As a test case, we applied the present approach to the analysis of an engineered protein channel, the mechanosensitive channel of large conductance.
Identification of novel biomarkers in septic cardiomyopathy via integrated bioinformatics analysis and experimental validation

Purpose: Septic cardiomyopathy (SCM) is an important world public health problem with high morbidity and mortality. It is necessary to identify SCM biomarkers at the genetic level to identify new therapeutic targets and strategies. Method: DEGs in SCM were identified by comprehensive bioinformatics analysis of microarray datasets (GSE53007 and GSE79962) downloaded from the GEO database. Subsequently, bioinformatics analysis was used to conduct an in-depth exploration of DEGs, including GO and KEGG pathway enrichment analysis, PPI network construction, and key gene identification. The top ten Hub genes were identified, and then the SCM model was constructed by treating HL-1 cells and AC16 cells with LPS, and these top ten Hub genes were examined using qPCR. Result: STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP were significantly elevated in the established SCM cells model. Conclusion: After bioinformatics analysis and experimental verification, it was demonstrated that STAT3, SOCS3, CCL2, IL1R2, JUNB, S100A9, OSMR, ZFP36, and HAMP might play important roles in SCM.
Image Representations of DNA allow Classification by Convolutional Neural Networks

In metagenomic analyses the rapid and accurate identification of DNA sequences is important. This is confounded by the existence of novel species not contained in databases. There exist many methods to identify sequences, but with the increasing amounts of sequencing data from high-throughput technologies, the use of new deep learning methods are made more viable. In an attempt to address this it was decided to use Convolutional Neural Networks (CNNs) to classify DNA sequences of archaea, which are important in anaerobic digestion. CNNs were trained on two different image representations of DNA sequences, Chaos Game Representation (CGR) and Reshape. Three phyla of archaea and randomly generated sequences were used. These were compared against simpler machine learning models trained on the 4-mer and 7-mer frequencies of the same sequences. It was found that the simpler models performed better than CNNs trained on either image representation, and that Reshape was the poorest representation. However, by shuffling sequences whilst preserving 4-mer count it was found that the Reshape model had learnt 4-mers as an important feature. It was also found that the Reshape model was able to perform equally well without depending on the use of 4-mers, indicating that certain training regimes may uncover novel features. The errors of these models were also random or in weak disagreement, suggesting ensemble methods would be viable and help to identify problematic sequences.
Functional characterisation of mechanical joints to facilitate its selection during the design of open architecture products

New trends in product design require the use of modularity as key feature aimed to improve functional performance and the generation of open architecture products. For mechanical systems, one of the challenges during early design stages of these products involves the proper selection of joining methods among their constructive components. A robust joint selection process must consider product requirements, life cycle analysis and eventual procedures for assembly and disassembly. However, the general approach towards a Design-for-Assembly (DFA)/Design-for-Disassembly (DFD) only considers design, manufacturing and in some cases final disposal stage. Additionally, most of the works found in the literature are merely focused on assembly operations, disregarding economic and environmental benefits from optimising disassembly complexity. Herein, a functional characterisation of mechanical joint methods for the assembly and disassembly activities that take place throughout the product life cycle is proposed, focusing on open architecture products. Additionally, a classification of joining methods, a joint complexity metric valuation and a selection process are proposed for the conceptual design stage. The approach integrates both DFA and DFD principles in a formal methodology. The proposed selection roadmap can be implemented to increase product sustainability positively regarding resources optimisation, operational time and costs in reuse, remanufacturing and recycling tasks.
A comparison of sawtooth oscillations in bean and oval shaped plasmas

The effect of plasma shape on sawtooth oscillations in the DIII-D tokamak plasmas is investigated by comparing discharges with cross-sections shaped like a bean and an oval. The two shapes are designed so that the Mercier instability threshold is reached when the axial safety factor is below unity for the bean and above unity for the oval cross-sections. This allows the role of interchange modes to be differentiated from that of the kink-tearing mode. The differences in the nature of the sawtooth oscillations in the bean and oval discharges are found to be determined primarily by extreme differences in the electron heat transport during the reheat. In both cases, the axial safety factor is found to be near unity following the crash.
An Optimal Control Model to Understand the Potential Impact of the New Vaccine and Transmission-Blocking Drugs for Malaria: A Case Study in Papua and West Papua, Indonesia

Malaria is one of the major causes of a high death rate due to infectious diseases every year. Despite attempts to eradicate the disease, results have not been very successful. New vaccines and other treatments are being constantly developed to seek optimal ways to prevent malaria outbreaks. In this article, we formulate and analyze an optimal control model of malaria incorporating the new pre-erythrocytic vaccine and transmission-blocking treatment. Sufficient conditions to guarantee local stability of the malaria-free equilibrium were derived based on the controlled reproduction number condition. Using the non-linear least square fitting method, we fitted the incidence data from the province of Papua and West Papua in Indonesia to estimate the model parameter values. The optimal control characterization and optimality conditions were derived by applying the Pontryagin Maximum Principle, and numerical simulations were also presented. Simulation results show that both the pre-erythrocytic vaccine and transmission-blocking treatment significantly reduce the spread of malaria. Accordingly, a high doses of pre-erythrocytic vaccine is needed if the number of infected individuals is relatively small, while transmission blocking is required if the number of infected individuals is relatively large. These results suggest that a large-scale implementation of both strategies is vital as the world continues with the effort to eradicate malaria, especially in endemic regions across the globe.
Dual embedding with input embedding and output embedding for better word representation

Recent studies in distributed vector representations for words have variety of ways to represent words. We propose a various ways using input embedding and output embedding to better represent words than single model. We compared the performance in terms of word analogy and word similarity with each input and output embeddings and various dual embeddings which are the combination of those two embeddings. Performance evaluation results show that the proposed dual embeddings outperform each single embedding, especially with the way of simply adding input and output embeddings. We figured out two things in this paper, i) not only input embedding but also output embedding has such meaning to represent the words and ii) combining input embedding and output embedding as dual embedding outperforms the single embedding when we use input embedding and output embedding individually.
IMPLICIT MANIPULATION OF CONSTRAINT SETS FOR GEOMETRIC MATCHING UNDER 2D TRANSLATION AND ROTATION

This paper presents a new algorithm in the RAST family of algorithms. RAST algorithms perform geometric matching by exploring intersections between query regions and constraint sets in the space of possible model transformations. RAST algorithms are closely related to hierarchical Hough transformations but have more desirable geometric and combinatorial properties for object recognition applications. Previous applications of the RAST algorithm were limited by the need to represent constraint sets explicitly in transformation space. This paper introduces methods of manipulating the constraint sets arising from image and model point correspondences implicitly, allowing RAST methods to be applied in situations where no simple representation of constraint sets is algebraically possible. The method is demonstrated for exact bounded error matching in the space of 2D translations and rotations.
SISTEM MONITORING BESARAN LISTRIK DENGAN TEKNOLOGI IoT (INTERNET of THINGS)

ABSTRAK Monitoring besaran listrik secara online perlu dilakukan untuk mengetahui kondisi real dari sebuah system tenaga listrik. Teknologi Internet of things (IoT) dimungkinkan untuk memantau secara langsung kondisi tersebut. Pemantauan harus memberikan informasi kompleks dengan konsep SMART (Specific, Measurable, Attainable, Relevant, Time-bound) yaitu spesifik, terukur, dapat dicapai, relevan, dan dalam rentang waktu. Adapun yang dimonitor dalam penelitian ini ialah besaran listrik berupa arus, tegangan, daya, dan faktor daya. Prototype sistem yang dibuat terdiri dari komponen – komponen elektronik seperti sensor arus, sensor tegangan, rangkaian pengkondisian sinyal, Arduino UNO, dan Ethernet Shield serta dilengkapidenganfasilitas server dan web. Masing-masing komponen diuji sebelum dirangkai menjadi sebuah sistem. Pengujian prototypedilakukan pada Lab. Teknik Pengukuran Besaran Elektrik Universitas Lampung (Unila) menggunakan Clamp meter (standard laboratorium). Dari hasil pengujian diperoleh nilai galat prototype < 10%, dengan demikian prototype yang dibangun pada penelitian tugasakhirini termasuk dalam golongan alat ukur kelas 1,5; 2,5; dan 5 yang bisa digunakan untuk pengukuran pada panel listrik. Kata kunci: prototype sistem monitoring, Arduino UNO, Ethernet Shield, sensor arus, sensor tegangan, IoT ABSTRACT Online monitoring of electrical quantities needs to be done in order to obtain the actual condition of a power system. Direct condition of monitored power system is possible to access in real-time by using Internet of Things (IoT) Technology. Monitoring should provide complex information with SMART (Specific, Measurable, Attainable, Relevant, Time-bound) concepts that are specific, measurable, achievable, relevant, and within the time frame. The electrical quantities recorded in this research were in the form of current, voltage, power, and power factor. Designed prototype was constructed by electronic components such as current sensors, voltage sensors, signal conditioning circuit, Arduino UNO, and Ethernet Shield integrated to server and web facilities. Each components were tested before being assembled into a system. Prototype was tested in Measurement of Electrical Quantities Laboratory, University of Lampung (Unila) using Clamp meter (laboratory standard). Results showed that < 10% of error value obtained, thus the prototype built in this research included for 1.5; 2.5; And 5 of measurment devices that can be used for measurement on electrical panels. Keywords: monitoring system prototype, Arduino UNO, Ethernet Shield, current sensor, voltage sensor, IoT
Analysis of Flexible Car Body of Straddle Monorail Vehicle

Based on the finite element model of straddle monorail vehicle, a rigid-flexible coupling dynamic model considering vehicle body’s flexibility is established. The influence of vertical stiffness and vertical damping of the running wheel on the modal parameters of the car body is analyzed. The effect of flexible car body on modal parameters and vehicle ride quality is also studied. The results show that when the vertical stiffness of running wheel is less than 1 MN / m, the car body bounce and pitch frequency increase with the increasing of the vertical stiffness of the running wheel, when the running wheel vertical stiffness is 1MN / m or more, car body bounce and pitch frequency remained unchanged; When the vertical stiffness of the running wheel is below 1.8 MN / m, the vehicle body bounce and pitch damping ratio increase with the increasing of the vertical stiffness of the running wheel; When the running wheel vertical stiffness is 1.8MN / m or more, the car body bounce and pitch damping ratio remained unchanged; The running wheel vertical damping on the car body bounce and pitch frequency has no effect; Car body bounce and pitch damping ratio increase with the increasing of the vertical damping of the running wheel. The flexibility of the car body has no effect on the modal parameters of the car, which will improve the vehicle ride quality index.
Torque coordinated control in engine starting process for a single-motor hybrid electric vehicle

To tackle the excessive output torque ripple during engine starting process of parallel hybrid electric vehicles, the engine starting process is divided into three phases in this study: engine cranking phase, speed synchronization phase, and after synchronization phase. The expressions of the vehicle jerk are derived from the vehicle dynamic formula in each phase, and the influences of various parameters on the jerk are analyzed. The coordinated control strategy for the clutch pressure and motor torque and that for the motor torque and engine torque are proposed. The simulation model for the single-motor parallel hybrid electric vehicle is established using MATLAB/Simulink, and the effectiveness of the proposed control algorithms is verified. Besides, a bench test is conducted and the test results show that the selected change rates of clutch pressure, motor torque, and engine torque can effectively coordinate the relation between clutch, motor, and engine. It can be concluded that the proposed control strategy satisfies the requirement of vehicle ride performance in engine starting in-motion process.
A Fast Computational Algorithm to Evaluate Large Transmit-arrays

Transmit-arrays (TAs) have become popular solutions for point-to-point communications due to their lowprofile and high-efficiency. However, simulation of these large and detailed structures demands a great amount of memory and time. Here, we propose a fast algorithm to estimate the performance of a beam-steering TA to accelerate its design optimization process. The results and the required resources of the algorithm are compared with the ones of Ansys HFSS. It is shown that this algorithm can fairly evaluate the key factors of the performance of a TA within couple of minutes with memory resource of only 1 GB.
A New Computational Model of Alignment and Overspecification in Reference

(Pechmann, 1989; Belke & Meyer, 2002; Engelhardt, Bailey, & Ferreira, 2006). Existing computational models – of which the Incremental Algorithm (IA; Dale & Reiter, 1995) is arguably the most popular – capture this by selecting properties of a target in order of preference (e.g. colour before orientation), stopping when the description is distinguishing. The IA only overspecifies if it selects a preferred property before a dispreferred one, when the dispreferred one alone would suffice. However, recent work suggests a complex interaction between alignment and preferences in interactive settings. Goudbeek and Krahmer (2010) showed that speakers who are primed with a dispreferred attribute are more likely to re-use it in later references to different targets. Crucially, these experiments showed evidence of overspecification even though the primes themselves were not overspecified. However, they raise the question whether overspecification is itself something that can be primed. Using a similar paradigm, we show that speakers primed with overspecified descriptions evince an increased likelihood to overspecify, using both preferred and dispreferred attributes, even when either one would suffice. We model this computationally as a process of competition between a preference-based selection process and a priming-based one acting in parallel. In our experiment, participants were primed with overspecified descriptions containing both a preferred and a dispreferred attribute and then were asked to refer to a different object which could be identified using either attribute alone. As shown in the adjacent figure, participants overspecified over 50% of the time, using both the preferred and dispreferred attribute. Note that the Incremental Algorithm would predict 0% overspecification, since it would always choose the preferred attribute first, and then stop on finding the description to be distinguishing. To account for the human data, we propose a model consisting of an incremental, preference-based search process based on the IA, which selects properties concurrently and in competition with a priming-based process, both contributing properties to a limited capacity working memory buffer. Priming is modelled through a process of spreading activation, whereby an attribute which has been used in a description activates all other properties which share the same attribute (e.g. all values of orientation or colour). A description is built by taking properties from working memory, and overspecification occurs when both concurrent processes add properties to the buffer, though only a subset would suffice to identify the target. As shown in the figure, our model’s rate of overspecification is much closer to the human data; indeed, we find no significant difference in percentage overspecification between the model and the human data, though both differ significantly from the baseline Incremental Algorithm, which never predicts overspecification in these experimental contexts.
Appendix 2: The estimation of ferric iron in electron microprobe analysis of amphiboles

Winehite. Named for H.J. Winch, who found the amphibole. Type locality: Kajlidqngri, JhabuaoState, India. X-ray data: a 9.834 A, b 18.062 A, c 5.300 ,~. B 104.4 ~ (PDF 20-1390) References: L.L. Fermor (1906. Trans. Mining Geol. Inst. India, 1: 79) naming the amphibole described in 1904 (Rec. Geol. Surv. India, 31: 236). Topotype material found by B.E. Leake, C.M. Farrow, F. Chao and V.K. Nayak (1986. Min. Mag. 50: 174) proved to be very similar in composition to that originally found by Fermor (I 909. Mere. Geol. Surv. India, 37: 149).
Indonesian Journal of Electrical Engineering and Computer Science

Received Oct 29, 2021 Revised May 11, 2022 Accepted Jun 19, 2022 For a colour conversion substance, we developed a new nanocomposite containing a CdSe/CdS/ZnS red-emitted quantum dot (QD), a greenemittedd Sr2SiO4:Eu phosphorus, and silicon resins. Regarding QD concentration and ingredients, the heat increase and optic features of the nanocomposite attributable to the QD inclusion were examined. According to the findings, a modest portion of QDs added to a photon converter at the emission wavelength of QD produced a considerable degree of heat. We used 0.2 wt% QDs over an InGaN blue-emitting light-emitted diodes (LED) chip to simulate a thermal increase in a nanocomposite. Consequently, we were able to produce a white-emitted LED module featuring a good 83.2 colour rendered index, an excellent 65.86 lm W-1 brightness, and a reasonable 94 °C thermal rise. The recently founded QD-phosphorus nanocomposite transformed white-emitted LED offers a lot of possibility of modern lighting.
Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead

This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this regard, certain mechanisms and processes observed in nature, including the animal brain, have proved themselves to excel not only in terms of efficiently capturing time-evolving stimuli, but also at undertaking complex tasks by virtue of mechanisms that can be extrapolated to computer algorithms and methods. This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS). This systematic survey is complemented by an initiatory taxonomic introduction to bioinspired computational intelligence, along with the basics of its constituent techniques. A focus is placed on which research niches are still unexplored by the community in different ITS subareas. The open issues and research directions for the practical implementation of ITS endowed with bioinspired computational intelligence are also discussed in detail.