Miracle-dz/UniFAR-FTUs huggingface dataset:
Findings and derived challenges concerning how school leaders should support ICT integration at schools

ABSTRACT One of the biggest challenges during the COVID-19 pandemic was to integrate ICT (Information and Communication Technology) into remote teaching. As managers, school leaders play a principal role in deciding how to overcome this challenge. This study aims to identify how school leaders support ICT integration at schools. To do so, the authors conducted a systematic literature review (SLR). A literature search using the ISI Web of Science, SCOPUS, and the Education Resources Information Center (ERIC) yielded a total of 537 articles published between 2011 and 2020 (a manual search was undertaken between September 2020 – January 2021). The results suggest that school leaders influence ICT integration at schools in seven ways: encouraging ICT integration in the school community, facilitating ICT use, providing financial support, giving ICT training, collaboration, planning, and monitoring. The findings from this SLR provide valuable data, which could help authorities to evaluate school administrators’ roles in integrating ICT at schools. Additionally, policymakers can use the research results to provide a baseline for future assessments and guidelines for school leaders’ professional development. Finally, future research could examine the relationships between the factors identified in the SLR and how to measure school leaders’ support for ICT integration.
C∙∙∙O and Si∙∙∙O Tetrel Bonds: Substituent Effects and Transfer of the SiF3 Group

The tetrel bond (TB) between 1,2-benzisothiazol-3-one-2-TF3-1,1-dioxide (T = C, Si) and the O atom of pyridine-1-oxide (PO) and its derivatives (PO-X, X = H, NO2, CN, F, CH3, OH, OCH3, NH2, and Li) is examined by quantum chemical means. The Si∙∙∙O TB is quite strong, with interaction energies approaching a maximum of nearly 70 kcal/mol, while the C∙∙∙O TB is an order of magnitude weaker, with interaction energies between 2.0 and 2.6 kcal/mol. An electron-withdrawing substituent on the Lewis base weakens this TB, while an electron-donating group has the opposite effect. The SiF3 group transfers roughly halfway between the N of the acid and the O of the base without the aid of cooperative effects from a third entity.
STRATEGIC PLAN ANALYSIS USING THE BALANCED SCORECARD FRAMEWORK AT YOGYAKARTA ISLAMIC HOSPITAL PDHI

Background: Personal protection equipment (PPE) is becoming more and more difficult to come by, which makes service management less and less effective. The expectation is that hospitals will be able to navigate this epidemic phase by converting all obstacles into assets and all chances into strengths. Thus, despite the current crisis and condition, the hospital can endure and possibly expand. Aim: The purpose of this study is to analyze the strategic plan using the BSC framework. Method: This research is qualitative research with a case study approach conducted at RSIY PDHI. This research design uses an approach to explore and understand the organization by presenting a comprehensive picture and reporting a detailed description of the source of information and setting it up without any intervention from the researcher. Findings: From a financial perspective, three strategic targets are set, namely revenue growth, cost reduction, and profitability improvement. From the customer's perspective, four strategic targets are set, namely, increasing the quality and quality of service, increasing customer satisfaction, increasing the quality of customer relationships, and improving the image of the hospital. From the perspective of internal business processes, three strategic targets are set, namely the integration of internal service processes, the improvement of the quality of the service process to customers, and the increasing speed of the service innovation process. From the growth and learning perspective, three strategic targets are set, namely increasing employee capabilities, increasing employee satisfaction, and increasing the use of integrated information systems.
Cascade recurrent neural network for image caption generation

A new cascade recurrent neural network (CRNN) for image caption generation is proposed. Different from the classical multimodal recurrent neural network, which only uses a single network for extracting unidirectional syntactic features, CRNN adopts a cascade network for learning visual-language interactions from forward and backward directions, which can exploit the deep semantic contexts contained in the image. In the proposed framework, two embedding layers for dense word expression are constructed. A new stacked Gated Recurrent Unit is designed for learning image-word mappings. The effectiveness of the CRNN model is verified with adopting the commonly used MSCOCO datasets, where the results indicate CRNN can achieve better performance compared with the state-of-the-art image captioning methods such as Google NIC, multimodal recurrent neural network and so on.
Spiral phase-change material for enhancing natural circulation solar water heater: An experimental study

This article studies the use of spiral phase-change material as an energy storage to improve the performance of a domestic solar water heater. The heating and cooling tests have been conducted for the vertical and horizontal position of the phase-change material in the water heater tank. The vertical position phase-change material yields better results than horizontal position. The charging energy and system thermal efficiencies of the tank are increased up to 20% and 12%, respectively, when the phase-change material is kept vertically. Also, it is observed that better heat transfer coefficient between water and phase-change material and upgraded thermal stratification during the cooling tests.
Effects of dietary leucine on growth, antioxidant capacity, immune response, and inflammation in juvenile yellow catfish Pelteobagrus fulvidraco

The experiment was conducted to investigate the effects of dietary leucine on growth, antioxidant capacity, immune response, and inflammation in juvenile yellow catfish. Five diets were formulated to contain five dietary leucine levels: 12.00 (control), 19.00, 26.00, 33.00, and 40.00 g kg−1. Each diet was randomly assigned to triplicate groups of 30 juvenile fish (5.02 ± 0.15 g) twice daily to apparent satiation for 56 days. Weight gain rate, specific growth rate, and activities of liver superoxide dismutase, glutathione peroxidase, and serum lysozyme, as well as immunoglobulin M content, significantly increased with increase in dietary leucine levels up to 26.00 g kg−1, but those values decreased significantly with a further increase in dietary leucine. On the contrary, the lowest malondialdehyde content was found in 26.00 and 33.00 g kg−1 leucine groups. The expression levels of IGF 1 and MYF 5 genes in muscle were significantly upregulated with increase in dietary leucine levels up to 26.00 g kg−1, but the expression of MSTN level showed the opposite trend. The lowest expression levels of IL 8 and TNFɑ genes in the liver were found in 26.00 g kg−1 leucine groups. The quadratic regression analysis on weight gain, specific growth rate, and feed conversion ratio against dietary leucine levels indicated that the optimal dietary leucine requirement was estimated to be 26.84–27.00 g kg−1of the dry diet.
Novel Shielding Technology and Interleaving Technology Realizes EMI CM Noise Suppression Base on Cascaded Power Module

In this article, a cascaded power module adopts novel shielding technology and interleaving technology to realize electromagnetic interference (EMI) common mode noise current suppression. The power module's first level adopts an LLC topological architecture converter, the second level adopts a buck topological architecture converter. To address the electromagnetic common mode noise problem, the first level LLC topological architecture converter's primary and secondary windings adopt shielding technology and cancellation technology. Analysis LLC topological architecture converter's transformer shielding structure model and noise current propagation path, researched and compared the LLC converter winding adopts novel shielding technology and cancellation technology transformer internal effective capacitance characteristics and electro-magnetic interference effect. The second-level buck topological architecture converter adopts a palar interleaving reverse coupled inductance, buck topological architecture converter's common mode noise model and propagation path model are established, studied, and analyzed the interleaving reverse coupled inductance winding effective capacitance. Experimental results show that a quarter brick cascaded power module with novel shielding technology and winding interleaving technology can effectively inhibit EMI common mode noise, reducing average common mode noise by 15 dB and peak common mode noise by 25 dB.
Near-lossless positron injection into a dipole magnetic field

E. V. Stenson1,2, U. Hergenhahn1, S. Nissl1,2, J. Horn-Stanja1, H. Saitoh1,3, T. Sunn Pedersen1,4, M. R. Stoneking5, M. Singer2, M. Dickmann2, C. Hugenschmidt2, J. R. Danielson6, C. M. Surko6 1 Max Planck Institute for Plasma Physics, Garching & Greifswald, Germany 2 Technische Universität München, Garching, Germany 3 The University of Tokyo, Kashiwa, Japan 4 University of Greifswald, Greifswald, Germany 5 Lawrence University, Appleton, WI, U.S.A. 6 University of California, San Diego, La Jolla, CA, U.S.A.
Nondestructive Inspection and Quantification of Soldering Defects in PCB Using an Autofocusing Digital Holographic Camera

The essential requirements for any digital holography-based system to get employed as an inspection device in electronic industry are portability and ability to automatically focus on the reconstruction plane. Any deviation from the exact propagation distance between the hologram plane and the reconstruction plane may result in erroneous measurement of the surface features under inspection. We report for the first time, to the best of our knowledge, the autofocus capability in a digital holography-based portable camera for full-field quantitative inspection of solder joint defects in a printed circuit board (PCB). An intensity gradient-based algorithm is developed and applied to the digital hologram to automatically locate the reconstruction plane and determine the propagation distance. For a given range of distance, the algorithm calculates the maximum value of high-frequency components in the reconstructed image, and the corresponding propagation distance is used in the reconstruction. The autofocus algorithm along with the double exposure digital holographic interferometry (DHI) is applied on the PCB under thermal loading. 2-D and 3-D deformation maps are analyzed to detect different kinds of solder joint defects, such as holes, irregular shapes, excess soldering, and bridging. The work presented in this article may further stimulate the development of digital holographic techniques for several applications in metrology and inspection in the electronics manufacturing industry.
GEOMETRIE ALGORITHMIQUE NON-LINEAIRE ET COURBES ALGEBRIQUES PLANAIRES

We tackle in this thesis the problem of computing the topology of plane algebraic curves. We present an algorithm that avoids special treatment of degenerate cases, based on algebraic tools such as Gröbner bases and rational univariate representations. We implemented this algorithm and showed its performance by comparing to similar existing programs. We also present an output-sensitive complexity analysis of this algorithm. We then discuss the tools that are necessary for the implementation of nonlinear geometric algorithms in CGAL, the reference library in the computational geometry community. We present an univariate algebraic kernel for CGAL, a set of functions aimed to handle curved objects defined by univariate polynomials. We validated our approach by comparing it to other similar implementations.
844-fs mode-locked fiber laser by carboxyl-functionalized graphene oxide

Abstract. We had proved that a kind of graphene nanomaterial “carboxyl-functionalized graphene oxide (GO-COOH)” possessed nonlinear saturable absorber (SA) property. The modulation depth of a GO-COOH water solution was measured as ∼8%. Moreover, a GO-COOH-based SA device was made and applied in an erbium-doped fiber laser. In this fiber laser, Q-switching pulses and mode-locked pulses were both obtained. With an increase in the pump power, the pulse width of Q-switching pulses decreased from 9.05 to 2.49 μs. The mode-locked pulse width was 844 fs, and the corresponding spectral bandwidth was 3.64 nm. Moreover, polarization adjusting or control was not needed during the whole process of mode locking. It illustrated that the proposed fiber laser incorporating GO-COOH could endure bigger intracavity birefringence. Our results indicated that the GO-COOH nanomaterial was a promising SA for generating high-performance pulse lasers.
Multi-industry stock forecasting using GRU-LSTM deep transfer learning method

After the Covid-19 pandemic, the number of investors in Indonesia has proliferated. In managing a good stock portfolio, investors need the right strategy too. One approach that can be applied is to predict stock movements by considering the company's industrial sector. This paper proposed a new framework for applying deep transfer learning for stock forecasting in multi-industry. The model used in the framework is a combined algorithm between Gated Recurrent Unit (GRU) and Long-Short Term Memory (LSTM). The author built the pre-trained model using Indeks Harga Saham Gabungan (IHSG) and transferred it to predict Indonesia's stock indexes based on industry classification (IDX-IC) as the measurer of stock movement in multiple industries. The outcomes reveal that this framework produces good model predictions and can be used to help analyze the evaluation of the pre-trained model to conduct transfer learning stock prediction in different industries efficiently. The model built using the IHSG indexes can predict stock prices best in the energy, technology, and industrial sectors.
Halfway to Automated Feeding of Chinese Hamster Ovary Cells

This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction. It also describes the development of a simulation environment whose core is a model of CHO cell kinetics. The latter allows the development of advanced control algorithms for nutrient dosing and the observation of the effects of different parameters on the growth and productivity of CHO cells. All developed models were validated and demonstrated to have a high robustness and predictive accuracy, which were reflected in a 40% reduction in the root mean square error compared to established methods. The results of this study provide valuable insights into the practical application of these methods in the field of monitoring and automated cell feeding and make an important contribution to the further development of process analytical technology in the bioprocess industry.
Experimental Investigation of Vibration Isolator for Large Aperture Electromagnetic MEMS Micromirror

The Micro-Electro-Mechanical-System (MEMS) micromirror has shown great advantages in Light Detection and Ranging (LiDAR) for autonomous vehicles. The equipment on vehicles is usually exposed to environmental vibration that may degrade or even destroy the flexure of the micromirror for its delicate structure. In this work, a mechanical low-pass filter (LPF) acting as a vibration isolator for a micromirror is proposed. The research starts with the evaluation of vibration influences on the micromirror by theoretical calculation and simulation. The results illustrate that mechanical load concentrates at the slow flexure of the micromirror as it is excited to resonate in second-order mode (named piston mode) in Z-direction vibration. A specific LPF for the micromirror is designed to attenuate the response to high-frequency vibration, especially around piston mode. The material of the LPF is a beryllium-copper alloy, chosen for its outstanding properties of elasticity, ductility, and fatigue resistance. To measure the mechanical load on the micromirror in practical, the on-chip piezoresistive sensor is utilized and a relevant test setup is built to validate the effect of the LPF. Micromirrors with or without the LPF are both tested under 10 g vibration in the Z-direction. The sensor output of the device with the LPF is 35.9 mV in piston mode, while the device without the LPF is 70.42 mV. The attenuation ratio is 0.51. This result demonstrates that the LPF structure can effectively reduce the stress caused by piston mode vibration.
Radar Based Precipitation Nowcasting Prediction by Using Deep Learning Techniques

Nowcasting is an emerging area in meteorology that focuses on accurately anticipating the severity of short-term rainfall for a particular location. It is essential to many facets of society. Owing to its significance, researchers are experimenting to predict short term rainfall using neural network approaches. This study analyses proposes a novel method of merging Convolutional Neural Network and Long Short-Term Memory neural networks on a radar echo dataset. The model was tested against a synthetic moving mnist dataset before applying on actual radar image dataset. Given the previous radar images, the model could successfully find future image sequences and obtained an accuracy of more than 90%.
An Improved SAMP Algorithm for Sparse Channel Estimation in OFDM System

Channel estimation of an orthogonal frequency division multiplexing (OFDM) system based on compressed sensing can effectively reduce the pilot overhead and improve the utilization rate of spectrum resources. The traditional SAMP algorithm with a fixed step size for sparse channel estimation has the disadvantages of a low estimation efficiency and limited estimation accuracy. An Improved SAMP (ImpSAMP) algorithm is proposed to estimate the channel state information of the OFDM system. In the proposed ImpSAMP algorithm, the received signal is firstly denoised based on the energy-detection method, which can reduce the interferences on channel estimation. Furthermore, the step size is adjusted dynamically according to the l2 norm of difference between two estimated sparse channel coefficients of adjacent phases to estimate the sparse channel coefficients quickly and accurately. In addition, the double threshold judgment is adopted to enhance the estimation efficiency. The simulation results show that the ImpSAMP algorithm outperforms the traditional SAMP algorithm in estimation efficiency and accuracy.
Predicting lameness in dairy cattle using untargeted liquid chromatography–mass spectrometry-based metabolomics and machine learning

Lameness in dairy cattle is a highly prevalent condition that impacts on the health and welfare of dairy cows. Prompt detection and implementation of effective treatment is important for managing lameness. However, major limitations are associated with visual assessment of lameness, which is the most commonly used method to detect lameness. The aims of this study were to investigate the use of metabolomics and machine learning to develop novel methods to detect lameness. Untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS) alongside machine learning models and a stability selection method were utilized to evaluate the predictive accuracy of differences in the metabolomics profile of first-lactation dairy cows before (during the transition period) and at the time of lameness (based on visual assessment using the 0–3 scale of the Agriculture and Horticulture Development Board). Urine samples were collected from 2 cohorts of dairy heifers and stored at −86°C before analysis using LC-MS. Cohort 1 (n = 90) cows were recruited as current first-lactation cows with weekly mobility scores recorded over a 4-mo timeframe, from which newly lame and nonlame cows were identified. Cohort 2 (n = 30) cows were recruited within 3 wk before calving, and lameness events (based on mobility score) were recorded through lactation until a minimum of 70 d in milk (DIM). All cows were matched paired by DIM ± 14 d. The median DIM at lameness identification was 187.5 and 28.5 for cohort 1 and 2, respectively. The best performing machine learning models predicted lameness at the time of lameness with an accuracy of between 81 and 82%. Using stability selection, the prediction accuracy at the time of lameness was 80 to 81%. For samples collected before and after calving, the best performing machine learning model predicted lameness with an accuracy of 71 and 75%, respectively. The findings from this study demonstrate that untargeted LC-MS profiling combined with machine learning methods can be used to predict lameness as early as before calving and before observable changes in gait in first-lactation dairy cows. The methods also provide accuracies for detecting lameness at the time of observable changes in gait of up to 82%. The findings demonstrate that these methods could provide substantial advancements in the early prediction and prevention of lameness risk. Further external validation work is required to confirm these findings are generalizable; however, this study provides the basis from which future work can be conducted.
Testing Frenchification: A Sociolinguistic Analysis of French Loan Morphology in Seventeenth- and Eighteenth-Century Dutch

There is a long history of social, cultural and political contact between the Dutch and French language areas, which has also resulted in language contact. In the Dutch language area, the cultural and linguistic contact situation has resulted in an anti-French discourse of alleged Frenchification from the sixteenth century onwards. The peak of influence from French is traditionally located in the eighteenth century. However, corpus-based research of the actual influence of French on Dutch in the Early and Late Modern periods is still scarce. We investigate the use of 31 French loan suffixes (e.g. the verbal suffix -eren, nominal suffixes such as -age and -teit, and adjectival suffixes such as -aal) in the Letters as Loot Corpus, which is a socially stratified corpus of private and business letters from the seventeenth and eighteenth centuries, written by men and women from various regions in the northern Low Countries. A regression analysis shows that the overall distribution of French loan suffixes is quite similar in the two periods, except for the capital Amsterdam, where there is a significant increase in the eighteenth century. Further significant effects are found for men and for the higher social ranks, and for business or mixed letters (as opposed to purely private letters). The results suggest that French-origin items entered the language of the northern Low Countries as relatively formal or conceptually written forms, mainly adopted by upper (middle) class men from the cosmopolitan city of Amsterdam.