Miracle-dz/UniFAR-FTUs huggingface dataset:
Teknologi Malaysia OPTIMIZATION OF OXIDATIVE COUPLING OF METHANE USING RESPONSE SURFACE METHODOLOGY

Oxidative Coupling of Methane (OCM) reaction over Li/MgO catalyst was optimized using experimental design from ‘Statsoft Statistica’ version 6.0 software. The manipulated variables chosen in this study were operating temperature, total flow rate per weight of catalyst (F/W), and weight percent of Li doped into MgO catalyst, whilst methane conversion, C2 product selectivity, and C2 product yield were the responses. The equation model was tested with Anova analysis with 99% degree confidence. The Response Surface Methodology (RSM) was employed to determine the optimum responses. By means of variance analysis and additional experiments, the adequacy of this model was confirmed.
Understanding Educational Leadership and Curriculum Reform : Beyond Global Economism and Neo-Conservative Nationalism

On a state level both curriculum policy work and educational leadership are increasingly challenged by new transnational phenomena in Europe, Asia, Africa and the Americas alike: expanding cultural neo-nationalism, more populist politics, economic protectionism, new forms of self-centered identity formation, religious fundamentalism, mistrust in democratic political participation, and decreasing respect for knowledge institutions and established media. These developments have many roots but appear partly as consequences of neoliberally driven policy initiatives and globalization. Consequently, there is increasing mistrust as to whether curriculum, leadership, and evaluation initiatives driven by a global neoliberal policy may provide sustainable solutions for guiding reforms in the public sector, including education. Not surprisingly, also the existing curriculum and educational leadership theory are under scrutiny. This article provides openings pointing to a hermeneutic and systems-oriented, multilevel and professional approach for reorienting national systems with respect to collaborative work on curriculum, leadership, and evaluation. Such a Bildung-centered view on human identity, growth, and citizenship is congruent with a non-affirmative education theory (NAT). It provides a conceptualization that is able to deal with curriculum and leadership genuinely based on an idea of education. Such a position grounds educational leadership, curriculum, and policy work, as well as evaluation and school reform, in education theory. As a general education theory the non-affirmative position is able to bring together an analysis of educational aims, contents, and methods of schooling, teacher professionalism, and leadership. In addition, NAT frames an understanding of how curriculum work at different levels is initiated, implemented, and enacted. In bridging these perspectives, it is argued that critical and hermeneutic NAT provides a theoretically productive approach to present-day local, national, and global education problems. As a foundational frame of reference, NAT allows us to perceive curriculum discourses as different forms of mediating, hermeneutic invitations, and summoning to selfactivity and self-formation (Bildung), within and for a democratic polity.
Tilburg University The Effect of Incentives in Non-Routine Analytical Teams Tasks-Evidence from a Field Experiment

Despite the prevalence of non-routine analytical team tasks in modern economies, little is known about how incentives influence performance in these tasks. In a field experiment with more than 3000 participants, we document a positive effect of bonus incentives on the probability of completion of such a task. Bonus incentives increase performance due to the reward rather than the reference point (performance threshold) they provide. The framing of bonuses (as gains or losses) plays a minor role. Incentives improve performance also in an additional sample of presumably less motivated workers. However, incentives reduce these workers’ willingness to “explore” original solutions. JEL-Codes: C920, C930, J330, D030, M520.
Response to the comments of the reviewers

The variability of the sea surface temperature (SST) in the Northwest Pacific has been studied 13 on seasonal, annual and interannual scales based on the monthly datasets of ERSST 3b (1854-2017, 164 14 years) and OISST V2 (1988-2017, 30 years). The overall trends, spatial-temporal distribution 15 characteristics, regional differences in seasonal trends, and seasonal differences of SST in the Northwest 16 Pacific have been calculated over the past 164 years based on these datasets. In the past 164 years, the 17 SST in the Northwest Pacific has been increasing linearly year by year with a trend of 0.033 °C/10 yr. 18 The SST during the period from 1870 to 1910 is slow decreasing and staying in the range between 25.2 °C 19 to 26.0 °C. During the period of 1910-1930, the SST as whole maintained a low value, which is at the 20 minimum over the 164 years.The period from 1880 to 1910 is a slow decreasing trend period in the past 21 164 years and the SST during the 1910-1930 period was a trough of the past 164 years. After 1930, SST 22 has continued to increase until now. The increasing trend in the past 30 years has reached 0.132 °C/10 yr 23 and the increasing trend in the past 10 years is 0.306 °C/10 yr, which is around ten times in the past 164 24 years. The SST in most regions of the Northwest Pacific showed a linear increasing trend year by year, 25 and the increasing trend in the offshore region was stronger than that in the ocean and deep-sea region. 26 The change in trend of the SST in the Northwest Pacific shows a large seasonal difference, and the 27 increasing trend in autumn and winter is larger than that in spring and summer. There are some 28 correlations between the SST and some climate indexes and atmospheric parameters, the correlation 29 between the SST and some atmospheric parameters have been discussed, such as NAO, PDO, SOI 30 anomaly, TCW, Nino 3.4, SLP, Precipitation, T2 and wind speed. The lowest SST in the Near China Sea 31 basically occurred in February and the highest in August. The SST fluctuation in the Bohai Sea and 32 Yellow Sea (BYS) is the largest with a range from 5 °C to 22 °C, the SST in the East China Sea (ECS) 33 is from 18 °C to 27 °C, the smallest fluctuations occurs in the South China Sea (SCS) maintained at range 34 of 26 °C to 29 °C. There are large differences between the mean and standard deviation in different sea 35 regions. 36
CRYSTAL STRUCTURE INVESTIGATION OF FERRITIC 73 FE 24 CR 2 SI 0 . 8 MN 0 . 1 NI STEEL FOR MULTI-PURPOSE STRUCTURAL MATERIAL APPLICATIONS

Microstructural identification of synthesized steel with significant local content has been carried out. Alloy ingot was prepared using a casting technique. The samples were then formed into bulk steel by a machining process. A high resolution powder neutron diffractometer (HRPD) was used as an equipment for characterization. By applying neutron diffraction techniques, a ferritic steel profile can be resulted in as well as ‘minor peaks’ belong to impurities formed in the sample. These impurities can be identified as small amounts of Al2O3 54SiO2, Al4C3, SiC and Cr23C6. Scanning transmission electron microscopy (STEM) combined with energy dispersive X-ray spectroscopy (EDX) confirmed and revealed neutron identified phase distributions. Joint Committee on Powder Diffraction Standards (JCPDS) least square curves calibration can precisely calculate the dhkl parameters of each reflection plane. As a comparison, another sample of alloy ingot was also investigated using neutron diffraction. The pattern was free from crystal impurities. Rietveld refinements provide satisfactory goodness of fits Rwp = 10.42% and reliability factor S = 1.7. This was so-called a ‘real bulky’ sample of a 73Fe24Cr2Si0.8Mn0. 1Ni ferritic steel alloy.
Questions and Research Methods in CSCL Research

While research questions play a critical role in research, their role has seldom been examined systematically. In this study, we examined what kinds of research questions are addressed in CSCL empirical research and what is the relationship between research questions and methods, in part using outcomes of an earlier analysis. The analysis showed that CSCL research has mainly focused on design and implementation of technological and/or curricular interventions. Research questions influenced research methods to a degree, although the relationships were not strict. The results confirmed, as well as contradicted, some of the intuitive conceptions about the relationship between research questions and methods. Implications for future CSCL research are discussed.
Plasma Urea Concentration and Urea and Creatinine Excretions in Steers

The work was carried out to evaluate the plasma N-urea concentration (NUP), the urea fractional excretion (UFE) and the creatinine (CE) and urea (EU) excretions for several experimental conditions; to establish the relation among NUP, the urea fractional excretion and the urea excretion with the dietary crude protein (CP) and NDF percentage; and to evaluate the urinary endogenous losses, by means of the creatinine excretion determination. In the experiments I and II, diets with 25.0, 37.5, 50.0, 62.5, and 75.0% of concentrate formulated were used to have different protein levels according to the dietary energy, or similar protein levels (12%CP), respectively. In the experiment III, concentrate level was fixed (40%), and the forage cut age was variable. In the experiment IV, diets were constituted on 20.0, 32.5, 45.0, 57.5, and 70.0% of concentrate, with approximately 12% CP. An experimental 5 x 5 latin square design, except for the experiment III (4 x 4), was used. From the total urine collected in 24 hours, the urea and creatinine excretions were determined. For the experiments I and III, the NUP concentration linearly changed as the dietary CP and NDF percentage increased. The fractional urea excretion and the urine excretions varied the same way for all treatments. The creatinine excretion was not influenced by the dietary CP content being proportional to the body weight for all experiments, with an average value of 27.36 mg/day/LW, and it could be used to estimate the endogenous losses of nitrogenous compounds.
Functional Data Analysis Technique on Daily Rainfall Data : A Case Study at North Region of Peninsular Malaysia . 1

Abstract: The study of rainfall features and patterns are very useful for water management systems, water resources engineering and also in agricultural planning. It can be beneficial in order to reduce the risks and losses. Functional data analysis technique is one of the method can be used to explore and display the pattern and variation of the rainfall data. This technique displays the pattern in the form of curves. The first and second derivatives of the curves represent the rate of change and the acceleration of the curves. The objective of the study is to model two rainfall features; rainfall amount and rainfall occurrence by using functional data analysis technique at eight rainfall stations from the north part of Peninsular Malaysia. Markov chain model has been used to model the rainfall occurrence and Fourier basis to smoothing the data. The results show that both of the rainfall features have similar bimodal pattern. Although the mean curves are slightly similar, the first peak of variance curve for rainfall occurrence is higher than the second peak which is difference with variance curve for rainfall amount. The relationship between rainfall amount and rainfall occurrence for both observed and estimated curve is also discussed.
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy Dovepress

php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 2016:9 185–199 Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy Dovepress
Verification of Continuous and Hybrid Systems ARCH-COMP 19 Category Report : Continuous and Hybrid Systems with Linear Continuous Dynamics

This report presents the results of a friendly competition for formal verification of continuous and hybrid systems with linear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2019. In its third edition, seven tools have been applied to solve six different benchmark problems in the category for linear continuous dynamics (in alphabetical order): CORA, CORA/SX, HyDRA, Hylaa, JuliaReach, SpaceEx, and XSpeed. This report is a snapshot of the current landscape of tools and the types of benchmarks they are particularly suited for. Due to the diversity of problems, we are not ranking tools, yet the presented results provide one of the most complete assessments of tools for the safety verification of continuous and hybrid systems with linear continuous dynamics up to this date. G. Frehse and M. Althoff (eds.), ARCH19 (EPiC Series in Computing, vol. 61), pp. 14–40 ARCH-COMP18 Linear Dynamics Althoff et al.
Molecular hydrogen can take part in phytohormone signal pathways in wild rice

Molecular hydrogen (H2) could be a novel signal in phytohormone signaling pathways in response to biotic and abiotic stresses. Here, we employed two wild rice species (Oryza rufipogon Griff. and O. minuta J. Presl) to test this hypothesis using hydrogen-rich water (HW). The expression differences of phytohormone and hydrogenase genes between conventional rice (Oryza sativa L,) and wild rice were determined by real-time quantitative polymerase chain reaction, and the effects of HW on gene expression of wild rice were detected during three growth stages. Expression of hydrogenase genes, synthesis genes, and receptor genes of salicylic acid (SA), jasmonic acid (JA), and ethylene (ET) signalling pathways was higher in six wild rice types than in conventional rice. Hydrogen-rich water up-regulated expression of two hydrogenase genes, SA, JA, and ET receptor genes and synthesis genes in the seedling stage of wild rice. But this positive regulation by HW was less significant in the vegetative and reproductive stages. Additional key words: abiotic and biotic stresses, ethylene, hydrogen-rich water, jasmonic acid, Oryza minuta, Oryza rufipogon, Oryza sativa, salicylic acid.
Computing the L 2 gain for linear periodic continuous-time systems ⋆

A method to compute the L2 gain is developed for the class of linear periodic continuous-time systems that admit a finitedimensional state-space realisation. A bisection search for the smallest upper bound on the gain is employed, where at each step an equivalent discrete-time problem is considered via the well-known technique of time-domain lifting. The equivalent problem involves testing a bound on the gain of a linear shift-invariant discrete-time system, with the same state dimension as the periodic continuous-time system. It is shown that a state-space realisation of the discrete-time system can be constructed from point solutions to a linear differential equation and two differential Riccati equations, all subject to only single-point boundary conditions. These are well-behaved over the corresponding one period intervals of integration, and as such, the required point solutions can be computed via standard methods for ordinary differential equations. A numerical example is presented and comparisons made with alternative techniques.
Approval Name : Degree : Title : Examining Committee : Date

The emergence of deep learning has attracted the attention from a wide range of fields and brought a large number of related applications. With the rapid growth of mobile computing techniques, numerous deep learning applications are designed for the mobile end. However, since deep learning tasks are computational-intensive, the limited computation resource on the mobile device cannot execute the application effectively. Traditional approach is to push the data and the workload to the remote cloud. Meanwhile, it introduces a high data transmission delay and possibly bottlenecks the overall performance. In this thesis, we apply a new rising concept, edge computing, for mobile deep learning applications. Comparing with cloud learning, the communication delay can be significantly reduced by pushing the workload to the near-end edge. Unlike the existing edge learning frameworks only concerning inference or training, this thesis will focus on both and put forward different optimization approaches towards them. Specifically, the thesis proposes a layer-level partitioning strategy for inference tasks and an edge compression approach with the autoencoder preprocessing for training tasks, to exploit all the available resources from the devices, the edge servers, and the cloud to collaboratively improve the performance for mobile deep learning applications. To further verify the optimization performance in practice, we formulate a scheduling problem for the multi-task execution and propose an efficient heuristic scheduling algorithm. Real-world experiments and extensive simulation tests show that our edge learning framework can achieve up to 70% delay reduction.
Predictive power of socio-emotional variables on academic performance and chance of dropout

Socio-cognitive and socio-affective variables are associated with learning and the process of adaptation to higher education. This research aimed to verify, in a longitudinal way, the predictive power of these variables in the academic performance and the chances of dropout perceived by university students. Participants included 136 physical education students from a private institution. A questionnaire on self-efficacy and performance expectations was administered at the beginning of the semester together with the Social and Emotional or Non-cognitive Nationwide Assessment. After knowing the results at the end of the semester, the questionnaire on self-efficacy and satisfaction with performance was administered. The results showed that in a negative sense, the main predictor of the chances of dropout was self-efficacy for conscientiousness. The belief in being organized, self-regulated, and self-motivated leads students to see themselves with lower changes of dropout, regardless of the immediate results of academic assessments.
Time-Expanded Graphs for Faster Timetable Information ?

We present an extension of the well-known time-expanded approach for timetable information. By remodeling unimportant stations, we are able to obtain faster query times with less space consumption than the original model. Moreover, we show that our extensions harmonize well with speed-up techniques whose adaption to timetable networks is more challenging than one might expect.
Spatially Parallel Convolutions

The training of convolutional neural networks with large inputs on GPUs is limited by the available GPU memory capacity. In this work, we describe spatially parallel convolutions, which sidestep the memory capacity limit of a single GPU by partitioning tensors along their spatial axes across multiple GPUs. On modern multi-GPU systems, we demonstrate that spatially parallel convolutions attain excellent scaling when applied to input tensors with large spatial dimensions.
Analysis of Heavy Metals and Physicochemical Properties in Honey Along the Ndola-Lusaka Road , Zambia . ( Conference ID : CFP / 382 / 2017 )

Analysis of the concentration of selected heavy metals i.e. cobalt, copper, zinc, iron, lead, manganese, and chromium in honey samples was done in order to determine the level of heavy metals in the study area and to determine the health safety of the honey consumed. The honey samples were from Kapiri Mposhi and Kaoma districts of Zambia. Acid aqueous solution was used in the digestion of the honey samples and an Atomic Absorption Spectrophotometer was used to analyze the concentration of heavy metals. Physicochemical properties such as moisture content, Ash content and pH of the honey were also determined. Kapiri Mposhi Sample and from Kaoma sample had moisture content of 13.77% and 15%, respectively. The rest of the samples had moisture content within the world honey standard of 18% to 23%. The ash content values ranged from 2.8% to 15.7%, exceeding the maximum limit allowed of 1%. The pH values recorded were within the accepted range (3.5 –5.5) according to codex standards. The concentrations of the heavy metals in decreasing order were as follows: Zn>Fe>Cu>Mn>Pb>Cr>Co. Zinc had the highest concentration in all samples with mean concentration range of 9.68 to 19.8 mg/kg, iron ( 6.02 to 11.95 mg/kg), copper (1.48 to 1.95 mg/kg), manganese ( 0.2 to 1.57 mg/kg), lead ( 30.0075 to 1.23 mg/kg), chromium ( <0.05 to 0.55 mg/kg ) cobalt which had concentrations in all samples less than 0.05 mg/kg. All the metals were found to be below the maximum residue limits of the WHO/FAO. It was thus concluded that the honey collected was of good quality in terms of its metal content and physicochemical properties. It can also be said that the environment from which the honey samples were collected was not contaminated with toxic heavy metals.
PROFILE OF AUTOMATIC SPEECH RECOGNITION ALGORITHM

Abstract This work provide mathematical insight to Automatic Speech Recognition (ASR) system’s algorithm such that, the intricacy of the system becomes a simplified correlation of the ASR algorithm to the physical form using the mathematical flowchart which clearly and uniquely show the link from one stage of the algorithm to the other. The mathematical profile of the ASR algorithm starts from the data input module, through noise cancellation module, voice activity detection module, pre-processing module, Linear Predictive Coding (LPC) based feature extraction module, then provides alternate root for both Dynamic Time Wapping (DTW) and Hidden Markov Model (HMM) based pattern matching module after which the output is fed to the final decision module of the ASR algorithm. The modern research outputs has improved the robustness of each stage of the algorithm but the approach used here focused on the basics of each stage which helps in easy and better understanding of the ASR system. It also aid in the evaluation and create necessary intuition for decoding problems of the recent ASR systems for new researchers in the research area.