bluuebunny/crossref_metadata_2025 huggingface dataset:
Large-amplitude dust acoustic solitons in an opposite polarity dusty plasma with generalized polarization force
Authors: Khaled Mahmood A. H. , Shukri Mohamed A. , Hager Yusra A. A.
Published: May 2021

Linear and nonlinear dust acoustic (DA) waves have been investigated in an opposite polarity dusty plasma comprising
negatively and positively charged dust grains, Maxwellian electrons and ions, including the generalized polarization force effect. The properties of linear DA waves have been significantly altered by the dual dust polarity and polarization force. Large amplitude DA solitons have been discussed in the framework of the Sagdeev potential technique. Our results show that both rarefactive and compressive solitons can exist in such a dusty plasma. The basic features of the Sagdeev potential have been examined under the effect of the polarization force parameter R, the ratio of the charge number of the positive dust to that of the negative dust Z, and the Mach number M. The results show that these parameters play a significant role in determining the region of existence of large amplitude DA solitons.
High-efficiency asymmetric diffraction based on PT-antisymmetry in quantum dot molecules
Authors: Cheng Guangling , Hu Yongsheng , Zhong Wenxue , Chen Aixi
Published: May 2021

We present preparation of asymmetric grating with higher diffraction efficiency in quantum dot molecules by combining the tunneling effect and parity-time antisymmetry. In the presence of tunneling between two quantum dots, the system exhibits the striking PT antisymmetry via spatially modulating the driving field and the detuning with respect to the driven transition. For this reason, the asymmetric grating could be achieved. The results show that the diffraction efficiency can be adjustable via changing the driving intensity, detuning, tunneling strength, and interaction length, and then the high-order diffraction can be reached. The scheme provides a feasible way to obtain the direction-controlled diffraction grating, which can be helpful for optical information processing and realization of controllable optical self-image.
Dynamics of a stochastic rumor propagation model incorporating media coverage and driven by Lévy noise*
Authors: Huo Liang-An , Dong Ya-Fang , Lin Ting-Ting
Published: May 2021

With the development of information technology, rumors propagate faster and more widely than in the past. In this paper, a stochastic rumor propagation model incorporating media coverage and driven by Lévy noise is proposed. The global positivity of the solution process is proved, and further the basic reproductive numberR0is obtained. WhenR0< 1, the dynamical process of system with Lévy jump tends to the rumor-free equilibrium point of the deterministic system, and the rumor tends to extinction; whenR0> 1, the rumor will keep spreading and the system will oscillate randomly near the rumor equilibrium point of the deterministic system. The results show that the oscillation amplitude is related to the disturbance of the system. In addition, increasing media coverage can effectively reduce the final spread of rumors. Finally, the above results are verified by numerical simulation.
Hardware for multi-superconducting qubit control and readout*
Authors: Wang Zhan , Yu Hai , Liu Rongli , Ma Xiao , Guo Xueyi , Xiang Zhongcheng , Song Pengtao , Su Luhong , Jin Yirong , Zheng Dongning
Published: May 2021

We have developed an electronic hardware system for the control and readout of multi-superconducting qubit devices. The hardware system is based on the design ideas of good scalability, high synchronization and low latency. The system, housed inside a VPX-6U chassis, includes multiple arbitrary-waveform generator (AWG) channels, analog-digital-converter (ADC) channels as well as direct current source channels. The system can be used for the control and readout of up to twelve superconducting transmon qubits in one chassis, and control and readout of more and more qubit can be carried out by interconnecting the chassis. By using field programmable gate array (FPGA) processors, the system incorporates three features that are specifically useful for superconducting qubit research. Firstly, qubit signals can be processed using the on-board FPGA after being acquired by ADCs, significantly reducing data processing time and data amount for storage and transmission. Secondly, different output modes, such as direct output and sequential output modes, of AWG can be implemented with pre-encoded FPGA. Thirdly, with data acquisition ADCs and controlAWGs jointly controlled by the same FPGA, the feedback latency can be reduced, and in ourtest a 178.4 ns latency time is realized. This is very useful for future quantum feedback experiments. Finally, we demonstrate the functionality of the system by applying the system to the control and readout of a 10 qubit superconducting quantum processor.
Multi-objective optimization approach for channel selection and cross-subject generalization in RSVP-based BCIs
Authors: Xu Meng , Chen Yuanfang , Wang Dan , Wang Yijun , Zhang Lijian , Wei Xiaoqian
Published: May 2021

Objective. Achieving high precision rapid serial visual presentation (RSVP) task often requires many electrode channels to obtain more information. However, the more channels may contain more redundant information and also lead to its limited practical applications. Therefore, it is necessary to reduce the number of channels to enhance the classification performance and users experience. Furthermore, cross-subject generalization has always been one of major challenges in electroencephalography channel reduction, especially in the RSVP paradigm. Most search-based channel selection method presented in the literature are single-objective methods, the classification accuracy (ACC) is usually chosen as the only criterion. Approach. In this article, the idea of multi-objective optimization was introduced into the RSVP channel selection to minimize two objectives: classification error and the number of channels. By combining a multi-objective evolutionary algorithm for solving large-scale sparse problems and hierarchical discriminant component analysis (HDCA), a novel channel selection method for RSVP was proposed. After that, the cross-subject generalization validation through the proposed channel selection method. Main results. The proposed method achieved an average ACC of 95.41% in a public dataset, which is 3.49% higher than HDCA. The ACC was increased by 2.73% and 2.52%, respectively. Besides, the cross-subject generalization models in channel selection, namely special-16 and special-32, on untrained subjects show that the classification performance is better than the Hoffmann empirical channels. Significance. The proposed channel selection method could reduce the calibration time in the experimental preparation phase and obtain a better accuracy, which is promising application in the RSVP scenario that requires low-density electrodes.
Target polarization characteristics analysis and detection in complex background
Authors: Zhang Guoyang , Nie Xiaofeng , Du Zhongqiang
Published: May 2021

Polarization feature contains important information of the target and expecially related to the surface properties of target. In some complex conditions, such as the background of bushes and shrubs, masking target detection is of great significance. In order to realize target detection in complex conditions, we propose a new method based on the traditional CEM detection method, utilizing the polarization feature of the material, and finally achieve the purpose of target detection by using the polarization dimension feature of the material.
Latent factor model based zero-shot learning
Authors: Zhang Y X Q , Peng Y D , Li J D , Zhang C X , Yu D R , Xia R T , Liu D B , Yang B
Published: May 2021

The key of zero-shot learning is to design an appropriate approach to capture the potential relevance between the image visual features and category-level semantic knowledge. There is a lot of data with matrix form in zero-shot recognition problem, such as image label matrix, visual feature matrix and semantic information matrix. Inspired by the latent factor model which tackles recommendation problems well avoid being influenced by sparse matrix, we regard an image as a user and analogize each dimension of the visual feature as an item. The key to our approach is that we model the image recognition as a special recommendation problem by aid of latent factor model. We evaluate our algorithm on three classic benchmark data sets for both conventional and generalized zero-shot setting, the classification results outperform significantly the state-of-art approaches with low calculation cost.
A Novel Method of Light Source Calibration under Near-field Lighting Condition
Authors: Ge Feng , Li Chen , Zhang Xu
Published: May 2021

The paper presents a novel method of photometric stereo calibration that works under near-field condition, and eight LED lights are installed on the system. In order to estimate the pose of the light source, light source position estimation based on iPad imaging is proposed. However, it is difficult to estimate principal optical axis direction of the light source which would influence the reconstruction result. Considering the problems of above, the new convolution kernel is used to find the brightest point on the chessboard paper, and the principal optical axis direction of light source can be precisely calculated according to geometric transformation. The experiments show the high-quality of 3-D reconstruction can be obtained by using the novel calibration method.
2D Light scattering images analyzed by deep learning algorithm for label-free differentiation of dead and live colonic adenocarcinoma cells
Authors: Li Shuaiyi , Li Ya , Yao Jianning , Chen Bing , Song Jiayou , Xue Qi , Yang Xiaonan
Published: May 2021

The detection of cell viability or the detection of the percentage of live and dead cells in a sample of cells is an important parameter. At present, the common methods for cell viability determination mainly rely on the responses to cell dyes. However, the additional need for cell staining will consequently cause time-consuming and laborious efforts. Furthermore, the determination of cell viability by cell staining is invasive and may damage the internal structure of cells. In this work, we proposed a label-free method to classify live and dead colonic adenocarcinoma cells by 2D light scattering combined with deep learning algorithm. The deep convolutional network of YOLO-v3 was used to identify and classify light scattering images of live and dead HT29 cells. This method achieved an excellent sensitivity (92.16%), specificity (94.23%), and accuracy (93.2%). The results show that the combination of 2D light scattering images and deep neural network may provide a new label-free method for cellular analysis.
Preliminary Design of 1000MW Nuclear Reactor
Authors: Yi Lei , Wei Wang , Qian Zhang , Qiang Zhao
Published: May 2021

In order to verify the practicality of a software named NECP, which is used for physical design of the nuclear power reactor, a preliminary design of 1000MWe, PWR were carried out by using this software. After the conceptual design, fuel assembly design and the full core dispersion design, the main parameters were determined and the 8 kinds of fuel assemblies were arranged. The homogenization calculation of various types of fuel assemblies and the arrangement of core fuel assemblies were completed while the full core dispersion calculation was performed. The power results, reactivity curves and non-uniform coefficients were determined after simulated and compared with the preset expected values. According to the reactor design criteria, the results are analyzed and improved. By modifying parameters such as the fuel enrichment of different components, the reactor design that meets the requirements is finally obtained. The results show that the calculated values are in good agreement with the expected values and meet the reactor design criteria, and the NECP software can be applied to operate the reactor physical parameters in the nuclear power plant.
A Sampling-based Distributed Source Coding Scheme
Authors: Mo H , Chen J , Li J , Zhao M
Published: May 2021

In many practical distributed source coding (DSC) schemes that apply channel coding and iterative decoding, correlation estimation provides the likelihood information about the source bits to initialize the decoding algorithms. Therefore, the decoding depends strongly on the accuracy of this correlation estimation information. In this study, a sampling-based scheme for DSC is proposed that takes advantage of the source redundancy without requiring prior knowledge of its statistics. At the sender, the source sequence is sampled to obtain the sampled and unsampled sub-sequences. Then, the un-sampled sub-sequence is compressed by an arithmetic coder. Meanwhile the syndromes of the sampled sub-sequence are also calculated. At the receiver, the correlation between the side information and the un-sampled sub-sequence is used to estimate the conditional marginal distribution of the sampled sub-sequence. Then, the estimated information is used to initialize the likelihood information of the decoding algorithms. Finally, the likelihood information is combined with the received syndromes to perform iterative decoding to recover the original sampled sub-sequence. Experiment results show that the proposed scheme achieves better rate-distortion performance compared with the existing DSC schemes.
LooPIM: A Loop-Oriented Acceleration Framework for Processing-in-Memory
Authors: Liu Chuangshi , Li Xianfeng
Published: May 2021

The latest development of 3D stacking technology provides a feasible solution for processing in memory (PIM) to solve the memory wall crisis. Since data can be processed locally in a logic layer adjacent to memory, large emerging big data applications may achieve significant performance and energy-efficiency benefits. Researchers continue to propose various PIM architectures to utilize the advantages of stacked memory efficiently. However, previous efforts relied on manually mapping specialized kernels to the logic layer, making it infeasible to perform more general workloads. We propose a loop-oriented acceleration framework that intelligently partitions loops and maps them onto appropriate execution units. The mapping mechanism can significantly reduce the communication overhead between the host processor and the PIM stack. Besides, we introduce a simple execution model that exploits data-level parallelism transparently. Experiments show that our framework achieves approximately 2.4x performance speedups comparing to the baseline 3D memory system.
An Improved Background Subtraction Method for Adaptive Rate Compressive Sensing
Authors: Wang Jianming , Chen Jianhua
Published: May 2021

In Compressive Video Sensing application, the using of Adaptive Rate Compressive Sensing (ARCS) method can predict and adjust the sampling rate for each video frame, reduce the total sampling rate and improve the quality of reconstructed image. In order to use the inter-frame correlation of the video signal efficiently and reduce the sampling rate of the whole signal, an Improved Background Subtraction (IBS) method is proposed in this paper. By alternately using the frame without any foreground objects and the previous frame as the background of the signal, the sparsity of the foreground signal is improved and the total sampling rate is reduced. Experimental results show that, compare with the traditional background subtraction method, the IBS method can significantly reduce the total video sampling rate without apparent degradation of reconstructed image quality under the same ARCS method.
Influence of heat source location on the performance of air-flow inclination sensor
Authors: Zheng Yonghong , Peng Shiliu
Published: May 2021

According to sensitive mechanism of air-flow inclination sensor, discussing the influence of heat source location on zero output, sensitivity, resolution and response time of the sensor. If the heat source deviates from the centre in horizontal direction, zero output is not zero. If the heat source deviates from the centre in vertical direction, sensitivity and resolution are decreased, while response time is increased. For resolving these problem a solution is proposed finally. Results show this method may reduce the influence of heat source location on the performance of air-flow inclination sensor.
Sensorless Starting Control of Brushless Synchronous Machine (BSM) For Aircraft
Authors: Tshambe Phinees Ngoy , Wei Jiadan
Published: May 2021

In this paper, the sensorless starting control of brushless AC synchronous machine (BSM) for aircraft is studied. Low speed sensorless control technology is the key to accomplish full speed sensorless control. After injected the high frequency voltage with the three-phase AC excitation, the initial rotor position as the response is shown in this paper. On that basis, the sensorless control from starting to low-speed operation is accomplished. The performance of the starting control system is researched using MATLAB simulation software and the feasibility of the proposed scheme is demonstrated by simulation results.
A 15-bit, 5 MSPS SAR ADC with on-chip digital calibration
Authors: Yu Wenxin , Hao Yunqiang , Yi Dongbai , Xi Jianxiong , Zhang Xiaowei , He Lenian
Published: May 2021

In this paper, a digital algorithm based on a 15-bit, 5 million samples per second (MSPS), high-speed successive approximation register (SAR) analog-to-digital converter (ADC) is presented. It features on comparator offset cancellation and capacitor mismatch calibration. The algorithm uses a 13-bit bypass array to measure the errors of both comparator and capacitors. During calibration, errors are read and put into the bypass array to compensate for the mismatches of the main array. Verified by MATLAB, the errors of the SAR ADC output can be corrected into 1 LSB with the help of calibration. The simulation results show that the proposed algorithm improves the precision of the SAR ADC significantly.
A Novel Start-up Strategy for MMC-based Medium-voltage DC Distribution System with Low Inrush Current
Authors: Yang Limin , Zhu Yiying , Lei Ming
Published: May 2021

To realize medium-voltage dc distribution system, the modular multilevel converter (MMC) is commonly adopted due to its high-level scalability and reliability for medium-voltage high-power energy conversion. Since the capacitors’ voltages of the MMC are insufficient when the uncontrolled pre-charging stage is finished, large inrush current exists when the MMC is unlocked for active charging, which can cause malfunction of protection and threat the successful start-up of the whole system. This paper presents a detailed analysis on the causes of inrush current during the MMC’s active charging process. Based on the analysis, a novel start-up strategy of the MMC is proposed to achieve improved controllability of the MMC’s ac-side currents for inrush current suppression. The simulation results from a full-scale simulation model demonstrate that the inrush current can be effectively suppressed during the MMC’s active charging process.
Compact Tunable Balanced Dual-band Bandpass Filter Using Tri-Stub Loaded Resonator
Authors: Yang Hong , Zhang Dewei , Zhang Junjie , Wang Xian , Pan Chenqing , Ma Zhuang , Fu Yufan
Published: May 2021

This letter proposes a tunable balanced dual-band bandpass filter (BPF) with high common-mode (CM) suppression based on tri-stub loaded resonator (TSLR). Two TSLRs terminated with varactor diodes are employed to obtain the tunable second differential-mode (DM) passband, which is independent of the first one. For validation, a tunable balanced dual-band BPF is designed, fabricated and measured that achieve the continuous frequency-tuning range of the second DM passband from 5.968 to 6.368 GHz with constant 3dB fractional bandwidth (FBW) of 6.8% ± 0.17% and CM rejection levels of over 30 dB. The measured results agree well with the simulated ones.
Polarization properties of mouse oocyte captured by Mueller matrix imaging
Authors: Liu M J , Tian N , Yu J
Published: May 2021

Non-invasive assessment of oocyte quality is of important basis in embryo transfer and other related fields. Until now, the oocyte quality assessment is mainly focus on the morphology observation. Oocyte quality is often reflected in varieties of its microstructure which can be provided by polarization imaging technology. The Mueller matrix is a label-free technique to characterize all polarization properties of samples. This paper used a Mueller matrix microscope for the first time for non-invasive imaging the mouse oocyte. By Mueller matrix analysis, we successfully examined the dichroism, phase delay and scattering depolarization of oocytes, furthermore used the above to compare the polarization properties of mouse oocytes at GV stage and MII stage and the polarization properties of matured fresh oocytes and postovulatory aged oocytes. In this paper changed phase delay were observed before and after oocyte maturation (and oocyte aging). It suggested that the Mueller matrix imaging may provide a potential breakthrough for oocyte quality assessment.
B-spline fitting of sunglasses lens contour based on adaptive sampling
Authors: Fan Xian-guang , Nie Ting , Shen Jie , Xu Ying-jie
Published: May 2021

To solve the problems of low accuracy and low efficiency of B-spline fitting in sunglasses lens contour cutting, a sampling method based on the characteristic function of a weighted combination of arc length and curvature square root was proposed, and then perform B-spline fitting according to the sampling points to improve the accuracy and efficiency. First, read and identify the sunglasses lens data in the DXF file, and disperse it to obtain a series of coordinate points; combine the defined feature function for uniform sampling, and adjust the parameter weight in the feature function to obtain a more reasonable sampling point for the sunglasses lens contour; Then use the LU decomposition and interpolation method to obtain the B-spline curve through all the sampling points; the maximum absolute error (MAE) and the root-mean-square error (RMSE) are used as the accuracy criteria, by increasing the number of sampling points iteratively until the accuracy is satisfied to construct a reasonable fitting curve.
Simulation Study on State Estimation for a Quadrotor Generalized Attitude Model
Authors: Yin H , Liu J C , Geng L H , Zhao S H , Ayele T B
Published: May 2021

This paper studies the state estimation of a quadrotor yaw-channel generalized attitude model (GAM) using the subspace identification based state estimation method under the conditions that the measurements for the control quantity and the output attitude angle are corrupted with disturbing noises and that there are no available models. The quadrotor attitude control loop for the yaw-channel is first designed by MATLAB using an observer based state feedback law. A high-order coprime factor (CF) model for the GAM is then identified from simulation data by the subspace identification method. Next, the estimation states for the GAM can be given by the similarity transform and the relations between the GAM and its CF model. Finally, the efficacy of the presented state estimation procedure and the accuracy of the state estimation are verified.
Component identification for Raman spectra with deep learning network
Authors: Wang Xin , Pan Qi-hang , Fan Xian-guang , Xu Ying-jie
Published: May 2021

Raman spectroscopy is widely used in the research of the molecular structure of substances because of the advantages of no invasion, no damage and no interference from water. Meanwhile, component identification for mixtures is still challenging in Raman spectra. In this paper, a graphics-based sample-generating method and a model based on deep-learning for component identification was proposed. Convolution neural network (CNN) model is an important part of deep learning network and CNN models was utilized to assess the possibility of the presence of components in samples. As is shown in the comparative studies, the model was sensitive to the relative position of the characteristic peaks and could learn spectra features in mixtures. The deep-learning based component identification method showed more robustness than conventional linear fitting methods. Therefore, the method provided a valid approach to component identification for mixtures and has the potential in spectra component analysis.
Self-attention Mechanism based Dynamic Fault Diagnosis and Classification for Chemical Processes
Authors: Chen Shuai , Luo Lin , Xia Qilei , Wang Lunjie
Published: May 2021

A dynamic fault detection and diagnosis technique based on deep encoder-decoder network with self-attention mechanism is proposed in this paper. Although traditional encoder-decoder networks exhibit capability in extracting the temporal dependencies, the architecture encodes the input sequence into a fixed-length internal representation. This limits the performance of these networks, especially when considering relatively long input sequences. The self-attention mechanism is used to weight the local feature vectors and retain the correlation between the local information of the signal and the process operation state, so as to extract the effective feature vectors. The extracted features are then fed into the bidirectional encoder-decoder network. The resulting deep network is not only generalizing the importance of local temporal feature, but it also allows the interpretable feature representation and classification simultaneously. The experiments on the benchmark Tennessee Eastman process show that the proposed model has better diagnostic performance on receiver operating characteristic (ROC) and precise-recall (PR) curves than the classical diagnostic method based on the long-short term memory (LSTM) network with convolution layers.
Data-Level Parallelism Oriented Memory Access and On-Chip Buffering Mechanisms for a Loop Accelerator
Authors: Zhou Weikang , Li Xianfeng , Chen Mingtao , Li Yang
Published: May 2021

Memory access latency is always a bottleneck for the performance improvement of data-intensive applications. Exploiting the memory access patterns of Data-Level Parallelism(DLP) is a promising way for loop accelerators to reduce the latency significantly. This paper proposes two DLP-oriented data provisioning mechanisms to alleviate memory access latency: 1) DLP-oriented memory access(DoMA) for efficiently utilizes the available memory bandwidth. 2) a data access patterns aware on-chip buffer(PABUF) for exploiting reuse in a user-transparent manner. Unlike those loop accelerators using traditional DMA to access global memory, DoMA efficiently reduces the transmission of useless data by adjusting the size of requests intelligently. In addition, PABUF, which manages data using DLP’s memory access patterns without software engineering efforts, allows the loop accelerators to access data in parallel. Experiments show that when our mechanisms are integrated into a loop accelerator based on Rocket Chip Coprocessor(RoCC), it can achieve 4.20x-10.65x(6.81x on average) speedups with negligible overhead of power and area compared to L1 Cache.
Global optimal solution on blending problem
Authors: Zheng Yingchun
Published: May 2021

In order to study the algorithm of the global optimal solution of the blending problem. The general optimization model for blending problems is given in this paper, at the same time, the blending problem was transformed into the concave minimization problem and linear programming problem, the relevant proof was also given. Research shows that the blending problem is formulated as a concave minimization problem and linear programming problem, can reduce the number of variables blending problem. Blending problem is transformed into a linear programming enable blending problem solving becomes simple and easy.
A Long-term Recurrent Convolutional Network for Stock Index Prediction
Authors: Yu Xinpeng , Li Dagang
Published: May 2021

Stock index prediction aims to predict the future price of stock indexes, which plays a key role in seeking the maximum profit from stock investment. However, It has been proven to be a very difficult task because of its innate complexity, dynamics, and uncertainty. With the rapid development of deep learning, more researchers have attempted to apply nonlinear learning methods such as long short-term memory networks (LSTMs) to capture the complex patterns hidden in market trends. In this paper, we propose a Long-term Recurrent Convolutional Network (LRCN), which combines convolutional layers and long-range temporal recursion and is end-to-end trainable. In the LRCN model, the two-dimensional convolutional neural network (2D-CNN) performs convolution on the most recent region to capture local fluctuation features, and the long short-term memory (LSTM) learns the long-term temporal dependencies to improve stock index prediction. To evaluate the effectiveness of LRCN, we collected real stock market data for stock indexes S&P 500 and DJIA, and the experimental results show that the proposed LRCN can significantly outperform several existing highly competitive methods.
A comparative study on pricing methods of infrastructural assets securitization: a case study of power distributing assets of X mall
Authors: Chen Shaozhen , Zhang Yang , He Jinlin
Published: May 2021

Although the securitization technique was wide applied in financial practice, moreover, oversea academics have abundant experience on such field, due to securitization’s late introduced to China, legislate infrastructure such as the supervised legislation and the tax laws are far beyond perfect, not mention the study of the pricing theories on securitization, especially in pricing the securities which backed by such infrastructural assets as electric power distributing network, theoretic research in this area is even rare. This paper conducts a case study, taking the electric-power-distribution asset of a mall as an example, to examine three different pricing methods for valuating asset backed securities, with the purpose to explore more an appliable pricing method for infrastructure asset securitization.
Research on the recognition of students’ classroom learning state based on facial expressions
Authors: Lin Qiuchen , Lai Xiangwei
Published: May 2021

The learning state of students in the classroom can not only affect the learning effect of students, but also reflect the teaching quality of teachers. In this paper, a new image database of classroom learning state is established according to students’ learning state in real classroom environment, and an improved ResNeSt network model based on spatial attention mechanism is proposed. In this method, spatial attention mechanism is introduced into the ResNeSt network, which is complementary to the channel attention thought of the original network, so as to improve the recognition accuracy of learning state images. Using this method to identify students’ classroom learning state can provide a basis for teaching administrators to evaluate teachers’ teaching quality, as well as provide guidance for teachers to better adjust teaching models and teaching methods, and improve teaching efficiency.
Extent of geotechnical site investigations for buildings in Estonia
Authors: Leetsaar L , Korkiala-Tanttu L , Jaaniso V , Kurnitski J , Idnurm J
Published: May 2021

Numerous researchers have described problems encountered during the construction and performance of a building that result from inadequate site investigations (SI). The type and coverage of in-situ tests can significantly affect the quality of SI results. The aim of this study is to explore the amount and methods of SI in Estonia during the last eleven years. Site investigations data from 92 private and public buildings were collected and analyzed. Both quantitative and qualitative research methods were adopted to identify the most commonly used in-situ and laboratory methods according to different soil types and building height. The impact of the SI contractor was analysed as well. It was found that the costs of investigations constituted approximately 0.1% of the building tender cost for two-to six-storey buildings. Almost half of the SI contractors were designers too. Regardless of the contractor of SI and the soil type, the extent of SI was always similar. The majority of the investigations were conducted only in one stage. Dynamic probing was the most frequent in-situ testing method. The amount and quality of SI was found to be too low for a reliable and optimum design.
A review on immobilization strategy of cyclodextrin glucanotransferase producing Escherichia coli as whole cell biocatalyst
Authors: Abdullah A A , Azam N Z F N , Mohamad C W S R , Illias R M
Published: May 2021

This paper reviewed the immobilization strategy on cyclodextrin glucanotransferase (CGTase) producing Escherichia coli as whole cell biocatalyst. Biotransformation of insoluble substrate such as starch and lignocellulosic biomass with whole cell leads to the application of biological steps to chemocatalysis, which makes the whole process more effective. In this whole cell biocatalysis process, the excretion of the enzymes into the extracellular space by the cell is crucial. However, the use of free whole cell biocatalysts especially Escherichia coli often face with problem related to the cell lysis and stability. Due to that, the biotransformation process becomes inefficient. Thus, in this study the immobilization strategy was investigated to overcome the bottle necks mentioned. First, this study began with a focus in Escherichia coli, immobilization techniques, cyclodextrin, cyclodextrin glucanotransferase and recombinant Escherichia coli, in which can give a better understanding and a bigger picture in order to overcome the bottle necks mentioned. The different immobilization strategy and condition identified expected to improve the whole cell stability and reusability which lead to efficient biotransformation process. The output of the research also will elucidate the mechanism on how different immobilization material, strategy and cultural conditions improve the overall cell performance.
Scaling-up and techno-economics of ethanol production from cassava starch via separate hydrolysis and fermentation
Authors: Choo B C , Ismail K S K , Ma’Radzi A H
Published: May 2021

Nowadays bioethanol is being used extensively in fuel production because of its technical feasibility, economically competitive, and environmentally friendly. It is expected that biofuel will contribute to 30% of the global energy demand by 2050. Therefore, it is very important to investigate any cost-effective bioprocessing that can improve the overall production yield. The study aims to produce bioethanol from cassava starch by Kluyveromyces marxianus at a laboratory scale and a 5L fermentation rig. A separate hydrolysis and fermentation (SHF) process of cassava starch was introduced due to high sugar content in starch, using a thermoanaerobe able to reduce the cooling time after hydrolysis. A combination of 0.35% v/w amylase and 0.20% v/w amyloglucosidase used in the hydrolysis of cassava starch produced 19.18 g/L of sugar. A 15 g/L of K. marxianus showed to be the best yeast concentration which could produce the highest bioethanol, 42.85 g/L. When the laboratory scale was scaled up to 5 L fermentation, the result was comparable at 42.33 g/L. The same SHF parameters in laboratory scale had been proven to be effective in a larger scale fermentation based on similar results obtained. Simulation using SuperPro software indicated that 50.13% of starch can be converted into ethanol.
Effect of bentonite nanoclay filler on the properties of bioplastic based on sago starch
Authors: Zamrud Z , Ng W M , Salleh H M
Published: May 2021

The non-biodegradability of plastic packaging has led to a renewed interest in packaging materials based on biopolymers. The biopolymer used in this study which is sago starch is versatile and abundantly available in nature. In the present study, sago starch (SS)/polyvinyl alcohol (PVA) films were plasticized with glycerol (GLY) and reinforced with nanoclay bentonite (BEN) at 1 wt.%, 3 wt.% and 5 wt.% using solution casting method. The films prepared were characterized for their physic-chemical properties using conventional methods. Findings have shown that 5 wt.% BEN reinforced thermoplastic sago starch (TSS) showed highest weight loss in degradation test, greatest improvement in tensile strength and the lowest water absorption in swelling test. The introduction of BEN in the TSS showed changes in the intensification of O-H bond and Si-O bond. Furthermore, inclusion of BEN had reduced the vacant spots of SS/PVA/GLY matrices. Hence, the introduction of BEN in TSS composite had improved the film’s properties. This study suggests that SS/PVA/GLY/BEN film has the potential for packaging application to lessen the dependence on non-degradable plastic packaging.
Chemically modified palm kernel shell biochar for the removal of heavy metals from aqueous solution
Authors: Imran-Shaukat M , Wahi R , Rosli N R , Aziz S M A , Ngaini Z
Published: May 2021

Heavy metals eradication from water is a complicated subject, therefore a viable, resilient, and green technology is imperative. Heavy metal removal can be accomplished through easy access, economical, and efficient sorbents derived from agricultural waste. In the current study, palm kernel shell (PKS) waste was converted into biochar (PKSC) via pyrolysis. Chemical modification was performed on PKSC via acid-base treatment to refine its adsorption properties. Batch experiments were conducted to study the efficiency of PKSC and acid-base treated PKSC (MPKSC) for removal of Cr(IV), Ni(II) and Cu(II). The surface area was increased from 112.934 m 2/g to 149.670 m 2/g by acid-based treatment. Batch adsorption study showed that the MPKSC afforded high removal efficiency for Cu (99.29%), Ni (96.77%) and Cr (42.97%). The Cr(IV) and Ni(II) adsorption by PKSC, as well as Cr(IV), Ni(II) and Cu(II) adsorption by MPKSC were best represented by Freundlich isotherm. However, Cu(II) adsorption by PKSC can explained by using Langmuir isotherm. All studied heavy metals fitted the pseudo-second-order kinetic.
Torrefaction of fish waste (Sardinella fimbriata) in a fixed bed reactor: Yields and biochar characterization
Authors: Norisham S F , Munajat N F
Published: May 2021

Torrefaction is a promising process to upgrade biomass and produce solid biofuel. In this study, the fish by-product (Sardinella fimbriata), a fish-processing waste from the food industry highly prevalent in the east coast region of Peninsular Malaysia was investigated in a fixed bed torrefaction reactor with varying reaction temperature (200, 250, and 300°C) under a nitrogen atmosphere and single residence time (30 minutes). The distribution and yield of the biochar, bio-oil, and biogas were examined, and further characterization of the biochar was performed (proximate and ultimate, SEM, and FTIR analysis). The results indicated that with the increased torrefaction temperature, solid char yield decreases down to ∼ 52 % while bio-oil and biogas fluctuate between 7 - 28 % and 21 – 27 &, respectively. The proximate and ultimate analysis of the torrefied solid char showed that the fixed carbon, carbon content increased while volatile matter, moisture, hydrogen, and oxygen content decreased with temperature rise. From the SEM analysis, the torrefaction temperature causes an increase in biochar porosity. Lastly, from the FTIR analysis, it can be seen that there are degradation and formation of certain functional groups such as O-H, C-H, C=C, C=O, C-O, and C-C with torrefaction temperature variations.
Chemical Compositions and Physical Properties of Selected Malaysian Rice: A Review
Authors: Shakri A N Ahmad , Kasim K F , Rukunudin I B
Published: May 2021

Rice is the main food for most countries in Asia and, about 90% of the global rice area, production and consumption are concentrated on Asia. In Malaysia, the main varieties of rice found in retail outlets are ordinary local and imported white rice, brown unpolished rice and specialty rice. In short, conventional rice or white rice is a medium-length, white kernel with translucent endosperm. Brown rice is also called unmilled rice that is produced by removing the outermost layer of the grain. Specialty rice is the rice with special characteristics which includes pigmented rice, fragrant rice, parboiled and glutinous rice. Various rice varieties have various chemical and physical properties. This staple food is found to have received the utmost attention because of its potent antioxidant properties especially pigmented rice. These properties were related mainly to their phytochemical content which was associated with the provision of health benefits in chronic disease reduction. Therefore, this paper will comprehensively review the chemical compositions and physical properties of the Malaysian rice and its relation with health benefits.
Torréfaction of Mangrove Wood by Introducing Superheated Steam for Biochar Production
Authors: Mustaza M N F , Mizan M N , Yoshida H , Izhar S
Published: May 2021

Torréfaction by superheated steam was carried out over mangrove wood in a rotary kiln carbonizer. The mangrove wood was obtained from Kuala Sepetang, Malaysia. The current biochar production process still applies the traditional carbonization method, which requires 30 days in hot air. The torrefaction process using superheated steam was performed by manipulating the operating time at 30, 60, 90, and 120 min operating at 300°C. The high heating value (HHV) for raw mangrove wood to solid char product raised from 16.7 MJ/kg up to about 30 MJ/kg. The HHV was 29.7 MJ/kg after 60 min torrefaction. The carbon percent increases from 47.4% to above 70%. For bio-oil products, the superheated steam torrefaction produces various acids, alcohols, ketones, and aldehydes range from 249 ml to 335 ml. In contrast, torrefaction products via hot air carried-out in Kuala Sepetang have a liquid that contains acetic acid, butanoic acid, and phenol. Torrefaction of mangrove wood by superheated steam significantly reduced the biochar production period from 30 days to one-hour operation.
Removal of Iron and Manganese from Palm Oil Mill Effluent (POME) using Activated Clinoptilolite Zeolite
Authors: Aziz R A , Li C K , Salleh M N , Saleh M
Published: May 2021

The adsorption capacity of activated clinoptilolite zeolite for the removal of heavy metal ions; iron Fe(III) and manganese Mn(II) from palm oil mill effluent (POME) was investigated in this batch sorption study. The physicochemical properties of both raw clinoptilolite zeolite and POME effluent were first characterized before the modification of the zeolite is made, chemically and physically. The raw filtered POME has an initial pH of 4.56 which was acidic, whereas the natural zeolite has a slightly alkaline pH at 7.52. The effects of pH (5, 7 and 9), sorbent dosage (15, 20 and 25 g in 125 ml of effluent), contact time (15, 30, 45, 60, 120, 150, 180 and 200 minutes) and agitation speed (120, 150, 180 and 200 rpm) on the sorption of heavy metal ions were evaluated. The concentration of heavy metals was analyzed using AAS. The concentration of heavy metal ions for each parameter has decreased significantly after batch study. The adsorption of heavy metal ions increased with the increasing of pH and adsorbent dosages. Optimum percentage of metal removal and the amount of metal ions adsorbed by the zeolite were determined at 85% of Fe(III) and almost 92% of Mn(II) in the experiments.
Biosorption of CU(II) Ions by Leucaena Leucocephala Leave from Aqueous Solution
Authors: Azmi M A , Mokhtar N , Shoparwe N F , Shukor H
Published: May 2021

As a preliminary study, this project aims to remove the copper (Cu II) from aqueous solutions using Leucaena leucocephala leaves. Effect of free cell and immobilized form of biosorbent on adsorption capacity were evaluated in this study. Kinetic models of pseudo-first order, second order and equilibrium isotherms of Langmuir and Freundlich were used to predict the efficiency of Cu 2+ absorption by Leucaena leucocephala leaves adsorption system. The functional groups involved during the adsorption were identified using Fourier Transform Infrared (FTIR) whereby the Leucaena leucocephala leaves has the large amount of hydroxyl functional groups at the wavelength of 3411.66 cm -1. Atomic Absorption Spectroscopy (AAS) were used to measure the metal concentration before and after the adsorption process. The maximum percentage removal of the Cu 2+ was achieved by immobilized form of Leucaena leucocephala leaves as biosorbent in aqueous solutions with a 72.3% of removal. Adsorption kinetics of pseudo-second order was well fitted with the adsorption data with the R 2 of 0.9622. In addition, the results reveal that Langmuir isotherm was the best model to explain the adsorption of Cu 2+ using immobilized Leucaena leucocephala leaves with the R 2 of 0.9974. This result showed that Leucaena leucocephala leaves as an effective and potential biosorbent for the adsorption of Cu 2+ from the wastewater.
Study on the opportunity of carbon footprint reduction through energy measurement by implementing good operating practices in PVC flooring mat manufacturing industry
Authors: Wahab F , Andrew A M , Azizan N Z N , Ragunathan S , Seman N A A , Tan W H , Munaaim M A C , Seah S Y
Published: May 2021

This paper outlines the carbon emission reduction opportunities through energy measurement by implementing good operating practices and good house-keeping in PVC floorcovering mat industry in Perlis, Malaysia. The baseline data was obtained through the energy audit to identify the area to be improved. Cleaner Production (CP) options through good operating practices and good housekeeping were suggested to be implemented in the area of concern. The performance of the suggested CP was analyzed in terms of (1) energy efficiency, (2) carbon emission reduction and (3) cost-saving according to the payback period. The results show that the CP option of utilization of rest time of intensive mixer has contributed to the most energy saving of 8.18%, followed by steam pipe insulation, 2.9%; steam trap maintenance, 0.95% and steam pipe leakage maintenance, 0.15% with the total CO 2 emission saved of 185,573 tCO 2/year corresponds to the overall energy save of 5.2%. Through the comparison between the estimated values and the measured values based on energy baseline model, it was found that the CO 2 intensity had decreased by 21.8%. The finding shows that there exists a significant opportunity to reduce CO 2 emissions through energy measurement in PVC flooring mat manufacturing industry by the implementation of good operating practices and good housekeeping.
Hydrogel synthesis of PVA/O. sativa for antimicrobial activity using freeze thaw method
Authors: Mohamad C W S R , Abdulah A A , Kasim K F , Sukhri M A
Published: May 2021

This research project was aimed to develop antibacterial hydrogel from blends of PVA and Oryza sativa L by applying freeze-thaw method (later on will refer as PVA/OS hydrogel). Agar well diffusion method was used to test the antimicrobial activity of the hydrogel against Gram-negative (E. coli) and Gram-positive bacteria (Staphy. aureus). The results indicate that PVA20/OS at pH 7 was effective as antimicrobial against both tested organisms while OS/PVA20 at pH 8 was effective for E. coli only. Based on the phytochemical test, the antimicrobial activity probably causes by the present of flavonoids content in the hydrogel. The higher PVA used in the blend’s hydrogel will increase the thickness of hydrogel. PVA40 at pH 7 and pH8 shows a good result in mechanical properties where the value of thickness is 0.750 and 0.770 mm respectively. PVA/OS blend hydrogel had good antimicrobial activity and good compatibility to form an antimicrobial hydrogel for medical purpose with the used of 20%.