Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobile games adoption: An extension of technology acceptance model and theory of reasoned action

This study aims to analyze the effect of game features and trust on the intention to play mobile games based on the technology acceptance model and theory of reasoned action. 408 respondents were engaged in this study to obtain the data which were then analyzed using PLS-SEM. The results show that game features, attitude, and subjective norms are the significant determinants of intention to play mobile games. Attitude toward playing online games is influenced by game features, perceived usefulness, and trust. Perceived ease of use does not have a significant effect on attitude. This study bridges the gap in the mobile game adoption literature by explaining how the relationship between game features and trust. This will drive the intention to play mobile games based on the technology acceptance model and theory of reasoned action to obtain a more comprehensive explanation of mobile game adoption.
The Waste Management of Polyethylene Terephthalate (PET) Plastic Waste: A Review

This paper reviews on the current techniques used for the management of Polyethylene Terephthalate (PET) plastic waste. The increasing amount of PET wastes and growing environmental concern had placed emphasis on various recycling methods such as chemical and mechanical recycling of PET waste. The focus of this review is on the various mechanical and chemical recycling methods for PET wastes. The conclusion with suggestion to the involved authority on the proper PET waste management to avoid pollution to the environment have been presented in last section.
A Review of Active Hand Exoskeletons for Rehabilitation and Assistance

Disabilities are a global issue due to the decrease in life quality and mobility of patients, especially people suffering from hand disabilities. This paper presents a review of active hand exoskeleton technologies, over the past decade, for rehabilitation, assistance, augmentation, and haptic devices. Hand exoskeletons are still an active research field due to challenges that engineers face and are trying to solve. Each hand exoskeleton has certain requirements to fulfil to achieve their aims. These requirements have been extracted and categorized into two sections: general and specific, to give a common platform for developing future devices. Since this is still a developing area, the requirements are also shaped according to the advances in the field. Technical challenges, such as size requirements, weight, ergonomics, rehabilitation, actuators, and sensors are all due to the complex anatomy and biomechanics of the hand. The hand is one of the most complex structures in the human body; therefore, to understand certain design approaches, the anatomy and biomechanics of the hand are addressed in this paper. The control of these devices is also an arising challenge due to the implementation of intelligent systems and new rehabilitation techniques. This includes intention detection techniques (electroencephalography (EEG), electromyography (EMG), admittance) and estimating applied assistance. Therefore, this paper summarizes the technology in a systematic approach and reviews the state of the art of active hand exoskeletons with a focus on rehabilitation and assistive devices.
ARAS measurements on the thermal decomposition of CO2 behind shock waves

The dissociation rate coefficient of CO2 has been measured behind reflected shock waves in the temperature range 2400 K≤T≤4400 K at pressures 1.2 bar≤p≤2.0 bar. By the use of the atomic resonance absorption spectroscopy (ARAS), the formation of O atoms could directly be observed in the post‐shock reaction zone. The experiments have been performed in mixtures of CO2 highly diluted in argon. The initial CO2 concentrations was varied from 10 to 5000 ppm. The Arrhenius expression for the thermal decomposition of CO2 was found to be: CO2+M→CO+O+M (R1) k1=3.65×1014exp(−52525 K/T) cm3 mol−1 s−1. The effect of impurities was minimized by keeping background pressures in the shock tube below 5×10−7 mbar.
Computational Study of Three-Dimensional Flow Past an Oscillating Cylinder Following a Figure Eight Trajectory

The paper presents a computational study of three-dimensional flow past a cylinder forced to oscillate in a uniform stream, following a figure-eight trajectory. Flow simulations were performed for Re = 400, for different cases, defined in terms of the oscillation mode (‘counter-clockwise’ or ‘clockwise’), for values of the ratio, F, of the transverse oscillation frequency to the Strouhal frequency close to 1.0. The results demonstrate that, for F ≤ 1.0, counter-clockwise cylinder motion is associated with positive power transfer from the flow to the cylinder, corresponding to excitation; for the clockwise motion, power transfer is negative at intermediate to high amplitudes, corresponding to damping. For the clockwise mode, in the range F = 0.9–1.1, a transition to two-dimensional vortex street is identified for transverse oscillation amplitude exceeding a critical value. This results from the induced suction of vortices, which moves vortex formation and shedding closer to the cylinder surface, thus resulting in a narrower wake, characterized by an effective lower Reynolds number. Both oscillation modes are characterized by higher harmonics in the lift force spectrum, with the third harmonic being very pronounced, while even harmonics are present for the case of clockwise mode, resulting from a wake transition to a “S + P” mode.
Students’ Satisfaction with the Library, Familial Reading Environments, and Reading Attitudes in Structural Equation Modeling Analyses

The main purpose of this study was to test the fit of model of the relationships among students’ satisfaction with the library, familial reading environments and whose reading attitudes. Further, it tried to find out whether the familial reading environments and satisfaction with the library affect the students’ reading attitudes, and then explored the direct, indirect and total effects between the students’ satisfaction with the library, familial reading environments, and reading attitudes. The participants of this study included 714 high school students (356 male, 358 female), and the findings suggested that (1) the theoretical model of students’ satisfactions with the library, familial reading environments, and reading attitudes using Structural Equation Modeling was supported; (2) students’ satisfactions with the library were in connection with their familial reading environments; (3) students’ satisfactions of library and familial reading environments had a significantly positive effect on students’ reading attitudes.
Simulating the spatiotemporal variations in aboveground biomass in Inner Mongolian grasslands under environmental changes

Abstract. Grassland aboveground biomass (AGB) is a critical component of the global carbon cycle and reflects ecosystem productivity. Although it is widely acknowledged that dynamics of grassland biomass is significantly regulated by climate change, in situ evidence at meaningfully large spatiotemporal scales is limited. Here, we combine biomass measurements from six long-term (> 30 years) experiments and data in existing literatures to explore the spatiotemporal changes in AGB in Inner Mongolian temperate grasslands. We show that, on average, annual AGB over the past 4 decades is 2561, 1496 and 835 kg ha−1, respectively, in meadow steppe, typical steppe and desert steppe in Inner Mongolia. The spatiotemporal changes of AGB are regulated by interactions of climatic attributes, edaphic properties, grassland type and livestock. Using a machine-learning-based approach, we map annual AGB (from 1981 to 2100) across the Inner Mongolian grasslands at the spatial resolution of 1 km. We find that on the regional scale, meadow steppe has the highest annual AGB, followed by typical and desert steppe. Future climate change characterized mainly by warming could lead to a general decrease in grassland AGB. Under climate change, on average, compared with the historical AGB (i.e. average of 1981–2019), the AGB at the end of this century (i.e. average of 2080–2100) would decrease by 14 % under Representative Concentration Pathway (RCP) 4.5 and 28 % under RCP8.5. If the carbon dioxide (CO2) enrichment effect on AGB is considered, however, the estimated decreases in future AGB can be reversed due to the growing atmospheric CO2 concentrations under both RCP4.5 and RCP8.5. The projected changes in AGB show large spatial and temporal disparities across different grassland types and RCP scenarios. Our study demonstrates the accuracy of predictions in AGB using a modelling approach driven by several readily obtainable environmental variables and provides new data at a large scale and fine resolution extrapolated from field measurements.
Predicting Secondary Structure of Protein Using Hybrid of Convolutional Neural Network and Support Vector Machine

Protein secondary structure prediction is one of the problems in the Bioinformatics field, which conducted to find the function of proteins. Protein secondary structure prediction is done by classifying each sequence of protein primary structure into the sequence of protein secondary structure, which fall in sequence labelling problems and can be solved with the machine learning. Convolutional Neural Network (CNN) and Support Vector Machine (SVM) are 2 methods that often used to solve classification problems. In this research, we proposed a hybrid of 1-Dimensional CNN and SVM to predict the secondary structure of the protein. In this research, we used a novel hybrid 1-Dimensional CNN and SVM for sequence labelling, specifically to predict the secondary structure of the protein. Our hybrid model managed to outperform previous studies in term of Q3 and Q8 accuracy on CB513 dataset.
The Sorption of Long-living Radionuclides 90Sr and 137Cs by Natural Polysaccharides

Sorption of long-living 90 Sr and 137 Cs radionuclides by natural polysaccharides -pectines and alginates in dependence on pH and ionic strength of a solution was studied. Radionuclides sorption was shown to be a minimal at low pH values and maximal at pH = 7. Nuclides sorption decreases sharply with the increase of solvent ionic strength. The main mechanisms of radionuclide sorption by polysaccharides were determined to be ion exchange and chelation.
Learning About Learning in Tourism: Indigenous Guide Perspectives on Their Personal and Professional Development

This empirically based article examines guides’ knowledge and skills acquisition. Using two cultural tourism attractions in New Zealand as field sites, the focus is on guides in cultural tourism contexts. Twenty-one semistructured in-depth interviews with guides and managers were conducted and analyzed using a social constructivist perspective. This article is thus among the first to add the voices of (Indigenous) guides to the discussions of guide knowledge acquisition and learning. The relevance of previous personal experience of guides, conceptualized as informal experiential (practice-based) learning and lifelong learning, is identified as critical in guided tour content selection and delivery, as well as in engagement with participants of a guided tour experience. Implications address power relationships, ownership of information and stories, and credibility of a message in (Indigenous) cultural tourism; the need to focus on recruitment of guides, and a call for perceiving guides as humans with a sophisticated, demanding, interpersonal role.
Label-free detection of rare cancer cells using deep learning and magnetic levitation principle

Magnetic levitation is an effective tool for separating target cells within a heterogeneous solution by utilizing density differences among cell lines. However, magnetic levitation cannot be used to identify target cells which have similar density profile as the other cells in the solution. Therefore, cell identification accuracy can dramatically reduce. In this study, we introduce, for the first time, the use of deep learning-based object detection approach for label-free identification of rare cancer cells within levitated cells. As a result, our novel and hybrid detection strategy could be used to identify circulating tumor cells for diagnosis and prognosis of cancer.
Computational Fluid Dynamics Study of Wake Recovery for Flow Across Hydrokinetic Turbine at Different Depth of Water

1 Fakulti Kejuruteraan Mekanikal (FKM), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia 2 Centre for Advanced Research on Energy (CARe), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia 3 Centre of Excellence Geopolymer and Green Technology (CeGeoGTech), Universiti Malaysia Perlis (UniMAP), 01000, Kangar, Perlis, Malaysia 4 Fakulti Teknologi Kejuruteraan Mekanikal dan Pembuatan (FTKMP), Universiti Teknikal Malaysia Melaka (UTeM), Hang Tuah Jaya, 76100 Durian Tunggal, Melaka, Malaysia
Implementation irrigation system using Support Vector Machine for precision agriculture based on IoT

The industrial revolution 4.0 is driving increased production and automation of systems using the internet of things and data mining. Climate change and young farmers knowledge make it necessary to create an internet-based and Artificial intelligent farming system. Farmers need a system that can help the process of watering and fertilising for crop growth. In several previous studies, calculating the prediction of water density from moisture sensor data and to predict future moisture values using SVM, the implementation of artificial intelligence for precision agriculture to detect field-grown cucumber, in other studies determining the amount of water using fuzzy systems and neural networks, parameters used in determining irrigation systems consist of environmental data including ambient temperature, humidity and soil moisture several additional variables in determining irrigation control using crop water demand, soil evapotranspiration and weather condos, forecasting agriculture water uses LS-SVM to improve accuracy and speed in the forecasting process implementation of automated irrigation using IoT. In this paper, we propose the integration of water quantity predictions on plants using several environment variables for forecasting using linear Support vector machine. Accuracy rate of SVM reaches 95%, 94.3% precision, 91% recall, F1-score 92.7%. The results of the forecasting will be sent to the end node using the MQTT protocol.
A probabilistic collaborative dictionary learning-based approach for face recognition

Although Sparse Representation based Classiﬁer (SRC), a non-parametric model, can obtain an interesting result for pattern recognition , a reasonable interpretation has been lacked for its classiﬁcation mechanism. What is more, the training samples are used as off-the-shelf dictionary directly in SRC, which can make the feature hidden in the training samples hard be extracted. At the same time, the complexity of the algorithm is increased because of too many atoms of the dictionary. The authors ﬁrst explains in detail the classiﬁcation mechanism of SRC from the view of probabilistic collaborative subspace and offer the process to improve the stability of the algorithm using the joint probability in the case of the multi-subspace. Then, the authors introduce the dictionary learning (DL) and Fisher criterion into the model to further enhance the discrimination of the coding coefﬁcient. In order to ensure the convexity of the discrimination term and further enhance the discrimination, the authors add the L 21 -norm term into the Fisher discrimination term and offer the proof for its convexity. Finally, the experimental result on a series of benchmark databases, such as AR, Extended Yale B, LFW3D-hassner, LFW3D-sdm and LFW3D-Dlib, show that PCDDL outperforms existing classical classiﬁcation models.
Development of Wearable Wireless Electrocardiogram Detection System using Bluetooth Low Energy

Wearable monitoring devices can provide patients and doctors with the capability to measure bio-signals on demand. These systems provide enormous benefits for people with acute symptoms of serious health conditions. In this paper, we propose a novel method for collecting ECG signals using two wireless wearable modules. The electric potential measured from a sub-module is transferred to the main module through Bluetooth Low Energy, and the collected values are simultaneously displayed in the form of a graph. This study describes the configuration and outcomes of the proposed system and discusses the important challenges associated with the functioning of the device. The proposed system had 84% signal similarity to that of other commercial products. As a band-type module was used on each wrist to check the signal, continuous observation of patients can be achieved without restricting their actions or causing discomfort.
Techno-Economic Assessment of Solar-Driven Steam Gasification of Biomass for Large-Scale Hydrogen Production

Solar biomass gasification is an attractive pathway to promote biomass valorization while chemically storing intermittent solar energy into solar fuels. The economic feasibility of a solar gasification process at a large scale for centralized H2 production was assessed, based on the discounted cash-flow rate of return method to calculate the minimum H2 production cost. H2 production costs from solar-only, hybrid and conventional autothermal biomass gasification were evaluated under various economic scenarios. Considering a biomass reference cost of 0.1 €/kg, and a land cost of 12.9 €/m2, H2 minimum price was estimated at 2.99 €/kgH2 and 2.48 €/kgH2 for the allothermal and hybrid processes, respectively, against 2.25 €/kgH2 in the conventional process. A sensitivity study showed that a 50% reduction in the heliostats and solar tower costs, combined with a lower land cost of below 0.5 €/m2, allowed reaching an area of competitiveness where the three processes meet. Furthermore, an increase in the biomass feedstock cost by a factor of 2 to 3 significantly undermined the profitability of the autothermal process, in favor of solar hybrid and solar-only gasification. A comparative study involving other solar and non-solar processes led to conclude on the profitability of fossil-based processes. However, reduced CO2 emissions from the solar process and the application of carbon credits are definitely in favor of solar gasification economics, which could become more competitive. The massive deployment of concentrated solar energy across the world in the coming years can significantly reduce the cost of the solar materials and components (heliostats), and thus further alleviate the financial cost of solar gasification.
Thin-barrier heterostructures enabled normally-OFF GaN high electron mobility transistors

In this paper, we present our recent research on the demonstration of normally-OFF operation and high-performance device merits in GaN high electron mobility transistors (HEMTs), which are enabled by the employment of thin-barrier AlGaN/GaN heterostructures. Two types of thin-barrier HEMTs are investigated: one is with a metal–oxide–semiconductor (MOS) gate structure, and the other is with a p-GaN gate stack. The MOSHEMTs feature an as-grown thin-barrier heterostructure with a gate-recess-free fabrication process and a high-k ZrO2 gate dielectric. Approaches including selective area barrier regrowth and selective area surface passivation are implemented to minimize the access resistance in the MOSHEMTs. For the p-GaN gate HEMTs, a T-shaped Schottky gate contact scheme is employed to realize superior gate stack reliability. Normally-OFF operation with a large threshold voltage ⩾1.5 V, a high maximum drain current ⩾450 mA mm−1, a steep subthreshold slope ⩽95 mV dec−1, and negligible hysteresis are achieved in all the demonstrated device structures. Moreover, in analyzing the reported device results with the corresponding heterostructure design, we discuss the benefits and tradeoffs of thin-barrier MOSHEMTs and p-GaN gate HEMTs, respectively.
Multimode Generative Adversarial Networks for Sequence Data Generation

As a new type of artificial intelligence technology, generative adversarial network (GAN) has good data understanding and generation capabilities, and has a wide range of application prospects in the fields of image and speech. However, due to the lack of prior knowledge, its training process is less robust and prone to occur the pattern ignore. Its development is restricted to a certain extent, and its application scope still needs to be expanded. To solve the above problems, this paper introduces a knowledge confidence multimode GAN (KC-MGAN) algorithm, calculates the confidence of the input data through the reasoning method, and then puts the confidence and the input data into the GAN system to generate new sample data. During the training process, the confidence of the input data is continuously calculated, while the generated data samples are continuously evaluated. The training process will end until the GAN system reaches a stable condition. Finally, this paper takes the generation of UAV flight trajectory data as an example to verify the effectiveness of the proposed method. Some explorations have been made for the application of data generation and GAN’s training mode with the prior knowledge.
Pharmaceutical Cocrystals—A Review

The design and synthesis of pharmaceutical cocrystals have received great interest in the recent years. Cocrystallization of drug substances offers a tremendous opportunity for the development of new drug products with superior physical and pharmacological properties such as solubility, stability, hydroscopicity, dissolution rates and bioavailability. This short review summarizes this highly topical field, covering why the topic is of interest in pharmaceutical formulation, the definitions and practical scope of cocrystals, cocrystal preparation and characterization, a comparison of different (traditional and novel) methods for cocrystal formation and the implications for regulatory control and intellectual property protection. Traditionally, cocrystals can be prepared by solvent evaporation method, grinding, and slurry method, but every method has its limitations for certain conditions. The current trend for cocrystal formation uses sophisticated methods such as the hot melt extrusion method, spray-drying method, supercritical fluid technology and the newest method: laser irradiation. The purpose of the development of a new method is not only to overcome the limitation of traditional cocrystallization methods, but also to generate simpler steps and a continuous process for the production of the cocrystal product. This article provides a brief explanation of each method that can be used to generate pharmaceutical cocrystals as well as evaluation of cocrystals. This article also covers how the developing field of cocrystallization may impact the pharmaceutical intellectual property landscape.