Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual Fuel Turbocharged Engine Operated with Exhaust Gas Recirculation

With good combustion characteristics, hydrogen has been developing as a clean alternative fuel of engines. This study is to develop a diesel/hydrogen dual fuel engine. The hydrogen was added at inlet port in a 4-cylinder direct injection turbocharged diesel engine with an EGR (Exhaust Gas Recirculation) system to investigate engine performance and exhaust pollutant. The measured items are composed of the gas pressure of cylinder, crank angle, consumption rate of diesel, consumption rate of hydrogen, air flow rate, emissions (HC, CO 2 , NO X , and Smoke), and so on. The authors analyze how the addition of hydrogen with EGR system influences the engine performance and emissions. The diesel/hydrogen dual fuel turbocharged engine can increase the brake thermal efficiency with a greater decrease in emissions compared with the turbocharged diesel engine. Furthermore, the authors little altered the engine structure to get the positive effect of energy saving and pollutant decreasing.
Digital Divides, Generational Gaps, and Cultural Overlaps: A Portrait of Media Use and Perspectives of Media in Thailand

ABSTRACT Although lower- and middle-income nations are experiencing a dramatic rise in digital media, scientific understanding of how young people and families in such nations navigate this altered landscape lags behind. This mixed methods study examines access to, use of, and perspectives of digital and traditional media in Thailand, a middle-income nation undergoing rapid technological change. Eighty participants, evenly divided by ecological context (rural, urban) and generational cohort (adolescent, parent), participated in this interview study. Quantitative analyses reveal ecological gaps in media access and use, and generational gaps in media use and perceived importance of media. Qualitative analyses reveal how perceived opportunities and challenges of media are customized by ecological context and generational cohort. Centering the experiences and perspectives of Thais in diverse communities and of diverse ages, this study highlights both within-nation heterogeneity and homogeneity in media integration and psychological orientations toward media. In so doing, it draws attention to how culture shapes media use and perspectives.
Establishing Multi-User MIMO Communications Automatically Using Retrodirective Arrays

Communications in the mmWave and THz bands will be a key technological pillar for next-generation wireless networks. However, the increase in frequency results in an increase in path loss, which must be compensated for by using large antenna arrays. This introduces challenging issues due to power consumption, signalling overhead for channel estimation, hardware complexity, and slow beamforming and beam alignment schemes, which are in contrast with the requirements of next-generation wireless networks. In this paper, we propose the adoption of a retro-directive antenna array (RAA) at the user equipment (UE) side, where the signal sent by the base station (BS) is reflected towards the source after being conjugated and phase-modulated according to the UE data. By making use of modified Power Methods for the computation of the eigenvectors of the resulting round-trip channel, it is shown that, in single and multi-user multiple-input multiple-output (MIMO) scenarios, ultra-low complexity UEs can establish parallel communication links automatically with the BS in a very short time. This is done in a blind way, also by tracking fast channel variations while communicating, without the need for ADC chains at the UE as well as without explicit channel estimation and time-consuming beamforming and beam alignment schemes.
Synthesis and characterization of pH-sensitive thin-layer molecularly imprinted membranes for selective solid-phase extraction of celecoxib from biological fluids followed by high-performance liquid chromatography with UV detection

Abstract In this work, the synthesis of thin-layer, pH-sensitive molecularly imprinted membrane using methacrylic acid and maleic acid as functional monomers, ethylene glycol dimethacrylate as cross-linker, 2,2′-azobisisobutyronitrile as initiator, poly (vinylidene fluoride) as porous membrane, and celecoxib as a target molecule have been reported. Different technique analyses characterized the membrane product. A novel molecularly imprinted membrane-solid phase extraction method was used for the extraction of celecoxib and concentrating it prior to be analyzed in the human plasma and urine matrix. High-performance liquid chromatography with ultraviolet detection was used and validated for the analysis of celecoxib which was extracted from biological fluids. The molecularly imprinted membrane showed selective performance for the template, and extraction recoveries of the drug by the membrane from human plasma and urine samples were between 96.2 and 97.3%. The obtained results showed that the detection and quantification limits of the drug were 0.12 and 0.40 ng mL−1, respectively. The adsorption capacity of 4.95 mg g−1 was obtained from the fitting by the Langmuir model. The molecularly imprinted membrane can be easily regenerated eight times with sustained efficiency. Therefore, the developed method can be a good candidate for the separation and detection of celecoxib in biological samples. GRAPHICAL ABSTRACT
Holocene evolution of the Banni Plain at the north‐east margin of the Arabian Sea: Constraints from a ca 50 m long sediment core

Holocene evolutionary history of the Banni Plain in the Great Rann of the Kachchh Basin is reconstructed from a subsurface sediment core of ca 50 m. Detailed data on textural and lithofacies variations, grain‐size analysis, environmental magnetism and accelerator mass spectrometry 14C dates on seven samples were generated on the sediment core retrieved from the Banni Plain near Berada. A high‐resolution record extending back to 10 ka has been reconstructed from the top ca 40 m of the core section comprising shallow marine sediments. The core is divisible into five depositional units. The basal part is a fluvial depositional unit followed upward by estuarine, sub‐tidal, intertidal and supra‐tidal environments. The sediment accumulation rate is highest in the sub‐tidal to intertidal facies (1.9 cm year−1) and decreases towards the supra‐tidal facies to 0.09 cm year−1. Environmental magnetic analysis, χlf coupled with the S‐ratio, indicates high magnetic mineral concentrations during the Early Holocene, suggesting a wet period accompanied by high monsoon precipitation. This is followed by the onset of semi‐arid conditions in the Great Rann of the Kachchh Basin as indicated by the low values of the χlf and S‐ratios. A westward and northward shift in the shoreline towards the deeper part of the basin is suggested during the Late Holocene, which is coupled with aridity and reduced monsoonal conditions. The change in depositional pattern from the retrogradational deposit of fluvial (Unit 1) to estuarine sediment (Unit 2), progressing to sub‐tidal (Unit 3), is attributed to sea‐level transgression followed by regressive intertidal (Unit 4) to supra‐tidal deposition (Unit 5), culminating in complete withdrawal of the sea, aided by tectonic uplift, during the Late Holocene. The results reveal that the sediment accumulation rates and depositional environments changed over time in response to changes in sea level from minima to maxima and then eventually to the present level.
Effect of Methanolic Extract of Platycerium angolence on Aluminium Chloride-induced Neurotoxicity in Rats

The aim of this study is to determine the ameliorative effects of methanolic extract of Platycerium angolence (MEPA)on aluminium chloride (AlCl3)-induced neurotoxicity in rats. Rats were randomly separated into five groups containing four rats each. Group 1 serves as the control group and received normal saline, group 2 rats were administered with 100 mg/kg body weight of AlCl3 orally for 28 days to induce neuronal damage, while groups 3 and 4 rats received 250 mg/kg and 500 mg/kg of MEPA for 7days after induction of neuronal damage by AlCl3 for 28 days. Group 5 rats were administered donepezil drug (0.2 mg/kg) which served as reference drug. Behavioural study such as elevated plus maze, Y-maze and open field tests were investigated on the rats. Biochemical assays such as lipid peroxidation, acetylcholinesterase activities, and metal concentration in the whole brain homogenates were estimated using standard procedures. Results revealed a significant (p<0.05) increase in the memory index (MI), lipid peroxidation of animals in the AlCl3 group when compared with the control and the MEPA treated groups. The significant (p<0.05) decrease on lipid peroxidation MEPA was dose dependent. The acetylcholinesterase activities observed in the brain of 250 mg/kg (0.49 μmol acetylcholine/hr/mg protein) group and 500 mg/kg of extract (0.66 μmol acetylcholine/hr/mg protein) has comparative effect with the group treated with the standard drug donepezil when compared with AlCl3 induced group (1.31 μmol acetylcholine/hr/mg protein). This study revealed that both doses of 250 and 500 mg/kg of MEPA has ameliorative potential against AlCl3-induced neurotoxicity in rats but 500 mg/kg of the extract shows better protection.
Toward Pitch-Insensitive Speaker Verification via Soundfield

Automatic speaker verification systems (ASVs) verify a person’s identity by his/her voice and have been widely deployed for user authentication. However, existing ASVs are based on traditional audio spectral features and hence, perform poorly in verifying pitch-changed utterances from speakers with cold or sore throat. In this article, we propose soundfield tracker (SOFTER), a soundfield-based speaker verification system that can verify speakers regardless of the pitch changes. SOFTER is based on the observation that soundfield features reflect the speaker’s vocal tract, mouth, head, torso, etc., which are less affected by the pitch changes in speech signals. SOFTER can be integrated into off-the-shelf smartphones without any hardware modifications. One major challenge is that the soundfield is sensitive to the distance between the speaker and the phone. To solve this problem, we propose a two-stage mechanism combining distance sensing and soundfield reconstruction, which enables to reconstruct the soundfield to a setting similar to the one in the enrollment phase, thus, the speaker can be verified from any distance to the phone. We compare SOFTER with six state-of-the-art academic and commercial ASVs on two data sets of 134 speakers and 31000 speech samples. Results show that SOFTER has an equal error rate (EER) of 2.18% and 1.61% on the two data sets, respectively. Moreover, SOFTER outperforms other ASVs by at least 24.67% on average in verifying pitch-varying or pathological speech samples, denoting an evidence of SOFTER’s effectiveness in both normal and unhealthy user conditions.
Infrared and Visible Light Image Fusion Algorithm Based on Compressed Sensing

The Nonsubsampled Contourlet Transform (NSCT) is translation invariant. It also has good line singular characteristics capturing feature in the image processing. The compressed sensing theory has good noise suppression capability. On the basis, this study proposed a novel compressed sensing image fusion algorithm in NSCT transform domain for the infrared image and visible light image fusion. This algorithm adopted the pixel feature energy weighted fusion rule and the neighborhood variance feature information fusion rule for the low-frequency coe‐cients and high-frequency coe‐cients fusion. The compressed sensing algorithm used the Toeplitz matices to observe the high-frequency sub-band coe‐cients. The experiment results show that this method efiectively decreases the amount of processing data, improves the convergence rate, and raises the fusion efiect.
The trait‐mediated trade‐off between growth and survival depends on tree sizes and environmental conditions

Interspecific relationships between growth and survival are critical determinants of tree species diversity maintenance in forests. The trade‐offs between growth and survival in co‐occurring tree species are believed to arise along a continuum of life‐history strategies. For example, co‐occurring species range from those that grow slowly and survive well in resource‐poor environments to those that grow quickly but have low survival rates in resource‐rich environments. However, uncertainties remain regarding how growth–survival trade‐offs are related to species traits, tree sizes or environmental conditions. We examined how the relationships between species traits and growth–survival relationships shift in response to changes in stem sizes and across census periods with different climate conditions (frequency of strong winds, drought intensity) across 45 co‐occurring tree species based on 23 years of growth and survival records in a warm temperate rainforest on Yakushima Island, Japan. We developed hierarchical Bayesian models of relative growth and survival rates, including leaf traits, wood density and 95‐percentile maximum stem diameter as explanatory variables. We tested the relationships between estimated trait‐mediated growth–survival relationships and the indices of climate events during five census periods. Each trait's effects on growth–survival relationships differed across the five census periods in response to climate conditions. Interspecific growth–survival relationships affected by a single trait axis for leaves or wood tended to be negative. In contrast, those affected by the maximum stem diameter tended to be positive. Such trends increased with more frequent strong winds or more intense droughts. The single‐trait effects on growth–survival relationships were stronger for smaller sizes than for larger sizes. For all traits combined, we found a significant growth–survival trade‐off only for small‐sized stems in three of five census periods. Synthesis. Our results indicate that the effect of species traits on the growth–survival relationships depended on tree sizes, the census periods or both in response to the frequency or intensity of climate events. We argue the importance of incorporating spatial and temporal variations in environmental conditions into long‐term data from tree census to predict forest dynamics.
Graph Structure Learning on User Mobility Data for Social Relationship Inference

With the prevalence of smart mobile devices and location-based services, uncovering social relationships from human mobility data is of great value in real-world spatio-temporal applications ranging from friend recommendation, advertisement targeting to transportation scheduling. While a handful of sophisticated graph embedding techniques are developed for social relationship inference, they are significantly limited to the sparse and noisy nature of user mobility data, as they all ignore the essential problem of the existence of a large amount of noisy data unrelated to social activities in such mobility data. In this work, we present Social Relationship Inference Network (SRINet), a novel Graph Neural Network (GNN) framework, to improve inference performance by learning to remove noisy data. Specifically, we first construct a multiplex user meeting graph to model the spatial-temporal interactions among users in different semantic contexts. Our proposed SRINet tactfully combines the representation learning ability of Graph Convolutional Networks (GCNs) with the power of removing noisy edges of graph structure learning, which can learn effective user embeddings on the multiplex user meeting graph in a semi-supervised manner. Extensive experiments on three real-world datasets demonstrate the superiority of SRINet against state-of-the-art techniques in inferring social relationships from user mobility data. The source code of our method is available at https://github.com/qinguangming1999/SRINet.
Resonant spatial tracking using nanostructured resonant waveguide grating for multispectral sensing by imaging

Resonant profile shift resulting from a change of resonant conditions is classically used for sensing, either liquid refractive index or immobilized biological layer effective thickness. Resonant waveguide gratings (RWG) allow sensing over a large spectral domain, depending on the materials and geometrical parameters of the grating. Profiles measurements usually involve scanning instrumentation. We recently demonstrated that direct imaging multi-assay RWGs sensing may be rendered more robust using spatial Fano profiles from “chirped” RWG chips. The scheme circumvents the classical but demanding scans: instead of varying angle or wavelength through fragile moving parts or special optics, a RWG structure parameter is varied. Our findings are illustrated with resonance profiles from nanostructured silicon nitride waveguide on glass. A sensitivity down to Δn=2x10-5 or biomolecules mass density of 10 pg/mm2 is demonstrated through theory and experiments. To assess different sensing wavelength, the period might also vary within the same chip support. We discuss guiding properties and sensing sensitivities of RWG sensing over the whole visible spectral range. Resonant profiles are analyzed using a correlation approach, correlating the sensed signal to a zero-shifted reference signal. This analysis was demonstrated to be more accurate than usual fitting, for analyzing signals including noise contribution. The current success of surface plasmon imaging suggests that our work could leverage an untapped potential to extend such techniques in a convenient and sturdy optical configuration. Moreover, extended spectral range sensing can be addressed by dielectric waveguide structures. This allows sensitive sensing of small volumes of analyte, which can be circulated close from the resonant waveguide. Together with the demonstration of highly accurate fits through correlation analysis, our scheme based on a “Peak-tracking chip” demonstrates a new technique for multispectral sensitive sensing through nanostructured chip imaging.
Proposal of a New System for Essential Oil Classification Based on Low-Cost Gas Sensor and Machine Learning Techniques

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.
Motion Area Division Method Based on Normal Component of Edge Optical Flow

To lessen the accuracy demand of traditional motion area division methods,a motion segmentation algorithm based on normal component of optical flow is proposed.Edgediscriminating same area decision function is used to give out the relevance measurement between each edge point and its surrounding area,which is the base for further clustering.Simulation on both synthesized and real image sequences was made.It is proved that under appropriate selection of parameters,the novel algorithm can give correct division result.Moreover,compared to those methods using complete optical flow vector,the new method costs less time because that only normal component of the edge point' optical flow is evolved in the algorithm.
An Entropy-based Adaptive PBIL Algorithm and Its Application

This paper analyzes the basic principle and disadvantage of population-based increased learning algorithm, and proposes an improved strategy which has adaptive function and mutation capability. Information entropy is introduced to evaluate population evolutionary degree, and learning rate and mutation probability are adaptively adjusted according to information entropy value in the new algorithm. The algorithm is applied to resolve the typical Flow Shop scheduling, problem and the result shows that compared with standard PBIL algorithm and genetic algorithm (GA), the calculation efficiency and local search capability are much improved, the optimum effect is satisfied, and the convergence process is very stable.
Adomian-modified decomposition method for large-amplitude vibration analysis of stepped beams with elastic boundary conditions

ABSTRACT The main objective of this paper is to apply an Adomian modified decomposition method for solving large amplitude vibration analysis of stepped beams with various general and elastic boundary conditions. Damaged or imperfect supports of beams can be modeled by using elastic boundary conditions composing of translational and rotational springs. For the beams subjected to dynamic severe loading, it is important to include the nonlinear term of axial stretching force developed by the large vibration amplitude in the governing equation for more accurate design. By using the method, the convergence studies for linear and nonlinear vibration analyses of stepped beams are shown for determining an appropriate number of terms in the solutions. The accuracy of the present results is validated numerically by comparing with some available results in the literature. New results of nonlinear frequency ratios of stepped beams with different boundary conditions are presented and discussed in detail. Aspects of step ratio, step location, boundary conditions, vibration amplitudes, etc., which have significant impact on linear and nonlinear frequencies of such beams are taken under investigation.
Optical and thermal characterization of pure CuO and Zn/CuO using laser-induced breakdown spectroscopy (LIBS), x-ray fluorescence (XRF), and ultraviolet–visible (UV–Vis) spectroscopy techniques

We present elemental analysis and thermal characterization of pure and two different concentrations of zinc-doped copper oxide (CuO) samples using different analytical techniques. The quantitative as well as qualitative investigations, were performed by incorporating calibration-free laser-induced breakdown spectroscopy (CF-LIBS) technique. The plasma was generated on the samples surface by focusing a first harmonic of Nd: YAG laser of 1064 nm wavelength and the time-integrated emission spectra was achieved through a set of five spectrometers equipped with a charge-coupled-device. The acquired optical emission spectra from laser-generated plasma show different concentrations of zinc and copper in different samples which are consistent with their percentage composition measured during the sample synthesis. Using LIBS emission analytical lines, the Boltzmann technique was utilized for estimating the excitation temperature, while the number density was evaluated using the well-known Stark broadening line profile method. Besides, the CF-LIBS elemental analysis was validated by the x-ray fluorescence technique. For the optical study, ultraviolet–visible spectroscopical analysis was performed to find the bandgap of pure and zinc-doped copper oxide samples and consequently, variation in bandgap was studied with the growth of zinc doping. In addition, the thermal properties of the samples were analyzed using the beam deflection spectroscopy technique. The results shows that thermal diffusivity ( D ) and thermal conductivity ( κ ), increased with an increase in zinc concentration.