Miracle-dz/UniFAR-FTUs huggingface dataset:
The diversity of Triassic South American sphenodontians: a new basal form, clevosaurs, and a revision of rhynchocephalian phylogeny

Rhynchocephalians are a group of lizard-like diapsid reptiles that were very diverse during the Mesozoic but are now restricted to a single extant genus in New Zealand. Recent cladistic analyses have revealed two major clades, Eusphenodontia and the more crownward Neosphenodontia, but relationships of individual taxa have remained difficult to determine because of missing data and an unrevised data matrix. Here we drastically revise the established data matrix on rhynchocephalians by reassessing, evaluating, and adding new characters and operational taxonomic units, differing from any previous analyses in our goal to consider all known rhynchocephalians. In addition, we describe a new genus and species of an early eusphenodontian taxon from the Norian of southern Brazil, with a unique mosaic of plesiomorphic and apomorphic traits, and we re-examine the craniodental anatomy of the eusphenodontian Clevosaurus brasiliensis with µCT imaging, revealing a unique form of acrodonty amongst rhynchocephalians. http://zoobank.org/urn:lsid:zoobank.org:pub:A9211C5A-D4F9-472A-B8AB-877D13ABFDD5
Crustal thickness estimation in Indonesia using receiver function method

The existence of seismic wave velocity difference in the Earth crust and mantle creates the possibility to use earthquake data for estimating the crustal thickness utilizing the Ps conversion phase in the boundary. The radial component signal was deconvolved from the vertical component in the frequency domain to estimate receiver function for Indonesia region. We implemented the water level deconvolution techniques with a Gaussian filter of 2.5 Hz to eliminate the high frequency noise in the receiver function. The H-k stacking technique was performed to all receiver functions from each event to predict the crustal thickness and the Vp/Vs ratio below the stations. We analyzed ten azimuthally distributed teleseismic earthquakes recorded by 108 stations of BMKG. The result shows that the crustal thickness in Indonesia varies from 20 to 39.9 km. The western part of Sumatera, northern part of Sulawesi Island, and North Maluku region show generally thinner crust with value about 20 to 25 km. The North Sumatera, Central Java, and East Java show a considerably thicker crust of up to 36 km. Furthermore, our result also reveals a difference of crustal thickness about 5 km with the previous studies.
SYNTHESIS OF CARBON DOTS (CDS) AND DETERMINATION OF OPTICAL GAP ENERGY WITH TAUC PLOT METHOD

Carbon Dots (CDs) which have been synthesized using the laser ablation method show the presence of UV-Vis absorption in the wavelength range of 303 nm to 333 nm for absorbance and 495 nm to 503 nm for fluorescence. Changes in the time duration 1, 2, 3 hours of CDs resulted in changes in the optical energy gap. The optical energy gap valuesare distinguished by the type of indirect transition (n=2) is 3.40 eV (1 hour), 3.15 eV (2 hour), 2.85 eV (3 hour) and direct transition (n=1/2) is 2.58 eV (1 hour), 2.31 eV (2 hour), 1.70 eV (1 hour).
Nonsingular Terminal Sliding Mode Based Finite-Time Dynamic Surface Control for a Quadrotor UAV

In this work, a tracking control strategy is developed to achieve finite-time stability of quadrotor Unmanned Aerial Vehicles (UAVs) subject to external disturbances and parameter uncertainties. Firstly, a finite-time extended state observer (ESO) is proposed based on the nonsingular terminal sliding mode variable to estimate external disturbances to the position subsystem. Then, utilizing the information provided by the ESO and the nonsingular terminal sliding mode control (NTSMC) technique, a dynamic surface controller is proposed to achieve finite-time stability of the position subsystem. By conducting a similar step for the attitude subsystem, a finite-time ESO-based dynamic surface controller is proposed to carry out attitude tracking control of the quadrotor UAV. Finally, the performance of the control algorithm is demonstrated via a numerical simulation.
Flow Instability Detection in Offshore Oil Wells with Multivariate Time Series Machine Learning Classifiers

In offshore petroleum exploration, subsea systems are susceptible to a variety of undesirable events or faults, in which oil wells operation is considered abnormal. Proper detection and classification of such events is crucial in order to reduce downtime, maintenance costs, and even damage to installations. Flow instability is a type of event inherently related to hydrocarbon multiphase flow and root cause of equipment stress and failure. This work investigates applying binary machine learning classifiers on real world captured data for the task of flow instability fault detection. Four different evaluation scenarios were considered. The mostly common scenarios used by the machine learning research community showed that even simple algorithms can reach high classification performance. The remaining scenarios, however, try to avoid the similarity bias problem and showed more realistic results.
A paper-based potentiometric sensing platform based on molecularly imprinted nanobeads for determination of bisphenol A

Currently, the electrode substrates of solid-contact potentiometric sensors for bisphenol A (BPA) are usually expensive glassy carbon, gold or platinum. This poses serious limits to the wide use of these sensors, especially in less developed countries. Herein, we present for the first time a simple and ultra-low-cost, disposable paper-based potentiometric sensor for determination of neutral BPA. Uniform-sized molecularly imprinted nanobeads are utilized as a recognition receptor to provide a higher affinity for BPA since they have larger binding capability and can be well dispersed in the polymeric membrane potentiometric sensor. Commercially available chromatography paper is used as the electrode substrate to build the sensor. It has been found that the proposed paper sensor exhibits similar analytical performance to the classical glassy carbon-based sensor. The present sensor shows a linear anionic potential response over the concentration range from 0.5 to 13 μM with a detection limit of 0.15 μM and exhibits good selectivity over other phenols. The proposed sensing platform has been successfully applied to determination of BPA released from real plastic samples.
All Work and No Play Makes Jack an Inefficient Employee – a Study on Video Games’ Effects on Sustained Attention

Taking short breaks throughout long work hours is proven to be beneficial to our attention spans and work efficiency. How we choose to fill that break time, however, can have a big effect on our performance. This research aims to measure whether video games, which have proven to induce flow and train our minds in numerous ways, can help boost sustained attention and therefore improve work performance if used as part of so-called 'microbreaks'. A total of 36 participants took part in an online experiment measuring their performance in sustained attention tests before and after playing video games compared to the control group activity of browsing “BuzzFeed Quizzes”, an entertainment website. Results of the study show that no significant improvements were measured in terms of attention, indicating that the potential of video games to induce flow does not translate to a playing a beneficial role in microbreaks. Future research is needed to investigate how and when video games should be applied to maximise the potential benefits.
Fully integrated hybrid microwave photonic receiver

We report the first demonstration of a fully integrated hybrid microwave photonic receiver (FIH-MWPR), to the best of our knowledge. In FIH-MWPR, All the optoelectronic components, including a laser source, phase modulators, tunable photonic filters based on microring resonators, and photodetectors, are hybrid integrated into the same substrate. Here, all the functionalities except the laser source are monolithically integrated on a silicon-on-insulator (SOI) chip, whereas the light is provided by a III-V distributed feedback (DFB) semiconductor laser chip. These two photonic integration platforms are combined through the hybrid integration method to demonstrate a completely packaged FIH-MWPR module, which occupies a compact volume of 30mm × 20mm × 10mm. In the experiment, we evaluate the system performance of the FIH-MWPR by measuring critical system performance metrics, including link gain, noise figure (NF), and spur free dynamic range (SFDR). When the FIH-MWPR operates at S-Ku bands (2-18 GHz), its link gain is measured to be -51.9 ~ -58.6 dB, its NF is measured to be 59.7~75.2 dB, and its SFDR is measured to be 89.0~94.1 dB*Hz2/3. This FIH-MWPR shows the advantages of broad operation bandwidth, tunable microwave photonic signal filtering, and superiority in size, weight, and power efficiency (SWaP), and it would provide a promising path to realize a practical microwave photonic receiver.
Innovative High-Pressure Fabrication Processes for Porous Biomaterials—A Review

Biomaterials and their clinical application have become well known in recent years and progress in their manufacturing processes are essential steps in their technological advancement. Great advances have been made in the field of biomaterials, including ceramics, glasses, polymers, composites, glass-ceramics and metal alloys. Dense and porous ceramics have been widely used for various biomedical applications. Current applications of bioceramics include bone grafts, spinal fusion, bone repairs, bone fillers, maxillofacial reconstruction, etc. One of the common impediments in the bioceramics and metallic porous implants for biomedical applications are their lack of mechanical strength. High-pressure processing can be a viable solution in obtaining porous biomaterials. Many properties such as mechanical properties, non-toxicity, surface modification, degradation rate, biocompatibility, corrosion rate and scaffold design are taken into consideration. The current review focuses on different manufacturing processes used for bioceramics, polymers and metals and their alloys in porous forms. Recent advances in the manufacturing technologies of porous ceramics by freeze isostatic pressure and hydrothermal processing are discussed in detail. Pressure as a parameter can be helpful in obtaining porous forms for biomaterials with increased mechanical strength.
Early Results of Time Domain Receiver Function Data Processing in Mt Merapi and Mt Merbabu

The receiver function method is a method to image the earth subsurface by utilizing Ps conversion waves that are formed due to the velocity boundary. In general, the receiver function method estimates depth of structures such as the mantle-crust boundary by deconvoluting the vertical component from the horizontal component. Typical receiver function data processing is done in the frequency domain where the deconvolution process can be seen as a division between two components. In this study, we tried to reprocess the data using a deconvolution technique in time domain, popularly known as iterative time-domain deconvolution. The principle of iterative time domain deconvolution consists of iterative cross-correlation between the horizontal and vertical component. We use data from the DOMERAPI seismic station network located in the vicinity of Mt Merapi and Mt Merbabu. Mt Merapi is one of the most active volcanoes in the world with frequent eruptions and located at the ring of fire chain volcano in Indonesia. Note that the previous receiver function study in this region showed complex signals at some stations that may be related to sediment at shallow sediment and possible layers of low velocity zone that interfering main signal for a crust-mantle boundary. Our current results show iterative time domain RFs have clearer and smoother signal than the frequency domain that help interpreting the waveform signals. We estimate a range of crust thickness between 26-31 km near Mt Merapi. However, we noticed that iterative time domain calculation requires longer computation time and input signal.
Optimization of the Composition of a Novel Bioactive Silage Produced by Mixing of Ground Maize Grains with Olive Mill Waste Waters, Grape Pomace and Feta Cheese Whey

In this work, the production of a novel and sustainable silage was realized and optimized. Three agro-industrial wastes produced in bulk: olive mill wastewater (OMWW), grape pomace (GP) and de-proteinized feta cheese whey (DFCW) were mixed with coarsely ground maize grains, and the mixture was inoculated with commercial lactic bacteria starter culture and fermented for 30 days under anaerobic conditions to obtain silage. Sixty-seven recipes with varying compositions of the three agro-wastes were ensilaged, and four silage quality indices: pH value, % acidity as lactic acid, total lactobacillus count (cfu/g) and total yeast and mold count (cfu/g) were monitored throughout the ensilage process, and the obtained data were used to perform multicriteria optimization of the silage composition. The optimization target was to simultaneously maximize the pH drop, % total acidity as lactic acid and lactobacillus count while minimizing the count of undesirable yeasts and molds. Following this optimization strategy, it was found that the best composition of the mixture of all three tested agro-industrial wastes to obtain a high-quality silage was the one containing: 20% w/w GP, 60% w/w OMWW and 20% w/w feta cheese whey. Finally, the produced silage was tested in broilers’ nutrition and by 10% w/w inclusion in the feed, which led to the production of high added-value bioactive meat rich in ω-3 fatty acids and with high antioxidant capacity.
Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning

A chemically explainable machine learning model was constructed with a small dataset to quantitatively predict the singlet-oxygen-scavenging ability. In this model, ensemble learning based on decision trees resulted in high accuracy. For explanatory variables, molecular descriptors by computational chemistry and Morgan fingerprints were used for achieving high accuracy and simple prediction. The singlet-oxygen-scavenging mechanism was explained by the feature importance obtained from machine learning outputs. The results are consistent with conventional chemical knowledge. The use of machine learning and reduction in the number of measurements for screening high-antioxidant-capacity compounds can considerably improve prediction accuracy and efficiency.
A Systematic Literature Review of the Impact of Human Resources Management Strategies on Employee’s Performance: Role of Job Embeddedness and Organizational Commitment as Mediator Variables

The present study seeks to examine the effectiveness of the practices of Human Resources (HR) on the performance of employees. It explores how job embeddedness and organizational commitment can have a mediation effect on the relation between the HR practices and staff’s performance. Moreover, this study discusses several dimensions of Human Resources Management System (HRMS) such as recruitment, involvement, performance appraisal, compensation, and training. Furthermore, task performance, the efficiency of work and creativity were taken as dimensions of Employee Performance. Data were collected through questionnaires from the participants. The study’s population consisted of 503 employees holding different positions in various sectors. Several results were reached by the study, the most significant of which is the finding that there is a positive relationship between the HRMS and Employee Performance. It was also deduced that an indirect partial mediation effect exists between HRMS and Employee Performance through both job embeddedness and organizational commitment.
An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.
Identification and Analysis of Small Interfering RNAs Associated With Heat Stress in Flowering Chinese Cabbage Using High-Throughput Sequencing

Endogenous small interfering RNAs (siRNAs) are substantial gene regulators in eukaryotes and play key functions in plant development and stress tolerance. Among environmental factors, heat is serious abiotic stress that severely influences the productivity and quality of flowering Chinese cabbage (Brassica campestris L. ssp. chinensis var. utilis Tsen et Lee). However, how siRNAs are involved in regulating gene expression during heat stress is not fully understood in flowering Chinese cabbage. Combining bioinformatical and next-generation sequencing approaches, we identified heat-responsive siRNAs in four small RNA libraries of flowering Chinese cabbage using leaves collected at 0, 1, 6, and 12 h after a 38°C heat-stress treatment; 536, 816, and 829 siRNAs exhibited substantial differential expression at 1, 6, and 12 h, respectively. Seventy-five upregulated and 69 downregulated differentially expressed siRNAs (DE-siRNAs) were common for the three time points of heat stress. We identified 795 target genes of DE-siRNAs, including serine/threonine-protein kinase SRK2I, CTR1-like, disease resistance protein RML1A-like, and RPP1, which may play a role in regulating heat tolerance. Gene ontology showed that predictive targets of DE-siRNAs may have key roles in the positive regulation of biological processes, organismal processes, responses to temperature stimulus, signaling, and growth and development. These novel results contribute to further understanding how siRNAs modulate the expression of their target genes to control heat tolerance in flowering Chinese cabbage.
Highly efficient photovoltaic parameter estimation using parallel particle swarm optimization on a GPU

Accurate modeling of solar photovoltaic (PV) systems is crucial to their control and performance optimization. We focus on the two most widely used PV models, i.e., the single‐ and double-diode model, and try to estimate their parameters from current-voltage data. This task is usually formulated as a nonlinear least squares problem and tackled by various metaheuristic algorithms. Despite the abundance of sophisticated metaheuristics in the literature, we employ an (almost) standard particle swarm optimization (PSO) algorithm and, somewhat unexpectedly, find that such primitive PSO is adequate to solve the problem to high accuracy, though it may take more fitness evaluations. Moreover, given the population-based nature of PSO, we take full advantage of modern graphics processing units (GPUs) and develop a highly efficient PV parameter identification method by effectively parallelizing PSO on a GPU. Numerical results on two benchmark datasets show that our approach can achieve high estimation accuracy on par with state-of-the-art methods while enjoying approximately a hundred-times speedup. Our code is publicly available at https://github.com/ShuhuaGao/PV-PPSO.
Peer Genius Bar: Using the Wisdom of the Crowd to Learn Technology Tools

Abstract Teaching technology when technology moves at the speed of light can be a challenging task. In addition to the fast-paced changes in this field, students possess a range of skills, experiences, and attitudes about technology such that there is no common baseline for content and activities. This article reflects on a technology-related crowdsourcing exercise implemented in a graduate professional course. The collaborative learning exercise fosters autonomy and peer-to-peer learning and has resulted in the exploration of more technology tools than could be shared by one instructor.
Hardware-Oriented Memory-Limited Online Artifact Subspace Reconstruction (HMO-ASR) Algorithm

Artifact Subspace Reconstruction (ASR) is a machine learning technique widely used to remove non-brain signals (referred to as “artifacts”) from electroencephalograms (EEGs). The ASR algorithm can, however, be constrained by the limited memory available on portable devices. To address this challenge, we propose a Hardware-Oriented Memory-Limited Online ASR (HMO-ASR) algorithm. The proposed HMO-ASR algorithm consists of (1) two-level window-based preprocessing including PCA-based and z-score-based preprocessing to clean the data in each window, (2) iterative mean, standard deviation, and covariance update using a parallel algorithm to achieve window-based processing, and (3) early eigenvector matrix determination to save the computation. With the three schemes, the HMO-ASR method can be implemented on mobile devices, application-specific integrated circuits (ASICs) or field programmable gate arrays (FPGAs) with limited memory. The study results showed that the proposed HMO-ASR algorithm can achieve comparable performance to those obtained by the offline ASR algorithm with a 98.64% reduction in memory size. An FPGA implementation is used for silicon proof of the proposed HMO-ASR algorithm.
Machine Operating Speed, Fault Security and Fault Tolerance for Performability Analysis in Industrial Automation

This paper focuses on FPGA-based controllers in the context of industrial automation. For high reliability applications, fault tolerance is often used. Fault security is another important aspect in these applications. Furthermore, machines are often operated at a lower-than-nominal speed. A Performability model is developed which simultaneously incorporates the effects of fault tolerance, fault security and different machine speeds on machine behavior. Another contribution of this paper is the derivation of an expression to obtain the average yearly cost of downtime as a function of the level of fault security of the design.