Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Ultrasonic Harbor Porpoise Clicks with Hetrodyning and Teager–Kaiser Energy Operator

This study advances research in the design of autonomous machine listening agents to identify the presence of marine mammals. We compare two common deep learning architectures in the detection of ultrasonic harbor porpoise clicks in noisy coastal soundscapes. We reduce the high dimensionality of the ultrasonic underwater recordings with a heterodyned Teager–Kaiser energy operator approach, which shifts the ultrasonic signal down to lower frequencies. We cross-validate our deep learning models by day to increase our model’s ability to generalize between the ever-changing background noise environment. We demonstrate our approach over a large dataset of (n=8286) examples of harbor porpoise click steams recorded in the Bay of Fundy. We discuss our results in the context of emphasizing the need to overcome such shifts in noise to design a robust porpoise click classifier that is capable of real-time deployment and able to generalize to all real-world conditions.
A Novel Cyclic Scheduling Approach to Time-Constrained Single-Arm-Robot Multi-Cluster Tools

In semiconductor wafer fabrication, it is incredibly challenging to operate a multi-cluster tool that is composed of several single-cluster tools. This paper deals with the scheduling problem of a time-constrained single-arm-robot multi-cluster tool. We first propose a characteristic single-arm-robot cluster tool and investigate its cyclic scheduling problem. Then, we devise a feature transformation method. By using such a novel approach, a single-arm-robot multi-cluster tool with arbitrary topology can be easily reduced into a characteristic single-arm-robot cluster tool. Thus, the robot tasks are reassigned and then a feasible cyclic schedule can be obtained if the system is schedulable. Finally, we provide illustrative experiments to validate the practicability and availability of the proposed approach.
Design and Performance Studies of Analog Linearizers for Thermistors

The paper proposes simple analog linearization circuits for thermistors. The linearizers are based on a single operational amplifier based circuit, and its basic version operates in a single mode. Multi-mode linearizers, which can give improved linearity with thermistors, are also designed and evaluated. The possible error sources of the proposed linearizers has been identified and analyzed for improved performance. The improvement in nonlinearity of linearizer with respect to K is at the cost of circuit complexity. The efficacy of the presented linearizers are evaluated with the help of simulation and experimental studies. On interfacing with a commercial thermistor, the circuits was shown to render a linearized indication of the input temperature. The maximum nonlinearity for the developed triple-mode linearizer is 1.05% for a wide span of temperature. Feasibility of the schemes for reduced temperature range is also verified. This linearized thermistor system can have a number of applications (e.g., industrial temperature regulation systems).
The MUSE 3D view of feedback in a high-metallicity radio galaxy at z = 2.9

We present a detailed study of the kinematic, chemical and excitation properties of the giant Ly$\alpha$ emitting nebula and the giant \ion{H}{I} absorber associated with the $z = 2.92$ radio galaxy MRC 0943--242, using spectroscopic observations from VLT/MUSE, VLT/X-SHOOTER and other instruments. Together, these data provide a wide range of rest-frame wavelength (765 \AA$\,$ -- 6378 \AA$\,$ at $z = 2.92$) and 2D spatial information. We find clear evidence for jet gas interactions affecting the kinematic properties of the nebula, with evidence for both outflows and inflows being induced by radio-mode feedback. We suggest that the regions of relatively lower ionization level, spatially correlated with the radio hotspots, may be due to localised compression of photoionized gas by the expanding radio source, thereby lowering the ionization parameter, or due to a contribution from shock-heating. We find that photoionization of super-solar metallicity gas ($Z/Z_{\odot}$ = 2.1) by an AGN-like continuum ($\alpha$=--1.0) at a moderate ionization parameter ($U$ = 0.018) gives the best overall fit to the complete X-SHOOTER emission line spectrum. We identify a strong degeneracy between column density and Doppler parameter such that it is possible to obtain a reasonable fit to the \ion{H}{I} absorption feature across the range log N(\ion{H}{I}/cm$^{-2}$) = 15.20 and 19.63, with the two best-fitting occurring near the extreme ends of this range. The extended \ion{H}{I} absorber is blueshifted relative to the emission line gas, but shows a systematic decrease in blueshift towards larger radii, consistent with a large scale expanding shell.
Composition dependence of bulk superconductivity in 
YFe2Ge2

In the layered iron-based superconductor ${\mathrm{YFe}}_{2}{\mathrm{Ge}}_{2}$, a high Sommerfeld ratio of $\ensuremath{\sim}100\phantom{\rule{0.16em}{0ex}}\mathrm{mJ}/\mathrm{mol}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{2}$ and a ${T}^{3/2}$ temperature dependence of the electrical resistivity at low temperature $T$ indicate strong electronic correlations and point toward an unconventional pairing state. We have investigated the role of composition and annealing conditions in optimizing the growth of high-quality ${\mathrm{YFe}}_{2}{\mathrm{Ge}}_{2}$. Our findings confirm that bulk superconductivity is observed in samples with disorder scattering rates less than $2{k}_{B}{T}_{c}/\ensuremath{\hbar}$. Fe deficiency on the Fe site is identified as the dominant source of disorder, which can be minimized by precipitating from a slightly iron-rich melt, followed by annealing.
The GALEX Ultraviolet Virgo Cluster Survey (GUViCS). VII. Brightest cluster galaxie UV upturn and the FUV-NUV color up to redshift 0.35

CONTEXT:At low redshift, early-type galaxies often exhibit a rising flux with decreasing wavelength in the 1000-2500 \AA{} range, called "UV upturn". The origin of this phenomenon is debated, and its evolution with redshift is poorly constrained. The observed GALEX FUV-NUV color can be used to probe the UV upturn approximately to redshift 0.5. AIMS: We provide constraints on the existence of the UV upturn up to redshift $\sim$ 0.4 in the brightest cluster galaxies (BCG) located behind the Virgo cluster, using data from the GUViCS survey. METHODS:We estimate the GALEX far-UV (FUV) and near-UV (NUV) observed magnitudes for BCGs from the maxBCG catalog in the GUViCS fields. We increase the number of nonlocal galaxies identified as BCGs with GALEX photometry from a few tens of galaxies to 166 (64 when restricting this sample to relatively small error bars). We also estimate a central color within a 20 arcsec aperture. By using the $r$-band luminosity from the maxBCG catalog, we can separate blue FUV-NUV due to recent star formation and candidate upturn cases. We use Lick indices to verify their similarity to redshift 0 upturn cases. RESULTS: We clearly detect a population of blue FUV-NUV BCGs in the redshift range 0.10-0.35, vastly improving the existing constraints at these epochs by increasing the number of galaxies studied, and by exploring a redshift range with no previous data (beyond 0.2), spanning one more Gyr in the past. These galaxies bring new constraints that can help distinguish between assumptions concerning the stellar populations causing the UV upturn phenomenon. The existence of a large number of UV upturns around redshift 0.25 favors the existence of a binary channel among the sources proposed in the literature.
The Close AGN Reference Survey (CARS): SOFIA Detects Spatially Resolved [C ii] Emission in the Luminous AGN HE 0433-1028

We report spatially resolved [C ii] λ158 μm observations of HE 0433-1028, which is the first detection of a nearby luminous active galactic nucleus (AGN; redshift 0.0355) with the Field-Imaging Far-Infrared Line Spectrometer (FIFI-LS) on board the airborne Stratospheric Observatory For Infrared Astronomy (SOFIA). We compare the spatially resolved star formation tracers [C ii], as provided by our SOFIA observations, and Hα from the Multi Unit Spectroscopic Explorer (MUSE) optical integral-field spectroscopy. We find that the [C ii] emission is mainly matching the extended star formation as traced by the extinction-corrected Hα line emission but some additional flux is present. While a larger sample is needed to statistically confirm our findings and investigate possible dependencies on AGN luminosity and star formation rate, our study underlines the necessity of collecting a spatially resolved optical–far-infrared data set for nearby AGNs, and shows that it is technically feasible to collect such data sets with FIFI-LS on board SOFIA.
Customer Loyalty Based On Internet Service Providers-Service Quality

Evaluating user loyalty attitudes and behavior based on perceived service is a strategy for internet service providers to survive and win the competition. This study aims to analyze customer attitudinal and behavioral loyalty based on the service quality of internet service providers, namely network quality, customer service, information quality, security, and privacy. Data was collected using a questionnaire distributed to 397 users who subscribe to fixed broadband in five major cities in Indonesia (DKI Jakarta, Tangerang, Depok, Bekasi, and Bogor). The purposive sampling technique was determined from the research area with the criteria of one year of subscription to fixed broadband internet. The author uses a quantitative method with Structural Equation Modeling based on variance (PLS) to answer the hypothesis. The study results prove that all hypotheses have a positive and significant effect. Security and privacy are the most potent factors in explaining customer attitudinal and behavior loyalty.
AKRUAL: Jurnal Akuntansi

The purpose of this study is to analyzed tensions faced by higher education in integrating sustainability into their curriculum, practices and policies during COVID-19 outbreak especially in accounting education. As we know, higher education has an important role in implementing economic, social, and environmental dimensions simultanously. During this pandemic, higher education gets greater tensions in integrating sustainability. This research used qualitative descriptive approach and single case study with several unit of analysis. This research begins with a study of sustainability integration that has been internalized in institutions, analyzing tensions faced by higher education and strategies to mitigate these tensions. We used Riau University and Polythecnic of State Finance STAN as our samples. We found that sustainability integration through curriculum, practices and policies in higher education is not fully implemented. Furthermore, while implementing sustainability higher education faces some tensions at level, change and context dimensions according to framework developed by Hahn et al, (2014). During this COVID-19 pandemic, Higher Education faces greater tensions to integrate sustainability into their curriculum, practices and policies. To mitigate those tensions, they can utilize acceptance and resolution strategies according to their respective needs and consequences
Advances in Magnetic Domain Walls and Their Applications

This article explores the recent developments in spin-based domain wall (DW) memories. The physics behind the DW motion, device materials, current challenges, and applications have been discussed in detail. DWs can propagate through a magnetic nanowire by the application of external magnetic fields or by spin-polarized electric currents. Great progress has been made in these devices since the introduction of electric current-induced DW motion. However, driving DWs necessitates large spin-current densities that are incompatible with low-power devices. Therefore, significant efforts have been made to achieve highly efficient and controlled DW motion by material engineering and different mechanisms such as spin-orbit-torque (SOT), Dzyaloshinskii-Moriya interaction (DMI), and voltage-controlled magnetic anisotropy. The controlled manipulation of DWs in magnetic materials has inspired numerous strategies for high-density memory and energy-efficient logic implementation. Moreover, these devices are the potential candidates for neuromorphic computing applications that can be combined with logic-in-memory. The displacement of the DW can achieve multiple resistance states that have opened up possibilities of building artificial neurons and synapses. Despite the rapid progress, DW devices face several challenges such as low read margin, low speed, and sub-20nm scalability. Future research directions have to focus on material engineering and fabrication techniques to address these issues. Simultaneously, efforts from the circuit and system perspectives are extensively required in exploring the possible uses of these devices.
Global Safety Characteristics of Wheeled Mobile Manipulators

Mobile manipulators have become increasingly popular in industry because they can be used for a large variety of tasks in a versatile and flexible manner. The perception and planning/control schemes of mobile robots enable them to share a workspace with humans. However, as undesired or unforeseen contacts can generally not be avoided, it is essential to understand the intrinsic safety properties of such systems. Then, collision safety can be systematically accounted for in mechanism design, planning, and control. In this paper, we derive the safety characteristics of wheeled mobile manipulators, more specifically the achievable reflected mass and velocity range, and show how they can be related to human injury data in the previously introduced Safety Map for a model-independent and interpretation-free safety assessment. We investigate two common types of wheeled mobile platforms and the combination of these with the seven-DOF Franka Emika robot. We analyze the influence of the vehicle parameters on the safety performance and derive the Safety Map representations for four practically relevant industrial collision scenarios.
Infall and SiO emission in V838 Mon

ABSTRACT We present moderate and high resolution infrared spectroscopyof the peculiar eruptivevariable V838 Mon, which underwent a series of remarkable outbursts in early 2002.During the period covered by our observations, 2002 December–2003 December, thenear-infrared spectrum continued to show many of the characteristics of a very coolsupergiant. However, throughout this period the spectrum also revealed strong andvariable SiO ﬁrst overtone emission, and Paβ emission. The 2003 December spectrumcontained a series of Tiilines with inverse P Cygni proﬁles. This is clear evidence thatsome material is falling inward towards the star.Key words: stars: individual: V838 Mon 1 INTRODUCTIONThe multiple outburst episode of V838 Mon inearly 2002 has been well documented (Munari et al.2002; Kimeswenger et al. 2002; Crause et al. 2003;Banerjee & Ashok 2002; Wisniewski et al. 2003;Rushton et al. 2005). The object was ﬁrst detected inoutburst on 2002 January 6 (V max = 10) (Brown et al.2002), and subsequent outbursts developed in 2002 February(V
Identification of Foliar Disease Regions on Corn Leaves Using SLIC Segmentation and Deep Learning Under Uniform Background and Field Conditions

Plant diseases lead to severe losses in crop yield worldwide. The conventional approach for diagnosing diseases relies on manual scouting. In recent years, advances in convolutional neural networks have motivated the use of deep learning-based computer vision for automatically identifying plant diseases. Although image classification techniques are commonly used for analyzing agricultural data, their use for accurately identifying diseased regions corresponding to different disease types on individual plant leaves is limited. In this study, Simple Linear Iterative Clustering (SLIC) segmentation was used on corn leaf images from the PlantVillage and CD&S datasets to create super-pixels, a cluster of pixels representing a region of interest on a corn leaf. The VGG16, ResNet50, DenseNet121, Xception, and InceptionV3, pre-trained deep learning models were utilized to identify diseased regions corresponding to five super-pixel classes (healthy, northern leaf blight (NLB), gray leaf spot (GLS), common rust, and background) for the PlantVillage dataset and four super-pixel classes (NLB, GLS, northern leaf spot, and background) for the CD&S dataset, on corn leaves. After setting the spatial proximity value (sigma) for SLIC segmentation to five, a total of 100 models were trained by using different numbers of segments per image (five and fifteen) in the SLIC algorithm for both datasets and choosing training: testing split ratios of 90:10, 80:20, 70:30, 60:40, and 50:50 for each of the five models. The highest overall testing accuracy of 97.77% was observed after training the DenseNet121 model to identify super-pixels created from the CD&S dataset, belonging to the four classes created using a sigma value of five, five segments per image, and an 80:20 training: testing split ratio. Web and mobile applications were developed to identify diseased regions on corn leaves using the best deep learning model as the classifier. The results suggest that SLIC segmentation on corn leaf images helps accurately identify diseased regions. This research demonstrates the potential of image-based scouting as an efficient alternative to manual scouting for disease monitoring.
A Fast Deep Learning Based Approach for Unsupervised Anomaly Detection in 3D Data

Anomaly detection is an important method in industrial manufacturing environments for defect detection, and consequently, for the quality requirements attainment. There is a strong trend to automate inspection systems using Artificial Intelligence techniques, especially through the use of 2D information. However, for dimensional inspection, it is necessary to have 3D data that provide detailed geometric information of the object. In this paper, we present a novel Deep Learning based approach for fast anomaly detection applicable to industrial domains by using point cloud voxelization. We evaluate our approach on a custom dataset in which our model achieves high accuracy, comparable to the state-of-the-art 3D Deep Learning models, while being faster than traditional anomaly detection methods.
Understanding Context of Use from Online Customer Reviews using BERT

For user experience (UX) analysis in product design, the context of use (such as task, activities and environment) is a valuable element that enables context-awareness in accordance with the available context of use elements of users. Typically, conventional methods such as interviews or questionnaires are used to extract the context of use elements, but they are labour-intensive and time-consuming and thus do not scale well. On the other hand, the automatic extraction approaches from existing studies are not as effective as the extracted context of use phrases are not as informative. In this study, we present an automatic approach to exploit and understand the context of use elements from online customer reviews using BERT. Firstly, the context of use elements from online customer reviews is labelled using a BERT-based approach. Secondly, a syntactic-based post-processing is designed to check the labelled results and form the phrases related to the context of use. Finally, the customers’ preferences related to the contexts of use is analysed by studying and aggregating it with its relevant hedonic quality (such as positive or negative) which can then be used to enrich the UX modelling. For product designers, the modelling results can facilitate the optimisation of product design. A case study was conducted to understand and leverage the context of use elements in UX from online customer reviews to support customer strategy creation and design activities.
Pulsation-triggered dust production by asymptotic giant branch stars

Eleven nearby (<300 pc), short-period (50-130 d) asymptotic giant branch (AGB) stars were observed in the CO J = 2-1 line. Detections were made towards objects that have evidence for dust production (K-s - [22] greater than or similar to 0.55 mag; AK Hya, V744 Cen, RU Crt, alpha Her). Stars below this limit were not detected (BQ Gem, epsilon Oct, NU Pav, II Hya, CL Hyi, ET Vir, SX Pav). K-s - [22] colour is found to trace mass-loss rate to well within an order of magnitude. This confirms existing results, indicating a factor of 100 increase in AGB-star mass-loss rates at a pulsation period of similar to 60 d, similar to the known 'superwind' trigger at similar to 300 d. Between similar to 60 and similar to 300 d, an approximately constant mass-loss rate and wind velocity of similar to 3.7 x 10(-7) M-circle dot yr(-1) and similar to 8 km s(-1) is found. While this has not been corrected for observational biases, this rapid increase in mass-loss rate suggests a need to recalibrate the treatment of AGB mass-loss in stellar evolution models. The comparative lack of correlation between mass-loss rate and luminosity (for L less than or similar to 6300 L-circle dot) suggests that the mass-loss rates of low-luminosity AGB-star winds are set predominantly by pulsations, not radiation pressure on dust, which sets only the outflow velocity. We predict that mass-loss rates from low-luminosity AGB stars, which exhibit optically thin winds, should be largely independent of metallicity, but may be strongly dependent on stellar mass.
Empirical Study of Utilizing the Information Retrieval in Similarity Checker Tools based on Academic Violation Findings

The development of plagiarism checking tools has been widely used to help improve academic performance. But contrary to that, many acts of fraud are also carried out to deceive these various tools. One of them is because there is still limited advanced technology to complete the tool. This paper is presented in order to provide information to readers about the need for optimization and development of information technology in the use of similarity checker. As one of the features used by a authors to check the originality of writing, Turnitin is one of the effective similarity checker currently used by many people. The advantages of Turnitin are inversely proportional to the number of individuals who commit fraud so that their writing is not detected, which is similar to data from previous research. The authors presents a finding as well as theoretical recommendations that can be done to develop technology in this Turnitin. One of them is the use of a Turnitin workflow that should be able to go through a procedure of checking the incoming text first before comparing it directly with existing data.
Comparison of Transfer Learning Models for COVID-19 Detection using Chest X-Ray Images

The transmission of cases of the COVID-19 virus (Coronavirus disease) is currently still racing. Even though the COVID-19 recovery rate has increased, new variants such as Omicron or Centaurus are still spreading in various countries. Detection COVID-19 based on Chest X-Ray is needed to avoid the wider transmission of the virus. This study uses the CNN (Convolutional Neural Network) transfer learning models like AlexNet, VGG19, Resnet50, InceptionV3 and the BasicCNN model. This study uses a dataset named the COVID-19 Radiography Database which contains three classes, namely COVID-19, Normal, and Viral Pneumonia. The results of this study indicate that the Resnet50 model is the best model that produces the highest accuracy compared to other models. The Resnet50 model obtained an accuracy of 98.68%. Then followed by other models in sequence, namely InceptionV3, VGG19, AlexNet, and BasicCNN. Evaluation in this study also uses Precision, Recall, and F1-Measure which show that Resnet50 obtains the highest value compared with other methods. This shows that the transfer learning model has a good performance for detection of COVID-19 based on Chest X-Ray.