Miracle-dz/UniFAR-FTUs huggingface dataset:
THE REPRESENTATION OF SUNDANESE FOLKLORE NYI MAS SANGHYANG DEWI SRI IN THREE LANGUAGES

Folklore about Dewi Sri, the goddess of fertility in Indonesian culture has various versions. The version of the folklore used in this article is the Sundanese version “Nyi Mas Sanghyang Dewi Sri” collected in a research done by lecturers of Faculty of Cultural Sciences, Universitas Padjadjaran. The folklore is represented in a written text in Sundanese language as the source language text and from the text, it is represented in Indonesian language as the first target language text, then in English language as the second target language text. How the folklore is represented in three languages and what methods of translation are used in target language texts are the objectives of this research. The research method used is descriptive-comparative method. The results show that the folklore is represented in different ways; some additional information are added particularly in the English target text and the method of translation used in the two target languages is mostly communicative translation but in the second target language text, free translation is also found. Despite these differences, the message that needs to be conveyed about Nyi Mas Sanghyang Dewi Sri as the goddess of fertility remains the same in all texts.
Gaussian mixture model for time series-based structural damage detection

In this paper, a time series-based damage detection algorithm is proposed using Gaussian mixture model (GMM) and expectation maximization (EM) framework. The vibration time series from the structure are modelled as the autoregressive (AR) processes. The first AR coefficients are used as a feature vector for novelty detection. To test the efficacy of the damage detection algorithm, it has been tested on the pseudo-experimental data obtained from the FEM model of the ASCE benchmark frame structure. Results suggest that the presented approach is able to detect mainly major and moderate damage patterns.
Report on Gynaikothrips uzeli (Zimmermann) (Thysanoptera: Phlaeothripidae) from Delhi

This study reports on the new record of thrips Gynaikothrips uzeli (Zimmermann, 1900) found at the Indian Agricultural Research Institute (IARI) campus, New Delhi during January 2018 in folded-leaf galls of Ficus benjamina L. (Moraceae). Predator, Chrysoperla sp. (Neuroptera: Chrysopidae) was also found in the leaf galls feeding on thrips, keeping it under check. Based on nucleotide sequence of partial mitochondrial cytochrome c oxidase subunit 1 (mtCOI) gene DNA barcode was generated for G. uzeli.
Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling’s T 2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by a large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T 2 based on PCA for IDS. The KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T 2 based PCA will be compared with conventional T 2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling’s T 2 based on PCA has excellent performance to detect an anomaly in the network. Compared to conventional T 2 control chart, the T 2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time.
Global crisis management and higher education: Agency and coupling in the context of wicked COVID‐19 problems

Abstract Campus crisis management remains an understudied topic in the context of COVID‐affected higher education. In this paper, we contrasted the ability to tame the wicked problems brought by the pandemic of COVID‐19 in private and public universities in Bangladesh, Cambodia, Colombia, India, Kazakhstan, Uganda, and Ukraine. The cross‐country analysis and diversity of institutional types allowed us to consider a wide range of challenges faced by academic leaders and their institutions during the global pandemic. By drawing on institutional policy reviews and interviews with university administrators, we have examined tensions between the human and institutional agencies on these crisis‐stricken campuses given differing institutional coupling, sizes, resources, and missions. The focus on agential co‐dependencies and institutional coupling lays the ground for conceptualizing campus crisis management as a culturally specific construct in the context of higher education affected by the global pandemic.
Preparation of a mixed Al/Sc nano-oxide derived from the bauxite residue (red mud) via the sulfuric acid roasting–leaching–precipitation process

In this study, three methods were used and compared for the selectable extraction of aluminum/ scandium with the least amount of iron in red mud (RM) samples from the Iran alumina plant in Jajarm as follows: 1) RM direct acid leaching with H2SO4, 2) RM washing with hydrochloric acid and oxalic acid before leaching with H2SO4, and 3) RM sulfuric acid roasting-leaching-precipitation. The aim was to extract the highest amount of scandium while preventing the leaching of other metals, especially iron. Due to any discriminative features, the selective separation of Al/Sc with methods 1 and 2 was impossible practically. While, method 3 resulted in 73.7% of extracted scandium under optimal conditions with only 0.6% of iron found in the final product. The characterization of the final oxide product was done via inductively coupled plasma mass spectrometry (ICP-MS) and energy-dispersive X-ray analysis (EDX). The morphology of the oxide product was examined by field emission scanning electron microscopy (FE-SEM). This mixture oxide had a nanosize spherical shape and was distributed uniformly. The pH of the remaining red mud after the acid roasting-leaching-precipitation method was 8, which was far more environmentally desirable than the primary red mud with a pH = 12.
Stellate Ganglion Block for Anosmia and Dysgeusia Due to Long COVID

Anosmia and parosmia refer to the loss or dysfunction of smell, respectively. Dysgeusia refers to taste disturbance. The coronavirus disease 2019 (COVID-19) pandemic and the subsequent phenomenon of Long COVID syndrome have been associated with an increased incidence of anosmia and dysgeusia. Smell and taste disturbances associated with COVID-19 are usually self-limiting but can persist for longer periods in some cases. Imbalances of the autonomic nervous system, especially dysregulation of the sympathetic system, are implicated in the persistence of anosmia and dysgeusia post-COVID-19 infection. Stellate ganglion block (SGB) can diminish the increased sympathetic activity and potentially resolve anosmia and dysgeusia occurring due to Long COVID. The authors report the successful resolution of persistent anosmia and dysgeusia due to Long COVID in a female patient after she underwent SGB.
A Switched Planar Multicoil Transmitter Antenna Designed With Nonuniform H-Field Forming for Small Device Localization

To monitor 3-D position and orientation of a small device having planar receiver (Rx) coil, a switched multicoil transmitter (Tx) antenna is proposed by adopting time-divisional approach. The existing multifrequency methods require eight or more input ports to feed the coils with multifrequency signals for localization. In contrast, the proposed Tx antenna is driven by a single frequency source and uses only three input ports for reduced complexity. Moreover, the multiple coils of the proposed antenna are optimally grouped to form nonuniform magnetic-field distribution using three switches (SWs) in such a manner that better localization accuracy is achieved as compared to the multifrequency design. Rigorous analysis and simulations are performed to investigate the performance of the proposed design and localization approach. To validate the claim, experimental study results proved the potential of the proposed antenna employed for tracking a planar Rx in applications like medical robots, navigation of people with limited vision, virtual reality, etc.
Relaxing or arousing environments (through color and music) for fostering designer’s creativity

ABSTRACT Within the literature devoted to studying the influence of environment factors on people’s creativity, the majority analyzed these factors as isolated elements and assess individual’s creativity in general. The aim of this work is to analyze the influence of some of these environmental factors on the design outcomes in terms of creativity, considering the potential emotion it can cause (relaxation or arousal). An experiment was developed to test the results generated in different settings combining environment factors that enhance creativity, in which designers were asked to develop different creative concepts. Different colors and music were combined in order to set arousing and relaxing environments. Results point to differences in the creativity parameters, according to the environment in which designers worked.
Effects of Moderate Enzymatic Hydrolysis on Structure and Functional Properties of Pea Protein

Pea protein (PP) was moderately hydrolyzed using four proteolytic enzymes including flavourzyme, neutrase, alcalase, and trypsin to investigate the influence of the degree of hydrolysis (DH) with 2%, 4%, 6%, and 8% on the structural and functional properties of PP. Enzymatic modification treatment distinctly boosted the solubility of PP. The solubility of PP treated by trypsin was increased from 10.23% to 58.14% at the 8% DH. The results of SDS-PAGE indicated the protease broke disulfide bonds, degraded protein into small molecular peptides, and transformed insoluble protein into soluble fractions with the increased DH. After enzymatic treatment, a bathochromic shift and increased intrinsic fluorescence were observed for PP. Furthermore, the total sulfhydryl group contents and surface hydrophobicity were reduced, suggesting that the unfolding of PP occurred. Meanwhile, the foaming and emulsification of PP were improved after enzymatic treatment, and the most remarkable effect was observed under 6% DH. Moreover, under the same DH, the influence on the structure and functional properties of PP from large to small are trypsin, alcalase, neutrase and flavourzyme. This result will facilitate the formulation and production of natural plant-protein-based products using PP.
Residues of Pesticides and Heavy Metals in Polish Varietal Honey

The levels of chemical pollutants were determined in 30 samples of varietal honey from southeastern Poland, including 223 pesticides (insecticides, herbicides, fungicides, acaricides, plant growth regulators, and veterinary drugs) and 5 heavy metals (Pb, Cd, Hg, Cu, and Zn). In 10% of the samples, no pesticide residues were found. The most frequently identified pesticides were thiacloprid (90% of the samples, max 0.337 mg/kg), acetamiprid (86.6%, max 0.061 mg/kg), carbendazim (60%, max 0.049 mg/kg), DMF (56.6%, max 0.038 mg/kg), total amitraz (53.3%, max 0.075 mg/kg), thiamethoxam (26.6%, max 0.004 mg/kg), thiacloprid-amide (13.3%, max 0.012 mg/kg), dimethoate (10%, max 0.003 mg/kg), azoxystrobin (10%, max 0.002 mg/kg), tebuconazole (6.66%, max 0.002 mg/kg), and boscalid (3.33%, max 0.001 mg/kg). The acceptable limits for the compounds were not exceeded in any sample. The Pb content ranged between 0.044 and 0.081 mg/kg. The concentration of Hg and Cd did not exceed 5.0 µg/kg and 0.02 mg/kg, respectively. The honey variety significantly (p < 0.01) influenced the content of Cu, which ranged from 0.504 (rapeseed honey) to 1.201 mg/kg (buckwheat). A similar tendency (p > 0.05) was observed for the Zn content, which ranged from 0.657 mg/kg (linden) to 2.694 mg/kg (buckwheat). Honey produced in southeastern Poland was shown to be safe for human consumption.
A Low-Profile Wearable Textile Antenna Using AMC for WBAN Applications at 5.8GHz

This paper presents a low-profile, wearable textile antenna, designed for Wireless Body Area Network (WBAN) applications operating in the 5.8GHz band for Industrial, Scientific, and Medical (ISM) applications. An Artificial Magnetic Conductor (AMC) structure was used to improve antenna performance and protect the human body from back-radiation. The antenna with the integrated AMC achieved a measured gain of 8.92dBi, an efficiency of 80%, a wide impedance bandwidth of 1.4GHz (24.1%), and SAR values of 0.00103 and 0.00034W/Kg for 10g and 1g tissues respectively. The proposed antenna was studied in a worn-on-body scenario using a multilayer numerical model of the human body. The influence of the thickness of each tissue layer of the human body was investigated. The results showed that the antenna maintained its performance, a stable gain was obtained, and the SAR values were also below the IEEE guidelines that guarantee the safety of the wearer.
Relationship Between Time of Class and Student Grades in an Active Learning Course

A factor related to students’ course performance that has seen limited research compared to other academic factors is the time of day a class is offered. Because of students’ chronotypes (i.e., preferred time of day to study or work), time of class can influence attendance, which has a strong correlation with students’ performance in a course.
The relationship between firms’ financial performance and performance measures of circular economy sustainability: an investigation of the G7 countries

To achieve sustainable development, companies are aware that they need to move from the ‘ take-make-consume-throw away ’ pattern toward an economic model based on sharing, re-use, repair, refurbishing, and recycling. Implementing the circular economy concept into a company ’ s business model is not an easy process, given that some eco-innovations have higher costs with direct effects on financial performance. Hence, this study aims to determine the relationship between corporate financial performance and the performance measures of the circular economy through multivariate econometric estimations. The observations considered in our sample reflect the financial and circular economy performance for 411 companies with headquarters in the European G7 members disclosed in the period 2014-2020. The EU was chosen to carry out the study because the member states subscribe to a general reporting framework and the European Green Deal agreement, making it the most active in implementing the circular economy model. The findings are based on the econometric models related to the nature and amplitude of the association relation between firms ’ financial performance and circular economy in connection with firms ’ performance. The results show that the performance of the circular economy has a relatively small influence on the firms ’ financial performance.
Association of meniscal tears with cruciate ligament injuries

Aim: To find the association of meniscal tears with isolated and combined cruciate ligament injuries, grades of injury, time of injury and type of injury. Method: Magnetic resonance (MR) imaging of 240 patients with knee pain who diagnosed with anterior cruciate ligament (ACL) and Posterior Cruciate Ligament (PCL) injury by arthroscopy were studied. Results: Road and traffic accidents (RTA) caused in 80.4% of cases. A total of 192 patients (80%) with isolated ACL involvement, seven patients (2.9%) with isolated PCL involvement, and 41 patients (17%) with combined ACL and PCL involvement. In cases with ACL involvement (n=192), 17 patients (8.8%) had grade 1 injury: sprain, 82 patients (42.7%) had grade 2/3 injury: partial tear and 93 patients (48.4%) had grade 4 injury: complete tear. Among partial ACL tear, lateral meniscus involvement was predominant in 45 patients (54.8%), whereas medial meniscus was involved in 26 patients (31.7%) and normal meniscus was observed in 19 patients (23.1%). In lateral meniscus, the anterior horn was majorly involved 36 patients (80%) as compared to posterior horn in 15 patients (33.3%). Conclusion: The present study indicates that the anterior horn of the lateral meniscus was predominantly involved in patients with isolated ACL and combined injury of ACL and PCL. The posterior horn of the medial meniscus was more commonly involved in chronic injury of ACL, whereas the posterior horn of the lateral meniscus was involved in acute ACL injury. Early ACL reconstruction is recommended for the prevention of secondary meniscal tears.
Early warning of reciprocating compressor valve fault based on deep learning network and multi-source information fusion

An early warning method of compressor valve fault based on multi-parameter signals (vibration, pressure, and temperature) is presented in this work. Due to the complexity working condition, the run data of the compressor are of problems like noise and feature aliasing, which makes it difficult to extract useful features and find out the running law from the original signals. In this work, an improved deep learning network Multi-Level Fusion long short-term memory based on Component Evaluating Empirical Mode Decomposition and Fuzzy C-Means (CEEMD-FCM & MLF-LSTM) for parameter prediction of reciprocating compressor and an information fusion strategy is proposed for compressor valve fault warning. The CEEMD-FCM & MLF-LSTM network consists of data processing block, information learning block, and prediction output block, which is mainly responsible for parameter prediction. In the data processing block, the CEEMD-FCM algorithm is used for parameter decomposition, noise removal, and fuzzy mode (FM) reconstruction, which generates the input for the information learning block to ensure the predicting accuracy and reduce model complexity. MLF-LSTM is constructed to predict the parameter in the future by learning the temporal and spatial characteristics of FMs of the run data. Then, an early warning strategy for compressor valve fault based on multi-source information fusion is developed. Experimental results have verified that the proposed CEEMD-FCM & MLF-LSTM model and early warning strategy could realize early warning of compressor valve fault effectively.
Voltage instability prediction based on reactive power reserve of generating units and zone selection

In this study, a voltage instability prediction algorithm is proposed based on Thevenin impedance matching theorem. This algorithm consists of three important parts: an adaptive network zoning algorithm, the Thevenin-based voltage instability risk predictor (VIRP) and a third-order extrapolation technique. The zoning algorithm, based on the concept of electrical distance, is proposed to divide the main network into several zones after the occurrence of any large event. Each zone consists of some load buses and generating units have the most influences on their voltage stability. The proposed VIRP is defined similarly to the Thevenin indicator multiplied with a contribution factor. This factor is calculated for each zone, based on the lack of the reactive power reserve in its generating units, to increase the predictor value, especially in the proximity of the instability. So, VIRP could reduce the instability detection time with respect to the Thevenin indicator. The further reduction in the instability detection time is obtained by the well known cubic spline extrapolation technique. This function is applied to the values of VIRP to predict the instant of the instability. The proposed algorithm is tested on the modified New England 39-bus test system supplied with the voltage-source converter-high-voltage dc link.
AB-DLM: An Improved Deep Learning Model Based on Attention Mechanism and BiFPN for Driver Distraction Behavior Detection

Driver distraction behavior causes a large number of traffic accidents every year, resulting in economic losses and injuries. Currently, the driver still plays an important role in the driving and control of the vehicle due to the low level of vehicle automation and the immature development of autonomous driving. Therefore, it is vital to research distraction detection for drivers. However, in realistic driving scenarios with uncertain information, they are still some challenges in efficient and accurate driver distraction detection. In this paper, an improved deep learning model based on attention mechanisms and bi-directional feature pyramid networks (BiFPN) is proposed to identify driver distractions. Firstly, an improved data augmentation strategy is introduced to increase the data diversity to enhance the generalization capability of the model. Secondly, the squeeze-and-excitation (SE) attention mechanism layer is used after the C3 module of the original backbone network to enhance the important feature information and suppress the minor feature information. Finally, the BiFPN module is introduced into the neck network to better achieve multi-scale feature fusion without increasing the calculation amount too much. The experimental results show that the method proposed in this paper has an average mean accuracy rate (mAP) of 0.956 on the test set. Compared to the original model the mAP has improved by 13.2%. The detection speed of the model is 71 frames per second, and the memory occupation is 15.9 MB. This method has the advantages of high recognition accuracy, fast detection speed, and small memory occupation of the model, which are important for achieving engineering deployment.