Miracle-dz/UniFAR-FTUs huggingface dataset:
Import demand potential for Indonesian rubber products in ASEAN countries

Increasing imports of rubber products from Indonesia is the key to the success of the downstream policy of rubber products to gain added value and job opportunities in the country. The rubber product market in ASEAN is more dynamic, and Indonesia must focus on strengthening competitiveness and exploiting potential markets. This paper aims to describe the potential demand for imports of Indonesian rubber products in ASEAN countries, especially Malaysia, Singapore, Thailand, the Philippines, and Vietnam. The flow of imports of rubber products uses the stochastic frontier panel data model to determine the potential demand for imports of Indonesian rubber products. Indonesian rubber products compete fiercely with similar industries in importing and exporting countries from East Asia. Indonesia mostly exports rubber products with HS codes 4015 and 4016 in the form of gloves, mittens, and mitts, rubber cellular, floor coverings and mats, erasers, gaskets and rings, boats, and fender docks to all markets in ASEAN countries. They were followed by rubber products with HS code 4010 in the form of conveyor belts or transmission belts from vulcanized rubber. The potential for import demand for other types of Indonesian rubber products (code HS4014, 4009, 4008) in ASEAN countries is relatively small and is growing slowly. Overall, the potential demand for imports of Indonesian rubber products tends to decline in ASEAN countries, as well as the actual imports of rubber products from Indonesia compared to their potential, which also declines slowly.
Confidence Intervals for Low-Dimensional Parameters With High-Dimensional Data

Abstract. The purpose of this paper is to propose methodologies for statistical inference of low-dimensional parameters with high-dimensional data. We focus on constructing confidence intervals for individual coefficients and linear combinations of several of them in a linear regression model, although our ideas are applicable in a much broad context. The theoretical results presented here provide sufficient conditions for the asymptotic normality of the proposed estimators along with a consistent estimator for their finite-dimensional covariance matrices. These sufficient conditions allow the number of variables to far exceed the sample size. The simulation results presented here demonstrate the accuracy of the coverage probability of the proposed confidence intervals, strongly supporting the theoretical results.
Misfire Detection in Spark Ignition Engine Using Transfer Learning

Misfire detection in an internal combustion engine is an important activity. Any undetected misfire can lead to loss of fuel and power in the automobile. As the fuel cost is more, one cannot afford to waste money because of the misfire. Even if one is ready to spend more money on fuel, the power of the engine comes down; thereby, the vehicle performance falls drastically because of the misfire in IC engines. Hence, researchers paid a lot of attention to detect the misfire in IC engines and rectify it. Drawbacks of conventional diagnostic techniques include the requirement of high level of human intelligence and professional expertise in the field, which made the researchers look for intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the misfire in IC engines. This paper proposes the use of transfer learning technology to detect the misfire in the IC engine. First, the vibration signals were collected from the engine head and plots are made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the misfire in the IC engines. In the present work, the pretrained networks such as AlexNet, VGG-16, GoogLeNet, and ResNet-50 are employed to identify the misfire state of the engine. In the pretrained networks, the effect of hyperparameters such as back size, solver, learning rate, and train-test split ratio was studied and the best performing network was suggested for misfire detection.
Dust Explosion Propagation in Small Diameter Pipes

In facilities handling combustible dusts, the isolation of propagating deflagrations requires great attention due to the potential catastrophic consequences of secondary dust explosions. While the ability of dust explosions to propagate is widely recognized, some misconceptions still exist. One of the common myths is that a dust explosion cannot propagate through small diameter pipes and that explosion isolation may not be required in that case.
The reader as subjective entropy: a novel analysis of multimodal readability

PurposeThe purpose of this study was to explore the viability of transinformation analysis as a multimodal readability metric. A novel approach was called for, considering that existing and established readability metrics are strictly used to measure linguistic complexity. Yet, the corpus of multimodal literature continues to grow, along with the need to understand how non-linguistic modalities contribute to the complexity of the reading experience.Design/methodology/approachIn this exploratory study, think aloud screen recordings of eighth-grade readers of the born-digital novel Inanimate Alice were analyzed for complexity, along with transcripts of post-oral retellings. Pixel-level entropy analysis served as both an objective measure of the document and a subjective measure of the amount of reader information attention. Post-oral retelling entropy was calculated at the unit level of the word, serving as an indication of complexity in recall.FindingsFindings confirmed that transinformation analysis is a viable multimodal readability metric. Inanimate Alice is an objectively complex document, creating a subjectively complex reading experience for the participants. Readers largely attended to the linguistic mode of the story, effectively reducing the amount of information they processed. This was also evident in the brevity and below average complexity of their post-oral retellings, which relied on recall of the linguistic mode. There were no significant group differences among the readers.Originality/valueThis is the first study that uses entropy to analyze multimodal readability.
Investigating the Mechanisms of Jieduquyuziyin Prescription Improves Lupus Nephritis and Fibrosis via FXR in MRL/lpr Mice

Lupus nephritis (LN) is one of the most serious complications of systemic lupus erythematosus (SLE) and one of the leading causes of death. An alternative effective treatment to ameliorate and relieve LN and delay the process of renal tissue fibrosis is urgently needed in the clinical setting. Jieduquyuziyin prescription (JP) has been successfully used to treat SLE, but its potential mechanisms are not sufficiently understood. In this study, we treated MRL/lpr mice with JP for 8 weeks and treated human renal tubular epithelial cells (human kidney 2 (HK-2)) with drug-containing serum to observe the antagonistic effects of JP on inflammation and fibrosis, as well as to investigate the possible mechanisms. Results demonstrated that JP significantly reduced urinary protein and significantly improved pathological abnormalities. Metabolomics combined with ingenuity pathway analysis illustrated that the process of kidney injury in lupus mice may be closely related to farnesoid X receptor (FXR) pathway abnormalities. Microarray biomimetic analysis and LN patients indicated that FXR may play a protective role as an effective therapeutic target for LN and renal fibrosis. JP significantly increased the expression of FXR and inhibited the expression of its downstream targets, namely, nuclear transcription factor κB (NF-κB) and α-smooth muscle actin (α-SMA), in the kidney of MRL/lpr mice and HK-2 cells, as confirmed by in vitro and in vivo experiments. In conclusion, JP may mediate the activation of renal FXR expression and inhibit NF-κB and α-SMA expression to exert anti-inflammatory and antifibrotic effects for LN prevention and treatment.
Three dimensional pole shape optimization of claw pole machines based on a hybrid model

This paper deals with the shape optimization of the claws of a claw-pole synchronous machine. A hybrid model combining 3D FEM and Permeance Network is developed for this purpose. The optimization of the average torque with fixed stator geometry is performed using Mesh Adaptative Direct Search methods. The developed methodology has led to improve the performances of claw-pole synchronous machines used in mild-hybrid vehicles.
Prediction of effective stimulated reservoir volume after hydraulic fracturing utilizing deep learning

Abstract It is very important to utilize hydraulic fracturing for unconventional reservoir development. Accurate prediction of effective stimulated reservoir volume (SRV) after fracturing facilitates production evaluation. However, the traditional methods to predict SRV are time consuming and the precision cannot fully meet the requirements. To overcome the shortcomings, a new approach was presented using deep learning which includes four procedures. Firstly, the datasets were collected by numerical simulation considering non-Darcy flow characteristics. Additionally, the Branched Deep Neural Network model (B-DNN) was established after data fusion through adding a branch neural network. Then the optimal hyperparameters were obtained after adjusting to satisfy model accuracy and reliability. Finally, the prediction results of B-DNN and convolutional neural network (CNN) and recurrent neural network (RNN) were compared. The results show that the model with Softplus activation function, four hidden layers, and 250 neurons in each layer would have the best calculation results. The proposed model has good agreement with actual field data which can reach 97%. Furthermore, compared with the CNN and RNN models, it is shown that the B-DNN model has considerable prediction accuracy and is less time-consuming. This deep learning model provides new insight for production evaluation in the fractured tight oil reservoir.
Temporal beta diversity patterns reveal global change impacts in closed mountain grasslands

Abstract Global changes are recognized as one of the main drivers of biodiversity changes over time, especially in mountain ecosystems. A key approach to detect and investigate the effect of climate and land use change on these ecosystems is represented by vegetation surveys. In this study, based on the resurvey of historical vegetation data, we assessed temporal beta-diversity patterns within and between subalpine and alpine vegetation types in the Dolomiti Bellunesi National Park. Resurvey was focussed on plots sampled in four main vegetation types: 1 and 2) Mesophytic and xerophytic grasslands on neutral or base-rich soils respectively dominated by Sesleria caerulea and Carex sempervirens and by Sesleria caerulea and Helictotrichon parlatorei; 3) Acidophytic grasslands dominated by Nardus stricta; 4) Snowbeds with dominance of dwarf willows (Salix herbacea or S. retusa) or Luzula alpinopilosa. Our results revealed a pattern of floristic homogenization in grasslands and snowbeds indicating a decreasing heterogeneity, both within and between different vegetation types that can be associated with different components of global change. The highest temporal beta diversity and floristic homogenization were detected in snowbeds. In general, the magnitude of temporal changes differ between communities, thus claiming for conservation activities that are tailored to each vegetation type.
Relationships among life satisfaction, meaning in life and need satisfaction with mixture structural equation modelling

ABSTRACT The purpose of the current study was to investigate the relationships among life satisfaction, meaning in life and basic psychological need satisfaction. Participants were 384 college students. Balanced Measure of Psychological Needs Scale, Meaning in Life Questionnaire, Satisfaction with Life Scale and demographic information form were utilised for data collection. Data were analysed using mixture structural equation modelling approach. Results of the study indicated that fulfilling competence and autonomy need is indirectly related to life satisfaction through meaning in life. Additionally, need for relatedness and autonomy satisfaction and meaning in life is directly associated with life satisfaction. Findings suggest two latent profiles: (a) a high satisfied and meaningful group and (b) a low satisfied and low meaningful group.
Improving Answer Retrieval from Web Forums with Topic Model and Ontology

Searching for information online has become an essential part of modern living. For a general domain, one can use a tool such as a search engine to find information. However, for domain-specific questions, other means, such as a web forum, are preferable. Common problems with web forums are post duplication and poor search results for long query strings, such as sentences. To overcome these issues, we propose a system based on a language model and ontology. Given a question, the system performs language processing to analyze the syntactic structure of the sentence and its category. The question sentence is classified using regular expressions, keyword matching, and query templates. Based on knowledge constructed from the existing information in the web forum, we can retrieve the suggested links to the answers for the given question. To the best of our knowledge, our paper is the first article that attempts to understand questions and to suggest existing sources of answers in a Thai web forum. We compared our 2 proposed subsystems, language-model-based and ontology-based, with the Google custom web search engine. We evaluated the systems by using a Thai breastfeeding web forum containing 6,823 threads with 75,906 messages. The evaluation results show that the proposed systems have fewer duplicated suggestions compared with the Google search engine. Moreover, for the input with some ambiguous keywords, our proposed system, based on a language model, outperforms the Google search engine, because the system based on a language model is better at finding related terms. For a question with no frequently found keywords, our proposed system, based on ontology, suggests answers which are more appropriate than answers from the Google search engine, because it contains related knowledge defined by experts.
DC compensated permeameter - the accuracy study

Abstract We describe D-permeameter originally designed by Trnka and later improved by Fajt and Kaspar and we analyze its precision for the measurement of different soft magnetic materials. The device measures DC hysteresis loops using compensation method, so that H and B can be calculated from the magnetization and compensation currents. The differences from results obtained by other standard methods are below 2%.
A Novel Piano Arrangement Timbre Intelligent Recognition System Using Multilabel Classification Technology and KNN Algorithm

In this paper, melody and harmony are regarded as the task of machine learning, and a piano arranger timbre recognition system based on AI (Artificial Intelligence) is constructed by training a series of samples. The short-time Fourier transform spectrum analysis method is used to extract the piano timbre characteristic matrix, and the electronic synthesis of timbre recognition is improved by extracting the envelope function. Using the traditional multilabel classification method and KNN (K-nearest neighbor) algorithm, a combined algorithm of these two algorithms is proposed. The experimental results show that the detection rate increases from 61.3% to 70.2% after using the combined classification algorithm. The correct rate also increased from 40.3% to 48.9%, and the detection rate increased to 74.6% when the K value was set to 6. The experimental results show that, compared with the traditional classification algorithm, this algorithm has a certain improvement in recognition rate. Using this system to recognize the timbre of piano arrangement has a high recognition accuracy, which is worthy of further popularization and application.
Phase I Solicitation

This subtopic addresses the unique problem of imaging and spectroscopic characterization of faint astrophysical objects that are located within the obscuring glare of much brighter stellar sources. Examples include planetary systems beyond our own, the detailed inner structure of galaxies with very bright nuclei, binary star formation, and stellar evolution. Contrast ratios of one million to ten billion over an angular spatial scale of 0.05-1.5 arcsec are typical of these objects. Achieving a very low background requires control of both scattered and diffracted light. The failure to control either amplitude or phase fluctuations in the optical train severely reduces the effectiveness of starlight cancellation schemes. This innovative research focuses on advances in coronagraphic instruments, starlight cancellation instruments, and potential occulting technologies that operate at visible and near infrared wavelengths. The ultimate application of these instruments is to operate in space as part of a future observatory mission. Measurement techniques include imaging, photometry, spectroscopy, and polarimetry. There is interest in component development, and innovative instrument design, as well as in the fabrication of subsystem devices to include, but not limited to, the following areas:
Association between Higher Circulating Leucine-Rich α-2 Glycoprotein 1 Concentrations and Specific Plasma Ceramides in Postmenopausal Women with Type 2 Diabetes

Background: Although ceramides are involved in the pathophysiology of cardiovascular disease and other inflammation-associated disorders, there is a paucity of data on the association between plasma ceramides and inflammatory biomarkers in type 2 diabetes mellitus (T2DM). Therefore, we explored whether there was an association between plasma leucine-rich α-2 glycoprotein 1 (LRG1) concentrations (i.e., a novel proinflammatory signaling molecule) and specific plasma ceramides in postmenopausal women with T2DM. Methods: We measured six previously identified plasma ceramides, which have been associated with increased cardiovascular risk [plasma Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/20:0), Cer(d18:1/22:0), Cer(d18:1/24:0) and Cer(d18:1/24:1)], amongst 99 Caucasian postmenopausal women with non-insulin-treated T2DM (mean age 72 ± 8 years, mean hemoglobin A1c 6.9 ± 0.7%), who consecutively attended our diabetes outpatient service during a 3-month period. Plasma ceramide and LRG1 concentrations were measured with a targeted liquid chromatography-tandem mass spectrometry assay and a Milliplex® MAP human cardiovascular disease magnetic bead kit, respectively. Results: In linear regression analyses, higher plasma LRG1 levels (1st tertile vs. 2nd and 3rd tertiles combined) were associated with higher levels of plasma Cer(d18:1/16:0) (standardized β coefficient: 0.289, p = 0.004), Cer(d18:1/18:0) (standardized β coefficient: 0.307, p = 0.002), Cer(d18:1/20:0) (standardized β coefficient: 0.261, p = 0.009) or Cer(d18:1/24:1) (standardized β coefficient: 0.343, p < 0.001). These associations remained significant even after adjusting for age, body mass index, systolic blood pressure, total cholesterol level, hemoglobin A1c, insulin resistance and statin use. Conclusions: The results of our pilot exploratory study suggest that higher plasma LRG1 concentration was associated with higher levels of specific high-risk plasma ceramide molecules in elderly postmenopausal women with metabolically well-controlled T2DM, even after adjusting for known cardiovascular risk factors and other potential confounding variables.
Cognitive and executive processes associated with children’s writing

Learning to write is key to successfully going through elementary education. Considerable progress in children’s writing is documented in the intermediate school grades, due to lexical and cognitive development. In line with this postulate, the goal of the study was to analyse the predictive value of attention, perception, planning, working memory and long-term memory in written expression tasks in Argentine schoolchildren. One hundred and sixty-eight boys and girls participated, between 8 and 11 years of age, who were in 3rd, 4th and 5th grade of Elementary Education. A regression analysis revealed that visuospatial perception, working memory, long-term memory, and planning may predict writing performance in children. Working memory denoted a high predictive value, along with long-term memory and visuospatial perception. The findings provide evidence about the involvement of cognitive and executive processes in written expression tasks in Spanish-speaking schoolchildren. Identifying cognitive predictors of writing skills makes it possible to design more comprehensive teaching programs that jointly contemplate training in specific writing skills and strengthening the cognitive resources of learners.
Tennessee Pharmacists’ Opinions on Barriers and Facilitators to Initiate PrEP: A Qualitative Study

Pre-exposure prophylaxis (PrEP) is recommended to prevent the transmission of the human immunodeficiency virus (HIV). Although an effective treatment, the uptake in the United States remains low. Pharmacists are well-positioned to initiate the conversation with patients about PrEP, but few studies exist exploring their unique roles. The objective of this study was to characterize Tennessee pharmacists’ perceptions about access to PrEP. A qualitative study was used to gather the data that consisted of virtual Focus Groups over four months in 2021 from practicing Tennessee pharmacists. Emails were sent to all Tennessee licensed pharmacists to recruit them to participate in the study. Recruitment continued until Thematic Saturation was obtained. The corpus of data was audio-recorded, transcribed, and analyzed by the research team. Thematic Analysis revealed two themes: (1) Barriers to accessing PrEP; (2) Potential solutions to address barriers identified. These findings highlighted barriers and identified solutions to improve access to PrEP in Tennessee; additional financial assistance programs and marketing programs targeting patients and providers are needed to enhance PrEP access.
Integrated AHP-TOPSIS under a Fuzzy Environment for the Selection of Waste-To-Energy Technologies in Ghana: A Performance Analysis and Socio-Enviro-Economic Feasibility Study

Energy recovery from waste presents a promising alternative for several countries, including Ghana, which has struggled with unsustainable waste treatment methods and an inadequate power supply for several decades. The current study adopts a comprehensive multi-criteria decision-making approach for the selection of an optimal waste-to-energy (WtE) technology for implementation in Ghana. Four WtE technologies are evaluated against twelve selection criteria. An integrated AHP-fuzzy TOPSIS method is applied to estimate the criteria’s weights and rank the WtE alternatives. From the AHP results, technical criteria obtained the highest priority weight, while social criteria emerged as the least important in the selection process. The overall ranking order of WtE technologies obtained by fuzzy TOPSIS is as follows: anaerobic digestion > gasification > pyrolysis > plasma gasification. The sensitivity analysis indicates highly consistent and sturdy results regarding the optimal selection. This study recommends adopting a hybrid system of anaerobic digestion and gasification technologies, as this offers a well-balanced system under all of the evaluation criteria compared to the standalone systems. The results of the current study may help the government of Ghana and other prospective investors select a suitable WtE technology, and could serve as an index system for future WtE research in Ghana.