Miracle-dz/UniFAR-FTUs huggingface dataset:
Parameter optimization for surface mounter using a self-alignment prediction model

Purpose The purpose of this paper is to develop a machine learning model that predicts the component self-alignment offsets along the length and width of the component and in the angular direction. To find the best performing model, various algorithms like random forest regressor (RFR), support vector regressor (SVR), neural networks (NN), gradient boost (GB) and K-nearest neighbors (KNN) were performed and analyzed. The models were implemented using input features, which can be categorized as solder paste volume, paste-pad offset, component-pad offset, angular offset and orientation. Design/methodology/approach Surface-mount technology (SMT) is the technology behind the production of printed circuit boards, which is used in several types of commercial equipment such as communication devices, home appliances, medical imaging systems and sensors. In SMT, components undergo movement known as self-alignment during the reflow process. Although self-alignment is used to decrease the misalignment, it may not work for smaller size chipsets. If the solder paste depositions are not well-aligned, the self-alignment might deteriorate the final alignment of the component. Findings It were trained on their targets. Results obtained by each method for each target variable were compared to find the algorithm that gives the best performance. It was found that RFR gives the best performance in case of predicting offsets along the length and width of the component, whereas SVR does so in case of predicting offsets in the angular direction. The scope of this study can be extended to developing this model further to predict defects that can occur during the reflow process. It could also be developed to be used for optimizing the placement process in SMT. Originality/value This paper proposes a predictive model that predicts the component self-alignment offsets along the length and width of component and in the angular direction. To find the best performing model, various algorithms like RFR, SVR, NN, GB and KNN were performed and analyzed for predicting the component self-alignment offsets. This helps to achieve the following research objectives: best machine learning model for prediction of component self-alignment offsets. This model can be used to optimize the mounting process in SMT, which reduces occurrences of defects and making the process more efficient.
Robust design of a novel regenerative heat exchanger with high porosity ceramic matrix to waste heat recovery of flue gas

ABSTRACT A novel regenerative heat exchanger was designed and fabricated for heat recovery of dryer flue gases with a high porosity matrix core that was made of alumina foams. The effects of matrix height, matrix linear speed, and flow rate on thermal effectiveness were investigated based on an L25 Taguchi model. Results show that the matrix height and flow rate are inversely related and the matrix speed is directly related to the thermal effectiveness. Finally, the highest thermal effectiveness, thermal improvement of the dryer, air preheating rate, and the number of transfer units were 31.75%, 27.13%, 186.42%, and 0.4652, respectively.
Plant Disease Detection Using Deep Convolutional Neural Network

In this research, we proposed a novel 14-layered deep convolutional neural network (14-DCNN) to detect plant leaf diseases using leaf images. A new dataset was created using various open datasets. Data augmentation techniques were used to balance the individual class sizes of the dataset. Three image augmentation techniques were used: basic image manipulation (BIM), deep convolutional generative adversarial network (DCGAN) and neural style transfer (NST). The dataset consists of 147,500 images of 58 different healthy and diseased plant leaf classes and one no-leaf class. The proposed DCNN model was trained in the multi-graphics processing units (MGPUs) environment for 1000 epochs. The random search with the coarse-to-fine searching technique was used to select the most suitable hyperparameter values to improve the training performance of the proposed DCNN model. On the 8850 test images, the proposed DCNN model achieved 99.9655% overall classification accuracy, 99.7999% weighted average precision, 99.7966% weighted average recall, and 99.7968% weighted average F1 score. Additionally, the overall performance of the proposed DCNN model was better than the existing transfer learning approaches.
Error Correction for FSI-Based System without Cooperative Target Using an Adaptive Filtering Method and a Phase-Matching Mosaic Algorithm

In our frequency scanning interferometry-based (FSI-based) absolute distance measurement system, a frequency sampling method is used to eliminate the influence of laser tuning nonlinearity. However, because the external cavity laser (ECL) has been used for five years, factors such as the mode hopping of the ECL and the low signal-to-noise ratio (SNR) in a non-cooperative target measurement bring new problems, including erroneous sampling points, phase jumps, and interfering signals. This article analyzes the impacts of the erroneous sampling points and interfering signals on the accuracy of measurement, and then proposes an adaptive filtering method to eliminate the influence. In addition, a phase-matching mosaic algorithm is used to eliminate the phase jump, and a segmentation mosaic algorithm is used to improve the data processing speed. The result of the simulation proves the efficiency of our method. In experiments, the measured target was located at eight different positions on a precise guide rail, and the incident angle was 12 degrees. The maximum deviation of the measured results between the FSI-based system and the He-Ne interferometer was 9.6 μm, and the maximum mean square error of our method was 2.4 μm, which approached the Cramer-Rao lower bound (CRLB) of 0.8 μm.
Plantation vegetation restoration enhances the relationship between rhizosphere microbial diversity and soil multifunctionality

Many studies have demonstrated the importance of rhizosphere microorganisms to soil nutrient cycling and plant stress resistance. However, we still lack a comprehensive understanding of the potential relationship between rhizosphere microbial diversity and soil multifunctionality during forest vegetation restoration. In this study, the diversity, assembly processes, and co‐occurrence patterns of rhizosphere microbial communities were explored along a 45‐year chronosequence of Robinia pseudoacacia plantations in China's Loess Plateau region. Soil multifunction index was calculated based on 11 measured available nutrient properties. The results showed that both α‐ and β‐diversities of rhizosphere microbial communities were significantly correlated with the soil multifunction index. Stochastic processes dominated rhizosphere microbial community assembly. Correlations between the abundances of sensitive operational taxonomic units for different forest ages (i.e. 15, 25, 35, and 45 years old) and soil multifunctional index varied in distinct network modules, being positive in module 1 and negative in module 2. There was a positive correlation between microbial network complexity (e.g. degree) and soil multifunctional index. The contribution of rhizosphere core microbiota to soil multifunctional index was higher than that of non‐core microbiota. To sum up, plantation vegetation restoration enhances the relationship between microbial diversity and soil multifunctionality, while affecting microbial co‐occurrence patterns in the rhizosphere.
Phase Transition in the 2D XY Model with 3-Fold Nematic Interactions

In this work the results of the study on a generalization of the XY model with an additional q-fold nematic-like term through Monte Carlo simulations in two dimensions (2D) have been presented. While the conventional 2D XY model has only integer vortexes, the generalized 2D XY model has both integer and non-integer 1/q vortexes, making the phase diagram of the generalized 2D XY model is much richer than that of the conventional 2D XY model. Here, we located the phase transition between the disordered phase (P), the quasi-long-range order phase (F), and the nematic phase (N) for the case of q = 3. We provided the numerical evidence to clarify the N−F phase transition of either the first-ordered or second-ordered phase transition. The results showed that the N−F phase transition is the second-ordered, not the first-order phase transition.
Relation Mapping For Question Answering Over Knowledge Graphs Using Large Corpus Of Free Text

With the advancements in semantic web technologies, researchers have developed several question answering systems that interpret the user’s natural language question and fetches the answer from knowledge graphs. One of the main challenges of building question answering systems is determining which relations within a knowledge graph matches the keywords found in the Natural Language question. In order to bridge the gap between the simple yet ambiguous natural language question, and the difficult relation mapping problem, we propose ReMLOFT, an interactive relation mapping approach which relies on external evidence from a large corpus of text for mapping relations to the keywords found in a Natural Language question without using any training data. Our approach builds a free-text knowledge graph from Wikipedia, with entities as nodes and sentences in which these entities co-occur, as edges. ReMLOFT interactively helps the user choose better candidate relations to build fine-grained SPARQL queries. In addition, we build a dictionary of the most frequent keywords that define the context of a relation in the knowledge graph without using contemporary lexical tools. Experiments on three question answering datasets show our approach can map high quality candidate relations in comparison to statistical and embedding-based relation mapping approaches.
Motor Imagery Recognition Method Based on Multisource Transfer Learning and Multiclassifier Fusion

There are two common problems in the field of motor imagery (MI) recognition, which are poor generalization and low recognition performance. A recognition method based on multisource transfer learning and multiclassifier fusion is therefore proposed to realize the MI classification. In this approach, multisource transfer learning method is used to transfer samples from multiple source domains to target domain. The source domain selection method based on distribution similarity is designed to select those source domains whose distribution is similar to the target domain, and samples with high information entropy are selected from these source domains for transferring. Then, an MI classification method is proposed through the fusion of multiple classifiers. The classifiers are trained by labeled samples in the target domain and the transferred samples in multiple source domains. The new sample in the target domain can be identified by the weight fusion of the results of these classifiers. In order to verify the effectiveness of the proposed method, four types of motor imagery in the BCI Competition IV dataset 2a were used to evaluate the recognition ability, and the results approved an excellent recognition and generalization performance as well as a better training efficiency comparing to the well-applied methods nowadays.
ADMM-Based Distributed OPF Problem Meets Stochastic Communication Delay

An increasing number of distributed generators will penetrate into the distribution power system in future smart grid, thus a centralized control strategy cannot effectively optimize the power loss problem in real-time. This paper examines the idea of a fully distributed optimal power flow (OPF) approach, based on the alternating direction multiplier method, to optimize the power loss. The objectives are not only to effectively obtain the minimization of power loss, but also to analyze the effect of communication time-delay on optimization performance. Both synchronous and asynchronous iterative algorithms are proposed to solve the OPF problem. In addition, four different strategies are proposed to improve convergence speed when delay occurs. The proposed weighted autoregressive strategy can reduce the fluctuation effectively. In comparison with synchronous algorithm, simulation results show that the asynchronous algorithm has a better optimization result.
Aging-associated and CD4 T-cell–dependent ectopic CXCL13 activation predisposes to anti–PD-1 therapy-induced adverse events

Significance Immune-related adverse events (irAEs) induced by immune-checkpoint blockade including antiprogrammed death receptor (PD)-1 therapy are a major problematic issue in cancer immunotherapy. Preclinical models for more physiologically occurring irAEs are potentially useful for the clarification of fundamental causes and natural developmental course of irAEs. Here, we found that in tumor-bearing aged, but not young, mice, anti–PD-(L)1 therapy alone induces irAE-like multiorgan toxicities through CD4 T-cell–derived interleukin (IL)-21 and subsequent age-specific CXCL13 expression in tertiary lymphoid structure. Consistent with this animal model, a systemic increase in CXCL13 correlates with irAE incidence in cancer patients. These findings provide insight into the development of management strategies for irAE that balance both irAE-related immune response and antitumor immune surveillance.
Analysis of the influence of asset allocation policy, investment manager performance, and risk level on the performance of Sharia money market mutual funds in Indonesia

Background: Islamic money market mutual funds have become an alternative to conventional investment instruments. This research has novelty in determining the variables of mutual fund performance by combining risk and return factors, asset allocation policy variables, and investment manager performance, which have a high impact on the return and risk level in reducing the risk of loss on investment. In addition, this research was conducted on the performance of Islamic money market mutual funds that have not been studied before. Methods: This research uses data on Islamic money market mutual funds registered with the Financial Services Authority in Indonesia. The performance of Islamic money market mutual funds was calculated using the Sharpe Method and tested using multiple regression analysis. Results: The results showed that the asset allocation policy, investment manager performance, and the level of risk simultaneously have a significant effect on the performance of Islamic money market mutual funds in Indonesia; however, partially, there is no significant effect between (1) the asset allocation policy on the performance of Islamic money market mutual funds in Indonesia: (2) investment manager performance on the performance of Islamic money market mutual funds in Indonesia, and (3) the level of risk on the performance of Islamic money market mutual funds in Indonesia. Conclusions: The results indicated that optimization of returns and risks was needed by considering the composition of asset allocation, choosing the right investment manager, and conducting a good risk level analysis to obtain optimal Islamic money market mutual fund performance.
Food Sources of Fiber and Micronutrients of Concern in Infants and Children in the United Arab Emirates: Findings from the Feeding Infants and Toddlers Study (FITS) and the Kids Nutrition and Health Survey (KNHS) 2020

We estimated the usual intakes of fiber, iron, zinc, calcium, folate, vitamin D, and vitamin A and the top foods that contribute to them among children in the UAE. Dietary intake was assessed using 24 h recalls among 5 age groups of infants and children. Foods were clustered into 54 food groups and ranked by their percentage contribution to the nutrients of interest in this study. The percentage achieving the adequate intake (AI) of fiber was negligible among all children. The top source of fiber was vegetables among children under 4 years, and white breads among those over 4 years. Only 45% of infants achieved iron adequacy, but iron standards were met by most children beyond the age of 1. The main contributors to iron intake were infant/young child formula and baby cereal in children under 4 years, while children over 4 years obtained it primarily from grains (fortified) and meat/fish. Vitamin D was inadequate across all age groups, with the percentage achieving adequacy ranging from 0 to 19% among pre-adolescents and toddlers, respectively. The top sources of vitamin D were fortified milks. Overall, nutrient inadequacies in fiber, calcium, and vitamin D highlight the need for greater intakes of whole grains and fortified dairy products in the UAE.
Analyzing Tweeting Patterns and Public Engagement on Twitter During the Recognition Period of the COVID-19 Pandemic: A Study of Two U.S. States

The abundance of available information on social media can provide invaluable insights into people’s responses to health information and public health guidance concerning COVID-19. This study examines tweeting patterns and public engagement on Twitter, as forms of social media, related to public health messaging in two U.S. states (Washington and Louisiana) during the early stage of the pandemic. We analyze more than 7M tweets and 571K COVID-19-related tweets posted by users in the two states over the first 25 days of the pandemic in the U.S. (Feb. 23, 2020, to Mar. 18, 2020). We also qualitatively code and examine 460 tweets posted by selected governmental official accounts during the same period for public engagement analysis. We use various methods for analyzing the data, including statistical analysis, sentiment analysis, and word usage metrics, to find inter- and intra-state disparities of tweeting patterns and public engagement with health messaging. Our findings reveal that users in Washington were more active on Twitter than users in Louisiana in terms of the total number and density of COVID-19-related tweets during the early stage of the pandemic. Our correlation analysis results for counties or parishes show that the Twitter activities (tweet density, COVID-19 tweet density, and user density) were positively correlated with population density in both states at the 0.01 level of significance. Our sentiment analysis results demonstrate that the average daily sentiment scores of all and COVID-19-related tweets in Washington were consistently higher than those in Louisiana during this period. While the daily average sentiment scores of COVID-19-related tweets were in the negative range, the scores of all tweets were in the positive range in both states. Lastly, our analysis of governmental Twitter accounts found that these accounts’ messages were most commonly meant to spread information about the pandemic, but that users were most likely to engage with tweets that requested readers take action, such as hand washing.
Mechanism of Huo-Xiang-Zheng-Qi in Preventing and Treating COVID-19: A Study Based on Network Pharmacology and Molecular Docking Techniques

Objective: The Chinese herbal formula Huo-Xiang-Zheng-Qi (HXZQ) is effective in preventing and treating coronavirus disease 19 (COVID-19) infection; however, its mechanism remains unclear. This study used network pharmacology and molecular docking techniques to investigate the mechanism of action of HXZQ in preventing and treating COVID-19. Methods: The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was used to search for the active ingredients and targets of the 10 traditional Chinese medicines (TCMs) of HXZQ prescription (HXZQP). GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmacogenomics Knowledge Base (PharmGKB), Therapeutic Target Database (TTD), and DrugBank databases were used to screen COVID-19-related genes and intersect them with the targets of HXZQP to obtain the drug efficacy targets. Cytoscape 3.8 software was used to construct the drug-active ingredient–target interaction network of HXZQP and perform protein–protein interaction (PPI) network construction and topology analysis. R software was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, AutoDock Vina was utilized for molecular docking of the active ingredients of TCM and drug target proteins. Results: A total of 151 active ingredients and 250 HXZQP targets were identified. Among these, 136 active ingredients and 67 targets of HXZQP were found to be involved in the prevention and treatment of COVID-19. The core proteins identified in the PPI network were MAPK1, MAPK3, MAPK8, MAPK14, STAT3, and PTGS2. Using GO and KEGG pathway enrichment analysis, HXZQP was found to primarily participate in biological processes such as defense response to a virus, cellular response to biotic stimulus, response to lipopolysaccharide, PI3K-Akt signaling pathway, Th17 cell differentiation, HIF-1 signaling pathway, and other signaling pathways closely related to COVID-19. Molecular docking results reflected that the active ingredients of HXZQP have a reliable affinity toward EGFR, MAPK1, MAPK3, MAPK8, and STAT3 proteins. Conclusion: Our study elucidated the main targets and pathways of HXZQP in the prevention and treatment of COVID-19. The study findings provide a basis for further investigation of the pharmacological effects of HXZQP.
Predicting Vaccine Confidence: Patient Self-advocacy and Fear of COVID-19 in the United States

ABSTRACT This study investigated the extent to which patient self-advocacy predicted COVID-19 vaccine hesitance in the U.S. (n = 1987). Results indicated being vaccinated, increasing age, and higher education were each positively related to increased vaccine non-hesitance. Also, those who sought more information and who were more assertive about their health had more non-hesitance toward the vaccine. Higher vaccine hesitance was found among participants who were Black and from other ethnic minorities, those who voted for Donald Trump or who did not vote in the 2020 election, and those who were more prone to nonadherence to health provider instructions. Targeted communication campaigns to connect to people with these specific characteristics and levels of advocacy can decrease vaccine non-hesitance.
Investigating the Difference in Factors Contributing to the Likelihood of Motorcyclist Fatalities in Single Motorcycle and Multiple Vehicle Crashes

In order to better understand the factors affecting the likelihood of motorcyclists’ fatal injuries, motorcycle-involved crashes were investigated based on the involvement of the following vehicles: single motorcycle (SM), multiple motorcycles (MM) and motorcycle versus vehicle (MV) crashes. Method: Binary logit and mixed logit models that consider the heterogeneity of parameters were applied to identify the critical factors that increase the likelihood of motorcyclist fatality. Results: Mixed logit models were found to have better fitting performances. Factors that increase the likelihood of motorcyclist fatality include lanes separated by traffic islands, male motorcyclists, and riding with BAC values of less than the legally limited value. Collisions with trees or utility poles lead to the highest likelihood of fatality in SM crashes. The effects of curved roads, same-direction swipe crashes, youth, and unlicensed motorcyclists are only significant in the likelihood of fatality in SM crashes. Conclusions: Motorcyclists tend to be killed if they collide with large engine-size motorcycles and vehicles, unlicensed motorcyclists, or drivers with speeding related or right-of-way violations with positive BAC values. Driving or riding should be prohibited for any amount of alcohol or for anyone with a positive BAC value. Law enforcement should focus on unlicensed, speeding motorcyclists and drivers, and those who violate the right of way or perform improper turns. Roadside objects and facilities should be checked for appropriate placement and be equipped with reflective devices or injury protection facilities.