Miracle-dz/UniFAR-FTUs huggingface dataset:
Examination and utilization of rare features in text classification of injury narratives

Huang, Hsin-Ying Ph.D., Purdue University, December 2016. Examination and Utilization of Rare Features in Text Classification of Injury Narratives. Major Professor: Mark R. Lehto. Thanks to the advances in computing and information technology, analyzing injury surveillance data with statistical machine learning methods has grown in popularity, complexity, and quality over recent years. During that same time, researchers have recognized the limitations of statistical text analysis with limited training data. In response to the two primary challenges for statistical text analysis, dimensionality reduction and sparse data, many studies have focused on improving machine learning algorithms. Less research has been done, though, to examine and improve statistical machine learning methods in text classification from a linguistic perspective. This study addresses this research gap by examining the importance of extremefrequency words in classifying injury narratives. The results indicate that adhering to the common practice of removing frequently-occurring prepositions from the text significantly decreased the classification performance for certain categories. Removing low-frequency words significantly improved the classification performance for Multinomial Naive Bayes (MNB), helped alleviate the problem of overfitting small categories for Logistical Regression (LR), but did not have any significant effect for Support Vector Machine (SVM). As a way to utilize low-frequency words, classic word normalization or grouping methods such as stemming and lemmatization are often used in the text preprocessing stage. Despite their popularity, these classic grouping methods are not without limitations. The proposed “Type M+S Word Grouping Method” groups
Multifunctional Theranostic Nanoparticles for Enhanced Tumor Targeted Imaging and Synergistic FUS/Chemotherapy on Murine 4T1 Breast Cancer Cell

Purpose Triple negative breast cancer (TNBC) is challenging for effective remission due to its very aggressive, extremely metastatic and resistant to conventional chemotherapy. Herein, a multifunctional theranostic nanoparticle was fabricated to enhance tumor targeted imaging and promote focused ultrasound (FUS) ablation and chemotherapy and sonodynamic therapy (SDT). A multi-modal synergistic therapy can improve the therapeutic efficacy and prognosis of TNBC. Methods AS1411 aptamer modified PEG@PLGA nanoparticles encapsulated with perfluorohexane (PFH) and anti-cancer drug doxorubicin (DOX) were constructed (AS1411-DOX/PFH-PEG@PLGA) to enhance tumor targeted imaging to guide ablation and synergistic effect of FUS/chemotherapy. FUS was utilized to trigger the co-release of doxorubicin and simultaneously PFH phase transition and activate DOX for SDT effect. The physicochemical, phase-changeable imaging capability, biosafety of nanoparticles and multi-mode synergistic effects on growth of TNBC were thoroughly evaluated in vivo and in vitro. Results The synthesized AS1411-DOX/PFH-PEG@PLGA (A-DPPs) nanoparticles are uniformly round with an average diameter of 306.03 ± 5.35 nm and the zeta potential of −4.05 ± 0.13 mV, displaying high biosafety and FUS-responsive drug release in vitro and in vivo. AS1411 modified NPs specifically bind to 4T1 cells and elevate the ultrasound contrast agent (UCA) image contrast intensity via PFH phase-transition after FUS exposure. Moreover, the combined treatment of A-DPPs nanoparticles with FUS exhibited significantly higher apoptosis rate, stronger inhibitory effect on 4T1 cell invasion in vitro, induced more reactive oxygen species (ROS), and enhanced anti-tumor effect compared to a single therapy (p < 0.05). Additionally, the joint strategy resulted in more intense cavitation effect and larger ablated areas and reduced energy efficiency factor (EEF) both in vitro and in vivo. Conclusion The multifunctional AS1411-DOX/PFH-PEG@PLGA nanoparticles can perform as a marvelous synergistic agent for enhanced FUS/chemotherapy, promote real-time contrast enhanced US imaging and improve the therapeutic efficacy and prognosis of TNBC.
Designing modular product architecture for optimal overall product modularity

ABSTRACT Designing modular product architectures is beneficial to both product development and manufacturing. Several techniques exist for clustering product components into modules; all these techniques, with few exceptions, do not consider the hierarchical structure of the product. Product architecture consists of a number of hierarchical levels, which add a useful dimension to modularity analysis. Designing product architecture that maximises its overall modularity over all levels of the product structure is the main focus of this research. A novel mathematical programming model has been developed to construct the product structure tree for a given product with optimal modularity at all its hierarchical levels without prior knowledge of their number. A genetic algorithm capable of handling practical problem sizes was also developed. A hand-held body massager is used for demonstrating and validating the proposed modular product architecture design method. A case study from the automotive industry was employed for comparing the obtained modularity analysis output with the relevant state-of-the-art research, and demonstrating achieved improvements. Product modularity is addressed in this research using a new perspective which promotes optimal overall modularity. It improves management of product changes and variety leading to more cost-effective product development and manufacturing.
Introducing the Postpartum Toolkit: An Examination of the Feasibility, Acceptability and Pilot Efficacy of an Online Clinical Tool to Enhance Postpartum Functioning and Emotional Wellbeing

During the postpartum period, a birth parent’s level of functioning (ability to perform the activities and roles required to maintain wellbeing) is critical in determining the health of parents and their infants. However, existing approaches to support postpartum parents are insufficient, especially in the United States, and these individuals face barriers to care. The utilization of internet-based intervention may be an effective solution allowing access to resources for this population. In this study, we developed a patient-centered online tool to bolster postpartum functioning, and collected data on the feasibility, acceptability, and initial impact of this tool on functioning and emotional wellbeing. Data collection took place between February and June 2021 from a sample of 124 individuals who were within the first ten months postpartum and living in the US. Results suggest that the tool is acceptable, though there are barriers to feasibility of use. Additionally, pilot-efficacy data suggest that this tool may be effective in improving postpartum emotional wellbeing, though further controlled testing is warranted. A future iteration of the tool that incorporates participant feedback to improve feasibility of use could prove an effective means of delivering support to an at-risk population.
Study on the Effect of PDCA Circulation Method on Nursing Quality Management in the Day Operating Room

Objective The main objective is to investigate the effect of PDCA circulation management on nursing quality in the day operation room. Methods A retrospective study was performed in 300 patients in the day surgery room. For the control group, 150 patients received routine nursing. For the observation group, 150 patients underwent PDCA circulation nursing management. The scores for nursing quality management, the hospital infection, the detection rate of pathogenic bacteria, the incidence rate of adverse events, the negative emotion of patients, and the satisfaction rate for the day surgery department were recorded and analyzed between two groups. Results Compared with the control group, the scores for nursing quality management and the satisfaction rate for the day surgery department were significantly increased (all P < 0.05), while the hospital infection, the detection rate of pathogenic bacteria, HAMA scores, HAMD scores, and the incidence rate of adverse events were obviously decreased (all P < 0.05). Significantly statistical differences were observed between the two groups. Conclusion PDCA circulation nursing management in the day operating room could optimize the nursing quality management, improve the satisfaction rate of the operating room, reduce the negative emotions of patients, and prevent adverse events in time, with lower hospital infections.
Barrier Function-Based Nonsingular Finite-Time Tracker for Quadrotor UAVs Subject to Uncertainties and Input Constraints

This study proposes an adaptive barrier functions-based non-singular terminal sliding mode control approach for the trajectory tracking of a quadrotor unmanned aerial vehicle subject to bounded uncertainties and input constraints. First, the state-space equations of the six degrees-of-freedom quadrotor system is introduced in the presence of bounded uncertainty and constrained input. Then, a compensation system is designed with the aim of removing the constrained input and leading to high performance. Afterwards, a linear switching surface is defined using the tracking error and virtual control input to guarantee the convergence of the tracking error in the presence of parametric uncertainties and input saturation. Later, a non-singular terminal sliding surface is proposed for fast convergence of the linear switching surface. To eliminate the need for approximating the upper bounds of uncertainties and ensure the fast convergence of the non-singular terminal sliding surface to a pre-specified neighborhood of the origin, we considered an adaptive barrier function scheme. The fast convergence rate of the proposed approach is verified via the Lyapunov stability theory. The accuracy and performance of the proposed approach is assessed using MATLAB/Simulink simulations and robustness analysis using the random number noise.
The impact of COVID‐19 trade measures on agricultural and food trade

Abstract This paper assesses the determinants of temporary non‐tariff measures (NTMs) in response to COVID‐19 and their implications for the agricultural and food trade. Using a control function approach, we show that economic and pandemic considerations played an essential role in implementing such NTMs. Relying on variation between treated and untreated varieties, we estimate a dynamic post‐event trade response of 5.4% for import facilitating and −27.5% for export restricting NTMs. After revoking them, their trade effects fade away, implying that these temporary trade policies were effective in achieving the set policy goals, causing only a limited degree of long‐term trade disruptions.
Silicon Oxynitride Thin Film Coating to Lossy Mode Resonance Fiber-Optic Refractometer

A fiber-optic refractometer for various liquids with refractive indices in the range from 1.33 to 1.43 has been manufactured and tested. The sensor is based on a thin silicon oxynitride (Si3N4-xOx) film coated thinned optic fiber section (taper) obtained in a multimode all-silica optical fiber by chemical etching of the reflective cladding. The film was deposited on the cylindrical surface of the thinned fiber by the surface plasma chemical vapor deposition method (SPCVD). Lossy mode resonance (LMR) was observed in the transmission spectrum of the coated taper at a wavelength dependent on the refractive index of the liquid in which the taper was immersed. We tested the obtained sensors in distilled water, isopropyl alcohol, dimethylformamide, and their aqueous solutions. It was found that with the help of the SPCVD, one can obtain a set of sensors in a single deposition run with the dispersion of sensitivity and spectral position of LMR no more than 5%. Maximum sensitivity of the manufactured sensors to surrounding media refractive index (SMRI) variation exceeds 1090 nm/RIU, which is the highest value recorded to date for a sensor with a non-oxide coating.
Random forest classification algorithm for medical industry data

Medical industry produces a significant portion of data whereas by adopting various Machine Learning models it can make accurate predictions about public healthcare that can be generalised. Transfer learning improves traditional machine learning by transferring the knowledge learned in one or more tasks and by using it for learning improvement in a related target task. In the current study, transfer learning with random forests was applied. Four datasets of medical interest obtained from the University of California, Irvine (UCI) Machine Learning Repository were used i.e., the BUPA-Liver Disease Dataset, the Breast Cancer Wisconsin Dataset, the Cleveland Heart Disease Dataset, and the Pima Indians Diabetes dataset. To our knowledge, there has been no study that applied Random Forests and Transfer Learning for these datasets. According to our results, our proposed method could provide significant accuracy rates in terms of diagnosing these disorders. Specifically, the classification accuracy of each dataset was similar or higher compared to the majority of similar studies that applied Random Forests. Limitations and suggestions regarding future research are also presented.
Iconic bias in Italian spatial demonstratives

ABSTRACT An iconic pattern across spoken languages is that words for ‘this’ and ‘here’ tend to have high front vowels, whereas words for ‘that’ and ‘there’ tend to have low and/or back vowels. In Italian, there are two synonymous Italian words for ‘here’, namely qui and qua, and two synonymous words for ‘there’, lì and là. Qui ‘here’ and là ‘there’ are iconic because qui has the high front vowel /i/ and là has the low vowel /a/, whereas qua ‘here’ and lì ‘there’ are counter-iconic, since their vowels are the opposite. Based on corpus, survey and computational data, we demonstrate that (i) qui ‘here’ and là ‘there’ have been consistently used more frequently throughout history compared to qua ‘here’ and lì ‘there’, respectively; and (ii) in present-day Italian, qui ‘here’ tends to refer to a location that is closer to the speaker than qua ‘here’ does, whereas là ‘there’ tends to refer to a location that is further away from the speaker than lì ‘there’ does. In summary, the iconic demonstrative pronouns (qui and là) are used more frequently and are closer to the prototypical meanings of ‘here’ and ‘there’. We argue that their frequency and prototypicality are motivated by their iconic power. This case study shows how iconicity may work as pressure on language use and language change.
Estimating water content of natural gas mixtures considering heavy hydrocarbons contribution using artificial neural networks

Abstract The accurate estimation of the water content of natural gas is the basis for proper equipment design in natural gas processing and transmission facilities. Methods for estimating the water content of natural gas have been reported based on pressure and temperature data, and the concentration of acid gases. This work aims to develop a predictive model based on artificial neural networks (ANN) to estimate the water content of natural gas, considering the contribution of heavier hydrocarbons than methane, commonly present in the rich gases, gas condensates, or liquefied petroleum gases. The novelty of the proposed model is the incorporation of natural gas richness (GPM) as an input parameter for calculating the water content of natural gas, in addition to temperature, pressure, and acid gas equivalent content. Experimental data from the literature is used in the ANN training and validation, with the help of the BigML machine learning platform to perform a multi-layer-feedforward neural network. The ANN performance showed the ability to accurately predict the water content for different gas mixtures (rich, lean, sweet, and sour gases). Comparisons with available models in the literature are performed, showing that the ANN model provides accurate estimations (AAD: < 10%).
Integrated Pharmacology Reveals the Molecular Mechanism of Gegen Qinlian Decoction Against Lipopolysaccharide-induced Acute Lung Injury

ALI is a severe inflammatory disease of the lungs. In previous studies, we found that GQD was effective against ALI, but specific molecular mechanism is still unclear. Therefore, this study was to examine effect of GQD on LPS-induced ALI rats and underlying mechanisms using multi-omics and molecular methods. The results showed that GQD significantly improved lung tissue damage, reduced pulmonary edema, inhibited MPO activity, and improved respiratory function in ALI rat. Additionally, GQD significantly reduced the levels of TNF-α, IL-1β, and IL-6 in serum and BALF. Furthermore, metabolomic analysis showed that GQD reduced pulmonary inflammation by improving metabolic remodeling. Moreover, transcriptomic analysis showed that GQD inhibited the activation of complement pathway and regulated Th17 and Treg cells balance. Additionally, GQD inhibited the expression of C3, C5a, and IL-17, and promoted the expression of TGF-β and CYP1A1 at the mRNA and protein levels. Gut microbial assay showed that GQD treatment increased the relative abundance of Firmicutes and their genera in intestinal microbiota, and increased short-chain fatty acids concentration. Overall, GQD treated ALI by improving metabolic remodeling, affecting immune-related pathways and regulating intestinal microbiota. This study provides a solid scientific basis for promoting the clinical use of GQD in treating ALI.
PuzzleMesh: A Puzzle Model to Build Mesh of Agnostic Services for Edge-Fog-Cloud

This paper presents the design, development, and evaluation of PuzzleMesh, an agnostic service mesh composition model to process large volumes of data in edge-fog-cloud environments. This model is based on a puzzle metaphor where pieces, puzzles, and metapuzzles represent self-contained autonomous and reusable software artifacts encapsulated into containers and published as microservices. A piece represents the integration of apps with I/O interfaces (loops/sockets), parallel processing, and management software. A puzzle represents a processing structure (e.g., workflows) built coupling pieces through loops and sockets. Puzzles integrate structures with a microservice architecture, implicit continuous dataflows, and transparent data exchange management software. A metapuzzle represents a recursive assemble of puzzles. A mesh represents a pool of pieces, puzzles, and metapuzzles available for designers to choose artifacts to build services. A prototype developed using PuzzleMesh model was evaluated through case studies about the automatic construction of processing services for the acquisition, pre-processing, manufacturing, preserving, and visualizing of satellite imagery. A qualitative comparison revealed that PuzzleMesh provides a flexible way to build reusable and portable services and to improve the usability of the services. The case study also revealed that PuzzleMesh yielded better performance results than other state-of-the-art tools.
Virtual Teams’ Dynamics in an Online Learning Environment During the Covid-19 Pandemic

During the Covid-19 pandemic, students need to adapt to the new way of learning, including collaborating in virtual teams. Thus, this paper aims to capture the students’ experience in collaborating virtually and describe the teams’ dynamics. The data were collected through individual feedback forms, group oral presentations, and course assessment documents from students enrolled in Group Communication and Organizational Communication course in Communication Science Study Program, Padjadjaran University. In general, the students are more satisfied working in teams that are "professional and fun" . These teams tend to be functional groups that focus on getting the job done while building positive relationships through socio-emotional communication, such as humor and non-task conversations. In addition, confirming responses and leadership roles were also found to contribute to team outcomes.
The Impact of Financial Inclusion on Income Inequality: Case Study in Europe

Financial inclusion is considered an important factor which contributes to reducing poverty and income inequality. A lot of previous researches showed the impact of some dimensions of financial inclusion on income inequality. However, financial inclusion is measured by many different dimensions; therefore, to assess the combined impact of financial inclusion on income inequality, the authors first used the principal components analysis to build a composite financial index, with the datasets from 29 high- and upper-middle-income countries in Europe in the period of 2011-2017. Next, the authors used the two-stage least squares (2SLS) regression method to estimate the impact of financial inclusion on income inequality. The research results found that the financial inclusion, the percentage of the population aged 25 and over graduating from secondary education, the economic openness had an opposite effect on income inequality. On the contrary, the employment-to-population ratio had a directional effect on income inequality. Based on the obtained estimates, the study proposed some solutions to reduce income inequality.
Single Actuator with Versatile Controllability of 2-DOF Assistance for Exosuits via a Novel Moving-Gear Mechanism

Decreasing the system weight while maintaining the assistance performance can help reduce the metabolic penalty in exosuits. Various researchers have proposed a bi-directional cable-driven actuator that can provide two degrees of freedom (2-DOF) assistance by using a single motor. However, such systems face limitations associated with the controllability of the assistance force. This study proposes a novel cable-driven system, that is, a dual pulley drive, that can provide versatile controllability of 2-DOF cable actuation by using a single motor via a novel moving gear mechanism. The moving gear winds the cable by switching both the side pulleys, which are then used for 2-DOF cable actuation. The spiral springs embedded between the pulley and base shaft work to release the cable. Results of experiments demonstrate that the dual pulley drive provides a versatile range of motion. The proposed system can provide 34.1% of overlapping motion per cable round trip time and support the non-overlapping motion. The preliminary integration of the dual pulley drive to the exosuit confirms that the novel exosuit is considerably lighter than the state-of-the-art exosuit. The calculations indicate that the operating cable speed and force generated using the proposed design are higher than the existing exosuit.