Miracle-dz/UniFAR-FTUs huggingface dataset:
Biodegradable Polymer Electrospinning for Tendon Repairment

With the degradation after aging and the destruction of high-intensity exercise, the frequency of tendon injury is also increasing, which will lead to serious pain and disability. Due to the structural specificity of the tendon tissue, the traditional treatment of tendon injury repair has certain limitations. Biodegradable polymer electrospinning technology with good biocompatibility and degradability can effectively repair tendons, and its mechanical properties can be achieved by adjusting the fiber diameter and fiber spacing. Here, this review first briefly introduces the structure and function of the tendon and the repair process after injury. Then, different kinds of biodegradable natural polymers for tendon repair are summarized. Then, the advantages and disadvantages of three-dimensional (3D) electrospun products in tendon repair and regeneration are summarized, as well as the optimization of electrospun fiber scaffolds with different bioactive materials and the latest application in tendon regeneration engineering. Bioactive molecules can optimize the structure of these products and improve their repair performance. Importantly, we discuss the application of the 3D electrospinning scaffold’s superior structure in different stages of tendon repair. Meanwhile, the combination of other advanced technologies has greater potential in tendon repair. Finally, the relevant patents of biodegradable electrospun scaffolds for repairing damaged tendons, as well as their clinical applications, problems in current development, and future directions are summarized. In general, the use of biodegradable electrospun fibers for tendon repair is a promising and exciting research field, but further research is needed to fully understand its potential and optimize its application in tissue engineering.
The precision of velocity measurements from H I profiles

The intrinsic precision of both systemic velocity and of the velocity width estimates derived from H I profiles of galaxies, is modelled by a Monte Carlo simulation of the effects of noise. This permits the achieved precision to be estimated directly from the S/N ratio. In particular, a S/N> or approx. =7 is required for reliable velocity estimates. Our results suggest that observers usually underestimate their precision. New observations of 21 galaxies were made from Arecibo, to compare velocity estimates derived from high S/N ratio profiles with the results of other observers. Surprisingly, these show that the 20% width (.delta.V/sub 20/) of many narrow profiles are significantly overestimated. With these cases identified, the remaining data have a precision of 10 km s/sup -1/, that does not scale with . delta.V/sub 20/. Furthermore, when S/N> or approx. =10 and the galaxies are smaller than the 3. /3 Arecibo beam, the precision of 21-cm estimates of the systemic velocity is 3.6 km s/sup -1/.
Real-Time Pose Tracking for a Continuum Guidewire Robot Under Fluoroscopic Imaging

Atherosclerosis is a medical condition that causes buildup of plaque in the blood vessels and narrowing of the arteries. Surgeons often treat this condition through angioplasty with catheter placements. Continuum guidewire robots offer significant advantages for catheter placements due to their dexterity. Tracking these guidewire robots and their surrounding workspace under fluoroscopy in real-time can be useful for visualization and accurate control. This paper discusses algorithms and methods to track the shape and orientation of the guidewire and the surrounding workspaces of phantom vasculatures in real-time under C-arm fluoroscopy. The shape of continuum guidewires is found through a semantic segmentation architecture based on MobileNetv2 with a Tversky loss function to deal with class imbalances. This shape is refined through medial axis filtering and parametric curve fitting to quantitatively describe the guidewire’s pose. Using a constant curvature assumption for the guidewire’s bending segments, the parameters that describe the joint variables are estimated in real-time for a tendon-actuated COaxially Aligned STeerable (COAST) guidewire robot and tracked through its traversal of an aortic bifurcation phantom. The accuracy of the tracking is ˜90% and the execution times are within 100 ms, and hence this method is deemed suitable for real-time tracking.
Classification and Mixture Approaches to Clustering Via Maximum Likelihood

Mixtures of distributions, in particular the normal distribution, have been used extensively as models in a wide variety of important practical situations where the population of interest may be considered to consist of two or more subpopulations mixed in varying proportions. The problem of decomposing such a mixture of distributions is of considerable interest and utility. Two commonly used clustering methods based on maximum likelihood are considered in the context of the classification problem where observations of unknown origin belong to one of the two possible populations. The basic assumptions and associated properties of the two methods are contrasted and illustrated by a series of simulations under two different sampling schemes, namely the mixture sampling scheme and the separate sampling scheme. A case study is presented to demonstrate the basic differences between these two methods.
Bidodeeloltag Neek'ahgo: Perceptions and Uses of Mathematics on the San Carlos Apache Reservation

This dissertation examines the perception and uses of mathematics on the San Carlos Apache reservation. The initial research questions were: 1) To what extent can the identification of Apache mathematics concepts provide a theoretical basis for the inclusion of Apache mathematical knowledge into the classrooms? 2) How will the identification of Apache mathematics use within the community affect the perception of community members’ mastery of mathematics in general? Eight enrolled adult Apache community members were interviewed and observed utilizing qualitative and quantitative methods regarding their use and perception of mathematics. The data suggests that the English word “mathematics” represents a narrow perception of mathematics whereas the study interviewees indicated that they engaged in complex mathematical concepts that are identified and discussed as a culturally distinct phenomena, Apache mathematics.
Light intensity matching between different intravascular optical coherence tomography systems

Currently two commercial intravascular optical coherence tomography (IVOCT) systems are available: Illumien Optis from St. Jude Medical (SJM) and Lunawave from Terumo. Both systems store the light intensity data in a raw vendor specific polar format. However, whereas SJM uses 16-bits per pixel Terumo uses 8-bits meaning the intensity values are in different ranges. This complicates quantitative light intensity based analysis when comparing results based on data from both systems. Therefore, this work aims to find an intensity transformation function from Terumo’s 8-bit OFDI data to SJM’s 16-bit range. The data consists of 8 pullbacks, 4 acquired with each system in the same arteries of 2 different patents pre- and post-stenting implantation. A total of 133 matching sections without stent struts from the two sets of pullbacks were identified based on landmarks such as side-branches and calcified regions. Since the main region of interest in the image is the tissue region only the pixels within 2mm behind the lumen border are used. In order to match the SJM data range, the Terumo data was rescaled and cumulative distribution functions (CDF) were calculated based on the histogram distributions. Comparing these CDFs, the transformation function can be determined. Application of this transformation function not only improves the visual similarity of matching slices it can also be used for further quantitative analysis.
Highly Sensitive Transistor Sensor for Biochemical Sensing and Health Monitoring Applications: A Review

Transistors have gained tremendous attraction in biochemical sensing due to their outstanding performance with high sensitivity, flexibility, selectivity, real-time monitoring, ease of fabrication, and biocompatibility for various point-of-care (POC) biomedical and healthcare applications. Here, critical sensing capability by different transistor classes of: 1) organic field-effect transistor (OFET); 2) organic electrochemical transistor (OECT); 3) low-dimensional (LD) semiconductor field-effect transistor (FET); and 4) hybrid transistors are summarized for biochemical (e.g., ions, glucose, antigen/antibody, DNA, bacteria, virus, and cancer biomarkers) detection. Recent advancements in the transistor field for bioanalyte sensing, including a new class of a photoelectrochemical (PEC) transistor, bioreceptor conjugation, strategies to improve sensitivity, and overcoming the limit of detection (LOD), are discussed in a focused manner. Furthermore, organic and inorganic active material-based transistors are compared to find the best fit for specific target biodetection and healthcare applications.
Co-Composted Biochar Improves Dry-Farmed Tomato Productivity in the Santa Clara Valley, Ca, with No Change in Soil Microbiology

CO-COMPOSTED BIOCHAR IMPROVES DRY-FARMED TOMATO PRODUCTIVITY IN THE SANTA CLARA VALLEY, CA, WITH NO CHANGE IN SOIL MICROBIOLOGY by Duncan A. Keller California is a globally important site for agricultural production, but the state is currently suffering from an unprecedented drought. One strategy to conserve water is dry farming, where growers cache moisture in the soil from off-season rain or early-season irrigation for crops to use throughout the growing season. Some California growers have dry-farmed tomatoes for decades. Recently, some have begun adding co-composted biochar (COMBI) to boost productivity and stimulate soil microbiology, particularly arbuscular mycorrhizal fungi. To the best of my knowledge, no field research on dry-farmed tomato systems currently exists, let alone on the effects of COMBI amendments on yield, soil microfauna, or the relative cost of production. This on-farm collaboration between San José State University (SJSU) and an organic grower was thus designed to test the effect of COMBI on dry-farmed tomato yield, soil microbiota, and cost implications. Compost treatments with and without a softwood biochar amendment were applied to a 4-acre dry-farm tomato field at Martial Cottle Park (San José, CA). To estimate productivity two weeks before fruit maturity, I nondestructively measured plant height and per-plant fruit number and size, then harvested and measured fruit and shoot biomass after maturity. I sent soil samples for phospholipid fatty acid analysis to describe the microbial community. COMBI improved tomato productivity enough to outweigh the cost of the amendment. Still, microbial community parameters did not differ between treatments, suggesting that chemical or physical mechanisms may drive
The Influence of Gait Phase on Predicting Lower-Limb Joint Angles

Machine learning has seen a rapid increase in applications that harness wearable signals to improve human mobility. Previous work has used machine learning predictors as a means of continuously estimating locomotor intent. Although previous studies perform gait phase classification and joint-level angular prediction, there are currently no studies that compare joint-level prediction performance at various phases of gait. As such, the purpose of this offline study was to analyze how machine learning and deep learning models perform at predicting future joint angles during various phases of gait. EMG, IMU, and joint kinematics collected during level-ground walking from thirty participants and data was separated into six distinct gait phases. Random forest, long short-term memory (LSTM), and bidirectional LSTM was used to predict lower-limb joint angles during the phases of gait. Results indicate that bidirectional LSTM is the most robust performer across the gait cycle, with a mean prediction RMSE of 1.42-5.71 degrees. Our study shows how deep learning methods, such as bidirectional LSTM, can accurately estimate joint angles throughout the gait cycle. Furthermore, we propose future work of deploying models which accurately predict future joint angles throughout the gait cycle for users to sufficiently operate a wearable exoskeleton during locomotion.
Fault Voiceprint Signal Diagnosis Method of Power Transformer Based on Mixup Data Enhancement

A voiceprint signal as a non-contact test medium has a broad application prospect in power-transformer operation condition monitoring. Due to the high imbalance in the number of fault samples, when training the classification model, the classifier is prone to bias to the fault category with a large number of samples, resulting in poor prediction performance of other fault samples, and affecting the generalization performance of the classification system. To solve this problem, a method of power-transformer fault voiceprint signal diagnosis based on Mixup data enhancement and a convolution neural network (CNN) is proposed. First, the parallel Mel filter is used to reduce the dimension of the fault voiceprint signal to obtain the Mel time spectrum. Then, the Mixup data enhancement algorithm is used to reorganize the generated small number of samples, effectively expanding the number of samples. Finally, CNN is used to classify and identify the transformer fault types. The diagnosis accuracy of this method for a typical unbalanced fault of a power transformer can reach 99%, which is superior to other similar algorithms. The results show that this method can effectively improve the generalization ability of the model and has good classification performance.
Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks

Mobile networks of the fifth generation have stringent requirements for data throughput, latency and reliability. Dual or multi-connectivity is implemented to meet the mobility requirements for certain essential 5G use cases, and this ensures the user’s connection to one or more radio links. Packet duplication (PD) over multi-connectivity is a method of compensating for lost packets by reducing re-transmissions on the same erroneous wireless channel. Utilizing two or more uncorrelated links, a high degree of availability can be attained with this strategy. However, complete packet duplication is inefficient and frequently unnecessary. The wireless channel conditions can change frequently and not allow for a PD. We provide a novel adaptive fractional packet duplication (A-FPD) mechanism for enabling and disabling packet duplication based on a variety of parameters. The signal-to-interference-plus-noise ratio (SINR) and fade duration outage probability (FDOP) are important performance indicators for wireless networks and are used to evaluate and contrast several packet duplication scenarios. Using ns-3 and MATLAB, we present our simulation results for the multi-connectivity and proposed A-FPD schemes. Our technique merely duplicates enough packets across multiple connections to meet the outage criteria.
Probability-Based Synthetic Minority Oversampling Technique

Many real-life datasets suffer from class imbalance, where one or more classes are under-represented in the dataset, resulting in reduced classifier performance, with the expected decline in quality of procedures depending on the classification results, such as financial losses to businesses or inferior product quality. Improving classifier accuracy by handling class imbalance will positively impact classifier accuracy. In this study, we present a Probability-Based Synthetic Minority Oversampling Technique P-SMOTE to generate new examples for the minority class. Our proposed solution improves classifier accuracy by enhancing the oversampled examples through sampling the probability distributions present in the data. Results show improved performance over algorithms in the literature, with an average F-score of 0.821 over 13 datasets using 5 classifiers.
Developmental Strategy and Validation of the Midwifery Interventions Classification (MIC): A Delphi Study Protocol and Results from the Developmental Phase

This study protocol aims to describe the rationale and developmental strategy of the first study in the Italian context which aimed to define a Midwifery Interventions Classification, an evidence-based, standardized taxonomy and classification of midwifery interventions. Midwifery interventions require a specific definition, developed through a consensus-building process by stakeholders to develop the Italian taxonomy of the Midwifery Interventions Classification with the potential for international transferability, implementation, and scaling up. A multi-round Delphi study was designed between June and September 2022, and data collection is planned between February 2023 and February 2024. The developmental phase of the study is based on a literature review to select meaningful midwifery interventions from the international literature, aiming to identify an evidence-based list of midwifery interventions. This phase led to including 16 articles derived from a systematic search performed on PubMed, CINAHL, and Scopus; 164 midwifery interventions were selected from the data extraction performed on the 16 included articles. Healthcare professionals, researchers, and service users will be eligible panelists for the Delphi surveys. The protocol designed a dynamic number of consultation rounds based on the ratings and interim analysis. A nine-point Likert scoring system is designed to evaluate midwifery interventions. Attrition and attrition bias will be evaluated. The results from the study designed in this protocol will inform the development of the Italian taxonomy of the Midwifery Interventions Classification. A shared classification of midwifery interventions will support audit and quality improvement, education, and comparable data collections for research, sustaining public recognition of midwifery interventions to promote optimal maternal and newborn health.
Facilitators and barriers for adherence of shoulder pain patients to a home-based exercise program: cross-sectional study

BACKGROUND AND OBJECTIVES : Home-based exercises can improve function and quality of life in shoulder pain patients. Knowing the facilitators and barriers is crucial for adherence to shoulder pain treatment. It is believed that individuals who adhere to home exercises have fewer environmental barriers, pain intensity and shoulder disability. The aim of this study is to identify facilitators and barriers for adherence to a home-based exercise program in shoulder pain individuals, and to investigate the influence of environmental barriers, shoulder pain and disability, and kinesiophobia on adherence to a program. METHODS: This is a cross-sectional study. Shoulder pain individuals performed home-based exercises for eight weeks based on a booklet in addition to supervised physical therapy or as their sole therapy. They reported the perceived facilitators and barriers to home exercises adherence and answered the Craig Hospital Inventory of Environmental Factors (CHIEF), Shoulder Pain and Disability Index (SPADI), and the Tampa Scale for Kinesiopho-bia (TSK) questionnaires. Logistic regression models analyzed the scores of the CHIEF, SPADI, and TSK as predictors of adherence. RESULTS : Fifty individuals participated in this study and 88% adhe-red to the program. The most frequent facilitator and barrier were
Taxonomic and phylogenetic contributions to Diatrypaceae from southeastern Tibet in China

In this study, we investigated the diversity of diatrypaceous fungi from southeastern Tibet in China. The phylogenetic analyses were carried out based on ITS and β-tubulin sequences of 75 taxa of Diatrypaceae from around the world. Based on a combination of morphological features and molecular evidence, a new genus—Alloeutypa, with three new species—A. milinensis, Diatrype linzhiensis, and Eutypella motuoensis, and a new combination—A. flavovirens, were revealed by the materials in China. Alloeutypa is characterized by stromatal interior olivaceous buff, stromata producing well-developed discrete, and ascospores allantoid, subhyaline. These characteristics separate the new genus from the similar genus Eutypa. Comprehensive morphological descriptions, illustrations, and a phylogenetic tree to show the placement of new taxa are provided. All novelties described herein are morphologically illustrated and phylogeny investigated to better integrate taxa into the higher taxonomic framework and infer their phylogenetic relationships as well as establish new genera and species. Our results indicate that the diatrypaceous fungi harbor higher species diversity in China.
Response to Freedman’s Critique of Path Analysis: Improve Credibility by Better Methodological Training

Before I started to read the Freedman contribution, I feared that it was going to be another criticism by a statistician of social science statistical methodology, where little interest is shown in the specific problems of social science applications and their overwhelming complexities, and where any attempt by social scientists to use complex modeling rather than exploratory data analysis is considered naive. I wondered if I would find factor analysis, path analysis, latent variables, and structural equation modeling discounted as tools for the social sciences. As an applied statistician, I have become bored with such an attitude, which I believe puts social scientists and statisticians in an adversarial relationship, fostering the social scientists' opinion that statisticians are only concerned with irrelevant problems. For a long time, I have been frustrated by misuses of path analysis and more general structural equation modeling in the social and behavioral sciences. I still see very few good applications and I definitely feel that the way path analysis is presently practiced is often depressingly bad. Therefore, I think that Freedman's contribution is very necessary and I welcome the opportunity to discuss these matters. After I read Freedman's paper, I found that it is not totally of the type described above and that I cannot disagree with the details of his critique from a statistical perspective. Omitted variables and poor measurement are probably the most important and common sources of misspecifications, certainly leading to very distorted results. Infatuation with expansive modeling, with or without latent variables, cannot cure such fundamental misspecification. Like Freedman, I too think that much more exploratory data analysis should precede rigorous modeling and that results should be interpreted more modestly.