Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning the Inverse Kinematics of a 6-DOF Concentric Tube Robot through a Generative Adversarial Network

Concentric tube robots (CTR’s) are a highly complex robotic system due to a combination of both joint and transitional spaces as well as having high degrees of freedom. Due to their advantages in having a small form factor, ability to navigate in constrained spaces, there is a great need for a robust control system. Analytical inverse kinematics (IK) offer one such approach, but suffer from numerical problems and instabilities in its Jacobian. Furthermore, redundancy is not addressed in such solutions. Neural Networks offer an opportunity to approximate the inverse kinematics, as well as offer advantages that numerical solution cannot offer. In particular, we approach inverse kinematics through a simulation intensive method by bootstrapping the Jacobian to generate data to simulate both the Jacobian and the forward kinematics with a shallow feed-forward neural network. We then perform a standard optimisation of the positional error through feedback to solve for IK. Furthermore, to tackle the issue of redundancy and generate starting conﬁgurations, we explore utilising an ensemble Generative Adversarial Network to generate a variety of robot conﬁgurations such that the end-effector is close to the target point. By comparing with standard inverse kinematic approaches, from a pure function approximation from target end poses to robot conﬁguration spaces, to a data-driven lookup process, we show that our mixture of Jacobian Learning + GAN offer relatively high accuracy (2.5% of total robot length) as well as low computational time (1 - 1.5 s/point), a signiﬁcant improvement over literature values of 10% of total robot length for neural network based implementations.
Carbon Metabolism in Scions of Colorado Blue Spruce: II. Carbon Storage Compounds

The study evaluated the roles of storage carbohydrates and neutral lipids in the success of Colorado blue spruce (Picea pungens Englemann ‘Hoopsi’) grafts. These scions do not require photosynthesis nor receive photosynthates from the rootstock during union development. Carbohydrate and neutral lipid contents, along with respiration and scion water relations, were measured during union development. Stored carbon compounds were sufficient to supply the needs of the scion during the 9 weeks of union development. Estimates of carbohydrate use indicated that decreases in sugar content (bark and needle) were insufficient to account for more than 25% of the estimated respiration. The results indicate that the quantity of carbon storage compounds is not a factor in graft success. We propose that neutral lipids may be the major carbon reserve of the scion during graft formation.
Controlling a Wheelchair using a Brain Computer Interface based on User Controlled Eye Blinks

Data published by different organizations such as the United Nations indicates that there are a large number of people who suffer from different types of movement disabilities. In many cases, the disability is so severe that they cannot have any kind of movements. Faced with this situation, Brain Computer Interface technology has taken up the challenge of developing solutions that allow delivering a better quality of life to those people, and one of the most important areas has been the mobility solutions, which includes the brain computer interface enabled electric wheelchairs as one of the most helpful solutions. Faced with this situation, the present work has developed a Brain Computer Interface solution that allows users to control the movement of their wheelchairs using the brain waves generated when blinks their eyes. For the creation of this solution, the Incremental Prototyping methodology has been used to optimize the development process by generating independent modules. The solution is made up of several components i.e. EEG System (OpenBCI), Main Controller, Wheelchair Controller and Wheelchair that allows to have a modularity to carry out updates (improvements) of their functionalities in a simple way. The developed system has shown that it requires a low amount of training time and has a real world applicable response time. Experimental results show that the users can perform different tasks with an acceptable grade of error in a period of time that could be considered as acceptable for the system. Considering that the prototype was created for people with disabilities, the system could grant them a certain level of independence. Keywords—BCI; EEG; brain computer interface; OpenBCI; eye blink detection
Masters Thesis: Phase Diversity for Blind Multi-Frame Deconvolution

When objects are imaged, often aberrations slip into the captured images. Many techniques exist to remove part of those aberrations using hardware, software or a combination of those. This research will focus on Phase Diversity for Blind Multi-Frame Deconvolution. In Blind Multi-Frame Deconvolution both the object and the aberrations are estimated from multiple images of the same object. Previous research has shown, Phase Diversity can be used to improve the quality of the reconstructed object in combination with Blind Multi-Frame Deconvolution. Using Defocus Phase Diversity is currently mainly used in literature. In this research new Phase Diversity methods are proposed. When there is no knowledge on the object or aberrations, Phase Diversities are proposed to improve the object reconstruction compared to Defocus Phase Diversity. We also propose a new method to optimize the Phase Diversity for the N + 1th image, when already N images have been taken. This method first determines the low values in the frequency spectra of the images. Next it estimates a Phase Diversity that results in a Modulation Transfer Function to enhances those frequencies. This method leads to an improved restoration quality in terms of Structural Similarity and Peak Signal to Noise Ratio when there is Gaussian noise or Poisson noise with a higher Signal to Noise Ratio. Recommendations are done to improve the method for lower Signal to Noise Ratios. Master of Science Thesis Dominique Vrijburg
Body-on-a-chip: Microfluidic based futuristic approach for Drug Screening

Preclinical testing using animal models has been crucial in drug development process, but issues of ethical factors and species variances still remain. However, human derived in vitro cell-based assays are being actively practiced to study drug’s pharmacokinetics and pharmacodynamics. Although, current in vitro cell assays give the drug's potential therapeutic benefits, it is only specific to a tissue but not to an entire body to accurately predict the compound’s efficacy, toxicity and inter-organ interactions. In recent years, ever-growing scientific research and innovative advancements in microfabrication technologies have impacted notably on bioengineering and biomedical engineering. One such approach is ‘Microfluidics’ that has emerged as a powerful tool to provide the opportunity to stimulate different organs. The technology of organ-on-a-chip has made it probable to physically and chemically recapitulate the in-vivo conditions by utilizing microfluidic approach. Till now, in vitro models of multi-organs including the gut, liver, kidney, lung, heart, and bone have been developed to mimic the organ physiology. Likewise, an integration of multiple organs on a microfluidic platform known as body-on-a-chip model has also been proposed to replicate organ cross talk. This review provides a overview on cell culturing within microfluidics systems, design, fabrication of biochips, organ and body on a chip models, and its applications in the coming future. Keywords― Microfluidics, organ-on-a-chip, body-on-a-chip, microfabrication, drug screening.
A New Key Generation Technique based on Neural Networks for Lightweight Block Ciphers

In recent years, small computing devices used in wireless sensors, radio frequency identification (RFID) tags, Internet of Things (IoT) are increasing rapidly. However, the resources and capabilities of these devices are limited. Conventional encryption ciphers are computationally expensive and not suitable for lightweight devices. Hence, research in lightweight ciphers is important. In this paper, a new key scheduling technique based on neural network (NN) is introduced for lightweight block ciphers. The proposed NN approach is based on a multilayer feedforward neural network with a single hidden layer with the concept of nonlinear activation function to satisfy the Shannon confusion properties. It is shown here that NN consisting of 4 input, 4 hidden, and 4 output neurons is the best in key scheduling process. With this architecture, 5 unique keys are generated from 64 bit input data. Nonlinear bit shuffling is applied to create enough diffusion. The 4-4-4 NN approach generates the se-cure keys with an avalanche effect of more than 50 percent and consumes less power and memory, thus ensuring better performance than that of the existing algorithms. In our experiments, the memory usage and execution cycle of the NN key scheduling technique are evaluated on the fair evaluation of lightweight cryptographic systems (FELICS) tool that runs on the Linux operating system. The proposed NN approach is also implemented using MATLAB 2021a to test the key sensitivity by the histogram and correlation graphs of several encrypted and decrypted images. Results also show that compared to the existing algorithms, the proposed NNcipher algorithm has lower number of execution cycles and hence less power consumption. Keywords—Lightweight cryptography; IoT; resource limited devices; neural network; avalanche effect; FELICS; MATLAB
Effect of Ethanol and Ultrasound Pretreatments on Pineapple Convective Drying

SUMMARY Research background Drying represents a viable unit operation for the preservation of food. Convective drying is the most used method for plant materials. However, it can result in negative changes in food nutrient composition, and other quality parameters, besides requiring high energy consumption. Pretreatments can represent an alternative to minimise these negative aspects of dried materials. This work aims to evaluate the use of ethanol and ultrasound before pineapple convective drying and its effect on the product´s colour, water activity, ascorbic acid and total carotenoid contents. Experimental approach For the pretreatment step, fruit samples were immersed in ethanol solutions of different volume fractions, and experiments were carried out for 10 min with and without using ultrasound (25 kHz). Fruit samples were dried at 60 ºC. A control group (without the pretreatment step) was also dried under the same condition. Semi-theoretical models were used for drying data fitting, and the diffusional model was used to describe the moisture transfer and calculate the effective diffusivity. Water activity, ascorbic acid, total carotenoids and colour analyses were performed. Results and conclusions The combination of ethanol and ultrasound as a pretreatment reduced the drying time of pineapple. Higher effective moisture diffusivities were obtained when ethanol and ultrasound were applied before drying. The two-term exponential model presented the best fit for drying experimental data. The dried samples had a darker colour than the fresh sample. The pretreatment with ethanol resulted in increased retention of the studied bioactive components. The satisfactory results of this study represent an improvement in the drying process. Novelty and scientific contribution Ultrasound and ethanol as a pretreatment to convective drying are promising. However, each food matrix has a typical structure and composition. Therefore, the application of the pretreatment in other products or using other conditions is still necessary to deeply understand and explain their effect on the process and the quality of the dried products.
Real Time Face Expression Recognition along with Balanced FER2013 Dataset using CycleGAN

Human face expression recognition is an active research area that has massive applications in medical field, crime investigation, marketing, online learning, automobile safety and video games. The first part of this research defines a deep neural network model-based framework for recognizing the seven main types of facial expression, which are found in all cultures. The proposed methodology involves four stages: (a) preprocessing the FER2013 dataset through relabeling to avoid misleading results and getting rid of non-face and non-frontal faces; (b) design of an efficient stable Cycle Generative Adversarial Network (CycleGAN), which provides unsupervised expression-to-expression translation. The CycleGAN has been designed and trained with a new cycle consistency loss. (c) Generating new images to overcome the class imbalance and finally (d) building the DNN architecture for recognizing the face sign expression, using the pretrained VGG-Face model with vggface weights. The second part encompasses the design of a GPU-accelerated face expression recognition system for real time video sequences using NVIDIAs Compute Unified Device Architecture (CUDA). OpenCV library has been compiled from scratch with CUDA and NVIDIA CUDA Deep Neural Network library, cuDNN. For face detection stage Haar Cascaded and deep learning were used and tested using both CPU and GPU as backend. Results show that the designed model run time to recognize a face sign is 0.44 seconds. Besides, the average test accuracy has been increased from 64% for the original FER2013 dataset to 91.76% for the modified balanced version using the same transfer learning model. Keywords—Facial expressions detection and recognition; multi-task cascaded convolutional networks; transfer learning; residual neural network; CycleGAN; FER2013; GPU and CUDA; HAAR
Object and Instance Detection Within Image Scenes

This paper compares state-of-the-art methods in object and instance detection and examines why YOLO (You Only Look Once) outperforms top detection methods. Different Pascal VOC dataset is used as the benchmark to explore mean average precision (mAP). YOLO is twice as accurate as prior works on real-time detection. The outcome of merging YOLO with Fast R-CNN is an increased mean average precision (mAP) which results in a performance boost. Hence, YOLO is an enhanced model of top detection methods.
Differentiated Key-Value Storage Management for Balanced I/O Performance

Modern key-value (KV) stores adopt the LSM-tree as the core data structure for managing KV pairs, but suffer from high write and read ampliﬁcations. Existing LSM-tree optimizations often make design trade-offs and are unable to simultaneously achieve high performance in writes, reads, and scans. To resolve the design tensions, we propose DiffKV, which builds on KV separation to carefully manage the ordering for keys and values. DiffKV manages keys using the conventional LSM-tree with fully-sorted ordering (within each level of the LSM-tree), while managing values with partially-sorted ordering with respect to the fully-sorted ordering of keys in a coordinated way for preserving high scan performance. We further propose ﬁne-grained KV separation to differentiate KV pairs by size, so as to realize balanced performance under mixed workloads. Experimental results show that DiffKV can simultaneously achieve the best performance in all aspects among existing LSM-tree-optimized KV stores.
Sarcasm Detection in Tweets: A Feature-based Approach using Supervised Machine Learning Models

Sarcasm (i.e., the use of irony to mock or convey contempt) detection in tweets and other social media platforms is one of the problems facing the regulation and moderation of social media content. Sarcasm is difficult to detect, even for humans, due to the deliberate ambiguity in using words. Existing approaches to automatic sarcasm detection primarily rely on lexical and linguistic cues. However, these approaches have produced little or no significant improvement in terms of the accuracy of sentiment. We propose implementing a robust and efficient system to detect sarcasm to improve accuracy for sentiment analysis. In this study, four sets of features include various types of sarcasm commonly used in social media. These feature sets are used to classify tweets into sarcastic and nonsarcastic. This study reveals a sarcastic feature set with an effective supervised machine learning model, leading to better accuracy. Results show that Decision Tree (91.84%) and Random Forest (91.90%) outperform in terms of accuracy compared to other supervised machine learning algorithms for the right features selection. The paper has highlighted the suitable supervised machine learning models along with its appropriate feature set for detecting sarcasm in tweets. Keywords—Machine learning; detection; sarcasm; sentiment; tweets
Clinical usage of non-invasive ventilation by physical therapists in cystic fibrosis centres across Australia: A cross-sectional survey study

BACKGROUND: This study investigated clinical usage of non-invasive ventilation during physical therapy for people with cystic fibrosis. Specific research questions were: 1. What are the clinical indications, contraindications and patient selection criteria for non-invasive ventilation use as an adjunct to physical therapy in people with cystic fibrosis? 2. Who implements non-invasive ventilation, what settings are used and how are they determined? 3. What outcome measures are used to determine the effectiveness of non-invasive ventilation as an adjunct to physical therapy and what are the main benefits and complications? METHODS: A purpose-designed survey was sent to 23 Australian cystic fibrosis centres. RESULTS: Fifteen centres (65%) responded, with 13 reporting current utilization of non-invasive ventilation to assist physical therapy. Non-invasive ventilation was most commonly (85%) used in patients with lung function <40% predicted. Physical therapy clinical indications included shortness of breath at rest (100%) and during airway clearance (100%), and fatigue during airway clearance (100%). Physical therapists were responsible for initiating non-invasive ventilation (62%), setting up (85%) and determining settings (62%). Bi-level ventilation was the only chosen ventilation mode. Benefits reported included improved ease of airway clearance (100%), reduced fatigue (92%) and decreased dyspnoea (85%). Only one complication of haemoptysis was reported. CONCLUSIONS: Non-invasive ventilation was used during physical therapy in people with cystic fibrosis who had severe disease, mostly during airway clearance to improve tolerability of treatment. Australian physical therapists initiated non-invasive ventilation when people with cystic fibrosis experienced shortness of breath or fatigue during treatment, aligning with current clinical guidelines. Clinical usage was largely consistent across centres, with numerous benefits and few complications reported. Further research is required to explore benefits of non-invasive ventilation use during physical therapy. Keywords: Cystic Fibrosis, Noninvasive Ventilation, Bilevel Positive Airway Pressure, Physical Therapy, Airway Clearance, Exercise
Genomic Surveillance of SARS-CoV-2 in Erie County, New York

Early in the SAR-CoV-2 pandemic, we established a whole genome sequencing pipeline to assess lineages circulating in Western New York. Initial sequences revealed entry into the region via Europe, similar to observations in New York City. However, as the pandemic progressed and variants of concern emerged, we observed distinct patterns in lineages relative to NYC. Notably, B.1. 427 became dominant in Western New York, before it was displaced by B.1. 1.7. Our hierarchical cluster analysis of B.1. 1.7 lineages, which by May 2021 made up ~ 80% of all cases, indicated both multiple introductions and community spread. Our work highlights the importance of widespread, regional surveillance of SARS-CoV-2 across the United States.
Eco-friendly photolithography using water-developable pure silk fibroin

We report that pure silk fibroin can be a green and biofunctional photoresist for deep ultraviolet photolithography. All processes are entirely water-based, from resist solvent to resist removal, and rely on the phototendering effect that decreases the crystallinity of silk fibroin films by DUV exposure. Additionally, the potential decrease in activity of bio-dopants due to high-energy irradiation is irrelevant to our positive-tone lithographic method.
Spontaneous Smoking Cessation in Parents

Purpose To determine the percentage of parents who report quitting spontaneously and examine the factors associated with these quits. Methods As part of a cluster randomized control trial addressing parental smoking in a pediatric outpatient setting, 12-month follow-up survey data were collected from parents who had self-identified as smokers when exiting from 10 control practices. Parents were considered to have made a spontaneous quit if they reported not smoking a cigarette, even a puff, in the last 7 days and chose the statement “I did not plan the quit in advance; I just did it” when describing how their quit attempt started. Results Of the 981 smoking parents enrolled at baseline, 710 (72%) completed the 12-month follow-up. Of these, 123 (17%) reported quitting, of whom 50 (41%) reported quitting spontaneously. In multivariable analysis, parents who reported smoking on some days vs. every day (OR 3.06 (95% CI 1.42, 6.62)) and that nobody had smoked in their home/car vs. someone had smoked in these settings in the past 3 months (OR 2.19 (95% CI 1.06, 4.54)) were more likely to quit spontaneously. Conclusions This study shows that, of parents who quit smoking, a substantial percentage report quitting spontaneously and that intermittent smoking and smoke-free home/car policies are associated with reports of quitting spontaneously. Promoting smoke-free home/car policies, especially when parents are not willing to make a plan to quit smoking, might increase the likelihood that parents decide to quit without advance planning. Pediatric healthcare providers are uniquely positioned to use the child's visit to motivate parents to quit smoking and eliminate their child's exposure to tobacco smoke, regardless of the frequency of smoking or a readiness to plan a quit attempt. Clinical Trial Registration. This trial is registered with NCT01882348.
Oops!... I Did It Again: Understanding Mechanisms of Persistence in Prosocial Behavior

We test whether asking individuals to donate blood leads to a persistent change in behavior, and examine the underlying mechanism. In a field experiment, we randomize a phone call, asking blood donors to turn out, and follow them over up to 18 months. We observe significant behavioral persistence for at least one year. We use naturally occurring rainfall as a second instrument for donor turnout to test whether behavioral persistence is due to habit formation (Stigler and Becker, 1977) or a persistent increase in motivation independent of past donation. Our results strongly favor habit formation as the underlying mechanism.