Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of chronic illness identities among women

The current qualitative study employed a modified form of constant comparative analysis to produce a model of the processes of developing chronic illness identities among women. Detailed interview data was collected from three participants, with part of the interview centering around the discussion of photos previously taken by the participant of events, people and things she felt were relevant to her chronic illness. Synthesis of the participant’s experiences produced a series of models of developmental experiences, including both phases and potentially influential factors. Of particular interest were findings of the potentially influential role played by disclosure experiences. In addition, developmental processes within the proposed model consisted of both a linear and a non-linear component. Although none of the findings can be taken as conclusive, due to the small sample size, several interesting areas for further inquiry were raised. INDEX WORDS: Identity development, Chronic illness, Women, Qualitative, Participantdriven interview format DEVELOPMENT OF CHRONIC ILLNESS IDENTITIES AMONG WOMEN
Brain Tumor Diagnosis Using CNN

Brain tumors are deadly neurological conditions caused by uncontrolled cell growth in the brain or skull. This disease's mortality rate continues to rise. Early detection of malignant tumors is essential for treating patients and improving survival rates. The evaluation and treatment of brain tumors rely heavily on the accurate categorization of the tumors. To spot malignant growths in the brain, neuroradiologists employ a variety of imaging methods. However, Magnetic Resonance Imaging is widely employed because it has a high picture quality as well as its absence of reliance on ionizing radiation. When it comes to classification and segmentation difficulties, among the machine learning subfields is deep learning (DL), which has lately demonstrated impressive results. Utilizing techniques of DL in the context of a big dataset medical field has shown to be quite advantageous, opening up completely new pathways and opportunities for the development of novel methods for the detection of illnesses. During this investigation, we are going to discuss a deep learning model that was created with the help of a CNN architecture to identify and categorize various forms of brain tumors from data found in the open-source dataset. The identification of brain tumors is accomplished with the help of a simple convolutional model, and the categorization of the primary kinds of brain tumors is carried out with the assistance of a transfer learning model known as ResNet50.
Conv2D Xception Adadelta Gradient Descent Learning Rate Deep learning Optimizer for Plant Species Classification

Almost every element of living beings is undergoing extraordinary transformation as a result of technological advances. While many areas of agriculture are leveraging cutting-edge technological innovations, farming remains far behind in adopting these advancements. Ayurvedic medicine and other plant-based medical systems employ the classification of plants by their leaflets for a variety of purposes, including ecosystem, farming, disease diagnosis, rare plant maintenance, and Herbal remedies. Due to the utilization of biological nomenclature, standard methods of plant recognition and classification are difficult, time-consuming, and aggravating for non-experts. For learners who are seeking to learn about species, this poses a real challenge. Based on this consequence, this paper recommends Conv2D Xception Adadelta Gradient Descent (CXAGD) Deep Learning to classify the plant species based on the leaflet feature structure. The proposed CXAGD model uses Leaf classification dataset from KAGGLE with 4500 plant leaflets of various species. One input, a dense average pooling layer, and an output layer were used in the development of the proposed Conv2D Adadelta Gradient Deep CNN model. The CXAGD model was constructed using 36 depth wise separable convolutional layers and a maximum pooling layer for each convolution and trained with learning rate of Adadelta optimizer. The CXAGD built with Xception network with the Adadelta optimizer with the dropout of 0.5, weight decay of L2 regularization. The leaf dataset subjected to the training and testing data division and the training data applied to CXAGD model and also applied to other CNN models for comparing the efficiency of the proposed CXAGD model. Python was used for programming, with a batch size of 64 and 30 training epochs, on a Geforce Tesla V100 Graphics card server. Implementation outcomes projects that proposed CXAGD model exhibits accuracy of 97.85%, Precision of 97.42%, Recall of 97.75% and FScore of 97.76% when compared with existing CNN models.
Potential improvement of XCO2 retrieval of the OCO-2 by having aerosol information from the A-train satellites

ABSTRACT Near-real time observations of aerosol properties could have a potential to improve the accuracy of XCO2 retrieval algorithm in operational satellite missions. In this study, we developed a retrieval algorithm of XCO2 (Yonsei Retrieval Algorithm; YCAR) based on the Optimal Estimation (OE) method that used aerosol information at the location of the Orbiting Carbon Observatory-2 (OCO-2) measurement from co-located measurement of the Afternoon constellation (A-train) such as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Observation (CALIPSO) and the MODerate-resolution Imaging Spectrometer (MODIS) onboard the Aqua. Specifically, we used optical depth, vertical profile, and optical properties of aerosol from MODIS and CALIOP data. We validated retrieval results to the Total Carbon Column Observing Network (TCCON) ground-based measurements and found general consistency. The impact of observed aerosol information and its constraint was examined by retrieval tests using different settings. The effect of using additional aerosol information was analyzed in connection with the bias correction process of the operational retrieval algorithm. YCAR using a priori aerosol loading parameters from co-located satellite measurements and less constraint of aerosol optical properties made comparable results with operational data with the bias correction process in three of the four cases subject to this study. Our work provides evidence supporting the bias correction process of operational algorithms and quantitatively presents the effectiveness of synergic use of multiple satellites (e.g. A-train) and better treatment of aerosol information.
Assessing the Potential of using Sentinel-1 and 2 or high-resolution aerial imagery data with Machine Learning and Data Science Techniques to Model Peatland Restoration Progress – a Northern Scotland case study

ABSTRACT Peatland is a globally important store of carbon. Peatland restoration efforts are being increasingly undertaken yet effective monitoring of landscape-scale restoration projects has been limited. A particular gap in our understanding is the length of time required before a site reaches the target state. To address this, a classification model based on remote sensing data was developed for a peatland restoration area on blanket bog in northern Scotland, UK, to evaluate whether post-restoration trajectories followed predictable trends over time. The model was trained against a chronosequence of sites within a 20 × 10 km study area that are being restored following drainage and intensive non-native afforestation. Two versions of the model were created to compare the accuracy obtainable from the suite of Sentinel-2 satellite data versus sub-metre resolution aerial imagery from GetMapping (RGB and IR). The Sentinel-2 based model greatly outperformed the aerial imagery-based model. Adding surface slope to the classification did not significantly improve the accuracy of prediction. Prediction of starting and target land covers was very robust, and both the most recent and oldest restoration sites were well predicted spatially. The main uncertainties in the model were within sites of intermediate restoration age, and sites which underwent additional treatments after the initial restoration. Using standard vegetation and wetness indices as indicators, it was possible to track the progression of areas that had been felled and rewetted towards the spectral signal of the control blanket bog locations. A further study examined the use of multiple years of satellite data (2015–2021) and including Sentinel-1 SAR imagery, and confirmed the findings obtained with only a single climatically average year, and furthermore examined the efficacy of different restoration methods. We observed consistent trends of restoration sites beginning to resemble the target hydrologically and ecologically functional blanket bog state after 10–20 years post intervention.
Cirrus Uncinus Generating Cells and the Evolution of Cirriform Clouds. Part I: Aircraft Observations of the Growth of the Ice Phase

Abstract The growth of the ice phase in cirrus uncinus and cirrostratus clouds was studied through aircraft measurement of cloud particle spectra at different altitudes. Five different cirrus uncinus clouds were studied; one of the cirrus uncinus evolved into cirrostratus. The temperature range of sampling was −19 to −58°C. In cirrus uncinus heads, crystals were determined to be nucleated and grown in the upshear region, before being carried into the trail region of the head downshear as a result of wind shear. The updraft region is upshear, and the downdraft region downshear. A “hole” was found to separate the up-and downshear regions of the head, with a horizontal extent of about 150 m. The concentrations of crystals in the head region were on the order of 0.5 cm −3, with 0.025-0.05 cm −3 longer than 100 µm. Accumulation of particles in the updraft region was noted. The mean length of crystals longer than 100 µm (precipitation size particles) ranged between 0.5 and 1.0 mm, and crystals as long as 2 mm w.
WasmSlim: Optimizing WebAssembly Binary Distribution via Automatic Module Splitting

Many web applications have adopted WebAssembly thanks to its near-native performance and high portability. However, as a WebAssembly application grows complex, it starts suffering from bloated file size and extensive startup time. This leads to poor usability, especially on low-end devices. Existing works attempt to address this issue by refactoring the codebase into several smaller shared libraries and dynamically linking them on demand. However, this approach requires source code access and extensive human effort, which are not always feasible. In this work, we present WasmSlim, a novel web server middleware that optimizes WebAssembly binary distribution without needing source code and user intervention. Our system exploits an observation that many functions are rarely used in the application's life cycle. Therefore, our system first serves users with an instrumented binary to collect execution profiles. It then performs a binary-level transformation to generate a slim main module and secondary modules. The main module contains frequently executed functions and patchable method stubs to load secondary modules on demand. Based on the module loading events, our system can also constantly refine the module partitioning scheme to suppress the module loading latency. Our preliminary experiments show that our system on average reduces the binary size by 69% and improves the startup speed by 71%.
Slipping Through Attackers' Fingers: Fast Neutron Communications for Space Cybersecurity

Multi-agent space systems are inherently promiscuous. Blasting radio signals to each other within a constellation or to a ground station, it is simple for adversaries to disrupt the confidentiality, integrity or availability of the communication. With increasing rendezvous proximity operations (RPO) capabilities, the communication security of multi-agent space systems - be they configured as co-orbital, in constellations or other topology, is vulnerable to attackers. Optical communications offer several security benefits compared to radiofrequency (RF) communications, but are fragile and easily disrupted given the precision required to point a laser into a small receptacle while satellites are flying at 11,300 kph. We propose engaging the nascent technique of communicating using fast neutrons for secure, out-of-band, multi-agent space communications. Fast neutrons offer unique physics-based security properties such as their the ability to penetrate materials and be difficult to intercept. Using attack trees, we conduct a comparative analysis across RF, optical and neutron communications to evaluate the difficulty to achieve an RPO-based eavesdropping mission. We demonstrate that fast neutron communications would be more secure than other multi-agent space communication modalities, should the technology be further developed and deployed on a spacecraft. Fast neutrons would literally slip through adversaries' fingers.
DragonClaw: A low-cost pneumatic gripper with integrated magnetic sensing

Advances in robotics and rapid prototyping have spurred interest in soft grippers across diverse fields ranging from medical devices to warehouse robotics. With this growing interest, it is imperative to create straight-forward soft grippers with embedded sensing that are more accessible to people outside of the soft robotics community. The DragonClaw - a 3D-printable, pneumatically actuated, three-fingered dexterous gripper with embedded magnetic tactile sensing - is intended to bridge this gap. The 2-DOF thumb design allows for a range of precision and power grasps, enabling the DragonClaw to complete a modified Kapandji test for dexterous ability. The operating range of the gripper is characterized through experiments on grip strength and finger blocking force. Further, the integrated magnetic sensor, ReSkin, is successfully demon-strated in a closed-loop control task to respond to external disturbances. Finally, the documentation, bill of materials, and detailed instructions to replicate the DragonClaw are made available on the DragonClaw website, encouraging people with wide ranging expertise to reproduce this work. In summary, the novelty of this work is the integration of soft robotic gripper feedback in a form factor that can easily be reproduced by inexpensive, simplified manufacturing methods.
Stochastic resonance in thalamic neurons and resonant neuron models

Neurons of the thalamus are major participants in gating sensory information for relay to the neocortex. Thalamic neurons are crucially involved in rhythmogenesis which determines the sleep/wake cycle. These roles require critical involvement of a T-type calcium current, conferring a frequency preference in response to subthreshold signals. We examine the interactions of this membrane resonance and noise using whole-cell patch clamp recordings in thalamocortical and reticular neurons of rat brain slices. We perform Monte-Carlo simulations and mathematical analysis using Hodgkin-Huxley-type and polynomial models of resonant neurons. We demonstrate stochastic resonance (SR) as maximal coherence between the input and stochastic output at intermediate noise levels. SR is measured by determining the signal-to-noise ratio under sine wave inputs, and from the reliability of detection measure under α-function inputs. In the experiments and neuron models with T-current, we demonstrate subthreshold resonance at 2-3 Hz, as well as noise dependent frequency dependence of SR for sine wave inputs. The simpler Hindmarsh-Rose model has a similar SR. This model also shows improved detection when the delay of consecutive EPSPs matches the preferred frequency. We show that the preferred frequency of the subthreshold and stochastic resonances depends on the time scale of the slow variable. The stochastic frequency preference arises from modulation of the firing probability of the fast subsystem. We develop a simple linear integrate-and-fire model with subthreshold resonance, which retains the main features of the more complicated models. An analytical solution of the stochastic equations shows that the eigenvalues determine frequency preferences in subthreshold resonance and stochastic resonance. SR can occur even with only noise. This autonomous SR depends on the resonance in our experiments and models. We demonstrate that preferred stochastic firing in the single neuron model translates into synchronized behaviour in a noisy network of resonant neuron models. With inhibitory synaptic coupling, noise can extend the parameter range of oscillations. With excitatory synaptic coupling, noise produces synchronized oscillations of the quiescent deterministic network. We speculate that combined subthreshold membrane resonance and stochastic resonance have physiological utility in coupling synaptic activity to preferred firing frequency, and in network synchronization under noise.
S-72, a Novel Orally Available Tubulin Inhibitor, Overcomes Paclitaxel Resistance via Inactivation of the STING Pathway in Breast Cancer

Microtubule-targeting agents are widely used as active anticancer drugs. However, drug resistance always emerges after their long-term use, especially in the case of paclitaxel, which is the cornerstone of all subtypes of breast cancer treatment. Hence, the development of novel agents to overcome this resistance is vital. This study reports on a novel, potent, and orally bioavailable tubulin inhibitor called S-72 and evaluated its preclinical efficacy in combating paclitaxel resistance in breast cancer and the molecular mechanisms behind it. We found that S-72 suppresses the proliferation, invasion and migration of paclitaxel-resistant breast cancer cells in vitro and displays desirable antitumor activities against xenografts in vivo. As a characterized tubulin inhibitor, S-72 typically inhibits tubulin polymerization and further triggers mitosis-phase cell cycle arrest and cell apoptosis, in addition to suppressing STAT3 signaling. Further studies showed that STING signaling is involved in paclitaxel resistance, and S-72 blocks STING activation in paclitaxel-resistant breast cancer cells. This effect further restores multipolar spindle formation and causes deadly chromosomal instability in cells. Our study offers a promising novel microtubule-destabilizing agent for paclitaxel-resistant breast cancer treatment as well as a potential strategy that can be used to improve paclitaxel sensitivity.
Risk Factors and the Impact of Multidrug-Resistant Bacteria on Community-Acquired Urinary Sepsis

Risk factors for multidrug-resistant bacteria (MDRB) in nosocomial urinary tract infection (UTI) have been widely studied. However, these risk factors have not been analyzed in community-acquired urinary sepsis (US), nor have its outcomes been studied. The aim of our study is to determine risk factors for MDRB in community-acquired US and its influence on outcomes. Prospective observational study of patients with community-acquired US admitted to a university hospital. We compared epidemiological and clinical variables and outcomes of US due to MDRB and non-MDRB. Independent risk factors for MDRB were analyzed using logistic regression. A total of 193 patients were included, 33.7% of them with US due to MDRB. The median age of patients was 82 years. Hospital mortality was 17.6%, with no difference between the MDRB and non-MDRB groups. The length of hospital stay was 5 (4–8) days, with a non-significant tendency to longer hospital stays in the MDRB group (6 (4–10) vs. 5 (4–8) days, p = 0.051). Healthcare-associated US was found to be an independent risk factor for MDR bacteria by multivariate analysis. In conclusion, the impact of MDR bacteria on the outcomes of community-acquired urinary sepsis was mild. Healthcare-associated US was an independent risk factor for MDR bacteria.
A Comparative Survey of Maize Leaf Diseases using Pre-Trained Convolutional Neural Networks

Transfer learning technique reduces the time for design and development of classification models. In this research, we developed maize leaf disease classification models using the most recent and widely used transfer learning techniques. Six pre-trained models were used for developing the maize leaf disease classification models, such as VGG19Net, ResNet152, InceptionV3Net, MobileNetV2, DenseNet201, and NASNetLarge. The pre-trained model was trained on the maize disease classes of the standard plant leaf disease dataset for 100 training epochs. There are 200 images that were used to test the performance of the classification models on maize leaf disease detection. Standard performance metrics such as classification accuracy, precision, recall, and F1 score were used to estimate the performance of the models. Densenet201 achieved a classification accuracy and an average F1 score of 99.25% and 98.5%, respectively. The performance metric scores of Densenet201 on maize leaf disease detection are better than those of other classification models. The research identified the classification performance of the Densenet201 as superior to VGG19Net, ResNet152, InceptionV3Net, MobileNetV2 and NASNetLarge on maize leaf disease classification.
ROSIS: Resilience Oriented Security Inspection System against False Data Injection Attacks

Current radar-based Air Traffic Service (ATS) providers lack the preservation of privacy for airspace operations of selected flight plans, positions, and state data; requiring security assurance. Recent events have shown that modern unmanned aerial vehicles (UAVs) are vulnerable to attack and subversion through software flaws or sometimes malicious devices that are present on urban air mobility (UAM) communication networks, which increases the need for cyber awareness including the risk of cyber intrusion. Many stealthy attacks (such as False Data Injection Attacks (FDIAs)) are hard to detect on avionics systems, as they can compromise measurements from sensors and bypass the sensor's basic “faulty data” detection mechanism and remain undetected. Such attacks on a UAM system may not even present their impact but propagate from the sensor to fool the system by predicting a delayed asset failure or maintenance interval. In this paper, we develop a Resilience Oriented Security Inspection System (ROSIS) to maximize UAM capability to secure data accessing and sharing among aircraft and Air Traffic Service (ATS) service providers. Specifically, we collect and demonstrate the effect of generalized FDIAs on wireless sensors of a turbofan engine using NASA's C-MAPSS simulator, and develop data-driven based deep learning methods (Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)) for detecting abnormal features. A graphical physics-informed Bayesian Network model is developed to represent the dynamic nature of the engine to predict health conditions accordingly. The ROSIS model characterizes the condition-symptom relationships of different engine components and sensors. A hybrid software-in-the-loop (SITL) and hardware-in-the-loop (HITL) design is also developed to evaluate the effectiveness of the ROSIS defense mechanisms. Our experiments validate the performance of ROSIS in detection accuracy and efficiency against FDIAs.