Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrochemically Exfoliated Graphene Quantum Dots Based Biosensor for CD44 Breast Cancer Biomarker

An innovative electrochemical biosensor based on graphene quantum dots (GQDs) is developed for a simple, rapid, and highly sensitive primary diagnosis of the breast cancer biomarker cluster of differentiation-44 (CD44) antigen. Herein, electrochemical exfoliation of waste dry batteries provides facile, eco-friendly, and cost-effective synthesis of GQDs. Transmission electron microscopy (TEM) analysis reveals that GQDs exhibit spherical shapes with an average diameter of 4.75 nm. Further, electrochemical analysis through cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) reveals that the electrochemical properties of GQDs are suitable for biosensing applications. Subsequently, GQDs have a large electroactive surface area that has been utilized for the immobilization of CD44 antibodies to fabricate the electrochemical biosensor. The electroanalytical performance of GQDs for CD44 biosensing capabilities is studied by differential pulse voltammetry (DPV). The developed electrochemical biosensor has high sensitivity with the lowest detection limit (LOD) of 2.11 fg/mL in the linear range of 0.1 pg/mL to 100.0 ng/mL in phosphate buffer saline (PBS). Further, the linear response of the electrochemical biosensor for CD44 antigen concentration is in the range of 1.0 pg/mL to 100.0 ng/mL with a LOD of 2.71 fg/mL in spiked serum samples. The outcomes suggest that the synthesized GQDs demonstrate promising attributes to be utilized as a viable nanomaterial in biosensing applications.
Adaptive Feedback Information Switching for Reliable Wireless Networked Control Systems

This paper proposes an adaptive switching method of feedback information for a wireless networked control system (WNCS) with unreliable communication links, which is based on a cross-layered design approach between communication and control layers. In the focused WNCS where communication errors can occur, the uncertainty in the estimation of control-state at the controller side consists of the following two factors. The one is the uncertainty as to whether the control-input has been correctly input (received). The other is the disturbance added to the controlled object (plant). This work considers the following two modes which determine the feedback information: (1)the mode in which the plant side transmits the communication result of feedforward channel (from the controller to the plant), which will be referred to as to ACK mode, (2)the mode in which the plant transmits the information of control-state observed by the sensor, which is referred to as STA mode. From a communication perspective, in the case with ACK mode, the use of ACK/NACK signaling allows a small number of bits (communication rate) to be used, and thus enables reliable feedback communication. As a result, the controller can reliably know the uncertainty of whether the control-input has been successfully received, but the uncertainty due to the disturbance still remains. On the other hand, in the case with STA mode, all the uncertainties can be eliminated only if the feedback communication is successful. However, the increase of information (rate) makes the feedback communication unreliable compared to the case with ACK mode. Furthermore, the control-input should be calculated considering the reliability of feedforward and feedback channels as well as the accuracy of the estimated control-state. As a result, the selection of feedback information deeply affects the quality of WNCS. This paper provides an adaptive switching method of feedback information and corresponding control-input, and shows that the proposed method can efficiently improve the quality of control in the WNCS with unreliable communication links.
Faster navigation of semi-structured forest environments using multirotor UAVs

Abstract Modern approaches for exploration path planning generally do not assume any structural information regarding the operational area. Therefore, they offer good performance when the region of interest is entirely unknown. However, for some applications such as plantation forest surveying, partial information regarding the survey area is known before the exploration process. Because the region of interest consists only of the lower portions of the tree stems themselves, the ground and high-elevation sections of the environment are unimportant and do not need to be observed. Due to these unconventional conditions, existing methods favoring faster survey speeds produce suboptimal surveys as they do not try and ensure even coverage across the entire exploration volume, while methods that favor reconstruction accuracy produce excessively long survey times. This work proposes a structured exploration approach specifically for plantation forests utilizing a lawnmowing pattern to maximize coverage while minimizing re-visited regions, guiding the unmanned aerial vehicle to visit all areas. Experiments are conducted in various environments, with comparisons made to state-of-the-art exploration planners regarding survey time and coverage. Results suggest that the proposed methods produce surveys with significantly more predictable coverage and survey times at the expense of a longer survey.
Counting and Computing by e

In this research and review paper, using some inequalities and relations involving e, we count the number of paths and cycles in complete graphs the number of derangements. Connection by e yields some nice formulas for the number of derangements, and using these relations allow us to compute some incomplete gamma functions and hypergeometric summations; these connections are hidden in the heart of a nice polynomial that we call it derangement function and a simple ordinary differential equation concerning it.
On the differentiability of the solution to an equation with drift and fractional diffusion

We consider an equation with drift and either critical or supercritical fractional diffusion. Under a regularity assumption for the vector field that is marginally stronger than what is required for Holder continuity of the solutions, we prove that the solution becomes immediately differentiable with Holder continuous derivatives. Therefore, the solutions to the equation are classical.
Measurement of High Water Holdup in Oil-in-Water Flows Using Reticular Microwave Resonant Sensor

Long-term waterflooding exploitation leads to the low flow rate and high water cut of the oil–water flows in oil wells, which has put forward a new challenge for the water holdup measurement. Therefore, a reticular microwave resonant sensor (RMRS) is proposed in this article, which can realize the high-sensitive water holdup measurement of oil-in-water flows with high water cut and low velocity at 1.85–2.3 GHz. Due to the optimum design in the quasi-TEM mode of the sensor based on the finite-element method, the resonant frequency shift measured by the RMRS is sufficiently sensitive to the water holdup. After the viability of the sensor is confirmed through a simulation experiment, a sensor prototype is prepared. And the measurement characteristics of the sensor to the water holdup are first investigated by the static experiment. Subsequently, the dynamic experiment of vertical upward oil-in-water flows is carried out. The response characteristics of the RMRS with the change of water salinity, flow rate, and water cut are analyzed. Finally, a satisfactory relationship between the resonant frequency and water holdup with the consideration of water salinity is obtained. It shows that the RMRS has a good resolution to the water holdup. Moreover, it can effectively restrain the influence of water salinity.
Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

This paper proposes to use deep reinforcement learning to teach a physics-based human musculoskeletal model to ascend stairs and ramps. The deep reinforcement learning architecture employs the proximal policy optimization algorithm combined with imitation learning and is trained with experimental data of a public dataset. The human model is developed in the open-source simulation software OpenSim, together with two objects (i.e., the stairs and ramp) and the elastic foundation contact dynamics. The model can learn to ascend stairs and ramps with muscle forces comparable to healthy subjects and with a forward dynamics comparable to the experimental training data, achieving an average correlation of 0.82 during stair ascent and of 0.58 during ramp ascent across both the knee and ankle joints.
HIGH ORDER PARAMETER-ROBUST NUMERICAL METHOD FOR A SYSTEM OF (M ≥ 2) COUPLED SINGULARLY PERTURBED PARABOLIC REACTION-DIFFUSION PROBLEMS

We present a high order parameter-robust numerical method for a sys- tem of (M ≥ 2) coupled singularly perturbed parabolic reaction-diffusion problems. A small perturbation parameter " is multiplied with the second order spatial deriva- tives in all the equations. The parabolic boundary layer appears in the solution of the problem when the perturbation parameter " tends to zero. To obtain a high or- der approximation to the solution of this problem, we propose a numerical method that employs the Crank-Nicolson method on an uniform mesh in time direction, together with a hybrid finite difference scheme on a generalized Shishkin mesh in spatial direction. We prove that the resulting method is parameter-robust or "- uniform of second order in time and almost fourth order in spatial variable, if the discretization parameters satisfy a non-restrictive relation. Numerical experiments are presented to validate the theoretical results and also indicate that the relation between the discretization parameters is not necessary in practice.
Influence of the Poly(ethylene Glycol) Methyl Ether Methacrylates on the Selected Physicochemical Properties of Thermally Sensitive Polymeric Particles for Controlled Drug Delivery

Thermosensitive copolymers P1–P5 of N-isopropylacrylamide (NIPA) and poly(ethylene glycol) methyl ether methacrylates (PEGMEMs) were synthesized via surfactant-free precipitation polymerization (SFPP) using ammonium persulfate (APS) at 70 °C. The polymerization course was evaluated by the conductivity. The hydrodynamic diameters and the polydispersity indexes (PDI) of P1–P5 in the 18–45 °C range, which were assessed via dynamic light scattering (DLS), were at 18° (nm): 26.07 ± 0.54 (PDI 0.65 ± 0.03), 68.00 ± 1.10 (PDI 0.56 ± 0,02), 45.12 ± 0.57 (PDI 0.51 ± 0.03), 62.78 ± 0.40 (PDI 0.53 ± 0.003), and 92.95 ± 1.56 (PDI 0.60 ± 0.04), respectively. The lower critical solution temperatures ranged from 31 to 33 °C. The electrophoretic mobilities estimated the zeta potential in the 18–45 °C range, and at 18 °C, they were (mV): −4.64 ± 1.30, −6.91 ± 2.67, −5.85 ± 3.17, −2.28 ± 0.30, and −3.60 ± 0.96 for P1–P5, respectively. The polymers were characterized by Attenuated Total Reflectance Fourier-Transform Infrared spectroscopy (ATR-FTIR), H nuclear magnetic resonance (1H NMR), thermogravimetric analysis (TGA/DTA), Differential Scanning Calorimetry (DSC), and powder X-ray diffraction analysis (PXRD). Stable amorphous polymers were obtained. We conclude that the length of the co-monomer chain nonlinearly influences the properties of the obtained thermosensitive polymer nanostructures.
Multi-Stage Generation of Tile Images Based on Generative Adversarial Network

Deep learning techniques have been recently widely used in the field of texture image generation. There are still two major problems when applying them to tile image design work. On the one hand, there is still lack of enough diverse ceramic tile images for the training process. On the other hand, the output image is difficult to control and adjust, and cannot meet the designer’s requirements of interactivity. Therefore, we propose a multi-stage generation algorithm of tile images based on generative adversarial network(GAN). First, the multi-scale attention GAN is applied to generate controllable texture image. Then, the SWAG texture synthesis GAN is also applied to obtain controllable and diverse image style. And finally, through the style iteration mechanism and the multiple step magnification method based on image super-resolution reconstruction network, the final tile images can be automatically generated with larger-size and higher-precision. The relevant experiments demonstrate that our method can not only generate high-quality tile images in a relatively short period of time, but also consider human interaction to a certain extent, and maintain a certain degree of control over the main texture and style of the final generated tile images. It has good and wide application value.
Improving Confidence in Model-Based Probable Maximum Precipitation: How Important is Model Uncertainty in Storm Reconstruction and Maximization?

We analyze uncertainty in model-based estimates of Probable Maximum Precipitation (PMP) as used in dam spillway design. Our focus is on model-based PMP derived from WRF (Weather Research and Forecasting) model reconstructions of severe historical storms, amplified by the addition of moisture in the boundary conditions (so-called Relative Humidity Maximization or RHM). By scaling moisture and predicting the resulting precipitation, the model-based approach arguably is more realistic than currently-used techniques (documented in NOAA Hydrometeorological Reports), which assume that precipitation scales linearly with moisture. Despite the important improvement this represents, model-based PMP is subject to several sources of uncertainty that have slowed adoption in operational settings. We analyze an ensemble of PMP simulations that reflect recognized sources of uncertainty including: (1) initial condition error; (2) choice of physics parametrizations; and (3) upscale propagating model errors. We apply this ensemble approach to the Feather River watershed (Oroville Dam) in California for the storms of February 1986 and January 1997, which produced some of the largest floods on record at that location, after carrying out in-depth evaluations of model reconstructions. Differences in the maximized 72-hour precipitation totals across the 56 ensemble members we produced for each storm are modest, ranging from +/− 7% of ensemble mean. Our results suggest that while model-based PMP estimates should be interpreted as a range of values, model uncertainty appears to be relatively small for the major atmospheric river-driven flood events we investigated.
A Statistical Learning Approach to Evaluate Factors Associated With Post-Traumatic Stress Symptoms in Physicians: Insights From the COVID-19 Pandemic

Physicians facing the COVID-19 pandemic are likely to experience acute and chronic, and often unpredictable, occupational stressors that can incur post-traumatic stress symptoms (PTSS), prevention of which is of utmost importance to enhance healthcare workforce efficiency. Unlike previous studies, in this paper we developed a generalized data-driven framework to generate insights into the complex, nonlinear associations of cognitive/occupational factors with physicians’ PTSS-risk. Data were collected from practicing physicians in the 18 states with the largest COVID-19 cases by deploying a cross-sectional, anonymous, web-based survey, following the second COVID-19 peak in the US. Analyses revealed that physicians directly treating COVID-19 patients (frontline) were at higher occupational risk of PTSS than those who didn’t (secondline). We implemented a suite of eight statistical learning algorithms to evaluate the associations between cognitive/occupational factors and PTSS in frontline physicians. We found that random forest outperformed all other models, in particular the traditionally-used logistic regression by 6.4% (F1-score) and 9.6% (accuracy) in goodness-of-fit performance, and 4.8% (F1-score) and 4.6% (accuracy) in predictive performance, indicating existence of complex interactions and nonlinearity in associations between the cognitive/occupational factors and PTSS-risk. Our results show that depression, burnout, negative coping, fears of contracting/transmitting COVID-19, perceived stigma, and insufficient resources to treat COVID-19 patients are positively associated with PTSS-risk, while higher resilience and support from employer/friends/family/significant others are negatively associated with PTSS-risk. Insights obtained from this study will help to bring new attention to frontline physicians, allowing for more informed prioritization of their care during future pandemics/epidemics.
Deep Imitation Learning of Nonlinear Model Predictive Control Laws for a Safe Physical Human–Robot Interaction

This article proposes motion planning algorithms for industrial manipulators in the presence of human operators based on deep neural networks (DNNs), aimed at imitating the behavior of a nonlinear model predictive control (NMPC) scheme. The proposed DNN solutions retain the safety features of NMPC in terms of speed and separation monitoring, defined according to the guidelines in the ISO/TS 15066 standard. At the same time, they improve the robot performance in terms of task completion time, and of a posteriori evaluation of the NMPC cost function on experimental data. The reasons for this improvement are the reduced computational delay of running a DNN compared to solving the nonlinear programs associated to NMPC, and the ability to implicitly learn how to predict the human operator's motion from the training set.
Stateful Logic Using Phase Change Memory

Stateful logic is a digital processing-in-memory (PIM) technique that could address von Neumann memory bottleneck challenges while maintaining backward compatibility with standard von Neumann architectures. In stateful logic, memory cells are used to perform the logic operations without reading or moving any data outside the memory array. Stateful logic has been previously demonstrated using several resistive memory types, mostly resistive RAM (RRAM). Here, we present a new method to design stateful logic using a different resistive memory-phase change memory (PCM). We propose and experimentally demonstrate four logic gate types (NOR, IMPLY, OR, NIMP) using commonly used PCM materials. Our stateful logic circuits are different than previously proposed circuits due to the different switching mechanisms and functionality of PCM compared to RRAM. Since the proposed stateful logic forms a functionally complete set, these gates enable the sequential execution of any logic function within the memory, paving the way to PCM-based digital PIM systems.
Classification of Stroke Severity Using Clinically Relevant Symmetric Gait Features Based on Recursive Feature Elimination With Cross-Validation

Stroke is a leading cause of disability among elderly individuals, and gait impairment is a typical characteristic related to the stroke severity experienced by patients. The aim of this study is to propose a novel stroke severity classification method using symmetric gait features with recursive feature elimination with cross-validation (RFECV). An experiment was conducted on data acquired from thirteen chronic stroke patients and eighteen elderly participants. They walked on a treadmill at four different speeds based on their preferred speed. Symmetric gait features representing the ratio between the left- and right-side values were used as inputs along with the general gait features that did not completely contain the patients’ gait characteristics. We used four different machine learning (ML) techniques to determine the optimal subset for differentiating between the elderly and stroke groups according to severity based on RFECV. In addition, to verify the performance of RFECV and the symmetric gait features, four different feature sets were used: 1) all forty-five general features, 2) all twenty-one symmetric features, 3) the optimal general feature subset obtained by using RFECV, and 4) the optimal symmetric feature subset obtained by using RFECV. The best classification result was obtained by RF-RFECV with an RF classifier derived from the symmetric features (accuracy: 96.01%). The result proved that the stroke severity classification performance increased when symmetric gait data and the RFECV technique were applied. The findings of this study can help clinicians diagnose the stroke severity experienced by patients based on information obtained using ML technology.
Neurogenic Supine Hypertension and Cardiovascular Autonomic Dysfunction in Patients with Parkinson's Disease

Background: Natural history and disease progression in patients with Idiopathic Parkinson's Disease (PD) is quite heterogeneous. Autonomic dysfunction occurs commonly among Idiopathic PD patients. Heart rate variability and ambulatory blood pressure monitoring are used to assess cardiac autonomic dysfunction. The prevalence and magnitude of supine hypertension in Indian PD patients has not been studied to date. The present study aimed to record cardiovascular autonomic functions and supine hypertension in PD patients and to correlate them with the age of onset, duration and severity of the disease, and non-motor symptom burden. Material and Methods: The cross-sectional study involved 60 PD patients. Webster rating scale was used to determine the disease severity. Non-motor symptom burden was assessed using the Non-Motor Symptom Scale (NMSS). Ambulatory blood pressure monitoring and heart rate variability parameters determined cardiac autonomic function. Supine hypertension was defined as Systolic Blood Pressure (SBP) ≥150 mmHg and/or DBP ≥90 mmHg. Less than 10% decrease or even increase in blood pressure during the night were classified as non-dippers. Pearson coefficient was used appropriately to establish correlation. P ≤ 0.05 was considered significant. Results: Age of onset was 61.2 ± 8.7 years and duration of disease was 1.7 ± 1.1 years. Mean Webster and non-motor symptom scores were 12.7 ± 4.4 and 15.5 ± 8.0, respectively. About 50 patients (83%) were non-dipper, while 32 (53%) had supine hypertension. Low Frequency oscillations (LF) (r = 0.28), High Frequency oscillations (HF) (r = 0.29), Standard Deviation NN intervals (SDNN) (0.26), and Root Mean Squared Successive Differences of NN intervals (RMSSD) (r = 0.28) correlated significantly with non-motor symptoms scale. LF (r = −0.39), HF (r = −0.43), SDNN (−0.40), RMSSD (r = −0.41), NN50 (r = −0.38), PNN50 (r = −0.42), mean SBP (r = 0.26), and mean DBP (r = 0.33) correlated significantly with disease duration. PNN50 (r = −0.255), mean SBP (r = −0.29), and mean DBP (r = −0.27) correlated significantly with age at onset. Conclusion: Awareness regarding neurogenic supine hypertension is needed as it occurs commonly among Indian PD patients. Heart rate variability (HRV) parameters and ambulatory blood pressure are of significant help in the detection of early cardiovascular autonomic dysfunction and correlate significantly with disease duration and non-motor symptom burden among PD patients.
Infinitesimal rigidity of convex surfaces through the second derivative of the Hilbert-Einstein functional I: Polyhedral case

The paper is centered around a new proof of the infinitesimal rigidity of convex polyhedra. The proof is based on studying derivatives of the discrete Hilbert-Einstein functional on the space of "warped polyhedra" with a fixed metric on the boundary. This approach is in a sense dual to using derivatives of the volume in order to prove the Gauss infinitesimal rigidity of convex polyhedra, which deals with deformations that preserve face normals and face areas. In the spherical and in the hyperbolic-de Sitter space, there is a perfect duality between the Hilbert-Einstein functional and the volume, as well as between both kinds of rigidity. We also discuss directions for future research, including elementary proofs of the infinitesimal rigidity of hyperbolic (cone-)manifolds and development of a discrete Bochner technique.