Good Signal Research Articles

Multi-stimuli-responsive molecular fluorescent probes for bioapplications.

Stimuli-responsive fluorescent probes have been widely utilized in detecting the physiological and pathological states of living systems. Numerous stimuli-responsive fluorescent probes have been developed due to their advantages of good sensitivity, high resolution, and high contrast fluorescent signals. In this feature article, the progress of multi-stimuli-responsive probes, including organic molecules and metal complexes, for the detection of various biomarkers for bio-applications is summarized. The feature article focuses on the applications of organic-molecule- and metal-complex-based molecular probes in biological systems for detecting different biomarkers of cancer or other diseases. The current challenges and potential future directions of these probes for applications in biological systems are also discussed.

Iron-chelated semiconducting oligomer nanoparticles for NIR-II fluorescence imaging-guided enhanced chemodynamic/photothermal combination therapy.

Chemodynamic therapy (CDT) has attracted increasing attention owing to its high tumor specificity and low number of side effects. However, the low absolute concentration of reactive oxygen species (ROS) within tumor cells restricts the CDT efficacy. Herein, we use dihydroartemisinin (DHA) to enhance the CDT efficacy and combine photothermal therapy (PTT) to further improve the anticancer effect. To achieve such a goal, an iron-containing semiconducting oligomer nanoparticle (DHA@FePSOD) is prepared by loading DHA into a Fe3+-chelated NIR-II fluorescent semiconducting oligomer (FePSOD). The Fe3+ ion within DHA@FePSOD can be reduced to the Fe2+ ion by glutathione (GSH) and subsequently catalyze the decomposition of hydrogen peroxide (H2O2) into the highly toxic hydroxyl radical (˙OH) for CDT. The loaded DHA may be further reduced by Fe2+ and generate a DHA radical to enhance the CDT efficacy. In addition, DHA@FePSOD shows a good photothermal effect and intense NIR-II fluorescence signal under 808 nm laser irradiation. Both in vitro and in vivo studies prove the better anticancer effect of DHA@FePSOD than FePSOD, which is attributed to the loaded DHA. Furthermore, DHA@FePSOD can effectively accumulate into a tumor and delineate the tumor via NIR-II fluorescence imaging. This study thus provides an efficient approach for developing a NIR-II fluorescence imaging-guided enhanced chemodynamic/photothermal combination therapeutic nanoplatform.

Electrochemical biosensing based on folic acid-triazine-grafted reduced graphene oxide: a highly selective breast cancer cell sensor.

Based on the results of this research, a new electrochemical sensor has been developed to detect human breast cancer cells (MCF-7). A folic acid (FA)-functionalized triazine-grafted reduced graphene oxide (RGOTrz) was used as the modifier of a glassy carbon electrode (GCE) for application as the sensing element. The composition of the resulting FA-RGOTrz/GCE was investigated using XRD (X-ray diffraction), FT-IR (Fourier-transform infrared) spectroscopy, SEM (scanning electron microscopy) and UV-vis spectroscopy studies. CV (cyclic voltammetry) and EIS (electrochemical impedance spectroscopy) techniques were also used to characterize the electrochemical proficiency of the new electrode. Further, MCF-7 cancer cells were examined in solutions of phosphate buffer and [Fe(CN)6]3-/4- as a suitable supporting electrolyte and a useful probe, respectively. The FA-RGOTrz/GCE provides a suitable substrate to reversible redox reactions and provides good electrochemical signals after binding to cancer cells. DPV (differential pulse voltammetry) results indicated that the binding of folate receptor (FR) in the MCF-7 cell to the RGOTrz-modified electrode, in the presence of [Fe(CN)6]3-/4-, reduced folic acid, diminished electron transfer and collapsed the current signal. During the measured flow, a detection limit of 50 human breast cancer cells per milliliter was obtained. The FA-RGOTrz/GCE, with its unique structural design, significantly increases the electron transfer and electrochemical activity towards the detection of MCF-7 cells. This FA-RGOTrz/GCE sensor, due to its special structure, shows high sensitivity to FR in MCF-7 cells and excellent, reliable and satisfactory performance and a great promise for use in industries and medical field.

Diagnosing of the Glass Fiber Reinforced Polymer Material High Performance Drilling Process State

The article presents the results of works on the analysis of using cutting forces and torque measurements to determine the state of dry fiberglass matrix polyester resin composite high performance drilling process. The research and analyzes were carried out for the different states of tool state and the process state itself. GFRP - Glass Fiber Reinforced Polymer was used to perform the experiment. As a result of the conducted analyzes, it was determined that booth the forces and torque generated in drilling process can constitute a good diagnostic signal, on the basis of which it is possible to conclude about the state of the process and tool.

A signal quality assessment method for fetal QRS complexes detection.

Non-invasive fetal ECG (NI-FECG) provides a non-invasive method to monitor the health of the fetus. However, the NI-FECG is easily interfered by noise, which makes the signal quality decline, leading to the fetal heart rate (FHR) monitoring becoming a challenging task. In this work, an algorithm for dynamic evaluation of signal quality is proposed to improve the multi-channel FHR monitoring. The innovation of the method is to assess the signal quality in the process of multi-channel fetal QRS (FQRS) complexes detection. Specifically, the detected FQRS is used as quality unit. Each quality unit can be a true R peak (TR) or a false R peak (FR). It is the basic quality information in this work. The signal quality of each channel is estimated by estimating the correctness of the detection results. Further, the TRs of all channels can be fused to obtain more reliable fetal heart rate monitoring. Analysis results demonstrate that the proposed algorithm is capable of selecting the good quality signal for FQRS detection achieving 97.40% PPV, 98.33% SE and 97.86% F1. This work sheds light on the quality assessment of fetal monitoring signal.

A Vehicle-Carried INS Positioning Accuracy Improvement Method by Using Lateral Constraint in GPS-Denied Environment

When the vehicle passes through the tunnel or bridge, Global Positioning System (GPS) will fail due to signal occlusion or interference. In order to improve the positioning accuracy of vehicle-carried inertial navigation system (INS) in GPS rejection environment, the lateral constraint method based on vehicle-carried INS is studied in this article. According to the characteristics of lateral constraint, in the area with good GPS signal, installation errors between the INS body coordinate frame and the vehicle coordinate frame are estimated at the same time of integrated navigation. In the GPS-denied area, the estimated installation errors are compensated to calculate the reference velocity at first. After compensation, the difference between INS velocity and reference velocity is taken as the measurement. Finally, the Kalman filter model is established to correct the inertial navigation position. In addition, the simulation and experimental verification of the proposed lateral constraint method are carried out. The results show that the INS positioning accuracy can be improved above 50% by using the lateral constraint method in GPS-denied environment, which verifies the effectiveness of the proposed lateral constraint method.

Trend analysis and prediction of temperature change in the continental, Thailand

Diurnal Temperature Range (DTR) is one of global warming indicator which using only daily minimum temperature (TMin) and daily maximum temperature (TMax) for calculation. The study aims 1) to analyse trend of TMax, TMin and DTR over the early period of 21st century (1987 - 2020) and 2) to forecast and analyse trend of TMax, TMin and DTR over the mid and late period of 21st century (2021 - 2040 and 2041-2100) using MIROC5 (rcp85) model, cooperated with EQM Statistical downscaling method. TMax, TMin, and and DTR trends of all periods were investigated by Mann Kendall Trend Test and Sen?s slope, then presented in the form of spatial maps. The most of TMax and TMin trends for all season and all regions tended to be increasing from year by year with the large increasing of changing in Northern and Northeast region especially in Winter, except for the last period which had a large increasing in Summer. However, DTR trends were quietly fluctuated with the large decreasing of changing in Eastern and Southern part, but the trends become to increase in the last period. A large DTR decreasing of changing usually occurred in Winter and Rainy season. The TMax and TMin of all periods were expanding year by year and leading the DTR decreased in the first and second period, however, a changing of TMax and TMin rate leaded DTR increasing for the last period. It is a good signal for heat transfer performance which can help the earth cooling in night time.

Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations. The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths. By considering multipath signal propagations from different APs, the inherent noise from these signal paths can be alleviated. Firstly, locations of the signal data with standard deviations higher than the threshold are identified. The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints. The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance. Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44% compared to the conventional interpolation techniques such as the Inverse Distance Weighting, Kriging, and single Path Loss Model. As a result, we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance.

THE ROLE OF INFLATION IN MODERATE FACTORS AFFECTING PROFITABILITY IN THE HOTEL, RESTAURANT AND TOURISM SECTOR

The company's goal is to make a profit. The level of profit is used as a basis for measuring the company's success. Companies that have prospects for future profits will provide a good signal for investors. This research contributes to explaining how inflation moderates the factors that influence profitability in the hotel, restaurant and tourism sub-sectors listed on the Indonesian stock exchange for the 2019-2022 period. The number of sample data used in this research was 104 data from 152 population data. This research uses linear regression analysis. The research found that Current assets (CR) had a negative and significant effect on Return on assets (ROA), Total asset turnover (TATO) had a positive but not significant effect on Return on assets (ROA), Debt to equity ratio (DER) had a negative and insignificant effect on Return on assets (ROA), Current ratio (CR) which has an insignificant negative effect on Return on Assets (ROA) with inflation as a moderating variable, Debt to equity ratio (DER) has an insignificant positive effect on Return on Assets (ROA).

Precision Design and Fabrication of Noise-free High-Frequency Amplifier Board for Enhanced Signal Processing

An optical signal processing system is widely used for long-distance signal transmission with high speed and low attenuation. It plays a fundamental role in optical communication systems that involves transmitting, receiving, amplifying, and manipulating optical signals which are high-frequency signals. This paper portrays the design of a high-frequency amplifier to amplify the low-amplitude high-frequency, electrical signal procured from the optical signal. A four-stage high frequency amplifier has been designed using an AD8009 high-frequency Op-amp for the highest frequency of 1GHz, and for the fastest rise time of 0.35 nanoseconds. This amplifier board incorporates the high-frequency PCB design rules related to the characteristic impedance, impedance matching, signal crosstalk, the propagation delay of the transmission lines, transmission techniques for the ground plane, and termination of transmission lines for the effective use of high-frequency signals. It has also counted on the issues of electromagnetic interference, power supply decoupling, noise, and high-speed operational amplifier. The proposed system resulted in suitable amplification of low amplitude high-frequency electrical signal, having no signal cross-talks, electromagnetic interference, signal ringing, signal reflections, and posed larger bandwidth. The improved quality of high frequency processed signals with good signal integrity and moderate circuit oscillations achieved by this system will benefit the optical communication system.

Implantable Loop Recorder with Long Sensing Vector: Safety, Acceptability, and Sensing Performance in Pediatric Patients

Implantable loop recorders (ILRs) are effective tools for detecting arrhythmias by long-term continuous heart rhythm monitoring. Benefits have been demonstrated even in pediatric patients. ILR with a long sensing vector has recently been designed to improve signal quality in terms of P wave visibility and R wave amplitude. However, there are no data on its use in pediatric patients. We considered a series of pediatric patients implanted with a long sensing vector ILR. Sensing performance, including R wave amplitude and P wave visibility, device-related complications, and diagnostic yield were collected. During follow-up, each patient guided by his/her parents/guardians was also asked to complete a brief questionnaire to assess patient acceptability of the device. Twenty-five consecutive pediatric patients (mean age 11.3 ± 3.5years, 72% male) were enrolled. The insertion success rate was 100% on the first attempt with no complications. The median amplitude of the R wave was 1.15mV (interquartile range, 1.01-1.42) with no significant differences between patients aged ≤ or > 10years (p = 0.726) and between female and male (p = 0.483). P wave was classified as 'always visible' in 24/25 patients (96%). ILR was generally well accepted and tolerated by all involved patients. During a median follow-up of 297days (117-317), we achieved in 5 patients a correlation between symptoms and rhythm disorders (20%)and ruled out significant arrhythmias in 6 symptomatic children (24%). Long sensing vector ILR showed to be well accepted, with good signal quality and an excellent safety profile even in pediatric patients.

Studies on the Performance of Equal Strength Cantilever Beam-Based FBG Acceleration Sensors

Based on the principle of FBG sensing and classical mechanics model of acceleration,the fundamental frequency and acceleration sensitivity coefficient of the fiber Bragg grating (FBG) acceleration sensor are theoretically analyzed in this study. An equal strength cantilever beam-based FBG acceleration sensor was designed based on the vibration frequency of conventional bridges and carried out experimental research. The results show that the fundamental frequency of the sensor increases when the cantilever beam thickness increases, while the sensitivity coefficient decreases. However, when the block mass increased, the fundamental frequency decreased in contrast to an elevated sensitivity coefficient. The experimental findings were found to be in line with the theoretical hypothesis. The sensors had good signal response performance and anti-transverse interference performance. In order to solve the problem of low sensitivity coefficient of sensor, the sensor was optimized and tested following the plane section assumption principle of classical mechanics. The result shows that the fundamental frequency of the optimized sensor remains unchanged, the sensitivity coefficient is increased by 2.6 times. The research provided an FBG acceleration sensor with suitable fundamental frequency, high sensitivity and other excellent performance for bridge monitoring.

Positioning of Tower Crane Trolley Based on D-S Evidence Theory and Kalman Filter

Due to the complex structure of the tower crane and the signal noise, it is difficult to estimate and predict the real position signal, which may lead to inaccurate positioning of the tower crane trolley. A tower crane positioning algorithm based on D-S evidence theory and unscented Kalman filter (DS-UKF) is established and developed. First, the nonlinear dynamic model, state equation, and detection equation of the tower crane are constructed based on the complex structure of the tower crane. Second, the arm angle is estimated, and the distance between the trolley and the root of the arm is positioned. Finally, in order to calculate the weight of a single sensor, the D-S evidence theory is used to process the measurement information of the sensor and the estimated value of the filter. The Kalman filter, which has good signal tracking and estimation ability, is used to predict and estimate the position of the trolley. The accurate positioning of the tower crane trolley is realized through the organic fusion of information. The simulation results show the effectiveness of the DS-UKF positioning algorithm.

An Electroconductive Hydrogel Scaffold with Injectability and Biodegradability to Manipulate Neural Stem Cells for Enhancing Spinal Cord Injury Repair

Spinal cord injury (SCI) generally leads to long-term functional deficits and is difficult to repair spontaneously. Many biological scaffold materials and stem cell treatment strategies have been explored, but very little research focused on the method of combining exogenous neural stem cells (NSCs) with a biodegradable conductive hydrogel scaffold. Here, a NSC loaded conductive hydrogel scaffold (named ICH/NSCs) was assembled by amino-modified gelatin (NH2-Gelatin) and aniline tetramer grafted oxidized hyaluronic acid (AT-OHA). Desirably, the well-conducting ICH/NSCs can be simply injected into the target site of SCI for establishing a good electrical signal pathway of cells, and the proper degradation cycle facilitates new nerve growth. In vitro experiments indicated that the inherent electroactive microenvironment of the hydrogel could better manipulate the differentiation of NSCs into neurons and inhibit the formation of glial cells and scars. Collectively, the ICH/NSC scaffold has successfully stimulated the recovery of SCI and may provide a promising treatment strategy for SCI repair.

Development of an LC/MS/MS Method for Simultaneous Detection of 11 Polyphenols in Rat Plasma and Its Pharmacokinetic Application after Oral Administration of Coreopsis tinctoria Extract.

Eleven polyphenols, classified as flavonoid glycosides, flavonoid aglycones, and phenolic acids, are important bioactive components in the capitula of Coreopsis tinctoria (CCT). Nevertheless, their full pharmacokinetic profiles have not been demonstrated simultaneously. Therefore, a liquid chromatography - tandem mass spectrometry (LC/MS/MS) method was developed in the present work and used it to study the pharmacokinetics of these 11 compounds. We performed LC/MS/MS with a gradient mobile phase composed of water containing 0.1 % formic acid and acetonitrile containing 0.1 % formic acid on a Proshell 120 SB C18 column (2.1 mm×100 mm, 2.7 μm). We achieved a good chromatographic peak shape, resolution, and mass signal response, and multiple reaction monitoring facilitated the simultaneous detection of 11 analytes. In addition, we validated the selectivity, correlation coefficient, precision, extraction recovery, matrix effects, and stability of the LC/MS/MS method to be acceptable for 11 analytes in rat plasma. Subsequently, rats were orally administered with 50 % ethanol eluent of CCT (ECCT). Nine of 11 polyphenols were absorbed quickly (except for QCD and TCA), and their plasma levels peaked within 40 min. The exposure and Cmax values of flavonoid glycosides and phenolic acids were lower than those of flavonoid aglycones. This is the first report to demonstrate the pharmacokinetics of 11 polyphenols in ECCT, which may play an important role in future studies of the bioactive components of ECCT and their bioactive mechanisms.

Ensemble Evaluation and Member Selection of Regional Climate Models for Impact Models Assessment

Climate change increasingly is affecting every aspect of human life on the earth. Many regional climate models (RCMs) have so far been developed to carefully assess this important phenomenon on specific regions. In this study, ten RCMs captured from the European Coordinated Downscaling Experiment (EURO CORDEX) platform are evaluated on the river Chiese catchment located in the northeast of Italy. The models’ ensembles are assessed in terms of the uncertainty and error calculated through different statistical and error indices. The uncertainties are investigated in terms of signal (increase, decrease, or neutral changes in the variables) and value uncertainties. Together with the spatial analysis of the data over the catchment, the weighted averaged values are used for the models’ evaluations and data projections. Using weighted catchment variables, climate change impacts are assessed on 10 different hydro-climatological variables showing the changes in the temperature, precipitation, rainfall events’ features, and the hydrological variables of the Chiese catchment between historical (1991–2000) and future (2071–2080) decades under RCP (Representative Concentration Path for increasing greenhouse gas emissions) scenario 4.5. The results show that, even though the multi-model ensemble mean (MMEM) could cover the outputs’ uncertainty of the models, it increases the error of the outputs. On the other hand, the RCM with the least error could cause high signal and value uncertainties for the results. Hence, different multi-model subsets of ensembles (MMEM-s) of 10 RCMs are obtained through a proposed algorithm for different impact models’ calculations and projections, making tradeoffs between two important shortcomings of model outputs, which are error and uncertainty. The single model (SM) and multi-model (MM) outputs imply that catchment warming is obvious in all cases and, therefore, evapotranspiration will be intensified in the future where there are about 1.28% and 6% value uncertainties for monthly temperature increase and the decadal relative balance of evapotranspiration, respectively. While rainfall events feature higher intensity and shorter duration in the SM, there are no significant differences for the mentioned features in the MM, showing high signal uncertainties in this regard. The unchanged catchment rainfall events’ depth can be observed in two SM and MM approaches, implying good signal certainty for the depth feature trend; there is still high uncertainty about the depth values. As a result of climate change, the percolation component change is negligible, with low signal and value uncertainties, while decadal evapotranspiration and discharge uncertainties show the same signal and value. While extreme events and their anomalous outcomes direct the uncertainties in rainfall events’ features’ values towards zero, they remain critical for yearly maximum catchment discharge in 2071–2080 as the highest value uncertainty is observed for this variable.

Seasonal and latitudinal variations of surface radio refractivity over Nigeria

Refractivity is a significant challenge of radio signal propagation. As it often distorts or leads to loss of signals. The seasonal and latitudinal variations of surface refractivity at three Nigerian tropospheric Observatory stations in Nigeria, namely Port-Harcourt, Lagos, and Makurdi, are presented in this paper. Values of monthly averages of pressure, temperature, relative humidity, and water vapour from the stations were used to compute surface refractivity at each station. The results show that the values of surface refractivity increase from the arid region in the north to the coastal area in the south. There was an increase in the values of surface radio refractivity from the minimum value of about 332.83 N-units at Makurdi station to a maximum value of 386.69 N-units at Lagos station. Seasonal variations are seen to be caused by weather conditions. The values of the surface refractivity increased significantly in the wet season and reduced in the dry season. It also shows that the values of surface radio refractivity are affected by the meteorological components mentioned above. The result of this study is needed for effective planning of good signal reception in these stations.

A Systematic Review of Machine Learning Techniques for GNSS Use Cases

In terms of the availability and accuracy of positioning, navigation, and timing (PNT), the traditional Global Navigation Satellite System (GNSS) algorithms and models perform well under good signal conditions. In order to improve their robustness and performance in less than optimal signal environments, many researchers have proposed machine learning (ML) based GNSS models (ML models) as early as the 1990s. However, no study has been done in a systematic way to analyze the extent of the research on the utilization of ML models in GNSS and their performance. In this study, we perform a systematic review of studies from 2000 to 2021 in the literature that utilizes machine learning techniques in GNSS use cases. We assess the performance of the machine learning techniques in the existing literature on their application to GNSS. Furthermore, the strengths and weaknesses of machine learning techniques are summarized. In this paper, we have identified 213 selected studies and ten categories of machine learning techniques. The results prove the acceptable performance of machine learning techniques in several GNSS use cases. In most cases, the models using the machine learning techniques in these GNSS use cases outperform the traditional GNSS models. ML models are promising in their utilization in GNSS. However, the application of ML models in the industry is still limited. More effort and incentives are needed to facilitate the utilization of ML models in the PNT context. Therefore, based on the findings of this review, we provide recommendations for researchers and guidelines for practitioners.

Mn2+ Bispidine Complex Combining Exceptional Stability, Inertness, and MRI Efficiency.

As an essential metal ion and an efficient relaxation agent, Mn2+ holds a great promise to replace Gd3+ in magnetic resonance imaging (MRI) contrast agent applications, if its stable and inert complexation can be achieved. Toward this goal, four pyridine and one carboxylate pendants have been introduced in coordinating positions on the bispidine platform to yield ligand L3. Thanks to its rigid and preorganized structure and perfect size match for Mn2+, L3 provides remarkably high thermodynamic stability (log KMnL = 19.47), selectivity over the major biological competitor Zn2+ (log(KMnL/KZnL) = 4.4), and kinetic inertness. Solid-state X-ray data show that [MnL3(MeOH)](OTf)2 has an unusual eight-coordinate structure with a coordinated solvent molecule, in contrast to the six-coordinate structure of [ZnL3](OTf), underlining that the coordination cavity is perfectly adapted for Mn2+, while it is too large for Zn2+. In aqueous solution, 17O NMR data evidence one inner sphere water and dissociatively activated water exchange (kex298 = 13.5 × 107 s-1) for MnL3. Its water proton relaxivity (r1 = 4.44 mM-1 s-1 at 25 °C, 20 MHz) is about 30% higher than values for typical monohydrated Mn2+ complexes, which is related to its larger molecular size; its relaxation efficiency is similar to that of clinically used Gd3+-based agents. In vivo MRI experiments realized in control mice at 0.02 mmol/kg injected dose indicate good signal enhancement in the kidneys and fast renal clearance. Taken together, MnL3 is the first chelate that combines such excellent stability, selectivity, inertness and relaxation properties, all of primary importance for MRI use.

Design of Nanostructured Hybrid Electrodes Based on a Liquid Crystalline Zn(II) Coordination Complex-Carbon Nanotubes Composition for the Specific Electrochemical Sensing of Uric Acid

A metallomesogen based on an Zn(II) coordination complex was employed as precursor to obtain a complex matrix nanoplatform for the fabrication of a high-performance electrochemical hybrid sensor. Three representative paste electrodes, which differ by the weight ratio between Zn(II) metallomesogen and carbon nanotubes (CNT), i.e., PE_01, PE_02 and PE_03, were obtained by mixing the materials in different amounts. The composition with the largest amount of CNT with respect to Zn complex, i.e., PE_03, gives the best electrochemical signal for uric acid detection by cyclic voltammetry in an alkaline medium. The amphiphilic structure of the Zn(II) coordination complex likely induces a regular separation between the metal centers favoring the redox system through their reduction, followed by stripping, and is characterized by enhanced electrocatalytic activity towards uric acid oxidation. The comparative detection of uric acid between the PE_03 paste electrode and the commercial zinc electrode demonstrated the superiority of the former, and its great potential for the development of advanced electrochemical detection of uric acid. Advanced electrochemical techniques, such as differential-pulsed voltammetry (DPV) and square-wave voltammetry (SWV), allowed for the highly sensitive detection of uric acid in aqueous alkaline solutions. In addition, a good and fast amperometric signal for uric acid detection was achieved by multiple-pulsed amperometry, which was validated by urine analysis.