Linear Squares Research Articles

The Effect of the Presence and Absence of DNA Repair Genes on the Rate and Pattern of Mutation in Bacteria.

Bacteria lose and gain repair genes as they evolve. Here, we investigate the consequences of gain and loss of 11 DNA repair genes across a broad range of bacteria. Using synonymous polymorphisms from bacteria and a set of 50 phylogenetically independent contrasts, we find no evidence that the presence or absence of these 11 genes affects either the overall level of diversity or the pattern of mutation. Using phylogenetic generalized linear squares yields a similar conclusion. It seems likely that the lack of an effect is due to variation in the genetic background and the environment which obscures any effects that the presence or absence of individual genes might have.

THE IMPACT OF INFLATION TARGETING MONETARY POLICY ON MACROECONOMIC PERFORMANCE OF TURKEY DURING 2002-2022

The main aim of the study is to investigate the impact of inflation targeting (IT) strategy in Turkey during 2002-2022, which was adopted in a three-year period of 2002-2004, on macroeconomic performance (actual inflation, exchange and interest rates) and economic growth of Turkey (in terms of the real GDP). The econometric and empirical investigation of this research focusing on the impact of inflation targeting on the selected macroeconomic variables were carried out by the linear squares method (LSM) regression taking the data of the period after implementation of the monetary policy. At this, the independent variable of inflation targeting was estimated against each chosen macroeconomic variable separately in four different models to catch its linear impact on the changes of these variables over the period after implementation of the strategy. The empirical outcomes demonstrated that inflation targeting monetary policy is strong enough to impact the macroeconomic performance of Turkey in terms of reducing inflation rates, boosting economy by pushing real GDP to grow, stabilize exchange rates and lower the nominal interest rates on deposits.

Functional incremental least square regression algorithm

Functional linear regression is one of the main modeling tools for working with functional data. Since functional data are usually stream data essentially and there are some noises in functional data. Many numerical research studies of machine learning indicate that the noise samples not only increase the amount of storage space, but also affect the performance of algorithm. Therefore, in this paper we consider a new learning strategy by introducing incremental learning, Markov sampling for functional linear regression and propose a novel functional incremental linear square regression algorithm based on Markov sampling (FILSR-MS). To have a better understanding of the proposed FILSR-MS, we not only estimate the generalization bound of the proposed algorithm and establish the fast learning rate, but also present some useful discussions. The performance of the proposed algorithm is validated by the numerical experiments for benchmark repository.

Empirical analysis of crude oil dynamics using affine vs. non-affine jump-diffusion models

This paper investigates the dynamics of the United States oil (USO) exchange traded fund (ETF). Daily USO returns are modelled using stochastic volatility (SV) frameworks derived from three different model classes: SV models with contemporaneous jumps in returns and volatility (SVCJ); SV model with jumps in returns only (SVJ); and a pure SV model class without jumps. Six affine and non-affine models are considered within each model class that depend on specification of the drift and the diffusion terms in the variance process, resulting in a total of 18 models that are estimated using particle Markov Chain Monte Carlo (PMCMC) approach. Model evaluation is conducted using the Deviance Information Criterion (DIC), Bayes factors, probability plots, and deviation measures to assess the discrepancy between the estimated volatility and key benchmarks, the crude oil ETF volatility index (OVX) and the realised volatility (RV). Our analysis indicates that models incorporating jumps, particularly the SVCJ-PLY-0.5 and SVCJ-PLY-1.0, more accurately capture USO dynamics than standard SV models. The SVCJ-PLY-0.5 model ranks highest based on DIC statistics and Bayes factors, and both models excel in aligning their estimated volatility with the OVX and RV benchmarks. Overall, the statistical criteria employed in our comparison favour models with jumps over the standard SV model class, suggesting that models incorporating jumps in both return and variance processes (SVCJ) are superior to those with jumps solely in the return process (SVJ). The affine models SVJ-LIN-0.5 and SVCJ-LIN-0.5 with linear variance drift and square root diffusion that are particularly interesting for theoretical finance applications are highly ranked among considered frameworks, outperforming several non-affine alternatives. Our analysis of the regression model for volatility forecasting reveals a significant predictive accuracy in the evaluated models, demonstrating their effectiveness in anticipating future volatility trends.

Ultrasound backscatter coefficient for fat quantification is affected by the measurement depth.

It has been reported that the estimate of ultrasound attenuation coefficient (AC) is affected by depth of measurement, with linear decrease of values with depth. It is unknown whether backscatter coefficient (BSC) has similar behavior. This retrospective study was performed with Sequoia US system equipped with ultrasound derived fat fraction (UDFF) algorithm (Siemens Healthineers, Issaquah, WA, USA) that combines BSC with AC. UDFF was obtained positioning upper edge of the region of interest at 1.5,2,3,4,5cm below liver capsule. BSC data were extracted from UDFF offline. A fractional polynomial regression, which selects the best model considering the polynomial development of the variables of interest, was used. Covariates included were age, sex, skin-to-liver-capsule distance, stiffness. Distance was included as linear factor or with a power ranging from -2 to 3, and the best fitting model was chosen according to partial F test. Body mass index (BMI) was not included because of collinearity with skin-to-liver capsule distance. 104 individuals (56 females; age: 57.9 ± 13.0years; BMI: 29.0 ± 6.5kg/m2; skin-to-liver-capsule distance: 2.3 ± 0.7cm; liver stiffness: 7.5 ± 5.5 kiloPascal) were studied. Best fitting model for BSC included a combination of depth as linear factor and square root. BSC showed a decrease of -13.98dB/cm-steradian for each logarithmic increase of 1cm depth (coefficient: -13.98; 95% CI: -21.016; -5.379; p = .001). Skin-to-liver-capsule distance and stiffness also were independent predictors of BSC. The estimation of the BSC in the liver exhibits a depth dependence that significantly affects results. A standardized acquisition protocol is needed to compare results and reliably assess changes over time.

Rapid and facile electrochemical detection and degradation of carbendazim in the spiked environmental trials using reduced graphene oxide/titanium dioxide-based sensor

This study aims to develop a rapid, facile, and sensitive sensor for electroanalytical carbendazim (CAR) detection. A novel and simple sensor was fabricated by incorporating a composite matrix of titanium dioxide nanoparticles (TiO2-NPs) with reduced graphene oxide (rGO) loaded carbon paste within a polytetrafluoroethylene (PTFE) tube for investigation. Characterization of the synthesized TiO2-NPs was conducted using SEM, XRD, and AFM techniques to assess their structural, morphological and functional features that could support understanding the electrocatalytic activity at the modified sensor (CPE/rGO/TiO2). Comparative analysis with the carbon paste electrode (CPE) revealed that CPE/rGO/TiO2 demonstrated sensitivity, achieving approximately a 6-fold higher detection current in CAR analysis. Cyclic voltammetry (CV), linear sweep voltammetry (LSV) and square wave voltammetry (SWV) approaches were employed for CAR's electrochemical detection and determination. The physicochemical attributes of the electrode activity were investigated under optimum experimental conditions, including electrolyte pH and accumulation time. The CPE/rGO/TiO2 exhibited a limit of detection of 7.66 nM, with a sensitivity of 1.08 µA.µM−1.cm−2 within a wide range of concentration linearity. The selectivity of CPE/rGO/TiO2 against interference metal ions with CAR was assessed. In real-time applications, the developed electrode was tested for the analysis of CAR in spiked soil and water samples, demonstrating significant detection capacity with good recovery. Moreover, the electrode exhibited stability across multiple measurements, highlighting CPE/rGO/TiO2 as a promising sensor for CAR detection.

Mixed-ligand based water-stable Mn(II)-MOF for quick, sensitive, and reusable IFE-PET-RET facilitated detection of formaldehyde and Cr(VI)-oxoanions in real-field samples like food and industrial water: experimental and theoretical insights.

We report the luminescence-based detection of Group-1 carcinogen formaldehyde (FA) and Cr(VI)-oxoanions with a mesoporous Mn(II)-MOF (1), featuring a uninodal 4-c net topology and linear 1D square channels forming a polymeric 2D network. The Mn-MOF i.e., [Mn(phen)(hia)(H2O)]∞ was solvothermally constructed using π-conjugated, chelating phenanthroline (phen) and µ3-η2:η1 binding 5-hydroxyisophthalic acid (hia) ligands. The 2D rod-like crystallites of 1 demonstrated excellent phase purity, high thermal and photostability, and robustness under harsh conditions. The SCXRD and XPS studies established the structural framework and elemental composition, while the Hirshfeld surface analysis and NCI-RDG plot confirmed the presence of π-π stacking and weak interactions in 1. We explored the bright-blue emission of 1 for recyclable and fast-responsive (∼70 s) 'turn-off' detection of FA, with a low limit of detection (LOD) of 8.49 µM. Based on this, a 04-input-03-output molecular logic gate was proposed, which can be useful as a molecular switch for future applications. Furthermore, a unique experimental setup using the MOF film demonstrated ∼57% quenching upon exposure to FA vapor (an indoor VOC). Additionally, 1 exemplified itself as an efficient probe towards Cr(VI)-oxyanions, depicting LODs of 79 and 170 ppb, Stern-Volmer constants (KSV) of 16.13 × 104 and 12.73 × 104 M-1, and response times of ∼48 and ∼40 s for CrO42- and Cr2O72-, respectively. DFT calculations and specific wet-chemical investigations elucidated the FA detection to be triggered by photo-induced electron transfer (PET), while the Cr(VI)-sensing involved a combination of PET, the inner-filter effect (IFE), resonance energy transfer (RET), and electrostatic H-bonding interactions. The FA detection was validated using food samples (fish and meat) and wastewater specimens, achieving excellent recovery rates of ∼92-95%. Furthermore, the MOF's efficacy in recognizing the Cr(VI)-species in complex matrices (coal mine wastewater, sewage, and tap water) was investigated to yield high KSV values (3.10-5.17 × 104 and 2.16-7.03 × 104 M-1 for CrO42- and Cr2O72-), which demonstrated the probe's consistency and reliability.

Electrochemical Determination of Thiophene by Dicyandiamide/Graphene Oxide/Modified Graphite Electrode in Gasoline by Square Wave Voltammetry (SWV)

An efficient stable electrochemical method for the determination of thiophene as a sulfur model compound in gasoline has been investigated based on the electrodeposition of graphene oxide and dicyandiamide on graphite electrode by cyclic voltammetry. The electroactive layer performance was considered by linear sweep voltammetry and square wave voltammetry by examining scan rates and their relevant equations to evaluate the electrochemical parameters. This modified electrode has high electrocatalytic characteristics to irreversible electrooxidation of thiophene. The electron transfer coefficient (α) was equal to 0.78, the electron transfer rate constant (log KS) was 3.2 s−1, and the saturated adsorptive capacity of the electroactive substance at the electrode surface (Γmax) was 1.14 × 10−8 mol cm−2 at pH 8 at low working potential of 0.5 V versus Ag/AgCl reference electrode. Scanning electron microscopy of the surface revealed the nanostructure of electrodeposited films and the morphology. This sensor is capable of electrochemical response to thiophene over the concentration range from 25 to 250 nmol L−1 with a detection limit of 8.31 nmol L−1, and a quantification limit of 25.18 nmol L−1. The method’s reliability was checked regarding the simulated gasoline samples and the desired response and sensitivity were provided. The wide dynamic range, low detection limit, favorable precision and accuracy, and fast response with no interferences confirm the ability of this sensor for thiophene determination. This nano-sensor can be used for electrochemical in-situ direct monitoring of liquid fuel desulfurization processes.

Bayesian denoising algorithm dealing with colored, non-stationary noise in continuous glucose monitoring timeseries

Introduction: The retrospective analysis of continuous glucose monitoring (CGM) timeseries can be hampered by colored and non-stationary measurement noise. Here, we introduce a Bayesian denoising (BD) algorithm to address both autocorrelation of measurement noise and temporal variability of its variance.Methods: BD utilizes adaptive, a-priori models of signal and noise, whose unknown variances are derived on partially-overlapped CGM windows, via smoothing approach based on linear mean square estimation. The CGM signal and noise variability profiles are then reconstructed using a kernel smoother. BD is first assessed on two simulated datasets, DS1 and DS2. On DS1, the effectiveness of accounting for colored noise is evaluated by comparison against a literature algorithm; on DS2, the effectiveness of accounting for the noise variance temporal variability is evaluated by comparison against a Butterworth filter. BD is then evaluated on 15 CGM timeseries measured by the Dexcom G6 (DR).Results: On DS1, BD allows reducing the root-mean-square-error (RMSE) from 8.10 [6.79–9.24] mg/dL to 6.28 [5.47–7.27] mg/dL (median [IQR]); on DS2, RMSE decreases from 6.85 [5.50–8.72] mg/dL to 5.35 [4.48–6.49] mg/dL. On DR, BD performs a reasonable tracking of noise variance variability and a satisfactory denoising.Discussion: The new algorithm effectively addresses the nature of CGM measurement error, outperforming existing denoising algorithms.

Hg(II)⋅d8 [M] Interactions: Are they Metallophilic Interactions or Spodium Bonds?

Some literature reports have shown the existence of short Hg(II)⋅⋅⋅d8 [M] (M=Pd, Pt) contacts between linear Hg(II) and square planar d8 [M] complexes that have been defined as heterometallophilic interactions. Linear L-Hg(II)-L complexes exhibit a π-hole or positive belt of electrostatic potential at the Hg atom, whereas late transition metals can serve as effective electron donors through their filled dz 2 orbitals. This study provides compelling evidence that Hg(II)⋅⋅⋅d8 [M] interactions should be more appropriately termed spodium bonds.

Synthesis of LQR Controller Based on BAT Algorithm for Furuta Pendulum Stabilization

In this study, a controller design method based on the LQR method and BAT algorithm is presented for the Furuta pendulum stabilization system. Determine the LQR controller, it is often based on the designer's experience or using trial and error to find the Q, R matrices. The BAT search algorithm is based on the characteristics of the bat population in the wild. However, there are advantages to finding multivariate objective functions. The BAT algorithm has an improvement for the LQR controller to optimize the linear square function with fast response time, low energy consumption, overshoot, and a small number of oscillations. Swarm optimization algorithms have advantages in finding global extrema of multivariate functions. Therefore, with a large number of elements of the Q and R matrices, they can also be quickly found and these matrices still satisfy the Riccati equation. The controller with optimal parameters is verified through simulation results with different scenarios. The performance of the proposed controller is compared with a conventional LQR controller and implemented on a real system.

Effect of multi-walled carbon nanotube/Porous Carbon/SiO2 on reinforcing of cement mortar matrix: Modeling studies of effective parameters

The enhancement of the mechanical properties of cement composites by various nanomaterials is now a trendy subject. This study assessed the impact of three nanomaterials on the compressive and tensile strength of Portland cement mortar: biomass-derived porous carbon, MWCNT, and SiO2. These nanoparticles were developed using environmentally friendly, inexpensive technologies, and they underwent material characterization analyses using FESEM, N2 adsorption-desorption, contact angle, FTIR, XRD, and TEM. After 7 and 28 days, the compressive and tensile strength was assessed. According to the findings, among the samples, cement-SiO2 had the maximum compressive strength (19.32 MPa). The FESEM micrographs amply support the creation of the C-H-S gel and the presence of the bridge. The outcomes further showed that the nanoparticle's presence in the matrix does not significantly impact the tensile strength. SiO2-cement had the highest tensile strength, 1.45 MPa, matching the conclusions drawn from the compressive strength. Moreover, the compressive strength of cement was unaffected by the total pore volume of nanomaterials or the pore-filling mechanism of cement. Modeling (Linear regression-least square method) made it possible to predict the compressive strength for a period of 92 days and examine the influence of effective ingredients on it. Ultimately, the results indicate that porous carbon and silica can be viable alternatives for MWCNT.

Developing a used car pricing model applying Multivariate Adaptive regression Splines approach

Although the used car market in India is enormous, with an annual 27.1 billion USD worth of car sales, no academic study examining the pricing of Indian used cars is available. The average on-road life of cars in India is high, exceeding twenty years and pass through multiple hands. The price of old cars is also a primary determinant of the prices of new cars. The article discusses the non-linear reduction of used car prices on account of age, kilometer driven, and the number of previous owners of the cars. In addition to using the Ordinary Linear Squares (OLS), we also applied Multivariate Adaptive Regression Splines (MARS) techniques to capture non-linear price relationships with other depreciating variables. The resulting error estimation from these two methods demonstrates that the complexity of non-linear modeling using MARS significantly improves fitment accuracy.

Estimation of interlayer textural relationships to discriminate the benignancy/malignancy of brain tumors

A computer-aided diagnosis system is a popular tool to predict the risk factors of brain tumors. However, the existing techniques are unable to provide high detection accuracy due to the inability of capturing the hidden features of brain tumors. In this view, the present work decomposes the MR modality images into different layers using non-sub-sampled shearlet transformation (NSST). Following this, the proposed detection pipeline employs adaptive pulse-coupled neural network (A-PCNN) module and a fuzzy c-means (FCM) clustering algorithm for enhancement and segmentation of suspicious regions respectively. Interlayer textural relationships of tumors are estimated in terms of the layer-wise difference of the gray-level cooccurrence matrix (GLCM), similarity indices, and entropy distributions. Thenceforth, those feature coefficients are coded with binary sequences to express the textural homogeneity/randomness of tumors. Finally, these handcrafted multi-scale feature quantifiers are fed to some standard classifiers such as the k-nearest neighbor (kNN) technique, the linear square support vector machine (LS-SVM), and the decision tree (DT) for discriminating the state of benignancy/malignancy of tumors. Experimental results ensure that the proposed detection model shows superior performance compared to existing methods.

Current electroanalytical approaches in the carbamates and dithiocarbamates determination.

Pesticides are a suitable tool for controlling plagues and disease vectors. However, their inappropriate use allows for contamination of the environment, soil, water, and foods. Carbamates and dithiocarbamates pesticides present accumulative effects in the human body resulting in hormonal, neurological and reproductive disorders, and some are still suspected or proven to give carcinogenic or mutagenic effects. This review provides a current electroanalytical approach in the carbamates and dithiocarbamates determination, showing the use of voltammetric techniques such as amperometry, cyclic and linear scan, differential pulse, and square wave voltammetry, indicating their advantages, disadvantages, and perspectives in electroanalytical detection of carbamates and dithiocarbamates in natural water and foods. Also are reported the different materials used in the preparation of working electrodes since their choice has an important impact on the success of the analytical applications, resulting in suitable sensitivity, selectivity, stability, and robustness.

Prediksi Parameter Kelembapan Udara Berdasarkan Data Penyinaran Matahari Menggunakan Metode Aproksimasi Kuadrat Terkecil

Humidity is the measure, generally expressed as a percentage, of water vapor that presents in the air. Each place has different percentage of humidity. It happens since humidity is affected by solar radiation intensity. In this study, the percentage of relative humidity is being predicted by applying least square method and Gauss elimination. The data used in this research is the data of relative humiditity and solar radiation intensity during 2018 until 2022 which have been collected by Trunojoyo Stationary of Meteorology. The result shows that the approximation function generated by linear square method is powerful enough in order to predict the relative humidty, based on the relatively small error accumulated.

Impact of the Coronavirus Disease 2019 Pandemic on Surgical Volumes Among Fellowship-Trained Glaucoma Subspecialists.

The change in glaucoma surgical volumes due to the coronavirus disease 2019 pandemic was not uniform across procedure types and was unequal between rural and urban practice locations. To quantify the impact of the coronavirus disease 2019 pandemic on surgical volumes performed by fellowship-trained glaucoma subspecialists. This retrospective cohort analysis of the Centers for Medicare and Medicaid Services Medicare Public Use File extracted all glaucoma surgeries, including microinvasive glaucoma surgeries (MIGSs), trabeculectomy, goniotomy, lasers, and cataract surgery, performed by fellowship-trained glaucoma surgeons in rural and urban areas between 2016 and 2020. Predicted estimates of 2020 surgical volumes were created utilizing linear squares regression. Percentage change between predicted and observed 2020 surgical volume estimates was analyzed. Statistical significance was achieved at P <0.05. In 2020, fellowship-trained glaucoma surgeons operated mostly in urban areas (N = 810, 95%). A 29% and 31% decrease in predicted cataract surgery volumes in urban and rural areas, respectively, was observed. Glaucoma surgeries experienced a 36% decrease from predicted estimates (N = 56,781). MIGS experienced an 86% and 75% decrease in rural and urban areas, respectively. Trabeculectomy in rural areas experienced a 16% increase relative to predicted estimates while urban areas experienced a decrease of 3% ( P > 0.05). The number of goniotomies decreased by 10% more in rural areas than in urban areas (-22% and -12%, respectively). Laser procedures decreased by 8% more in urban areas than in rural areas (-18% and -10%, respectively). Among glaucoma-trained surgeons, glaucoma surgeries experienced a greater volume loss than cataract surgeries. In urban US areas, relative reductions in MIGS and goniotomy volumes in urban areas may have been compensated by greater laser and trabeculectomy volumes. Trabeculectomies in rural areas were the only group exceeding predicted estimates. Glaucoma subspecialists may utilize these findings when planning for future events and in overcoming any remaining unmet need in terms of glaucoma care.

Performance Analysis of Active RIS-Aided Systems in the Face of Imperfect CSI and Phase Shift Noise

The linear minimal mean square error (LMMSE) estimator for active reconfigurable intelligent surface (RIS)-aided wireless systems is formulated. Furthermore, based on the moment-matching method, we employ the Gamma distribution to approximate the distribution of the instantaneous received signal-to-interference-plus-noise ratio (SINR), and then derive the closed-form outage probability and ergodic channel capacity in the presence of realistic channel estimation errors, the thermal noise of RIS amplifiers and the RIS phase shift noise. Our theoretical analysis and simulation results show that the introduction of RIS amplifiers is equivalent to increasing of the transmit power, and also present the performance degradation resulting from the channel estimation error and the RIS phase noise.

MATHEMATICAL MODELING OF THE CORROSION BEHAVIOR OF AUSTENITIC STEELS IN CHLORIDE-CONTAINING ENVIRONMENTS DURING THE OPERATION OF PLATE HEAT EXCHANGERS

Purpose. It consists in the development of mathematical models that describe the dependence of the critical pitting temperatures of AISI 304, 08X18N10, AISI 321, 12X18N10T steels in model circulating waters with pH 4...8 and chloride concentration from 350 to 600 mg/l.&#x0D; Research methods. The developed mathematical models are based on linear square regression and a neural network of direct signal propagation for a reduced set of features.&#x0D; Results. It was established that the critical pitting temperatures of the studied austenitic chrome-nickel steels increase with an increase in the pH of the circulating water, the number of oxides up to 3.95 μm in size, the average distance between titanium nitrides, the Cr content and a decrease in the concentration of chlorides in the circulating water, the average distance between oxides, and the austenite average grain diameter.&#x0D; Scientific novelty. Based on the established relationships between the critical pitting temperatures of corrosion-resistant steels AISI 304, 08Х18Н10, AISI 321, 12Х18Н10Т, their chemical composition within the standard and structural heterogeneity, the mechanisms of their influence on the pitting resistance of these structural materials in circulating chloride-containing waters have been developed. It was established that metastable pitting is formed in a solid solution of austenite of steels around oxides with a size of 1,98...3,95 microns and repassivates before reaching critical dimensions of about 5 microns, which contributes to the growth of their pitting resistance in reversible chloride-containing environments.&#x0D; Practical value. The developed mathematical models are proposed to be used for the selection of optimal melts of austenitic chrome-nickel steels for the production of heat exchangers and prediction of their pitting resistance during their operation in circulating waters. The processes contributing to the perforation of heat transfer elements of heat exchangers during their operation have been identified.

A simple and low-cost portable potentiostat with real-time data sharing for wireless electrochemical analyses

In this work, we developed a new wireless and battery-powered potentiostat. The device interfaces with a smartphone using a Bluetooth protocol to receive the experimental data and visualize them in real-time. This approach simplifies the design and decreases both size and cost of the device. We developed an open Android application named “BluChem” for controlling the device wirelessly, in addition to an API and a website that can be used in remote locations for data processing and management. This new integration model is presented for the first time for data acquisition and displaying of the results. It allows users non-present locally in the experiment to access, edit, process, and manage the results, opening new opportunities for real-time data acquisition and application in extreme or remote onsite conditions. The developed device is also the first to perform five different electrochemical techniques in a single device: chronoamperometry, cyclic voltammetry, linear sweep voltammetry, differential pulse, and square wave voltammetry. Different measurements validated the performance of the proposed device compared with analogous performance using a conventional benchtop instrument. The results obtained by the two instruments were almost superimposable, and current values were statistically equal (ρ > 0.05). The current and voltage operational ranges of the new potentiostat (±1.5 V, ±1 mA) are more limited than benchtop potentiostats. However, its operating range is sufficient for most electroanalytical analyses. The potentiostat is also simple, small in size, inexpensive (< 40 USD), wireless, and completely integrated and controlled by a smartphone and a website. Therefore, this device can be considered a remote, wearable, and onsite electroanalysis landmark.