Low Standard Deviation Research Articles

Development of forecasting of monthly SAR time series in river systems: A multivariate data decomposition-based hybrid approach

The sodium adsorption ratio (SAR) is a critical variable in assessing the quality of water resources, and accurately forecasting its time series is operationally valuable. This study developed a hybrid approach using the multivariate variational mode decomposition (MVMD) model for signal analysis, Boruta model for feature selection, pure linear neural network (PLNN), support vector regression (SVR), Lasso regression, and Elman neural network (ENN) models to forecast monthly SAR time series for rivers. Data from two rivers were used to enhance result reliability, and the developed models were compared with corresponding basic models to evaluate their impact. Numerical and graphical criteria demonstrated the significant superiority of the developed models over the basic ones. Among the basic models, the ENN model exhibited the highest accuracy, while the MVMD-Boruta-ENN model surpasses all investigated models. This finding suggested that the ENN model's structure is more suitable for SAR time series forecasting than other basic models. Analyzing the models' residuals revealed lower mean, standard deviation, skewness, and error range in the developed models, indicating their robust behavior in forecasting the SAR time series. Notably, forecasting extreme SAR values holds greater importance than other values. Anderson-Darling and Kolmogorov-Smirnov tests identified the dominance of the generalized logistic and log-logistic (3-parameters) functions in SAR time series. Probability distribution functions were used to estimate extreme values, and the studied models exhibited more accurate estimations compared to the basic models, indicating their enhanced resilience. The consistent patterns observed in comparing developed and basic models for the entire series, as well as extreme values and residuals across the two investigated rivers, emphasized the reliability of the results.

Silica Nanochannels as Nanoreactors for the Confined Synthesis of Ag NPs to Boost Electrochemical Stripping Chemiluminescence of the Luminol-O2 System for the Sensitive Aptasensor.

Herein, we, for the first time, synthesize silver nanoparticles (Ag NPs) within the nanochannels of amino group-functionalized vertically ordered mesoporous silica films (NH2-VMSF) and investigate their coreaction accelerator role in the luminol-dissolved oxygen (O2) electrochemical stripping chemiluminescence (ESCL) system. The synthesized Ag NPs are capable of electrocatalytic reduction of O2 to superoxide radicals, and meanwhile, sliver ions (Ag+) electrochemically stripped from Ag NPs can promote the amount of luminol anion radicals, generating the boosted ECL intensity of the luminol-dissolved O2 system. This proposed Ag NPs@NH2-VMSF on the indium tin oxide electrode was applied to construct the ESCL aptasensor for quantitative determination of prostate-specific antigen (PSA), yielding a low detection limit [0.19 pg/mL (S/N = 3)] and a broad linear dynamic range (1 pg/mL to 100 ng/mL). Furthermore, good analytical performance of PSA in serum with satisfactory recoveries and low relative standard deviation values is achieved by our developed ESCL aptasensor, rendering it a convenient and sensitive method for PSA determination in clinical applications and further broadening the strategy of ESCL techniques.

Cardiac electrophysiology, structure and diastolic function in patients with diabetic foot versus those without diabetic foot.

To evaluate the differences in cardiac autonomic function, cardiac structure and diastolic function between individuals with diabetic foot (DF) and those with diabetes but without DF. A total of 413 individuals with DF and 437 without DF who underwent a 24-h electrocardiogram Holter and a Doppler echocardiogram were included. The heart rate variability parameters to evaluate cardiac autonomic function, and the indices for the assessment of cardiac structure and left ventricular (LV) diastolic function, including left atrium, LV posterior wall thickness, interventricular septum and E/e' ratio, were measured or calculated. Propensity score matching was used for the sensitivity analysis to minimize potential imbalance. In both the crude and propensity score matching analyses, significant differences were observed in heart rate variability between individuals with and without DF, as evidenced by lower standard deviation of the normal sinus interval, lower low-frequency power/high-frequency power ratio, lower standard deviation of the 5-min average RR intervals, lower low-frequency power, lower percentage of normal adjacent RR interval difference >50 ms, lower root mean square of successive RR interval differences and lower high-frequency power (all P < 0.05). In multivariate analysis, DF showed an independent negative correlation with the aforementioned indices of heart rate variability (all P < 0.05). Individuals with DF showed higher left atrium, LV posterior wall thickness, interventricular septum and a higher E/e' ratio than those without DF in the crude analysis (all P < 0.05), whereas these indices were no longer associated with DF in the multivariate analysis and the propensity score matching analyses. Cardiac autonomic modulation was more severely impaired in individuals with DF than in their counterparts without DF. There has been insufficient evidence to demonstrate the independent association of DF and LV diastolic dysfunction.

Benchmarking the performance and uncertainty of machine learning models in estimating scour depth at sluice outlets

ABSTRACT This study investigates the performance of six machine learning (ML) models – Random Forest (RF), Adaptive Boosting (ADA), CatBoost (CAT), Support Vector Machine (SVM), Lasso Regression (LAS), and Artificial Neural Network (ANN) – against traditional empirical formulas for estimating maximum scour depth after sluice gates. Our findings indicate that ML models generally outperform empirical formulas, with correlation coefficients (CORR) ranging from 0.882 to 0.944 for ML models compared with 0.835–0.847 for empirical methods. Notably, ANN exhibited the highest performance, followed closely by CAT, with a CORR of 0.936. RF, ADA, and SVM performed competitive metrics around 0.928. Variable importance assessments highlighted the dimensionless densimetric Froude number (Fd) as significantly influential, particularly in RF, CAT, and LAS models. Furthermore, SHAP value analysis provided insights into each predictor's impact on model outputs. Uncertainty assessment through Monte Carlo (MC) and Bootstrap (BS) methods, with 1,000 iterations, indicated ML's capability to produce reliable uncertainty maps. ANN leads in performance with higher mean values and lower standard deviations, followed by CAT. MC results trend towards optimistic predictions compared with BS, as reflected in median values and interquartile ranges. This analysis underscores the efficacy of ML models in providing precise and reliable scour depth predictions.

Territorial planning support tool for sustainable forest management in the Amazon

In forest management, it is crucial to ensure process efficiency to maintain economic viability, especially in native forests where timber volume varies due to the uneven distribution of commercially valuable species. This variability poses a challenge for harvest planning, as the inherent heterogeneity of these forests often results in inconsistent productivity. This study employs the SOTER-PA system, an algorithm-based tool utilizing a minimalist tabu search, to achieve a more homogeneous territorial planning structure for better utilization of natural resources and increased economic returns from forestry activities. The study area is the Tapajós National Forest in the state of Pará, with a forest inventory database covering 2,218.7 hectares and 28,369 trees across 93 species. After applying exclusion criteria and data processing, 8,644 trees were deemed suitable for harvest. The tests suggested a new organization of Work Units, enabling a more balanced income distribution among them, and delineating the areas to be exploited in each cutting cycle. The results show that SOTER-PA not only improves the equitable distribution of natural resources but also enhances economic productivity. This improvement is attributed to the more homogeneous and optimized distribution of high-value species like red cedar and purpleheart. Furthermore, when comparing the SOTER-PA generated parcels with conventional management, the parcels created by SOTER-PA were more homogeneous, with better productivity quality and lower standard deviation, indicating greater uniformity. Thus, the use of SOTER-PA proves to be an efficient alternative for optimizing sustainable forest management planning, offering a more effective method for sustainable territorial planning in native forests, providing both economic and environmental benefits.

Deep learning classification of EEG-based BCI monitoring of the attempted arm and hand movements.

The primary objective of this research is to improve the average classification performance for specific movements in patients with cervical spinal cord injury (SCI). The study utilizes a low-frequency multi-class electroencephalography (EEG) dataset from Graz University of Technology. The research combines convolutional neural network (CNN) and long-short-term memory (LSTM) architectures to uncover neural correlations between temporal and spatial aspects of the EEG signals associated with attempted arm and hand movements. To achieve this, three different methods are used to select relevant features, and the proposed model's robustness against variations in the data is validated using 10-fold cross-validation (CV). The research also investigates subject-specific adaptation in an online paradigm, extending movement classification proof-of-concept. The combined CNN-LSTM model, enhanced by three feature selection methods, demonstrates robustness with a mean accuracy of 75.75 % and low standard deviation (+/- 0.74 %) in 10-fold cross-validation, confirming its reliability. In summary, this research aims to make valuable contributions to the field of neuro-technology by developing EEG-controlled assistive devices using a generalized brain-computer interface (BCI) and deep learning (DL) framework. The focus is on capturing high-level spatiotemporal features and latent dependencies to enhance the performance and usability of EEG-based assistive technologies.

Versatile simplistic correction of T-higrow sensors for improved soil moisture measurement accuracy

The use of soil moisture sensors for irrigation can help reduce water and energy consumption and risks of groundwater contamination, which are essential aspects for pursuing sustainable development goals. However, increased adoption of this technology is limited by calibration requirements, technical complexities, and sensor costs. In this work, a simplified method for reducing the measurement error of a recently released low-cost soil sensor (T-Higrow) is presented. The method only requires measurements of a dry sample from the target soil, which are inputted into a simple correction formula to reduce the measurement error at higher moisture levels. The requirements of the proposed method are simple enough for most labs or extension services. This method was compared to the commonly used linear, polynomial, and logarithmic regression models based on repeated bench-scale experiments within 0%–35% moisture range in silt and sandy loam soils and in silica sand. Uncorrected sensor readings correlated well with soil moisture (r: 0.94–0.98), but with significant overestimation (25%–60% error). The simplified correction method showed comparable error reduction to regression models across all soil types. All methods reduced error down to 2%–10% (0.02–0.1 cm3 cm−3) and maintained high correlations (r > 0.94), except for logarithmic regression which reduced correlation by around 3%. Variability amongst sensor measurements was generally low (Standard Deviation: 0.01–0.03) particularly at moisture ranges below 20%, this was also the case for sensor-to-sensor variability (Standard Deviation: 0.01–0.03). Sensor evaluation and calibration works are needed to increase the accessibility to this technology for improved water and energy conservation.

A Novel Clustering Algorithm Integrating Gershgorin Circle Theorem and Nonmaximum Suppression for Neural Spike Data Analysis

(1) Problem Statement: The development of clustering algorithms for neural recordings has significantly evolved, reaching a mature stage with predominant approaches including partitional, hierarchical, probabilistic, fuzzy logic, density-based, and learning-based clustering. Despite this evolution, there remains a need for innovative clustering algorithms that can efficiently analyze neural spike data, particularly in handling diverse and noise-contaminated neural recordings. (2) Methodology: This paper introduces a novel clustering algorithm named Gershgorin—nonmaximum suppression (G–NMS), which incorporates the principles of the Gershgorin circle theorem, and a deep learning post-processing method known as nonmaximum suppression. The performance of G–NMS was thoroughly evaluated through extensive testing on two publicly available, synthetic neural datasets. The evaluation involved five distinct groups of experiments, totaling eleven individual experiments, to compare G–NMS against six established clustering algorithms. (3) Results: The results highlight the superior performance of G–NMS in three out of five group experiments, achieving high average accuracy with minimal standard deviation (SD). Specifically, in Dataset 1, experiment S1 (various SNRs) recorded an accuracy of 99.94 ± 0.01, while Dataset 2 showed accuracies of 99.68 ± 0.15 in experiment E1 (Easy 1) and 99.27 ± 0.35 in experiment E2 (Easy 2). Despite a slight decrease in average accuracy in the remaining two experiments, D1 (Difficult 1) and D2 (Difficult 2) from Dataset 2, compared to the top-performing clustering algorithms in these categories, G–NMS maintained lower SD, indicating consistent performance. Additionally, G–NMS demonstrated robustness and efficiency across various noise-contaminated neural recordings, ranging from low to high signal-to-noise ratios. (4) Conclusions: G–NMS’s integration of deep learning techniques and eigenvalue inclusion theorems has proven highly effective, marking a significant advancement in the clustering domain. Its superior performance, characterized by high accuracy and low variability, opens new avenues for the development of high-performing clustering algorithms, contributing significantly to the body of research in this field.

Enantioanalysis of Leucine and Arginine: A Key Factor in Lung Cancer Metabolomics

Metabolomics has become a key factor for identifying new biomarkers able to be used for early diagnosis of cancer. A zinc oxide-α-cyclodextrin nanocarbon nanocomposite 3D stochastic microsensor (α-CD/ZnO/nanoC) was designed and integrated into an intelligent 3D stochastic enantioselective platform for the enantioanalysis of arginine and leucine in whole blood samples. The α-CD/ZnO/nanoC pastes were morphologicaly characterized by scanning electron microscopy, and electrochemically characterized by cyclic voltammetry and electrochemical impedance spectroscopy. L-Leucine was determined on a working concentration range between 1 × 10−11–1 × 10−8 mol l−1 with a limit of detection of 3 × 10−12 mol l−1; D-leucine was determined on a working concentration range between 1 × 10−16–1 × 10−11 mol l−1 with a limit of detection of 3 × 10−17 mol l−1; L-arginine was determined on a working concentration range between 1 × 10−12–1 × 10−9 mol l−1 with a limit of detection of 3 × 10−13 mol l−1; D-arginine was determined on a working concentration range between 1 × 10−14–1 × 10−11 mol l−1 with a limit of detection of 3 × 10−15 mol l−1. High sensitivities, selectivities, and enantioselectivities were recorded when the platform was used for the enantioanalysis of arginine and leucine. High recoveries were recorded at the enantioanalysis of arginine/leucine, with low relative standard deviations.

Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler.

Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide Variants (nsSNVs) are the most common genetic variations. They result from missense mutations where a single base pair substitution changes the genetic code in such a way that the triplet of bases (codon) at a given position is coding a different amino acid. Since genetic mutations sometimes cause genetic diseases, it is important to comprehend and foresee which variations are harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a classification problem. Computational methods using machine intelligence are gradually replacing repetitive and exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently, strong and more exact approaches are needed to address these problems. In the present work paper, we show a consensus classifier built on the holdout sampler, which appears strong and precise and outflanks all other popular methods. We produced 100 holdouts to test the structures and diverse classification variables of diverse classifiers during the training phase. The finest performing holdouts were chosen to develop a consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs. Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent results, highly accurate with low standard deviation. The advantage of our method emerges from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.

Analysis of the Repeatability of the Pencil Lead Break in Comparison to the Ball Impact and Electromagnetic Body-Noise Actuator

Acoustic emission testing is used to monitor the structural health by recording and analyzing sound signals originating from within a structure. Usually, sensors are attached at key positions on a structure under test to record and analyze these acoustic signals. For reliable measurements, the coupling of these sensors has to be assessed. For this purpose, artificial sound sources are commonly used which induce signals in the form of elastic waves into a structure allowing to approximately assess the frequency response function of the sensor coupling. To assess the coupling of sensors reliably by means of an artificial sound source, it has to exhibit high repeatability, i.e. small relative standard deviation (RSTD) with respect to some characteristic of interest. In this work, the repeatability of the pencil lead break, ball impact and an electromagnetic body-noise actuator is assessed across several repetitions. Through controlled and repeated excitation of the respective artificial sound sources on a concrete girder, the RSTD of the magnitude spectra of the artificial sound sources across frequency is assessed in the frequency range of about 0 kHz to 650 kHz. The ball impact and the actuator exhibited a RSTD of below 5 % of their magnitude spectra for frequencies below about 20 kHz, the maximum frequency for the actuator, whereas for the pencil lead break a RSTD of above 11 % was observed across the entire frequency range. An increase of the RSTD with increasing frequency was observed for the ball impact and the pencil lead break. The repeatability of the pencil lead break, for which a RSTD between 15 % to 40 % was observed for a broad frequency range of its amplitude spectrum, usually achieved a lower relative standard deviation than the ball impacts with 4 mm and 5 mm balls above a frequency of about 60 kHz. A significant increase of the relative standard deviation for the 4 mm and 5 mm ball was observed starting at about 20 kHz. Above about 60 kHz, the relative standard deviation of the 5 mm ball impact had risen to 30 % and more oscillating between about 30 - 50 %. At 200 kHz and more all signals were attenuated to background noise level by the concrete girder. It is concluded, that the pencil lead break is inferior with respect to repeatability below 40 kHz compared to the 4 mm and 5 mm ball impact and electromagnetic body-noise actuator, but performs equally well or better above 60 kHz. The repeatability of the investigated artificial sound sources depends on the investigated frequency range with no unique best. The most important finding is the increasing relative standard deviation of the ball impact with increasing frequency. As a result, the repeatability of the ball impact is questionable and should be investigated before using it in high precision experiments.

Impact of Thyroid Autoimmunity on the Clinical and Biochemical Characteristics of Type 1 Diabetes Mellitus Patients.

Type 1 diabetes mellitus (T1DM) is frequently associated with other autoimmune disorders that are characterized by the presence of organ-specific autoantibodies. Autoimmune thyroid disease (AIT) is the most frequent autoimmune disorder associated with T1DM. Thyroid peroxidase antibodies (TPOAb) serve as a marker for diagnosing AIT. Prior research indicates that thyroid dysfunction can negatively impact linear growth and glycemic control in subjects with T1DM. The present study was done to determine the impact of thyroid autoimmunity on the clinical and biochemical characteristics of patients with newly diagnosed T1DM. In this single-center, hospital-based, observational cross-sectional study, we enrolled 70 patients with newly diagnosed T1DM ≤18 years of age. Type 1 diabetes mellitus was diagnosed based on the acute onset of osmotic symptoms with or without diabetic ketoacidosis (DKA), severe hyperglycemia (blood glucose >13.9 mmol/l (>250 mg/dl)), and insulin requirement from the onset of diabetes. Secondary diabetes, pancreatic diabetes (Type 3c), and maturity-onset diabetes of the young (MODY) were excluded. Participants were screened for AIT disease using TPOAb testing. Based on the presence or absence of TPOAb, the participants were categorized into two groups: Group A comprised individuals with T1DM who tested positive for TPOAb, while Group B consisted of those who tested negative for TPOAb. Out of 70 patients, 41.4% were girls and 58.6% were boys, with a mean age of 9.8±4.4 years. The prevalence of TPOAb among the cohort was 18.6%. A significant majority of patients (71.4%), presented with DKA. Group A showed significantly lower mean height standard deviation scores (SDS) compared to Group B (-0.3±0.6 vs. -0.8±0.5, p = 0.004), but no differences in weight SDS or BMI SDS. Hemoglobin A1C (HbA1c) levels, C-peptide levels, and frequency of DKA did not differ between groups. Group A had higher mean thyroid-stimulating hormone (TSH) levels (4.8±3.7 µU/ml vs. 2.6±1.5 µU/ml, p = 0.001) and a greater proportion of patients with TSH levels above the upper limit of normal compared to Group B (38.4% vs. 7.1%, p = 0.008). Additionally, Group A exhibited a higher frequency of glutamic acid decarboxylase antibody (GADA) positivity compared to Group B (46.1% vs. 17.5%, p = 0.04). Patients positive for TPOAb exhibited significantly lower height SDS compared to TPOAb-negative patients. Additionally, T1DM patients with TPOAb positivity showed an increased frequency of GADA compared to those without TPOAb. However, no significant differences were found in HbA1c levels, C-peptide levels, or hematological parameters between TPOAb-positive and TPOAb-negative patients. These findings emphasize the impact of TPOAb on growth parameters in T1DM and advocate for routine screening of TPOAb in all T1DM patients, starting at the time of diabetes diagnosis.

Unlocking the electrochemical signature of Naringenin: Implications for screening in pharmaceutical formulations

Naringenin (NAR) is a versatile herbal flavonoid known for its broad pharmacological benefits, including antioxidant, anti-inflammatory, and anticancer properties. These properties underscore its potential and necessity for industrial applications within the pharmaceutical sector, making its detection and quantification crucial for ensuring the quality and efficacy of pharmaceutical products. Addressing the imperative for a sensitive and sustainable screening approach, this research leverages electrochemical analysis techniques including electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and differential pulse voltammetry (DPV). Complementary to experimental investigations, the electronic structure of NAR was probed using B3LYP/Def2-TZVPPD calculations alongside Fukui and dual descriptor analyses. Our findings delineate the dissociation kinetics of the hydroxyl groups within NAR, with particular emphasis on the phenolic group’s proton as the primary dissociation site, elucidating pivotal insights into the oxidation mechanism of NAR. Noteworthy is the irreversibility of the electrochemical oxidation process observed on both non-modified glassy carbon electrode (GCE) and gold electrode (GE), indicating a one-electron-one-proton mechanism. Utilizing the external calibration method and GCE, a linear range from 1.36 to 19.1 mg L-1 in Britton-Robinson (BR) supporting electrolyte was established, exhibiting a limit of detection (LOD) of 0.11 mg L-1 (R2 = 0.9945). For GE, the standard addition calibration approach revealed one linear range, from 0.27 to 0.48 mg L-1 (LOD of 0.04 mg L-1, R2 = 0.9960). Importantly, the proposed methodologies demonstrate satisfactory selectivity against potential interferences, with intra- and inter-day precision exhibiting low relative standard deviation (RSD) values. This study introduces a simple, sensitive, stable, selective, and sustainable electroanalytical method for the detection of NAR in pharmaceutical formulations, harnessing the advantages of unmodified electrodes and rigorous experimental validation.

Fagonia arabica extract-stabilized gold nanoparticles as a highly selective colorimetric nanoprobe for Cd2+ detection and as a potential photocatalytic and antibacterial agent

In the current study, polar leaves extract of the plant Fagonia arabica (FALE) from the Zygophyllaceae family was used to synthesize sunlight-assisted gold nanoparticles (FALE@AuNPs) with defined size and shape and of excellent colloidal stability. The effect of different physiochemical parameters (FALE dosage, Au3+ ions concentration, sunlight exposure time) on the formation of AuNPs was also studied. The synthesized FALE@AuNPs were characterized using UV–visible spectrophotometry, scanning electron microscopy-energy dispersive X-rays (SEM-EDX), Fourier-transform infrared spectroscopy (FTIR), powder XRD, zeta potential (ZP) and dynamic light scattering (DLS). The successful synthesis of FALE@AuNPs in solution was confirmed by the appearance of a localized surface plasmon resonance (LSPR) characteristic peak at 535 nm in UV–Vis spectra. The size estimated by UV–Vis (∼59 nm) was found in good agreement with that determined by SEM (∼60 nm) and DLS (∼61 nm). A zeta potential value of -28±1.5 mV confirms the colloidal stability of FALE@AuNPs. The FALE@AuNPs were utilized as a colorimetric nano-sensor for Cd2+ ion 15 nM limit of detection (LOD) and linear range detection of 0–600 nM. When utilized to identify Cd2+ ion in actual water samples, the nano-probe demonstrated high recovery rates of over 98 % and a low relative standard deviation (RSD) of about 4 %, demonstrating its dependability and efficiency in doing so. FALE@AuNPs were found to be antibacterial against gram-positive as well as gram-negative bacteria. An exceptionally high photocatalytic activity was shown by FALE@AuNPs with 80 % efficiency of degradation examined against methylene blue and methyl orange dye.

Development and validation of rapid screening of 192 veterinary drug residues in aquatic products using HPLC-HRMS coupled with QuEChERS

The presence of veterinary drug residues in aquatic products represents a significant challenge to food safety. The current detection methods, limited in both scope and sensitivity, underscore the urgent need for more advanced techniques. This research introduces a swift and potent screening technique using high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS) and a refined QuEChERS protocol, allowing simultaneous qualitative and semi-quantitative analysis of 192 residues. A comprehensive database, employing full scan mode and data-dependent secondary mass spectroscopy, enhances screening accuracy. The method involves efficient extraction using 90% acetonitrile, dehydration with Na2SO4, and acetic acid, followed by cleanup using dispersive solid-phase extract sorbent primary secondary amine. It is suitable for samples with varying fat content, offering detection limits ranging from 0.5 to 10 μg/kg, high recovery rates (60–120%), and low relative standard deviations (<20%). Practical application has validated its effectiveness for multi-residue screening, marking a significant advancement in food safety evaluation.

Stochastic time series analysis of atrial fibrillatory electrograms in paroxysmal atrial fibrillation

Abstract Background/Introduction Regional dominant frequency and instantaneous frequency modulation of short fractionated atrial electrograms have been associated with ablation targets and the prognosis of atrial fibrillation (AF). However, inconsistent and not fully comprehensible results have been reported. Purpose To construct a simple computational algorithm for the analysis of continuous fibrillatory atrial electrograms and evaluate it in clinical practice, among patients with paroxysmal AF. Methods Before the first cryo-balloon ablation, 3-minute continuous local electrogram recordings during pace-induced AF were obtained by the circumferential catheter from the left atrial appendage (LAA) and each pulmonary vein: left superior (LSPV), left inferior (LIPV), right superior (RSPV) and right inferior (RIPV). Following time- and frequency-domain analysis, non-linear detrended fluctuation analysis (DFA α) with analysis for short-term (DFA α1) and long-term fluctuations (DFA α2) was performed. The signals were detrended by subtracting time series polynomial fit and badpass filtered in the MATLAB environment (cut-off voltage 0.1 mV to avoid scar and artifacts). Results Fifteen patients were studied. Among different atrial areas, higher mean peak rate (pr) along with lower standard deviation of between peaks intervals (sdpp) and root square of peak-to-peak differences (rmppd) were observed in the LAA and left veins (Fig. 1). In patients with longer AF history (&amp;gt; 2 yrs), lower α, α1 and α2 exponents of DFA were observed (Fig. 2). Conclusions This algorithm depicted dissimilar impact on atrial electrical activity between different atrial areas. Patients with longer AF history had less self-similarity in local LAA electrograms. This might indicate possible ablation targets and reveal patients with greater AF burden and worse arrhythmic prognosis.Leftheriotis - Figure 1Leftheriotis - Figure 2

Revenue Collectors Competency on Achievement of Own Source Revenue in Kenyan County Governments

An alarming trend has emerged from an analysis of own source revenue as a percentage of the targeted annual revenue over the past five financial years, revealing a consistent decline in performance with counties raising less than 40% of their estimated own source revenue potential despite using agents and having undertaken automation of the revenue collection processes. A deeper examination further reveals that a significant number of counties that consistently outperform the national average in achieving own source revenue targets have already embraced automation, in addition to agency revenue collection. A mixed research design was adopted by this study, targeting a population of 708 employees working in six selected county governments. By use of the Yamane formula, the determined sample was 280, who were reached through both census and stratified random sampling technique. Primary was collected through structured questionnaires. In order to examine quantitative data, descriptive statistics such as frequency, mean, and standard deviation were used. The study findings reveal that there was an overall mean of 4.321 and a relatively low standard deviation of 0.823. This underscores the significance of skilled and competent revenue collection employees in achieving own source revenue targets. This finding has profound implications for county governments, emphasizing the critical need to invest in the training, development, and ongoing support of revenue collection personnel.

Tailoring Rhodamine Probes: Oxyethylene Chains, Conjugation, and End Substituents in the Quest for Superior Hg2+ Sensing

AbstractThis study explores the impact of incorporating oxyethylene (OE) chains into rhodamine‐based dye molecules for the enhancement of photophysical properties in the context of chemical sensing. The introduction of OE chains provides several advantages, including improved water solubility, photostability and resistance to self‐assembled nanoaggregates formation. Despite these, the compound with oxyethylene chains showed poor response (~2‐fold) towards Hg2+ ions than that of dimeric probe (~20 fold) with no such substituents. Notably, dimeric compound shows a remarkable three‐fold larger response to Hg2+ ion compared to monomeric compound. This intriguing outcome is attributed to the extended conjugation within the dimeric structure, which can induce conformational change of the overall molecule. Moreover, the study investigates the effects of end substituents, such as electron‐donating (−OMe, improved response) and electron‐withdrawing (−NO2, poor response) groups, on the interaction with Hg2+ ions. The excellent sensitivity of the probe molecules is also evident in significantly low detection limits for Hg2+ ion (5–8 ppb) in real‐life water samples. The high recovery values (&gt;95 %) with sufficient low standard deviation (&lt;5%) indicates that quantitative analysis of Hg2+ is achievable using the current protocol. Beyond that, we have employed the probe 1 for intracellular (HEK293T cells) fluorescence imaging of Hg2+.

Unravelling a complex catalyst deactivation in which selectivity affects conversion: oxygen-assisted styrene synthesis at industrially-relevant conditions

The oxidative dehydrogenation of ethylbenzene (EB) into styrene (ST) has been proposed as an alternative to the conventional energy-consuming synthesis of styrene. Various types of catalysts have been reported as promising for the reaction under industrially-relevant conditions. However, they show a complex deactivation behaviour. The EB conversion and the ST selectivity decay, with an increased COx selectivity. This phenomenon was investigated by means of experimental data and reaction model analysis, using two reference inorganic catalysts. The active catalyst is the deposited coke (ODH-coke) and not directly the inorganic material. The coke is formed in the initial reaction phase and promoted by the Lewis acid sites of the inorganic material. The reaction shows an activation period in which the ODH-coke is deposited and the EB conversion reaches a maximum where O2 is fully converted. From that point onwards, the reaction model is applicable and the experimental data fit very well with low standard deviations. The model explains that the EB conversion′s decay with time on stream is associated to changes in the selectivity. Hence, EB conversion is not an independent parameter. This simplifies the understanding of this complex deactivation; the deactivating parameter is the selectivity. At this moment, we cannot discriminate between increased COx and decreased ST, or both effects, because both routes are competitive. This case represents a new type of catalyst deactivation behaviour, in which selectivity affects conversion.

Overcoming Dimensionality Constraints: A Gershgorin Circle Theorem-Based Feature Extraction for Weighted Laplacian Matrices in Computer Vision Applications.

In graph theory, the weighted Laplacian matrix is the most utilized technique to interpret the local and global properties of a complex graph structure within computer vision applications. However, with increasing graph nodes, the Laplacian matrix's dimensionality also increases accordingly. Therefore, there is always the "curse of dimensionality"; In response to this challenge, this paper introduces a new approach to reducing the dimensionality of the weighted Laplacian matrix by utilizing the Gershgorin circle theorem by transforming the weighted Laplacian matrix into a strictly diagonal domain and then estimating rough eigenvalue inclusion of a matrix. The estimated inclusions are represented as reduced features, termed GC features; The proposed Gershgorin circle feature extraction (GCFE) method was evaluated using three publicly accessible computer vision datasets, varying image patch sizes, and three different graph types. The GCFE method was compared with eight distinct studies. The GCFE demonstrated a notable positive Z-score compared to other feature extraction methods such as I-PCA, kernel PCA, and spectral embedding. Specifically, it achieved an average Z-score of 6.953 with the 2D grid graph type and 4.473 with the pairwise graph type, particularly on the E_Balanced dataset. Furthermore, it was observed that while the accuracy of most major feature extraction methods declined with smaller image patch sizes, the GCFE maintained consistent accuracy across all tested image patch sizes. When the GCFE method was applied to the E_MNSIT dataset using the K-NN graph type, the GCFE method confirmed its consistent accuracy performance, evidenced by a low standard deviation (SD) of 0.305. This performance was notably lower compared to other methods like Isomap, which had an SD of 1.665, and LLE, which had an SD of 1.325; The GCFE outperformed most feature extraction methods in terms of classification accuracy and computational efficiency. The GCFE method also requires fewer training parameters for deep-learning models than the traditional weighted Laplacian method, establishing its potential for more effective and efficient feature extraction in computer vision tasks.