Zero Values Research Articles

C, Ge-doped h-BN quantum dot for nano-optoelectronic applications

Emerging materials, particularly nanomaterials, constitute an enduring focal point of scientific inquiry, with quantum dots being of particular interest. This investigation is centered on elucidating the exceptional structural, electromagnetic, and optical characteristics of hexagonal boron nitride (h-BN) quantum dots and h-BN quantum dots doped with carbon (C) and germanium (Ge). The employed methodology in this study hinges on density functional theory coupled with the Vienna Ab initio simulation package. The outcomes of this research unveil the structural stability of hexagonal honeycomb structures upon optimization. Comprehensive examinations encompassing structural properties, electromagnetic characteristics, and charge density variations have been systematically conducted. Furthermore, this work delves into the elucidation of multi-orbital hybridizations that give rise to σ bonds and π bonds. Notably, the outcomes of the optical property analysis divulge intriguing observations. Specifically, the absorption coefficient exhibits zero values within select energy ranges within the visible light spectrum, a phenomenon observed in both pristine and C-doped configurations. This discovery underscores the material’s optical transparency at these specific radiation energies. Additionally, the 0x and 0y components of the dielectric function display negative values across particular energy ranges, a characteristic that holds significant promise for potential applications in nanotechnology communications, offering minimal energy loss.

Prediction of diabetes progress based on machine learning approach

Uropathy is a serious chronic disease whose prevalence is increasing at an alarming rate. Early detection and prediction of diabetes in women is important because of the increased risk of diabetes-related complications during pregnancy. This study introduces machine learning models to assess the likelihood of diabetes in women. The importance of studying characteristics and improving prediction accuracy to understand the nuances of categorization. Specifically, for data preprocessing, experiments are conducted to solve the problem of missing values and outliers by replacing the zero values of certain features with the median values of the corresponding features. This step reduces the impact of less reliable data on model performance. As recognition models, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), and Random Forest (RF) are built. Performance analysis is performed along with a careful exploration of the hyperparameter space. Scores for Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) are used to compare various models. Different features affect the classification to different degrees. The experimental findings indicate that the modified random forest model demonstrates superior prediction accuracy and robustness. These findings can assist physicians in predicting a patient's risk of developing diabetes earlier.

Do women on boards break the glass ceiling or face the glass cliff?

PurposeThis paper aims to appraise the effectiveness of gender quotas in breaking the glass ceiling for women on boards (WoBs) in companies that are legally obliged to comply with quotas (listed companies and state-owned companies, LP) and in those that are not (unlisted companies and nonstate-owned companies, NLNP). Furthermore, it investigates the glass cliff phenomenon, according to which women are more likely to be appointed to apical positions in underperforming companies.Design/methodology/approachA balanced panel data of the top 116 Italian companies by total assets, which are present in both 2010 and 2017, is used for estimating ANOVA tests across sectors and fixed-effects panel regression models.FindingsWoBs significantly increased in both the LP and the NLNP companies, and this increase was greater in the financial sector. Furthermore, the relationship between the percentage of WoBs and firm performance is not linear but depends on the financial corporate health. Specifically, the situation in which a woman ascends to a leadership position in challenging circumstances where the risk of failure is high (glass cliff phenomenon) is only present in companies with the lowest performance in the sample, in other words, when negative values of Roe and negative or zero values of Roa occur together.Practical implicationsThese findings have relevant policy implications that encourage the adoption of gender quotas even in specific top positions, such as CEO or president, as this could lead to a “double spillover effect” both vertically, that is, in other job positions, and horizontally, toward other companies not targeted by quotas. Practical interventions to support women in glass cliff positions, on the other hand, relate to the extent of supervisor mentoring and support to prevent women from leaving director roles and strengthen their chances for career advancement.Originality/valueThe authors explore the ability of gender quotas to break through the glass ceiling in companies that are not legally obliged to do so, and to the best of the authors’ knowledge, for the first time, the glass cliff phenomenon in the Italian context.

Studying the theoretical basis and advantages and disadvantages of Logistic methods of the total horizontal Gradient

Edge detection is one of the most important steps in interpretation of magnetic and gravity data. In magnetic and gravity maps, it is difficult to distinguish adjacent sources due to their field superposition. Many different techniques have been used to determine the edges of sources. These techniques are based on vertical or horizontal gradients of magnetic and gravity data or combinations of them, and the edges of the geological structures are determined by maximum, minimum, or zero values in the output maps. One of the most popular techniques is the total horizontal gradient which is based on horizontal gradients of magnetic and gravity data. The capability of the total horizontal gradient technique in mapping the boundaries of deep bodies is very limited when competing with large-amplitude shallow bodies. Some enhanced modifications of the total horizontal gradient technique have been introduced to improve the boundary estimation results. These techniques are based on logistic functions and derivatives of the total horizontal gradient. In this study, we aim to estimate the effectiveness of the logistic filters of the total horizontal gradient. To obtain optimum results, these filters were tested on synthetic gravity and magnetic data and real magnetic data from the Zhurihe region (China). The findings show that the logistic filters can provide more accurate and sharper boundaries without false source edges than the total horizontal gradient. These techniques can determine the edges of shallow and deep structures at the same time. These results demonstrate that the logistic filters are useful tools for the qualitative interpretation of potential field data.

Dispersion of optical vortices in twisted elliptical-core optical fibers with torsional stresses

In this paper, the dispersion of optical vortices (OVs) with a topological charge l >= 1 in twisted elliptical fibers (TEF) with torsional stresses (TS) is studied. Analytical expressions for polarization, topological, and hybrid dispersion are derived from the spectra of vortex modes in step- and gradient-index TEFs with TS. It is shown that in strongly twisted fibers with a gradient refractive index profile, new types of dispersion appear in comparison with the case of a step-index profile. The dependence of the dispersion of OVs on the material and geometrical parameters of the fiber is studied numerically. It is established that in step-index TEFs with TS, there is a spectral region near which all types of dispersion for OVs with l >= 1 take a near zero value. It is also demonstrated that for TEFs with a gradient profile, this regime is realized for strongly twisted strongly elliptical fibers.

Further analysis of stabilization of neutral type singular systems with mixed time‐varying delays and multiple random states

AbstractIn this paper, the stabilization problem of neutral type singular systems (NTSSs) with mixed time‐varying delays and multiple random states is further analyzed. Lyapunov Krasovskill functional (LKF) and generalized Itô formula, together with linear matrix inequality (LMI), are used to represent a new stability criterion for NTSSs with mixed time‐varying delays (MTVDs) in multiple random states. First, the regularity, impulse free, and stochastic stability of the system are satisfied by designing a state feedback controller (SFC). Second, on the basis of Leibniz Newton formula, the stability criterion is also feasible by integrating zero value equation technology, and then combined with singular decomposition, the gain of SFC is obtained. Finally, our paper is a further analysis of the latest literature, so two numerical examples are used to verify the feasibility and effectiveness of this method.

Design considerations of series type hybrid circuit breaker (S‐HCB)

AbstractThe series‐type direct current (DC) hybrid circuit breaker (S‐HCB) concept was previously reported to offer better performance than solid‐state circuit breakers (SSCB) and hybrid circuit breakers (HCB). S‐HCB offers low conduction power loss like an HCB and ‐scale interruption time, which is even faster than an SSCB. It uses a pulse transformer to isolate the lower‐voltage high‐inductance power electronic circuit from the high‐voltage, low‐inductance main power loop. This paper provides analysis of the impact of the S‐HCB circuit components on the overall system performance and a scalable S‐HCB design guide for different DC system voltage and current ratings. In addition, system energy flow analysis is performed in the time domain to provide an understanding of how energy is delivered, dissipated, and released throughout the entire fault interruption process. The S‐HCB prototype was experimentally tested at 3 kV/30 A and 6 kV/150A with the results showing the interruption of the low fault current of 30 A and the high fault current of 150 A within 8 and maintaining the fault current at a near zero value for 300 to enable an arcless opening of a series mechanical switch. The key design challenges of S‐HCB at high voltage and high current ratings were discussed and possible solutions to mitigate those challenges were introduced.

The Gibbs-Helmholtz equation and the enthalpy–entropy compensation (EEC) phenomenon in the formation of micelles in an aqueous solution of surfactants and the cloud point effect

Here, the hidden thermodynamic processes in the micellization of an aqueous surfactant solution are analyzed analytically. Based on the hidden processes, an equation for the self-association process was obtained, which represents the Gibbs-Helmholtz equation. For the existence of the enthalpy–entropy compensation (EEC) effect, the temperature dependence of the Gibbs free energy of micellization is necessary. In order to compare the EEC parameters of the clouding point effect with the EEC parameters of micellization, it is necessary to observe the clouding process in the direction of the surfactant transfer from the aqueous solution to the crystalline phase (opposite to what is usual in the literature). EEC parameter b (enthalpy change when entropy changes have the zero value) in the micellization process always has a negative value. In contrast, in the clouding point effect process, it can have both positive and negative values.

Load Flow Calculation For Electrical Power System Based On Run Length Encoding Algorithm

This paper presents Data Compression Simulated algorithm for load flow calculation in electrical power systems. Real time monitor of grids required less computation time in calculation of power system analysis. Load flow problem is heart of this analysis and it basically required calculate active and reactive power flow in lines connected between buses in networks. Many topology and structures for Transmission and Distribution Systems has been proposed to reduce CPU times and memory. The proposed algorithm used Data compression technique tested different systems and results shows it is efficiency. More accuracy for large systems will need more iterations calculation which mean increasing time consumption, while Run Length Encoding (RLE) algorithm is fitness to optimized calculation numbers to exact number cause it has no zero values included. Network structure was represented as one dimension vector instead of 2D Matrix and it is effectiveness results was valid, by avoid exponential increased, by utilized this algorithm. Matlab results obtained by applied this algorithm match theoretical results.

Time series regression models for zero-inflated proportions

Time series of proportions are often encountered in applications such as ecology, environmental science and public health. Strategies for such data include linear regression after logistic transformation. Though easy to fit, the transformation approach renders covariate effects uninterpretable on the scale on which they were observed owing to Jensen's inequality. An alternative to the transformation approach has been to directly model the response via the beta distribution. In this paper, we extend zero-inflated beta regression models for independent proportions to time series data that is bounded over the unit interval and that may take on zero values. Estimation is within the partial-likelihood framework and is computationally feasible to implement. We outline the asymptotic theory of our maximum partial likelihood estimators under mild regularity conditions and investigate their bias and variability using simulation studies. The utility of our method is illustrated using two real data examples.

Deep learning analysis of smart meter data from a small sample of room air conditioners facilitates routine assessment of their operational efficiency

Room air conditioners (RACs) are crucial household appliances that consume substantial amounts of electricity. Their efficiency tends to deteriorate over time, resulting in unnecessary energy wastage. Smart meters have become popular to monitor electricity use of home appliances, offering underexplored opportunities to evaluate RAC operational efficiency. Traditional supervised data-driven analysis methods necessitate a large sample size of RACs and their efficiency, which can be challenging to acquire. Additionally, the prevalence of zero values when RACs are off can skew training. To overcome these challenges, we assembled a dataset comprising a limited number of window-type RACs with measured operational efficiencies from 2021. We devised an intuitive zero filter and resampling protocol to process smart meter data and increase training samples. A deep learning-based encoder–decoder model was developed to evaluate RAC efficiency. Our findings suggest that our protocol and model accurately classify and regress RAC operational efficiency. We verified the usefulness of our approach by evaluating the RACs replaced in 2022 using 2022 smart meter data. Our case study demonstrates that repairing or replacing an inefficient RAC can save electricity by up to 17 %. Overall, our study offers a potential energy conservation solution by leveraging smart meters for regularly assessing RAC operational efficiency and facilitating smart preventive maintenance.

Assessment of linkage disequilibrium and haplotype block structure in indigenous cattle populations of Tamil Nadu, India usingwhole genome sequence data

Understanding the nature of linkage disequilibrium (LD) between molecular markers reflects the degree of non-random association between their alleles, which is essential for genome-wide association studies. The main objective of this study was to assess the chromosome-wise linkage disequilibrium and haplotype block structure in indigenous cattle breeds of Tamil Nadu, India. A total of 79 animals belonging to five indigenous cattle breeds were considered as single indigenous cattle population and three samples per breed were sequenced using Illumina NovaSeqTM 6000 and Illumina HiSeq 2500. The LD parameter (r2) was estimated for a total of 62,095,778 pair- wise SNPs for all autosomes. The chromosome-wise number of SNP pairs, mean r2±standard deviation and median r2 values were 2,141,234, 0.484±0.246 and 0.493, respectively. Chromosome-wise mean r2 of different distance bins were calculated and it showed the maximum r2 value of 0.839 at 31-70 kb distance for Chromosome 11 and minimum r2 value of zero for Chromosome 7, 11, 15, 20 and 29, respectively at a distance 71-100 kb. This study also revealed a total of 413, 277 haplotype blocks which covered 2.34% of the autosomal genome. There was a total of 1,589,118 SNPs distributed within the haplotype blocks, covering a total length of 53.62 mb. The results of the study suggest the need for breed-specific reference populations for indigenous cattle breeds, which have a greater density of molecular markers of economic significance and thereby to identify breed-specific haplotypes in future.

ScIALM: A method for sparse scRNA-seq expression matrix imputation using the Inexact Augmented Lagrange Multiplier with low error

Single-cell RNA sequencing (scRNA-seq) is a high-throughput sequencing technology that quantifies gene expression profiles of specific cell populations at the single-cell level, providing a foundation for studying cellular heterogeneity and patient pathological characteristics. It is effective for developmental, fertility, and disease studies. However, the cell-gene expression matrix of single-cell sequencing data is often sparse and contains numerous zero values. Some of the zero values derive from noise, where dropout noise has a large impact on downstream analysis. In this paper, we propose a method named scIALM for imputation recovery of sparse single-cell RNA data expression matrices, which employs the Inexact Augmented Lagrange Multiplier method to use sparse but clean (accurate) data to recover unknown entries in the matrix. We perform experimental analysis on four datasets, calling the expression matrix after Quality Control (QC) as the original matrix, and comparing the performance of scIALM with six other methods using mean squared error (MSE), mean absolute error (MAE), Pearson correlation coefficient (PCC), and cosine similarity (CS). Our results demonstrate that scIALM accurately recovers the original data of the matrix with an error of 10e-4, and the mean value of the four metrics reaches 4.5072 (MSE), 0.765 (MAE), 0.8701 (PCC), 0.8896 (CS). In addition, at 10%-50% random masking noise, scIALM is the least sensitive to the masking ratio. For downstream analysis, this study uses adjusted rand index (ARI) and normalized mutual information (NMI) to evaluate the clustering effect, and the results are improved on three datasets containing real cluster labels.

Analysis of the Thermal Regime of a Residential Building Premises after an Emergency Shutdown of the Heating System

Maintaining favorable microclimatic conditions in a residential dwelling is directly related to the stable operation of the heating system. An emergency shutdown of the heating system, especially in the winter season, can lead to serious negative consequences: disruption of thermal comfort for people in the residential building, rupture of pipelines and heating devices, flooding of adjacent premises, etc. Thus, the issue of predicting the thermal regime of a residential dwelling after the termination of heat supply is of practical relevance. The object of study: Residential premises in buildings. Subject of study: Patterns of change in the temperature of the indoor air (in dimensional and dimensionless forms), the rate of temperature drop, and the loss of thermal energy during an emergency shutdown of the heating system. Objective of the study: To forecast the thermal regime of a room in a residential building after an emergency shutdown of the heating system. Research methods: Classical theory of thermal stability of building enclosures; theory of regular thermal regime, according to which the temperature field at all points of the cooled body (in this case, the heating system) changes in the same way, obeying the exponential law; software computer calculations. Research results: In a room of a residential building, the indoor air temperature will reach the dew point (12.5 ℃) within 8 hours 42 minutes after an emergency shutdown of the heating system and zero value - after 23 hours 42 minutes. Based on the conducted scientific research, it can be stated that the thermal storage capacity of external enclosures, due to their design features, plays a primary role in preserving the thermal regime of a residential dwelling after the heating system is turned off.

ScCAN: Clustering With Adaptive Neighbor-Based Imputation Method for Single-Cell RNA-Seq Data.

Single-cell RNA sequencing (scRNA-seq) is widely used to study cellular heterogeneity in different samples. However, due to technical deficiencies, dropout events often result in zero gene expression values in the gene expression matrix. In this paper, we propose a new imputation method called scCAN, based on adaptive neighborhood clustering, to estimate the zero value of dropouts. Our method continuously updates cell-cell similarity information by simultaneously learning similarity relationships, clustering structures, and imposing new rank constraints on the Laplacian matrix of the similarity matrix, improving the imputation of dropout zero values. To evaluate the performance of this method, we used four simulated and eight real scRNA-seq data for downstream analyses, including cell clustering, recovered gene expression, and reconstructed cell trajectories. Our method improves the performance of the downstream analysis and is better than other imputation methods.

New insights into the effects of small permanent charge on ionic flows: A higher order analysis.

This study investigated how permanent charges influence the dynamics of ionic channels. Using a quasi-one-dimensional classical Poisson-Nernst-Planck (PNP) model, we investigated the behavior of two distinct ion species-one positively charged and the other negatively charged. The spatial distribution of permanent charges was characterized by zero values at the channel ends and a constant charge $ Q_0 $ within the central region. By treating the classical PNP model as a boundary value problem (BVP) for a singularly perturbed system, the singular orbit of the BVP depended on $ Q_0 $ in a regular way. We therefore explored the solution space in the presence of a small permanent charge, uncovering a systematic dependence on this parameter. Our analysis employed a rigorous perturbation approach to reveal higher-order effects originating from the permanent charges. Through this investigation, we shed light on the intricate interplay among boundary conditions and permanent charges, providing insights into their impact on the behavior of ionic current, fluxes, and flux ratios. We derived the quadratic solutions in terms of permanent charge, which were notably more intricate compared to the linear solutions. Through computational tools, we investigated the impact of these quadratic solutions on fluxes, current-voltage relations, and flux ratios, conducting a thorough analysis of the results. These novel findings contributed to a deeper comprehension of ionic flow dynamics and hold potential implications for enhancing the design and optimization of ion channel-based technologies.

A Novel Low-Pressure Robotic Glove Based on CT-Optimized Finger Joint Kinematic Model for Long-Term Rehabilitation of Stroke Patients.

Wearing robotic gloves has become increasingly crucial for hand rehabilitation in stroke patients. However, traditional robotic gloves can exert additional pressure on the hand, such as prolonged use leading to poor blood circulation and muscle stiffness. To address these concerns, this work analyzes the finger kinematic model based on computerized tomography (CT) images of human hands, and designs a low-pressure robotic glove that conforms to finger kinematic characteristics. Firstly, physiological data on finger joint flexion and extension were collected through CT scans. The equivalent rotation centers of finger joints were obtained using the SURF and RANSAC algorithms. Furthermore, the trajectory of finger joint end and the correlation equation of finger joint motion were fitted, and a comprehensive finger kinematic model was established. Based on this finger kinematic model, a novel under-actuated exoskeleton mechanism was designed using a human-machine integration approach. The novel robotic glove fully aligns with the equivalent rotation centers and natural motion trajectories of the fingers, exerting minimal and evenly distributed dynamic pressure on the fingers, with a theoretical static pressure value of zero. Experiments involving gripping everyday objects demonstrated that the novel robotic glove significantly reduces the overall pressure on the fingers during grasping compared to the pneumatic glove and the traditional exoskeleton robotic glove. It is suitable for long-term use by stroke patients for rehabilitation training.

Refined model of leather

The structure of finished leather and semi-finished products differ in physical and mechanical parameters. To obtain the properties of elasticity, pliability, and viscosity required in leather production, it is necessary to ensure their values that guarantee the high quality of the resulting product. In this article, a model for finished leather is obtained based on combining the viscoelastic Kelvin model with the deformationinert Khusanov model and the Bingham–Shvedov viscoplastic model. This model makes it possible to improve the elastic-viscous plastic deformation of a fibrous material (leather). The possibility of clarifying the pattern of change in the deformation curve depending on the time the fibrous material is loaded by a certain force was explored. The aim of refining the deformation curve is that in known models, at the initial moment of loading, deformation occurs instantly up to a certain value, which does not fully describe the real physical process of deformation of the material. Consequently, in the proposed refined model, the deformation is fixedat zero value, which provides a real physical pattern of deformation of the fibrous material. The refined model will serve designers of technological machines to develop parameters of deformation during the mechanical processing of elastic-viscous plastic materials.

Application of the Group Method of Data Handling Network in Intermittent Time Series Data Forecasting

Intermittent data, characterized by sporadic and irregular occurrences, have successive zero values in time series, had present unique challenges for modeling and analysis. Forecasting using intermittent data is not easy to do. This paper presents an application of the Group Method of Data Handling (GMDH) in modeling and forecasting intermittent data. GMDH models excel in capturing non-linear patterns, handling missing or sparse data, and adapting to changing dynamics. By iteratively selecting the most informative variables and estimating their coefficients, GMDH constructs a hierarchical network of interconnected models that can effectively handle intermittent data. The forecasting results show that GMDH is able to follow the pattern of actual data. The mean deviation accuracy analysis shows a value of 72.22%. The findings showcase the potential of GMDH as a valuable tool in addressing the challenges associated with intermittent data and offer insights into its application across various domains.

Ambient temperature anomalies induce electrocardiogram abnormalities: Findings from a nationwide longitudinal study

Electrocardiogram (ECG) outcomes serve as early manifestations of cardiovascular functional or structural changes. While temperature fluctuation has been demonstrated to be a risk factor for cardiovascular diseases, few epidemiological studies have reported its relationship with ECG outcomes. In this study, we employed temperature anomaly (TA) as an innovative indicator of temperature fluctuation to quantify its detrimental impacts on ECG outcomes. A longitudinal study design was conducted using the repeated ECG records of the China National Stroke Screening Survey from 2013 to 2019. Only individuals undergoing at least two ECG tests were included. The daily temperature was assimilated by combining three kinds of data: in situ observations, satellite remote sensing measurements and weather research forecast simulations. We used generalized estimating equations to control for autocorrelation among repeated records and to estimate the association between TA and the risk of ECG abnormalities. We found 6837 events of ECG abnormalities in 47,286 individuals with 102,030 visits. Each unit increment of TA increased the risk of ECG abnormalities [odds ratio (OR) = 1.009, 95% confidence interval (CI): 1.001–1.017] and the risk of myocardial ischemia (OR = 1.061, 95% CI: 1.012–1.111). Hierarchic analyses presented a similar association of TA with both ECG abnormalities (OR = 1.017, 95% CI: 1.008–1.026) and myocardial ischemia (OR = 1.061, 95%CI: 1.011–1.114) in Northern China, but not in Southern China. The exposure-response relationship was estimated as a U-shaped curve centered at the TA value of zero. Sudden warming tended to increase the risk of ECG abnormalities and myocardial ischemia, and sudden cooling tended to increase the risk of atrial fibrillation. All these detrimental effects of TA could be modified by specific individual characteristics. In summary, ambient temperature fluctuation increased the risk of ECG abnormalities. This result indicated that regular ECG tests could be an early-warning measure for monitoring the adverse health effects of temperature fluctuations.