Bias Factor Research Articles

Research on Low Temperature Drift Suppression of Micro-Machined Gyroscope Based on Self-Calibration Technology

Micro-machined gyroscope is an important inertial sensor, which has the advantages of high integration, small size and low power consumption. However, due to the temperature sensitivity of silicon and electronic devices, the bias and scale factor of micro-machined gyroscopes have temperature drift, which limits their engineering application the relatively large process error of micro-machining makes the frequency mismatch between driving mode and detection mode of micro-machined gyroscope, which not only deteriorates the mechanical sensitivity of the gyroscope, but also causes the frequency mismatch between driving mode and detection mode, it also leads to the quadrature error of driving coupling in gyro detection mode. Based on this, firstly, the relationship between the bias factor and the scale factor of MEMS gyroscope and its dynamic and electrical parameters is analyzed. Secondly, a self-calibration capacitance detection scheme based on triangular-electrode based (TEB) is proposed, and the temperature effect of the scheme is analyzed. Finally, the simulation results show that the temperature coefficient of the scale factor decreases from −8845 ppm/°C to 1660 ppm/°C when the temperature range is from −10 °C to 60 °C, the bias temperature coefficient decreased from −0.97°/s/°C to −0.42°/s/°C. The experimental results show that the scheme effectively reduces the temperature sensitivity of the bias and scale factor of the gyroscope.

Analysis of the Advancement and Practice of Smart Education Models on Adolescent Physical and Mental Health Education

Abstract This paper utilizes a correlation-based smart prediction model to construct a smart teaching model for mental health. In the ABaisSvd model, the GD algorithm is used to iteratively update the matrix and optimize it by minimizing the error between the reconstructed achievement matrix and the original achievement matrix. The potential impact of attendance behavior and bias factors on grade prediction was also considered, and the prediction function was implemented and optimized after analyzing the correlation between student grades, attendance behavior, and bias factors through dimensionality reduction. According to the analysis of smart psychological teaching practices, 8.1% and 0.65% of students at University X experienced moderate and severe psychological problems, respectively. The smart classroom achieved a score of more than 87 points for the dimensions of appropriate use of educational techniques and obvious educational effects. The level of smart classroom and mental health in Group I was significantly higher than before the experimental intervention. There was no significant change in Group II before and after the intervention. Still, the level of mental health after the intervention (1.75) was also slightly higher than that before the intervention (1.74), and more than half of the students got a sense of theoretical, practical, and emotional gain in wisdom teaching. To sum up, the wisdom education model in this paper has the potential to significantly improve the mental health of college students.

Cross Cultural Empathetic Behavior in Health Care Providers: A Review of 3 Countries.

This paper examines empathetic behavior in the United States, a strongly individualistic country, as contrasted with Hungary and Ethiopia, which are moderately individualistic and strongly collectivistic respectively. It suggests that empathy may have a wider than originally perceived application in diverse settings to combat factors of ethnic bias and discrimination that adversely impact health. Models that distinctly focus on the development of healthcare provider empathic care are needed to enable the needs of resource scarce regions of the world to be met, including pockets of the U.S. More investigation is warranted on how empathic behavior can positively impact health outcomes and disparities.

Temperature drift suppression for micro‐electro‐mechanical system gyroscope based on vibrational‐displacement control with harmonic amplitude ratio

AbstractMicro‐electro‐mechanical system gyroscope detection output is susceptible to drift due to ambient temperature variations. Furthermore, the drive‐mode vibration amplitude is affected by the signal pickup circuit and structural parameters, which significantly influence the temperature drift of the bias and scale factor (SF). This study proposes a vibration amplitude control method based on harmonic amplitude sideband‐ratio (SBR), namely SBR‐AGC, which characterizes the vibration amplitude using the harmonic amplitude ratio of the vibrational electrical signal. The constancy of vibration amplitude is maintained via closed‐loop control to suppress bias and SF temperature drift. The experimental results on cobweb‐like disk resonator gyroscope reveal that the temperature coefficient of bias in the SBR‐AGC mode lies within −20 to 60°C and decreases by 36%, and the temperature coefficient of the SF lies within −10 to 50°C and decreases by 49.7%. Therefore, the environmental adaptability of the gyroscope is effectively improved.

Attitudes Toward Implicit Bias Among Athletic Trainers.

Implicit biases are attitudes, emotions, or stereotypes that occur in an unconscious manner and have the potential to negatively affect behaviors, actions, and decisions. Recent studies have suggested that even when certain factors are controlled for, health care workers do not provide equitable care to patients from different demographics. When patients are not receiving equitable health care, there is a potential for disparities in patient-related outcomes. The purpose of this study was to determine attitudes toward implicit bias among athletic trainers. A secondary purpose of this study was to assess differences and correlations between attitudes toward implicit bias and demographic factors including age, years of experience, gender identity, sexual orientation, and race. Participants were recruited for this study by emailing athletic trainers from publicly available staff directories at institutions of higher education and high schools, and athletic training education program directors. The survey consisted of questions gathering demographic information and questions taken from the Attitudes Toward Implicit Bias Instrument. A total of 218 athletic trainers (age = 38 [11]y, years of certified experience = 14 [11]y) opened and completed the survey. On average, participants scored 71.0 [11.2] on the Attitudes Toward Implicit Bias Instrument. This mean score indicated that the average participant felt that implicit bias had the potential to negatively impact patient care and needed to be addressed through education. There was a significant, negligible negative correlation between age and attitudes toward implicit bias (r[216] = -.157, P = .02). Examining implicit bias among athletic trainers warrants further research to understand how implicit bias can negatively affect access to equitable health care opportunities. The development of high-quality interventions for identifying and addressing implicit bias is crucial to ensuring optimal patient outcomes in athletic training and all medical settings.

Study Progress and Influencing Factors of Bias in Ensemble Perception of Average Size

Study Progress and Influencing Factors of Bias in Ensemble Perception of Average Size

Designing machine learning for big data: A study to identify factors that increase the risk of ischemic stroke and prognosis in hypertensive patients.

Ischemic stroke (IS) accounts large amount of stroke incidence. The aim of this study was to discover the risk and prognostic factors that affecting the occurrence of IS in hypertensive patients. Study data were obtained from the Medical Information Mart for Intensive Care (MIMIC)-IV database. To avoid biased factors selection process, several approaches were studied including logistic regression, elastic net regression, random forest, correlation analysis, and multifactor logistic regression methods. And seven different machine-learning methods are used to construct predictive models. The performance of the developed models was evaluated using AUC (Area Under the Curve), prediction accuracy, precision, recall, F1 score, PPV (Positive Predictive Value) and NPV (Negative Predictive Value). Interaction analysis was conducted to explore potential relationships between influential factors. The study included 92,514 hypertensive patients, of which 1746 hypertensive patients experienced IS. The Gradient Boosted Decision Tree (GBDT) model outperformed the other prediction model terms of prediction accuracy and AUC values in both ischemic and prognosis cases. By using the SHapley Additive exPlanations (SHAP), we found that a range of factors and corresponding interactions between factors are important risk factors for IS and its prognosis in hypertensive patients. The study identified factors that increase the risk of IS and poor prognosis in hypertensive patients, which may provide guidance for clinical diagnosis and treatment.

Monte Carlo global variance reduction method combining source bias and weight window

The global tally problem has a wide range of applications in major research fields such as Monte Carlo simulations of pin-by-pin reactor models and time-dependent particle transport problems in multi-physics coupling calculations. Due to the uneven power distribution of the simulated system, the statistical errors of all tallies are unevenly distributed, resulting in some low global efficiency. For this kind of problem with global characteristics, it is necessary to develop global variance reduction techniques to obtain the accurate distribution of target tallies in the entire space. A large number of global variance reduction algorithms have been studied based on the consideration of flattening global tally error distribution, so as to improve global efficiency. This work focuses on the combination of two efficient global variance reduction algorithms, namely, the uniform tally density algorithm and the weight window algorithm, which belong to source bias and transport process bias, respectively. In tally, a method is proposed to adjust the weight window parameters by using the bias factor of the uniform tally density algorithm. Then, the weight window method will be used to reduce the weight fluctuation caused by the uniform tally density method. In this way, an organic combination of these two algorithms can be realized. A series of comparative tests are carried out based on the Hoogenboom-Martin pressurized water reactor benchmark, and it is verified that the hybrid global variance reduction algorithm proposed in this work is better than the single weight window algorithm or the uniform tally density algorithm. In terms of reducing the maximum error, the global efficiency of the hybrid algorithm is 2.6 times and 3 times that of the weight window algorithm and the uniform tally density algorithm, respectively. In addition, through the comparative analysis of computational asymmetry degree and computational efficiency, it is verified that the uniform tally density algorithm has better performance than the classical uniform fission site algorithm, and the performance advantages of the uniform tally density algorithm are quantitatively evaluated based on some new indicators. The results show that the hybrid global variance reduction algorithm proposed in this work can solve the global tally problem efficiently, thereby further promoting research in related fields.

Label-aware debiased causal reasoning for Natural Language Inference

Recently, researchers have argued that the impressive performance of Natural Language Inference (NLI) models is highly due to the spurious correlations existing in training data, which makes models vulnerable and poorly generalized. Some work has made preliminary debiased attempts by developing data-driven interventions or model-level debiased learning. Despite the progress, existing debiased methods either suffered from the high cost of data annotation processing, or required elaborate design to identify biased factors. By conducting detailed investigations and data analysis, we argue that label information can provide meaningful guidance to identify these spurious correlations in training data, which has not been paid enough attention. Thus, we design a novel Label-aware Debiased Causal Reasoning Network (LDCRN). Specifically, according to the data analysis, we first build a causal graph to describe causal relations and spurious correlations in NLI. Then, we employ an NLI model (e.g., RoBERTa) to calculate total causal effect of input sentences to labels. Meanwhile, we design a novel label-aware biased module to model spurious correlations and calculate their causal effect in a fine-grained manner. The debiasing process is realized by subtracting this causal effect from total causal effect. Finally, extensive experiments over two well-known NLI datasets and multiple human-annotated challenging test sets are conducted to prove the superiority of LDCRN. Moreover, we have developed novel challenging test sets based on MultiNLI to facilitate the community.

Modelling the Effect of mercury on the Growth Rate of an SDS-degrading Pseudomonas sp. strain Maninjau1

The SDS-degrading bacterium Pseudomonas sp. strain Maninjau1 experienced significant inhibition by mercury. Observations of bacterial growth at varying mercury concentrations revealed a sigmoidal pattern, with lag periods extending from 7 to 12 hours. Increasing mercury concentrations progressively impeded growth, with a concentration of 1.0 mg/L nearly halting bacterial activity. To analyze these effects, the modified Gompertz model was employed to determine growth rates across different mercury concentrations. These rates were then subjected to several models, including the modified Han-Levenspiel, Wang, Liu, Shukor, modified Andrews, and Amor models. Among these, the Amor model failed to appropriately fit the growth curves. Statistical analysis indicated that the Shukor model performed the best, evidenced by the lowest values for Root Mean Square Error (RMSE) and Akaike’s Information Criterion corrected (AICc), the highest adjusted correlation coefficient (adR2), and values of Accuracy Factor (AF) and Bias Factor (BF) closest to unity. The parameters obtained from the Shukor model, which are mmax (h-1) and Sm (mg L-1) and n which represent maximum growth rate, critical heavy metal ion concentration and empirical constant values were 0.187, 1.126 and 2.406, respectively. The Shukor model allows for the prediction of the critical heavy metals concentration which can completely inhibited bacterial growth. This robust modeling approach underscores the Shukor model's suitability for predicting the impact of mercury on the growth dynamics of Pseudomonas sp. strain Maninjau1 under toxic stress conditions.

Modeling the Degradation Rate of the Azo Dye Congo Red by Acinetobacter baumannii

One of the challenges that face the textile industry is the release of effluents that are not wanted, most notably colors that do not degrade. This is one of the issues that plagues the industry. This is a concern since it affects the environment. Bioremediation using dye-degrading bacterium is appealing as bacterial metabolism converts hazardous dye to harmless carbon dioxide and water as byproducts. In this study, various secondary growth models such as Luong, Yano, Teissier-Edward, Aiba, Haldane, Monod, Han, and Levenspiel were employed. Following thorough statistical analyses such as root-mean-square error (RMSE), adjusted coefficient of determination (adjR2), bias factor (BF), and accuracy factor (AF), the Luong model emerged as the most optimal choice. The half-saturation constant for maximal growth, maximal growth rate and maximal concentration of substrate tolerated and curve parameter that defines the steepness of the growth rate decline from the maximum rate symbolized by Ks, qmax and Sm, and n were 76.54 mg/L (95% C.I., 50.51 to 102.57), 0.240 per h (95% C.I., 0.219 to 0.270), 1135.37 mg/L (95% C.I., 1041.04 to 1229.72) and 5.34 (95% C.I., 2.36 to 8.32), respectively. These novel constants discovered during the modeling process could serve as valuable inputs for subsequent modeling pursuits.

Primary Mathematical Modeling of the Growth of Diesel by a Bacterium Isolated from a Hydrocarbon-contaminated Soil

Mathematical modeling of microbial growth via nonlinear regression is essential for determining key parameters such as the maximum specific growth rate, which are foundational for secondary modeling. Models such as modified Gompertz, modified Logistic, modified Richards, Buchanan-3-phase, Baranyi-Roberts, modified Schnute, von Bertalanffy, Morgan-Mercer-Flodin (MMF), and Huang elucidate the impact of substrates on bacterial growth and biotransformation processes, vital for biotechnological applications like wastewater treatment and bioremediation. A previously isolated diesel-degrading Pseudomonas sp. strain Neni-4 growth on diesel was modeled using the aforementioned primary models. Experimental data showed that diesel concentrations from 0.25 to 3.5% (v/v) are toxic, slowing bacterial growth and increasing lag periods from 3 to 15 hours. Among the primary models tested, the Baranyi-Roberts model provided the best fit, evidenced by a high adjusted coefficient of determination, low RMSE and AICc values, and favorable accuracy (AF) and bias factors (BF). The reliability of the Baranyi-Roberts model underscores its suitability for modeling bacterial growth under toxic conditions, offering valuable insights for optimizing biotechnological processes involving bacterial adaptation and growth under stress conditions.

Primary Modeling of Microbial Growth under Toxic Conditions with the Modified Schnute Model

Primary modeling of microbial growth is essential for determining key parameters such as the maximum specific growth rate (μm), which are foundational for secondary modeling. These models, including those by Monod, Haldane, Aiba, and Teissier, elucidate the impact of substrates on bacterial growth and biotransformation processes, vital for biotechnological applications like wastewater treatment and bioremediation. Experimental data showed that acrylamide from 250 to 1250 mg/L as a sole nitrogen source is toxic, slowing bacterial growth at higher concentrations resulting in an increase in lag periods ranging from 3 to 9 hours. Various primary models were tested, with the modified Schnute model providing the best fit based on statistical analysis, normality tests, and key parameters such as adjusted coefficient of determination near to unity, lowest values for RMSE and AICc values and good values of accuracy (AF) and bias factors (BF). The modified Schnute model's reliability underscores its suitability for modeling bacterial growth under toxic conditions, offering valuable insights for optimizing biotechnological processes involving bacterial adaptation and growth under stress conditions.

Financial Literacy and Behavioral Bias Towards Consumptive Behavior: Evidence from Bandung, Indonesia

Bandung is a city with a notably elevated per capita spending rate. It denotes a considerable degree of consumptive behavior, which can be attributed to various factors. This research delves into the impact of financial literacy and behavioral bias on consumptive behavior within the productive-age population of Bandung. Employing a quantitative methodology, the study gathered data by administering surveys to 400 participants using the Slovin formula and purposive sampling methods. The data analysis was conducted through multiple linear regression. It reveals that financial literacy and behavioral bias possess significant partial and simultaneous influences on consumptive behavior. The study's results suggest that the Financial Services Authority (OJK) needs to enhance financial education initiatives among the productive-age population. Its implications for future research involve supplementary independent variables, such as framing bias and demographic factors.

Financial Literacy and Behavioral Bias Towards Consumptive Behavior: Evidence from Bandung, Indonesia

Bandung is a city with a notably elevated per capita spending rate. It denotes a considerable degree of consumptive behavior, which can be attributed to various factors. This research delves into the impact of financial literacy and behavioral bias on consumptive behavior within the productive-age population of Bandung. Employing a quantitative methodology, the study gathered data by administering surveys to 400 participants using the Slovin formula and purposive sampling methods. The data analysis was conducted through multiple linear regression. It reveals that financial literacy and behavioral bias possess significant partial and simultaneous influences on consumptive behavior. The study's results suggest that the Financial Services Authority (OJK) needs to enhance financial education initiatives among the productive-age population. Its implications for future research involve supplementary independent variables, such as framing bias and demographic factors.

Enhancing accuracy and privacy in speech-based depression detection through speaker disentanglement

Speech signals are valuable biomarkers for assessing an individual’s mental health, including identifying Major Depressive Disorder (MDD) automatically. A frequently used approach in this regard is to employ features related to speaker identity, such as speaker-embeddings. However, over-reliance on speaker identity features in mental health screening systems can compromise patient privacy. Moreover, some aspects of speaker identity may not be relevant for depression detection and could serve as a bias factor that hampers system performance. To overcome these limitations, we propose disentangling speaker-identity information from depression-related information. Specifically, we present four distinct disentanglement methods to achieve this — adversarial speaker identification (SID)-loss maximization (ADV), SID-loss equalization with variance (LEV), SID-loss equalization using Cross-Entropy (LECE) and SID-loss equalization using KL divergence (LEKLD). Our experiments, which incorporated diverse input features and model architectures, have yielded improved F1 scores for MDD detection and voice-privacy attributes, as quantified by Gain in Voice Distinctiveness (GVD) and De-Identification Scores (DeID). On the DAIC-WOZ dataset (English), LECE using ComparE16 features results in the best F1-Scores of 80% which represents the audio-only SOTA depression detection F1-Score along with a GVD of −1.1 dB and a DeID of 85%. On the EATD dataset (Mandarin), ADV using raw-audio signal achieves an F1-Score of 72.38% surpassing multi-modal SOTA along with a GVD of −0.89 dB dB and a DeID of 51.21%. By reducing the dependence on speaker-identity-related features, our method offers a promising direction for speech-based depression detection that preserves patient privacy.

Effects of spore purity on the wet heat resistance of Clostridium perfringens, Bacillus cereus and Bacillus subtilis spores

Heat resistance of spores of Clostridium perfringens 8238 (Hobbs Serotype 2), Bacillus cereus NCTC 11143 (4810/72), and Bacillus subtilis PS533, an isogenic derivative of strain PS832 (a 168 strain) was determined in ground beef at 95 °C. Spore purification was by centrifugation and washing with sterile distilled water (dH2O), followed by sonication and then Histodenz centrifugation for B. subtilis and C. perfringens, and centrifugation and washing with sterile dH2O followed by Histodenz centrifugation for B. cereus. Bags containing inoculated beef samples were submerged in a temperature-controlled water bath and held at 95 °C for predetermined lengths of time. Surviving spore populations were enumerated by plating on mannitol egg yolk polymyxin agar (MYP) agar plates for B. cereus and B. subtilis, and on tryptose-sulfite-cycloserine agar (TSC) agar plates for C. perfringens. Survivor curves were fitted to linear, linear with tail, and Weibull models using the USDA Integrated Pathogen Modeling Program (IPMP) 2013 software. The Weibull model provided a relatively better fit to the data since the root mean square error (RMSE), mean square error (MSE), sum of squared errors (SSE), and Akaike information criterion (AIC) values were lower than the values obtained using the linear or the linear with tail models. Additionally, the Weibull model accurately predicted the observed D-values at 95 °C for the three spore-formers since the accuracy factor (Af) values ranged from 1.03 to 1.08 and the bias factor (Bf) values were either 1.00 or 1.01. Times at 95 °C to achieve a 3-log reduction decreased from 206 min for C. perfringens spores purified with water washes alone to 191 min with water washes followed by sonication and Histodenz centrifugation, from 7.9 min for B. cereus spores purified with water washes alone to 1.4 min with water washes followed by Histodenz centrifugation, and from 20.6 min for B. subtilis spores purified with water washes alone to 6.7 min for water washes followed by sonication and Histodenz centrifugation. Thermal-death-time values reported in this study will assist food processors to design thermal processes to guard against bacterial spores in cooked foods. In addition, clearly spore purity is an additional factor in spore wet heat resistance, although the cause of this effect is not clear.

Nucleic acid sensing promotes inflammatory monocyte migration through biased coagulation factor VIIa signaling

AbstractProtease activated receptors (PARs) are cleaved by coagulation proteases and thereby connect hemostasis with innate immune responses. Signaling of the tissue factor (TF) complex with factor VIIa (FVIIa) via PAR2 stimulates extracellular signal-regulated kinase (ERK) activation and cancer cell migration, but functions of cell autonomous TF-FVIIa signaling in immune cells are unknown. Here, we show that myeloid cell expression of FVII but not of FX is crucial for inflammatory cell recruitment to the alveolar space after challenge with the double-stranded viral RNA mimic polyinosinic:polycytidylic acid [Poly(I:C)]. In line with these data, genetically modified mice completely resistant to PAR2 cleavage but not FXa-resistant PAR2–mutant mice are protected from lung inflammation. Poly(I:C)-stimulated migration of monocytes/macrophages is dependent on ERK activation and mitochondrial antiviral signaling (MAVS) but independent of toll-like receptor 3 (TLR3). Monocyte/macrophage-synthesized FVIIa cleaving PAR2 is required for integrin αMβ2-dependent migration on fibrinogen but not for integrin β1-dependent migration on fibronectin. To further dissect the downstream signaling pathway, we generated PAR2S365/T368A-mutant mice deficient in β-arrestin recruitment and ERK scaffolding. This mutation reduces cytosolic, but not nuclear ERK phosphorylation by Poly(I:C) stimulation, and prevents macrophage migration on fibrinogen but not fibronectin after stimulation with Poly(I:C) or CpG-B, a single-stranded DNA TLR9 agonist. In addition, PAR2S365/T368A-mutant mice display markedly reduced immune cell recruitment to the alveolar space after Poly(I:C) challenge. These results identify TF-FVIIa-PAR2-β-arrestin–biased signaling as a driver for lung infiltration in response to viral nucleic acids and suggest potential therapeutic interventions specifically targeting TF-VIIa signaling in thrombo-inflammation.

Analisis Kelonggaran Dispensasi Kawin dalam Pernikahan Dini Menggunakan Vos- Viewer

Analysis of marital dispensation leniency in early marriage using vos-viewer. This study aims to determine the development and research opportunities of marital dispensation in early marriage in Google Scholar-indexed journals which are conducted using Publish or Perish software using the keywords early marriage, marriage dispensation, internet, mass media, gender, poverty, education, family, religious courts, requests for dispensation from marriage, culture, gender bias, unwanted pregnancy, domestic violence, adolescent sexuality, and customary factors. To analyze the development of the number of publications, author productivity, and top articles that are most frequently cited, we used Microsoft Excel. Additionally, to analyze the relationship between research topics, research trends, and research topics that are most often researched, we used VOSViewer. In this search, 2660 articles obtained from searches with publish or perish were grouped into 8 clusters, each of which is assigned to a certain color. The resulting density visualization map shows the opportunity to determine the topic that we selected.

THE INFLUENCE OF FINANCE KNOWLEDGE, FINANCIAL LITERACY, INDIVIDUAL EMOTIONAL INTELLIGENCE, AND PSYCHOLOGICAL BIAS FACTORS ON PERSONAL FINANCIAL MANAGEMENT

This study aims to determine the effect of Financial Knowledge, Financial Literacy, Individual Emotional Intelligence and Psychological Bias Factors on Personal Financial Management. The subject of this research is the z generation in Buleleng Regency, Bali. This research is a quantitative study using multiple regression analysis techniques, classical assumption test, analysis of the coefficient of determination test, and the t-test (Partial). The results of the study show that Financial Knowledge, Financial Literacy, Individual Emotional Intelligence and Psychological Bias Factors influence Personal Financial Management in the z generation in Buleleng Regency with an adjusted R-Square value of 0.508 or 50.8%. Proving the hypothesis is done by comparison between t-table and t-count in each regression model.