Higher Degree Of Discrimination Research Articles

Deep Attention Networks With Multi-Temporal Information Fusion for Sleep Apnea Detection.

Sleep Apnea (SA) is a prevalent sleep disorder with multifaceted etiologies that can have severe consequences for patients. Diagnosing SA traditionally relies on the in-laboratory polysomnogram (PSG), which records various human physiological activities overnight. SA diagnosis involves manual scoring by qualified physicians. Traditional machine learning methods for SA detection depend on hand-crafted features, making feature selection pivotal for downstream classification tasks. In recent years, deep learning has gained popularity in SA detection due to its capability for automatic feature extraction and superior classification accuracy. This study introduces a Deep Attention Network with Multi-Temporal Information Fusion (DAN-MTIF) for SA detection using single-lead electrocardiogram (ECG) signals. This framework utilizes three 1D convolutional neural network (CNN) blocks to extract features from R-R intervals and R-peak amplitudes using segments of varying lengths. Recognizing that features derived from different temporal scales vary in their contribution to classification, we integrate a multi-head attention module with a self-attention mechanism to learn the weights for each feature vector. Comprehensive experiments and comparisons between two paradigms of classical machine learning approaches and deep learning approaches are conducted. Our experiment results demonstrate that (1) compared with benchmark methods, the proposed DAN-MTIF exhibits excellent performance with 0.9106 accuracy, 0.9396 precision, 0.8470 sensitivity, 0.9588 specificity, and 0.8909 [Formula: see text] score at per-segment level; (2) DAN-MTIF can effectively extract features with a higher degree of discrimination from ECG segments of multiple timescales than those with a single time scale, ensuring a better SA detection performance; (3) the overall performance of deep learning methods is better than the classical machine learning algorithms, highlighting the superior performance of deep learning approaches for SA detection.

A refined representation method of the particle size distribution curve based on grading entropy

The particle size distribution (PSD) curve is an important expression of the basic properties of the soil. The characteristic parameters such as median particle size D50, effective particle size D10, and some combinations of characteristic parameters can only represent some points of the PSD curve, failing to represent all information of the PSD curve. In this paper, a fraction characteristic parameter F that could reflect the fraction size variation of the PSD curve was introduced based on grading entropy. And a new presentation method of the A-B-F three parameters was proposed for a refined representation of the PSD curve. It was found that the newly constructed model not only better represents the differences in the width of PSD curves but also has a higher sensitivity than the A-B two-parameter model proposed by Lőrincz. For PSD curves with similar distributions, the new presentation method has a higher degree of discrimination than that of the D50-Cu-Cc three-parameter or the four-parameter combination Gc proposed by Arshad. Finally, the application of this new method in describing the spatial nonuniformity distribution of depositions and predicting the soil hydraulic conductivity was discussed. The research results can provide a reference for the refined representation of PSD curves.

장애인의 차별경험이 사회참여에 미치는 영향

This study reviews the effects of the discrimination experiences of the adults with disability on their social participation. Through its route, it verifies the mediation effects of the daily living ability. For this review, this study used data on the real state of the adults with disability collected in 2020 by the Korea Institute for Health and Social Affairs. Data samples were made by the stratified cluster sampling method. The major findings of this study are as follows: First, the daily living ability and social participation of the adults with disability show a statistically significant difference by gender, age, education, registered disability type, and marriage. Females show higher the daily living ability and social participation than males. On the other hand, discrimination experiences do not show a significant gender difference. Second, discrimination experiences affect social participation. With a higher degree of discrimination, they show lower social participation. Also, with a higher the daily living ability, they do more social participation. Third, discrimination experiences of the adults with disability directly affect their social participation. The daily living ability as a mediation role indirectly affects the adults with disability trial of social participation.

Case-based reasoning for shipwreck emergency salvage scheme assisted decision

Shipwrecks that adversely affect the marine ecological environment and the passage of vessels should be recovered urgently. The formulation of emergency salvage plans is still based on experts’ judgment. Therefore, this paper proposes an assisted decision model for the emergency salvage plan for wrecks based on case reasoning. This paper uses the Improved Game Theory (IGT) method to calculate the combined weight of multiple subjective and objective methods to overcome the one-sidedness of a single method to determine the weight. To further improve the sensitivity of the interval similarity calculation, this paper improves the method of interval similarity calculation in shipwreck cases. Based on the comparison and analysis of multiple methods based on the real shipwreck salvage case, the standard deviation of the case similarity obtained by the model method in this paper is 0.134, which is the highest compared with the classical algorithm, which shows that the similarity calculation results of this paper are reasonable and have a higher degree of discrimination. This method can provide decision support for the formulation of a marine emergency salvage plan for shipwrecks.

Leaf morphological characteristics of section Quercus based on geometric morphometric analysis.

We conducted leaf geometric morphometric analysis (GMMs) for five Quercus species (Section Quercus) of Fagaceae. In total, 887 leaves chosen from 182 individuals of 20 natural popu-lations were marked with GMMs. Leaf morphological characteristics of these samples were digitized to visualize leaf morphological differences. Generalized Procrustes analysis could effectively exclude the influence of leaf position and size on leaf shape. Results of principal component analysis at tree-level showed that the leaf morphology of Q. dentata was different with Q. aliena and Q. serrata. Canonical variates analysis at tree-level showed that leaf morphology of Q. aliena could be accurately distinguished from the other four species in leaf symmetric components. The results of multivariate analysis of asymmetrical components in leaves showed no distinction among the five species. The analysis at leaf-level showed that the two groups with a higher degree of discrimination were Q. aliena vs. Q. dentata (99.5% vs. 100%) and Q. dentata vs. Q. serrata (99.0% vs. 100%), which could be accurately distinguished by leaf shape. The two groups with a slightly lower degree of discrimination were Q. fabri vs. Q. serrata (90.5% vs. 86.8%) and Q. dentata vs. Quercus mongolica (85.1% vs. 82.9%). Our results provided new insights for the leaf shape identification among species with frequent hybridization and introgression.

Adaptive feature extraction and fault diagnosis for three-phase inverter based on hybrid-CNN models under variable operating conditions

The increasing reliability and availability requirements of power electronic systems have drawn great concern in many industrial applications. Aiming at the difficulty in fault characteristics extraction and fault modes classification of the three-phase full-bridge inverter (TFI) that used as the drive module of brushless DC motor (BLDCM). A hybrid convolutional neural network (HCNN) model consists of one-dimensional CNN (1D-CNN) and two-dimensional CNN (2D-CNN) is proposed in this paper, which can tap more effective spatial feature for TFI fault diagnosis. The frequency spectrum from the three-phase current signal preprocess are applied as the input for 1D-CNN and 2D-CNN to conduct feature extraction, respectively. Then, the feature layers information are combined in the fully connected layer of HCNN. Finally, the performance status of TFI could be identified by the softmax classifier with Adam optimizer. Several groups of experiments have been studied when the BLDCM under different operating conditions. The results show that the fusion features can get a higher degree of discrimination so as to the presented network model also obtains better classification accuracy, which verify the feasibility and superiority to the other networks.

Phylogenetic studies of larval digenean trematodes from freshwater snails and fish species in the proximity of Tshwane metropolitan, South Africa.

The classification and description of digenean trematodes are commonly accomplished by using morphological features, especially in adult stages. The aim of this study was to provide an analysis of the genetic composition of larval digenean trematodes using polymerase chain reaction (PCR) and sequence analysis. Deoxyribonucleic acid (DNA) was extracted from clinostomatid metacercaria, 27-spined echinostomatid redia, avian schistosome cercaria and strigeid metacercaria from various dams in the proximity of Tshwane metropolitan, South Africa. Polymerase chain reaction was performed using the extracted DNA with primers targeting various regions within the larval digenean trematodes’ genomes. Agarose gel electrophoresis technique was used to visualise the PCR products. The PCR products were sequenced on an Applied Bioinformatics (ABI) genetic analyser platform. Genetic information obtained from this study had a higher degree of discrimination than the morphological characteristics of seemingly similar organisms.

The efficacy of quantitative fetal fibronectin in predicting spontaneous preterm birth in symptomatic women: A retrospective cohort study.

Recent data suggest that quantitative measurements of fetal fibronectin can be used accurately to predict increased risk of preterm birth. The purpose of this study was to demonstrate that the quantification of fetal fibronectin improves diagnostic accuracy in women who present with symptoms suggestive of threatened preterm labour (TPL) using a quantitative fetal fibronectin (qfFN) bedside analyser. This was a retrospective cohort study of pregnant women who presented between 22+6 and 32+6 weeks gestation with symptoms of TPL who had qfFN measured using the Rapid fFN Q10 system. The ability to predict spontaneous preterm birth (sPTB) within 48h, 14days and <34weeks gestation at qfFN thresholds of 10, 50 and 200ng/mL was assessed. The overall rate of sPTB <34weeks was 4.1% (n=373). For deliveries within 48h, within 14days and <34weeks, a qfFN threshold of 200ng/mL had positive predictive values of 26.7%, 42.9% and 46.7%, respectively, when compared to patients with qfFN values of 0-9ng/mL. The corresponding relative risks were 68.5, 53.8 and 38.0, respectively CONCLUSION: Quantitative fetal fibronectin testing with thresholds of 10, 50 and 200ng/mL allows for more accurate prediction of preterm birth in symptomatic women. This higher degree of discrimination allows for more directed interventions for high-risk patients and reduces the cost and burden of unnecessary treatment for low-risk patients.

Discrimination against women in Turkish labor market: an analysis of gender wage gap with Blinder-Oaxaca and Juhn-Murphy-Pierce decomposition methods, 2004-2017 period

Purpose - The average education level of female in the labor force is higher than the education level of their male counterparts in Turkey. Since education level is the most important factor in the determination of wages, the average wage of female employees is expected to be higher than that of male employees. However, the data shows the reverse. In this paper, the gender wage gap in the 2004-2017 period is analyzed to understand what portion of the wage gap is due to discrimination and what portion is due to human capital factors. The sample used in the econometric analysis is the Household Labor Force Survey microdata in the 2004-2017 period. Blinder-Oaxaca and Juhn- Murphy-Pierce Decomposition methods are applied, and the results show that the gender wage gap in favor of male employees in the said period cannot be explained by human capital differences, but by discrimination against women in the labor market. Methodology - First wage equation regressions are set to see whether there is a difference between the effects and significance of explanatory variables on wages of male employees and females. Then, Blinder Oaxaca and Juhn Murphy Pierce decomposition methods are applied to see whether the wage gap can be explained by human capital differences or not. Findings- Data shows that there is a gender wage gap in favor of men in Turkish labor market and a strong discrimination against women. As for 2004, considering the human capital endowments, on average, women would have earned 5.2% more than what male employees earn. However, data show that male employees earn 15.1% more than what female employees earn, which can be interpreted that female employees have a wage-loss of about 20.3% due to discrimination. Both the wage gap and discrimination effects vary between regions. Most industrialized/developed and regions with high agriculture share in employment are usually the ones with larger gap and higher degree of discrimination. The wage gap and discrimination effects have declined until 2010, but they increased after that. Conclusion- Gender wage gap and discrimination based loss in wages are seem to be persistent so far in Turkish labor market, but as the average level of female employees increase the gap is expected to decline in long run.

Validation of a model to estimate survival in ambulatory patients with hepatocellular carcinoma: a single-centre cohort study.

Survival of patients with hepatocellular carcinoma (HCC) is determined by hepatic function and tumour extent. Recently, a new Model to Estimate Survival in Ambulatory HCC patients (MESIAH) was proposed to predict overall survival in ambulatory HCC patients. This study aimed to evaluate the prognostic performance of the MESIAH score in an independent cohort of HCC patients. A cohort of 1969 patients newly diagnosed with HCC at the National Cancer Center, Korea between January 2004 and December 2009 was used for validation of the MESIAH score. The model's performance was assessed using C-statistics, the likelihood ratio (LR) χ2 value and Akaike information criterion (AIC). Patients in the cohort had a median age of 56 years and 83.2% were men. Hepatitis B virus infection was present in 74.6 and 81.6% had a Child-Pugh class A. The median overall survival was 21.4 months. The MESIAH score had a higher degree of discrimination, with a C-statistic of 0.792 [95% confidence interval (CI), 0.782-0.803], when compared with the Barcelona Clinic Liver Cancer (BCLC) staging system [0.665 (95% CI, 0.653-0.678), P<0.001]. The LR χ2 value and the AIC of MESIAH were also better than those of BCLC, Cancer of the Liver Italian Program, Japan Integrated Scoring and Tokyo score. The observed survival in the cohort closely matched that predicted by the MESIAH score. The new prognostication model MESIAH accurately estimated the overall survival of Korean HCC patients and may be useful in future research as well as individual patient care.

Development, standardization and assessment of PCR systems for purity testing of avian viral vaccines

The European Pharmacopoeia (Ph. Eur.) requires avian viral vaccines to be free of adventitious agents. Purity testing is an essential quality requirement of immunological veterinary medicinal products (IVMPs) and testing for extraneous agents includes monitoring for many different viruses. Conventional virus detection methods include serology or virus culture, however, molecular tests have become a valid alternative testing method. Nucleic acid testing (NAT) is fast, highly sensitive and has a higher degree of discrimination than conventional approaches. These advantages have led to the development and standardization of polymerase chain reaction (PCR) assays for the detection of avian leucosis virus, avian orthoreovirus, infectious bursal disease virus, infectious bronchitis virus, Newcastle disease virus, infectious laryngotracheitis virus, influenza A virus, Marek's disease virus, turkey rhinotracheitis virus, egg drop syndrome virus, chicken anaemia virus, avian adenovirus and avian encephalomyelitis virus. This paper reviews the development, standardization and assessment of PCR for extraneous agent testing in IVMPs with examples from an Official Medicines Control Laboratory (OMCL).

Invariant Description and Retrieval of Planar Shapes Using Radon Composite Features

This paper proposes a novel feature-based invariant descriptor termed Radon composite features (RCFs) for planar shapes. Instead of analyzing shapes directly in the spatial domain, some shape features are extracted from the Radon transform plane using statistical and spectral analysis. The proposed method overcomes the drawbacks of existing shape representation techniques since it accomplishes the invariances to common geometrical transformations without any normalization process, which usually causes inaccuracies. A novel hierarchical strategy with RCFs can achieve low complexity and coarse-to-fine retrieval, and perform accurately when retrieving shapes, while remaining robust against variations. Experiments demonstrate that RCF provides a higher degree of discrimination as compared with several state-of-the-art approaches.

Relationship between molecular and enological features of Patagonian wine yeasts: relevance in selection protocols

In this paper, a study of the relationship between genetic patterns, obtained by the combination of mtDNA-RFLP and PCR-amplified inter-δ sequence DNA polymorphism analysis, and relevant enological phenotypic data (fermentative power, specific productivity, volatile and total acidity) was carried out on Argentinean Saccharomyces cerevisiae isolates from north Patagonia. The use of a powerful statistical tool, Generalized Procrustes analysis, allowed us to weigh the relationship for each isolate in particular, denoting a good enough degree of agreement between molecular and physiological data for most of the population analysed. The inclusion of a physiological feature, as the killer sensitivity biotype, within identification methods resulted in a higher degree of discrimination among isolates and in better correlation between both characterizations. The combined use of methods based on molecular polymorphisms and killer biotype could be applied so as not to miss any isolate with differential enological properties in selection protocols.

Occurrence of Bacillus species on evergreen oak leaves

Thirty-five spore-forming bacilli were isolated from evergreen oak leaves at four stages (one-year-old and two-year-old leaves, upper layer and underlayer little leaves) and over the four seasons within one year. These isolates, plus five reference strains, were characterized morphologically and physiologically by a total of 100 tests and genetically by DNA/DNA hybridization. Phenotypic similarities of all strains were determined by numerical taxonomy, with the unweighted average linkage (UPGMA) algorithm and simple matching (SSM) and Jaccard (SJ) coefficients used as measures for similarity. Three groups (A to C) were defined at a similarity level of 71% (SSM) or 57% (SJ). They contained leaf isolates phenotypically related to Bacillus cereus, B. pumilus and B. circulans species respectively. The majority of the leaf isolates were asigned to B. cereus (34%) and B. pumilus (63%). DNA/DNA hybridization also discriminated three groups (genomic groups 1, 2 and unclassified strains) which presented a good correlation with the numerical analysis. Yet, DNA/DNA hybridization grouping revealed a higher degree of discrimination by defining four subgroups (1a, 1b, 2a, and 2b). Genomic subgroup 1a contained leaf isolates belonging to the B. cereus species; isolates from genomic subgroup 1b belonged to B. mycoides species and isolates from genomic subgroup 2a belonged to B. pumilus species. Subgroup 2b consisted of a new genomic subspecies of B. pumilus which exhibited a degree of homology ranging from 53 to 64% with the B. pumilus type strain and a coefficient of divergence (ΔTm) ranging from 5.5 to 7°C. The different genomic groups presented different substrate metabolism capacities and a different spatial distribution on evergreen oak leaves. B. cereus strains (group 1) were predominantly located on litter leaves whereas B. pumilus strain (group 2) were found on the phyllosphere. In contrast with group 1, group 2 was able to metabolize some sugars and pectin, while group 1 isolates were able to hydrolyze starch and glycogen. Thus, our hypothesis is that group 1 succeeds group 2 when the leaves are littered.

Discrimination of planar shapes using shape matrices

An algorithm is presented for describing and discriminating binary images of planar shapes. The descriptor is a matrix that the dimensions dependent on the maximum radius of the shape. The descriptor is obtained by a polar quantization of the shape and is independent of the shape's position, orientation, and size. The descriptors discriminate two or more shapes by measuring their degree of similarity. The shapes considered include letters of the alphabet, numerals, geometrical figures, and physical objects. The results obtained for the letters were compared to those reported by A. Goshtasby (1985). The comparison showed that the proposed algorithm provides a higher degree of discrimination.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Operant conditioning studies of taste discrimination in the pigeon ( Columba livia)

In the conditioning procedure employed in this study, pigeons were trained to dip their bills into a container and then to peck either at a left or at a right key, depending on whether the container was full of distilled water or of a solution of the substance being tested. The discrimination thresholds found (from one bird each) were 2 g/l (0.034 M) for NaCl, 2 g/l (0.024 M) for NaHCO 3, 1 g/l (0.013 M) for KCl, 0.5 g/l (0.005 M) for KHCO 3. Following the same procedure, two birds were then faced with the choice between the solution of the known substance and of a novel one. A higher degree of discrimination was found between NaHCO 3 and KHCO 3 than between NaHCO 3 and NaCl; in the latter case, choices appeared to be mostly based on the concentration of the two substances.