Variations In Tone Research Articles

Effect of the application technique on the colorimetric coordinates for a ceramic enamel

In ceramic tiles production processes for ornamental purposes, the industry uses enamel as raw materials that support the pigments that give coloration and tonal constancy in a glassy matrix. These parameters are of great interest in the acceptance of the final product by the consumer. Due to the increasing number of complaints related to variations in tone in ceramic enamel of the tiles produced by a ceramic company in northeastern Colombia, this work studied the enamel approval methodology’s effect on the final tone of the ceramic pieces. A ceramic enamel was prepared and applied by the enameling pad technique on tiles manufactured by dry pressing. By applying the spectrophotocolorimetric technique, the colorimetric parameters were determined. The Tukey test’s statistical results did not show statistical significance in the tile samples’ tonal variation.

Variation of the human mu-opioid receptor (OPRM1) gene predicts vulnerability to frustration

Understanding the emotional reaction to loss, or frustration, is a critical problem for the field of mental health. Animal models of loss have pointed to the opioid system as a nexus of frustration, physical pain, and substance abuse. However, few attempts have been made to connect the results of animal models of loss to human behavior. Allelic differences in the human mu opioid receptor gene, notably the A118G single nucleotide polymorphism, have been linked to individual differences in pain sensitivity, depressive symptoms, and reward processing. The present study explored the relationship between A118G and behavior in two frustrating tasks in humans. Results showed that carriers of the mutant G-allele were slower to recover behavior following a reward downshift and abandoned a frustrating task earlier than those without the mutation. Additionally, G-carriers were more sensitive to physical pain. These results highlight the overlap between frustration and pain, and suggest that genetic variation in opioid tone may contribute to individual differences in vulnerability and resilience following emotional disturbances.

Josephus’ Nabataeans: a vision of Roman power in the Near East

AbstractNabataean history is significantly overlooked in the works of ancient historians. Josephus is an exception to this, as he includes several important events from Nabataean history inDe Bello JudaicoandAntiquitates Judaicae. However, his retelling of these events differs between the two works. In this paper, I argue that Josephus became more “pro-Roman” over time and eventually overshadowed an accurate portrayal of Nabataean history in his later narrative. He undermined moments of tension between Rome and Nabataea in order to showcase Roman power in the Near East, even beyond reality. In support of this argument, I analyze the Nabataean sections of Josephus’ works, as each passage has overlapping details, but significantly different emphasis, characterization, and perspective through variations in terminology, imagery, and tone. Though problematic, Josephus has preserved a unique look into Roman-Nabataean relations in the two centuries prior to the incorporation of Nabataea into the Empire.

Year and Industry-Level Accounting Narrative Analysis: Readability and Tone Variation

ABSTRACT In this study, I examine variations in the textual complexity of annual report narrative disclosures using the Fog Readability Index and Fin-Neg word list Tone Index given year and industry effects. I analyze accounting narrative readability and tone based on firm years, associations between the two narrative measures, and industry data. Tests of the relationship between readability and tone show that negative narratives have higher readability scores, supporting the obfuscation hypothesis that bad news tends to be more difficult to read. A year analysis shows that the negative relationship between readability and tone increases in significance over time (2006–2011). An industry analysis shows that the observed obfuscation tends to persist in basic materials, consumer services, financial, technology, and utilities industries. This study shows that considering the effect of variations between industry and firm years can inform annual report textual complexity research and associated empirical analyses. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M41; M49.

Computational Study of Growth and Remodeling in Ascending Thoracic Aortic Aneurysms Considering Variations of Smooth Muscle Cell Basal Tone.

In this paper, we investigate the progression of Ascending Thoracic Aortic Aneurysms (ATAA) using a computational model of Growth and Remodeling (G&R) taking into account the composite (elastin, four collagen fiber families and Smooth Muscle Cells—SMCs) and multi-layered (media and adventitia) nature of the aorta. The G&R model, which is based on the homogenized Constrained Mixture theory, is implemented as a UMAT in the Abaqus finite-element package. Each component of the mixture is assigned a strain energy density function: nearly-incompressible neo-Hookean for elastin and Fung-type for collagen and SMCs. Active SMCs tension is additionally considered, through a length-tension relationship having a classic inverted parabola shape, in order to investigate its effects on the progression of ATAA in a patient-specific model. A sensitivity analysis is performed to evaluate the potential impact of variations in the parameters of the length-tension relationships. These variations reflect in variations of SMCs normal tone during ATAA progression, with active stress contributions ranging between 30% (best case scenario) and 0% (worst case scenario) of the total wall circumferential stress. Low SMCs active stress in the worst case scenarios, in fact, affect the rates of collagen deposition by which the elastin loss is gradually compensated by collagen deposition in the simulated ATAA progression, resulting eventually in larger aneurysm diameters. The types of length-tension relationships leading to a drop of SMCs active stress in our simulations reveal a critical condition which could also result in SMCs apoptosis.

Do White People See Variation in Black Skin Tones? Reexamining a Purported Outgroup Homogeneity Effect

Previous research has reported that white survey interviewers remember black respondents’ skin tones in a much narrower range than recollections by black interviewers. This finding has been used to suggest that, in line with the one-drop rule, whites do not perceive meaningful differences between light- and dark-skinned black people. The authors reanalyze evidence thought to demonstrate relative homogeneity in white interviewers’ evaluation of black skin tones. In contrast to previous studies, this examination of several data sources reveals significant heterogeneity in the ratings assigned by white interviewers when taking into account the ordinal nature of the skin tone measures. The results are consistent with theories of social cognition that emphasize that beyond formal racial classification schemes, skin tone is used to implicitly categorize others along a continuum of “blackness.” The findings also align with research suggesting that rather than nullifying within-race skin tone, increases in white racism intensify white colorism.

The Correlation of Tonal Shifts and Dialect Use with Socioeconomic Class in Dalian, China

Drawing on Professor William Labov’s seminal 1962 experiment, this paper examines tonal variation amongst employees of department stores targeting three different socioeconomic classes in Dalian, China. The experiment recorded pronunciations of the tone of yī (the word “first” in the phrase “first floor” 一楼), which is pronounced in first tone in standard Mandarin and shifted to the third tone in Dalian dialect. In this experiment, it was hypothesized that for the four department stores studied, an employee’s tone in pronunciation of first tone words would shift towards the third tone the most in the store catering to lower socioeconomic classes and shift the least in the store catering to higher socioeconomic classes. From analyzing the data collected, the non-first tone pronunciations were the most frequent in the lowest ranking store and less frequent in the higher ranking store. Therefore, the salespersons’ tonal shift in pronunciation is shown to correlate with the socioeconomic class of the customers being targeted.

Regionally accented Mandarin lexical tones

This study investigated tone variations in regionally accented Mandarin (i.e., Standard Mandarin [SM] spoken by native speakers of other regional dialects in China). Yantai, Shanghai, and Guangzhou dialects were selected because their tone systems are different in various ways from the Beijing dialect, which is the basis of the SM tone system. 16 female regional speakers (4 speakers × 4 dialectal regions) were recruited to produce SM monosyllabic words that allow minimal contrasts among the four Mandarin lexical tones (i.e., level, rising, dipping, and falling tones). The overall f0 contours within and across the four regional accents were modelled with growth curve analysis up to second-order orthogonal polynomials. The averaged tone shapes were significantly different within each of the regional accents, indicating that each group of regional Mandarin speakers successfully differentiated the four Mandarin lexical tones. However, the tone shape for each of the non-Beijing groups deviated significantly from Beijing Mandarin in two ways: (1) The quadratic term for the regional accents' dipping tones each differed significantly from Beijing accent; (2) The slopes of the regional accents' rising and falling tones each differed significantly from Beijing accent. These two differences facilitate better understanding of tone variations triggered by regional accents.

Automatic Detection of Negative Symptoms in Schizophrenia via Acoustically Measured Features Associated With Affective Flattening

Among the characteristic symptoms of schizophrenia, negative symptoms are a category. The existence of negative symptoms results in a diminution of normal behaviors and functions for schizophrenic patients. In this research, we focus on the affective flattening of negative symptoms to explore potential biomarkers to achieve automatic diagnosis. This work proposes an automatic procedure for detecting the negative symptoms of schizophrenic patients based on speech signal processing. In this procedure, three features are initially proposed: the symmetric spectral difference level (SSDL), quantization error and vector angle (QEVA), and standard dynamic volume value (SDVV). The SSDL feature is designed with the aim of emphasizing spectral differences that contribute to the evaluation of emotional richness. The QEVA is proposed to reflect the variations in tone using one cumulative error indicator and one variation evaluation indicator. The SDVV feature aims to represent the modulation of speech intensity, considering the speaking behavior of schizophrenic patients. Experiments evaluating the discriminative capabilities of the three features are conducted using a speech database collected from 56 participants (28 schizophrenic patients and 28 healthy controls). The classifier employed in these experiments is a simple decision tree. Three other binary classifiers [linear discrimination (LD), logistic regression (LR), and a support vector machine (SVM)] are also tested to compare their performances with the decision tree in this work. Based on the decision tree classifier, the discrimination accuracy levels of schizophrenic patients and control subjects using the SDLL, QEVA, and SDVV features are in the range of 80.5%-83%, 65%-73%, and 87%- 91.5%, respectively. When the three features are combined, the best discrimination accuracy of schizophrenic patients and control subjects is 98.2%, with an area under the receiver operations characteristic curve (AUC) value of 98%. Note to Practitioners-This article is motivated by the problems of the diagnosis of negative symptoms in schizophrenia and the timely monitoring of schizophrenic patients. In clinic, there exists a high patient-to-clinician ratio and the diagnostic result depends on the subjective experience of the clinician. It is necessary to develop an automatic and objective procedure for the diagnosis and monitoring of schizophrenic patients. Speech disorders are among the salient characteristics of negative symptoms. In this work, an automatic procedure for detecting the negative symptoms based on speech signal processing is proposed. This automatic procedure is achieved based on three newly presented acoustic features involving the characteristics of schizophrenic speech, which consider the speaking behavior of schizophrenic patients. This automatic procedure and the information of the three acoustic features may serve as an aid to clinicians and could potentially help them in providing better monitoring of schizophrenic patients.

Patterns of Circadian Variation in 24-Hour Ambulatory Blood Pressure, Heart Rate, and Sympathetic Tone Correlate with Cardiovascular Disease Risk: A Cluster Analysis.

To investigate whether specific time series patterns for blood pressure (BP), heart rate (HR), and sympathetic tone are associated with metabolic factors and the 10-year risk of atherosclerotic cardiovascular disease (ASCVD). A total of 989 patients who underwent simultaneous 24-hour ambulatory BP and Holter electrocardiogram monitoring were enrolled. The patients were categorized into sixteen groups according to their circadian patterns using the consensus clustering analysis method. Metabolic factors, including cholesterol profiles and apolipoprotein, were compared. The 10-year ASCVD risk was estimated based on the Framingham risk model. Overall, 16 significant associations were found between the clinical variables and cluster groups. Age was commonly associated with all clusters in systolic BP (SBP), diastolic BP (DBP), HR, and sympathetic tone. Metabolic indicators, including diabetes, body mass index, total cholesterol, high-density lipoprotein, and apolipoprotein, were associated with the four sympathetic tone clusters. In the crude analysis, the ASCVD risk increased incrementally from clusters 1 to 4 across SBP, DBP, HR, and sympathetic tone. After adjustment for multiple variables, however, only sympathetic tone clusters 3 and 4 showed a significantly high proportion of patients at high risk (≥7.5%) of 10-year ASCVD (odds ratio (OR) = 5.90, 95% confidential interval (CI) = 1.27–27.46, and P value = 0.024 and OR = 15.28, 95% CI = 3.59–65.11, and P value < 0.001, respectively). Time series patterns of BP, HR, and sympathetic tone can serve as an indicator of aging. Circadian variations in sympathetic tone can provide prognostic information about patient metabolic profiles and indicate future ASCVD risk.

Effects of age and bilingualism on sensitivity to native and nonnative tone variation: Evidence from spoken word recognition in Mandarin Chinese learners.

Most children learn a language such as Mandarin Chinese that uses lexical tone to communicate meaning. This study aimed to examine the phonological specificity of tone representations in monolingual and bilingual learners of Mandarin. Two age-groups were tested: toddlers (2.5 to 3.5 years) and preschoolers (4 to 5 years; N = 80). Using a preferential looking paradigm, children were presented with correct and mispronounced (Cantonese or Mandarin tone substitutions) forms of familiar words. In the aggregate, participants demonstrated preferential fixation to visual targets (naming effects) for correctly pronounced words, words mispronounced with native (Mandarin) tone substitutions, and for words mispronounced with nonnative (Cantonese) tone substitutions. However, naming effects were greater for correct pronunciations and for Cantonese tone mispronunciations relative to Mandarin tone mispronunciations. There were no interactions of responses to each trial type with age or with language exposure. Results are discussed in terms of possible determinants of tone sensitivity in early childhood. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

CtrlFaceNet: Framework for geometric-driven face image synthesis

Abstract In this work, we introduce a novel framework based on Generative Adversarial Networks to control the pose, expression and facial features of a given face image using another face image. It can then be used for data augmentation, pose invariant face identification, face verification, and lightweight image editing. Generating new realistic face images with controllable poses, facial features, and expressions is a challenging generative learning problem due to skin tone variations, the identity preservation problem, necessity to deal with unseen large poses, and the absence of ground truth images in the training process. We make the following contributions. First, we present a network, CtrlFaceNet that can control a source face image while preserving the identity and skin tone. Second, we introduce a method for training the framework in fully self-supervised mode using a large-scale dataset of unconstrained face images. Third, we show that the style loss function can be used to preserve the skin tone of the source image. The experimental results show that our approach outperforms all other baselines. Furthermore, to the best of our knowledge, we are the first to train such a model using large-scale dataset of unconstrained face images.

Development of a Wearable Remote Cardiac Health Monitoring with Alerting System

Remote monitoring with alerting systems becomes unavoidable in the field of remote healthcare. The efficiency of such a system depends on accuracy, easy-to-use, versatility, and cost-effectiveness. In this study, a remote cardiac health monitoring along with the alert raising facility is presented. Special design techniques have been adopted to craft the photoplethysmography (PPG) sensor to overcome the congenital limitations like pressure disturbance, the influence of ambient light and motion artifacts. Three key components like pulse rate (PR), blood pressure (BP) and heart rate variability (HRV) are considered to monitor the cardiac health status. A novel algorithm has been developed to detect the cardiac health status of any subject from the above-mentioned physiological parameters. Also, the variation of human skin tone across demography is considered to minimize the impact of skin structure variability in PPG-based cardiac parameter measurement. Decentralized remote monitoring architecture is implemented using the virtual network computing (VNC) platform. We conducted a trial where the training dataset consisted of data from 70 volunteers and the trial dataset considered 20 new volunteers. The overall accuracy of cardiac health monitoring achieved is 98.5%. The experimental results demonstrate the good promise of reliable and efficient system architecture.

Plasticity in Second Language Learning: The Case of Mandarin Tones

ABSTRACT Adults typically struggle to perceive non-native sound contrasts, especially those that conflict with their first language. Do the same challenges persist when the sound contrasts overlap but do not conflict? To address this question, we explored the acquisition of lexical tones. While tonal variations are present in many languages, they are only used contrastively in tonal languages. We investigated the perception of Mandarin tones by adults with differing experience with Mandarin, including naïve listeners, classroom learners, and native speakers. Naïve listeners discriminated Mandarin tones at above-chance levels, and performance significantly improved after just one month of classroom exposure. Additional evidence for plasticity came from advanced classroom learners, whose tonemic perception was indistinguishable from that of native speakers. The results suggest that unlike many other non-native contrasts, adults studying a language in the classroom can readily acquire the perceptual skills needed to discriminate Mandarin tones.

Automatic diagnosis of skin diseases using convolution neural network

Skin diseases are becoming a most common health issues among all the countries worldwide. The method proposed in this work detects four types of skin diseases using computer vision. The proposed approach involves Convolutional Neural Networks with specific focus on skin disease. The Convolutional Neural Network (CNN) used in this paper has utilized around 11 layers viz., Convolution Layer, Activation Layer, Pooling Layer, Fully Connected Layer and Soft-Max Classifier. Images from the DermNet database are used for validating the architecture. The database comprises all types of skin diseases out of which we have considered four different types of skin diseases like Acne, Keratosis, Eczema herpeticum, Urticaria with each class containing around 30 to 60 different samples. The challenges in automating the process includes the variation of skin tones, location of the disease, specifications of the image acquisition system etc., The proposed CNN Classifier results in an accuracy of 98.6% to 99.04%.

Studying Self-Narrative Activity: The Self-Report and Verbal Expression

The relationship between internal and verbal self-narrative activity is rarely studied directly. The general aim of this correlational study was to verify the links within and between the self-reported aspects of self-narrative activity (in terms of self-narrative inclination and internal dialogical activity) and the selected characteristics of verbal expressions obtained in response to a stimulus encouraging self-narration. The sample consisted of 73 participants (68.5% women, Mean age = 34.36), who completed online version of self-report measures (Internal Dialogical Activity Scale, The Questionnaire of Self-Narrative Inclination – Brief Version, relationship status declaration) and a narrative task involving written self-narration about an important relationship. Selected features of verbal expression (productivity, dynamics of narration, emotional tone) were assessed. Results showed for example, the greater intensification of ruminative dialogs, the higher dynamics of narration and more negative emotional tone. Multivariate linear regression analysis has shown that supportive dialogs, ruminative dialogs and identity dialogs, together with the status of the relationship, explain 35% of the variation in emotional tone of the self-narration. We conclude that: (1) internal self-narrative activity, as a phenomenon experienced by a person, is connected with performing a verbal task only in certain aspects, so equalization of these two aspects of self-narrative activity is not justified; and (2) people, for personal (biographical) and situational reasons, can tell stories with different properties on a prompted topic, thus we recommend controlling these factors in narrative studies.

A interferência da modulação da voz do docente na aprendizagem de universitários cegos

O docente utiliza a fala para a transmissão do conhecimento aos seus alunos, em especial aos alunos cegos, ajudando-os a superar suas limitações. Este trabalho analisou, sob o ponto de vista dos deficientes visuais, como a modulação da voz do docente interfere no aprendizado, interesse e motivação ao longo do curso de graduação. Foi realizada pesquisa qualitativa com a utilização de entrevistas semiestruturadas em 11 estudantes cegos regularmente matriculados no ensino superior em diversas áreas do conhecimento, de ambos os sexos. Os resultados mostraram que os cegos percebem na modulação da voz do docente sua empatia e quando os mesmos sabem ou não como proceder de forma adaptada à deficiência. Há a necessidade de materiais adaptados, mobilidade adequada e professores capacitados para melhorar o ensino para cegos. Foi percebido que a variação no timbre, altura e frequência da fala do professor pode motivar ou desmotivar o aluno cego. Concluiu-se que a modulação de voz do docente serve como um intermédio entre o cego e o conhecimento efetivo e eficiente.

Melanogenic Difference Consideration in Ethnic Skin Type: A Balance Approach Between Skin Brightening Applications and Beneficial Sun Exposure.

Human skin demonstrates a striking variation in tone and color that is evident among multiple demographic populations. Such characteristics are determined predominantly by the expression of the genes controlling the quantity and quality of melanin, which can alter significantly due to the presence of small nucleotide polymorphism affecting various steps of the melanogenesis process and generally linked to the lighter skin phenotypes. Genetically determined, constitutive skin color is additionally complemented by the facultative melanogenesis and tanning responses; with high levels of melanin and melanogenic factors broadly recognized to have a protective effect against the UVR-induced molecular damage in darker skin. Long-term sun exposure, together with a genetic makeup responsible for the ability to tan or the activity of constitutive melanogenic factors, triggers defects in pigmentation across all ethnic skin types. However, sun exposure also has well documented beneficial effects that manifest at both skin homeostasis and the systemic level, such as synthesis of vitamin D, which is thought to be less efficient in the presence of high levels of melanin or potentially linked to the polymorphism in the genes responsible for skin darkening triggered by UVR. In this review, we discuss melanogenesis in a context of constitutive pigmentation, defined by gene polymorphism in ethnic skin types, and facultative pigmentation that is not only associated with the capacity to protect the skin against photo-damage but could also have an impact on vitamin D synthesis through gene polymorphism. Modulating the activities of melanogenic genes, with the focus on the markers specifically altered by polymorphism combined with differential requirements of sun exposure in ethnic skin types, could enhance the applications of already existing skin brightening factors and provide a novel approach toward improved skin tone and health in personalized skincare.

Implementation of a parallel algorithm of image segmentation based on region growing

In computer vision and image processing, image segmentation remains a relevant research area that contains many partially answered research questions. One of the fields of most significant interest in Digital Image Processing corresponds to segmentation, a process that breaks down an image into its different components that make it up. A technique widely used in the literature is called Region Growing, this technique makes the identification of textures, through the use of characteristic and particular vectors. However, the level of its computational complexity is high. The traditional methods of Region growing are based on the comparison of grey levels of neighbouring pixels, and usually, fail when the region to be segmented contains intensities similar to adjacent regions. However, if a broad tolerance is indicated in its thresholds, the detected limits will exceed the region to identify; on the contrary, if the threshold tolerance decreases too much, the identified region will be less than the desired one. In the analysis of textures, multiple scenes can be seen as the composition of different textures. The visual texture refers to the impression of roughness or smoothness that some surfaces created by the variations of tones or repetition of visual patterns therein. The texture analysis techniques are based on the assignment of one or several parameters indicating the characteristics of the texture present to each region of the image. This paper shows how a parallel algorithm was implemented to solve open problems in the area of image segmentation research. Region growing is an advanced approach to image segmentation in which neighbouring pixels are examined one by one and added to an appropriate region class if no border is detected. This process is iterative for each pixel within the boundary of the region. If adjacent regions are found, a region fusion algorithm is used in which weak edges dissolve, and firm edges remain intact, this requires a lot of processing time on a computer to make parallel implementation possible

RETRACTED ARTICLE: Preserving learnability and intelligibility at the point of care with assimilation of different speech recognition techniques

The neoteric introduction of 5G technology in mobile internet is transforming Internet of mobile Things (IoMT) massively by addressing low latency, support for a large number of IoMT devices, and less power consumption, thereby delivering cost-effective solutions to low-end devices. This transformational technology enables a ubiquitous connected critical communication network between the healthcare system and IoT, as they largely depend on low-end devices for gathering data at the point of care. Gathering and interpolation of data from things are moved over to the cloud, making the extraction of knowledge and decision-making capabilities more robust. Vocal signals form the basis of communication between human beings with the transfer of complex data with variations in thrust, pitch, and tones. The representation and recognition of these analog signals by digital systems prove to be quite exciting and challenging. The spoken language models are converted to digital signals to be identifiable based on different cues like phonetic, prosodic, phonotactic, and lexical features. Voice patterns tend to be specific for every individual with a slight orientation towards the language spoken by the individual of a particular region. While speech patterns tend to alter the meaning of words with tones, high and low pitches in the utterance of the words, NLP tends to learn specific associations of words through vectors. The focus on learned networks in solving the problem of speech synthesis to text with minimal loss and high predictability of syllable of word, sentence, and paraphrase is needed. The creation of a knowledge base corpus of learned variable prosody of features helps in the learnability of interestingness directly without any perturbations. The learning algorithms to realize the degree of understandability of speech with the word, sentence identified, and transcription with substantial noise interference. The transfer of the acoustic features learned by algorithms proves to be quiet challenging as they are distorted by sudden environmental changes. Syllable extracted from the speech translation may or may not represent the Sentiment of the word, with different phonetical modulation. Utilization of the MobileNets and DistillBERT to transfer the language extraction and the edge reducing the time of processing and reducing the corpus of the size, reducing the adversial learning of the voice features and the patterns, reducing the Transfer of learned corpus and patterns.