Intra-personal Variations Research Articles

"At school, I feel interrogated": Differences in willingness to communicate in Iranian schools and private English institutes

This qualitative study explored the underlying forces that shape Iranian EFL learners’ second language willingness to communicate (L2WTC) in two distinct contexts of public schools and private English institutes. To this end, we collected several bodies of data from both contexts and analyzed them through a grounded theoretical approach. The three major themes that emerged from data analysis were learner, teacher, and context issues, each bearing different weights and manifesting distinctively in the two contexts. Our findings confirm that both interpersonal and intrapersonal variations in L2WTC are subject to inter-contextual variation, making the prediction of L2WTC a challenge for both teachers and researchers. The study concludes by discussing the insights that can be gained from our findings for better understanding the dynamic and context-sensitive nature of L2WTC.

Too depressed and anxious to speak up: The relationships between weekly fluctuations in mental health and silence at work.

While it is widely acknowledged that some employees are more prone to silence than others, emerging research suggests that silence is much more dynamic than previously indicated, as even the most vocal employee will withhold input in some situations. However, given scant empirical attention to intraindividual fluctuations in silence, several important questions remain regarding its etiological antecedents, the mechanisms underlying such effects, and potential factors mitigating them. We respond by integrating the silence and mental health literature to consider how fluctuations in employees' experiences of depression and anxiety relate to fluctuations in silence via distinct silence motives. Specifically, we propose that employees are likely to engage in silence while experiencing episodes of depression because depressive symptomology shifts perceptions toward voice being pointless (i.e., ineffectual silence motive). Likewise, we propose that employees are likely to engage in silence while experiencing flare-ups of anxiety because anxious symptomology shifts perceptions toward voice being dangerous (i.e., defensive silence motive). Finally, we argue that voice endorsement attenuates these relationships by interrupting the link between silence motives and behaviors, such that employees experiencing heightened ineffectual and defensive silence motives are less likely to remain silent during weeks in which they experience high voice endorsement. We find support for these predictions via an experience sampling methodology study conducted with 136 employees across 4 weeks. We discuss how these results enhance theoretical clarity on the dynamic links between mental health and silence and offer insights into how organizations can counteract intrapersonal variations in silence. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Changes in Sperm Parameters with Time in Men with Normal and Abnormal Baseline Semen Analysis

The association between paternal age and sperm quality in the population level has been previously studied. Only limited data exists regarding the intra-personal variations in semen parameters among fertile and infertile men over time. We aimed to assess trends over time in semen parameters among men with normal and abnormal baseline sperm parameters and investigate potential risk factors for sperm quality deterioration. This retrospective cohort study was conducted at a university-affiliated medical center in vitro fertilization (IVF) unit. Patients with at least two semen analyses (SA) performed > 1 year apart, with the last SA done between 2017 and 2021, were included. The study consisted of two main analyses—comparison of intra-patient’s sperm parameters changes in men with normal and abnormal baseline SA (BSA) and analysis of risk factors for developing abnormal semen parameters over time in men who had normal BSA parameters. This study included a total of 902 men assessed for infertility with normal and abnormal BSA. The average time interval between tests was 1015 days (range 366–7709 days). Among individuals with normal BSA, there was a mild decline in most parameters—concentration (− 6.53 M/ml), motility (− 7.74%), and total motile count (TMC) (− 21.80 M) (p < 0.05 for all parameters). In contrast, a slight improvement in most parameters, except for concentration, was noted in men with abnormal BSA—volume (+ 0.21 ml), motility (+ 8.72%), and TMC (+ 14.38 M) (p < 0.05 for all parameters). Focusing on men with normal BSA, 33.5% of individuals developed abnormality in one or more of their sperm parameters over time, within a mean time of 1013 ± 661 days. We also found that only time between tests emerged as an independent prognostic factor for the development of abnormal SA later. Interestingly, sperm deterioration in participants in their third, fourth, and fifth decades of life with normal initial semen analysis was similar. Our study provides evidence of a decline in semen quality over time in individuals with normal BSA, in contrast to men with abnormal BSA. Longer time intervals between tests independently increase the risk of sperm abnormalities.

Doing More with Less on Intracranial Pressure Monitoring

Intracranial pressure (ICP) management based on predetermined thresholds is not accurate in light of recent research on cerebrovascular physiology. Interpersonal and intrapersonal variations will lead ICP elevations to reach individualized thresholds for intracranial compliance impairment from one subject to another. Therefore reuniting the modern techniques of neuromonitoring besides ICP enables practitioners to have a more whole picture in anticipating neuro worsening and improving timing in decision making. Brief literature review. For the severely brain-injured patient, current evidence indicates a personalized and physiology-based multimodal monitoring care to be required rather than decision making according to ICP predetermined cut-offs. The authors' point of view is of particular importance for regions with resource heterogeneity and scarcity, where ICP monitoring is not available for all those in need and noninvasive techniques may provide a surrogate approach. If physicians who deal with acute-brain-injured patients in lower-resource areas understand that several tools besides ICP may improve their practice, it is possible to reduce acute brain injury morbimortality.

A wireless patch for the monitoring of C-reactive protein in sweat.

The quantification of protein biomarkers in blood at picomolar-level sensitivity requires labour-intensive incubation and washing steps. Sensing proteins in sweat, which would allow for point-of-care monitoring, is hindered by the typically large interpersonal and intrapersonal variations in its composition. Here we report the design and performance of a wearable and wireless patch for the real-time electrochemical detection of the inflammatory biomarker C-reactive (CRP) protein in sweat. The device integrates iontophoretic sweat extraction, microfluidic channels for sweat sampling and for reagent routing and replacement, and a graphene-based sensor array for quantifying CRP (via an electrode functionalized with anti-CRP capture antibodies-conjugated gold nanoparticles), ionic strength, pH and temperature for the real-time calibration of the CRP sensor. In patients with chronic obstructive pulmonary disease, with active or past infections or who had heart failure, the elevated concentrations of CRP measured via the patch correlated well with the protein's levels in serum. Wearable biosensors for the real-time sensitive analysis of inflammatory proteins in sweat may facilitate the management of chronic diseases.

Multidimensional Indicators as Enablers on Perception of Facial Beauty and Appearance among Indian Population: A Descriptive Cross-Sectional Study.

Facial appearance has been a flagbearer of "beauty" since time immemorial. Perception of beauty is highly influenced by cultural, interpersonal, and intra-personal variations. This study aimed to assess the perception of facial beauty and appearance through multidimensional influencing indicators among the Indian population, and to determine whether the physically attractive person possesses more personal and socially desirable traits than the comparatively less attractive individual. A study population of 474 with equal male and female population of Indian origin was selected. Their perception was assessed based on the prevalidated, self-administered questionnaire using a tool with five major multidimensional indicators. Six images were selected, three each of male and female subjects, and labeled as A, B, and C, in descending order of attractiveness. The multidimensional influencing indicator tool was self-administered to the participants and the responses were recorded individually. Photograph A scored the highest out of the three grading scales in both males and females. The most attractive photograph, in both males and females, was deemed to be associated with higher scores of attractiveness and success.

Age and Gender Identification Using Neural Networks

Face recognition is still very challenging and complex problem. This problem can be credited as large intra-personal variations and large inter personal similarity. Facial recognition is the application of biometric breakthroughs that can observe or verify a person by observing and examining designs based on the individual's shape. Despite the increased interest in other applications, face recognition is still mostly utilized for well-being. Generally, advancements in face recognition are worthwhile since they may have a wide range of legal applications and commercial uses.

The influence of intra-personal variations in human hand odor on the determination of sample donor

A dual effort investigation was conducted to study (a) the naturally occurring variation in same-donor human hand odor samples over time and (b) the accuracy in associating same-donor human hand odor samples. Hand odor samples were collected from 8 donors throughout 5 sampling sessions; samples were collected in triplicate and analyzed by HS-SPME-GC-MS at each sampling session. The resulting human hand odor profiles were analyzed to investigate (a) the variability of human hand odor profiles as a function of time and (b) the ability to determine the source origin of human hand odor samples, determining samples to be from the same source or different sources. The researchers observed greater variation in 2-dimensional human scent profile patterning schemes among inter-day, inter-subject samples and less variation in inter-day, intra-subject samples. Although intra-subject samples revealed less variation than inter-subject samples, there was still notable variability among inter-day, intra-subject human scent profiles, with an observed time dependency. Two proof of concept models for the source determination of human hand odor samples were developed with maximum performance measuring TPR = 0.817/ FPR = 0.308 and TPR = 1.000/ FPR = 0.206 for models one and two, respectively. The study quantified same-donor human hand odor profile variation over time displayed within a larger goal of determining sample source origin.

Breast Cancer Human Epidermal Growth Factor Receptor 2 mRNA Molecular Testing Compared to Immunohistochemistry with Correlation to Neoadjuvant Therapy Response

BACKGROUND: Breast cancer is the most common cancer type among women worldwide. Human epidermal growth factor receptor 2 (HER-2) is amplified in 10–34% of breast carcinomas and offers a therapeutic option from HER2-targeted therapy. Hence, HER2 is tested routinely in all breast cancer patients using immunohistochemistry (IHC) and in situ hybridization. Yet, some pitfalls do exist due to tumoral heterogeneity, inter and intrapersonal variations. mRNA expression assays can provide an alternative method for accurately measuring HER-2 avoiding these limitations. AIM: Comparing results of mRNA gene expression analysis for HER2 with IHC results and correlating it with the therapy response. MATERIALS AND METHODS: One hundred breast cancer core biopsies were tested for HER-2 using IHC and the same blocks were sectioned and tested for mRNA gene expression for HER2 by the Xpert breast cancer STRAT4 device. RESULTS: Concordance rate between mRNA expression and IHC for HER-2 was 93% with Kappa measurement showing perfect agreement (κ = 0.81, 95% CI, p &lt; 0.0005). CONCLUSION: The study reveals high concordance between HER2 measurement using IHC and mRNA analysis. Molecular testing can provide an effective standardized method for HER-2 measurement in breast cancer patients.

Deep low-rank feature learning and encoding for cross-age face recognition

Cross-age face recognition (CAFR) is a challenging task, due to significant intra-personal variations. Furthermore, the training and testing data may contain random noise components. To address these issues, this paper proposes a deep low-rank feature learning and encoding method. Firstly, our method employs manifold learning in the low-rank optimization, which preserves the global and local structure of the data samples, while learning the clean low-rank features. Secondly, we encode the low-rank features using our locality-constrained feature encoding method, which learns an age-insensitive codebook from training data, and enables the intra-class samples to share the same local bases in a codebook. In the testing stage, the gallery and probe features are encoded by the learned codebook, which represents the images of the same identity by similar codewords for recognition. Furthermore, the periocular region of human faces is investigated for CAFR. Extensive experiments on five datasets demonstrate the effectiveness of our method.

The influence of panel effects and inertia on travel cost elasticities for car use and public transport

Studies on the impact of changes in travel costs on car and public transport use are typically based on cross-sectional travel survey data or time series analysis and do not capture intrapersonal variation in travel patterns, which can result in biased cost elasticities. This paper examines the influence of panel effects and inertia in travel behaviour on travel cost sensitiveness, based on four waves of the Mobility Panel for the Netherlands (comprising around 90,000 trips). This paper analyses the monetary costs of travel. Panel effects reflect (within wave) intrapersonal variations in mode choice, based on three-day trip diary data available for each wave. The impact of intrapersonal variation on cost sensitiveness is shown by comparing mode choice models with panel effects (mixed logit mode choice models with error components) and without panel effects (multinomial logit models). Inertia represents variability in mode choice between waves, measured as the effect of mode choice decisions made in a previous wave on the decisions made in the current wave. Additionally, all mode choice models include socio-economic and spatial variables but also mode preferences and life events. The effect of inertia on travel cost elasticities is measured by estimating mixed logit mode choice models with and without inertia effects. The main conclusion is that the inclusion of intrapersonal effects tends to increase cost sensitiveness whereas the inclusion of inertia effects decreases travel cost sensitiveness for car and public transport modes. Car users are identified as inert travellers, whereas public transport users show a lower tendency to maintain their usual mode choice. This paper reveals the inertia effects over four waves of repeated respondent’s data repeated yearly.

Human posture tracking with flexible sensors for motion recognition

AbstractThe integration of conventional clothes with flexible electronics is a promising solution as a future‐generation computing platform. However, the problem of user authentication on this novel platform is still underexplored. This work uses flexible sensors to track human posture and achieves the goal of user authentication. We capture human movement pattern by four stretch sensors around the shoulder and one on the elbow. We introduce the long short‐term memory fully convolutional network (LSTM‐FCN), which directly takes noisy and sparse sensor data as input and verifies its consistency with the user's predefined movement patterns. The method can identify a user by matching movement patterns even if there are large intrapersonal variations. The authentication accuracy of LSTM‐FCN reaches 98.0%, which is 10.7% and 6.5% higher than that of dynamic time warping and dynamic time warping dependent.

Human-in-the-Loop Auditory Cueing Strategy for Gait Modification

External feedback in the form of visual, auditory and tactile cues has been used to assist patients to overcome mobility challenges. However, these cues can become less effective over time. There is limited research on adapting cues to account for inter and intra-personal variations in cue responsiveness. We propose a cue-provision framework that consists of a gait performance monitoring algorithm and an adaptive cueing strategy to improve gait performance. The proposed approach learns a model of the person's response to cues using Gaussian Process regression. The model is then used within an on-line optimization algorithm to generate cues to improve gait performance. We conduct a study with healthy participants to evaluate the ability of the adaptive cueing strategy to influence human gait, and compare its effectiveness to two other cueing approaches: the standard fixed cue approach and a proportional cue approach. The results show that adaptive cueing is more effective in changing the person's gait state once the response model is learned compared to the other methods.

Iterative Dynamic Generic Learning for Face Recognition From a Contaminated Single-Sample Per Person.

This article focuses on a new and practical problem in single-sample per person face recognition (SSPP FR), i.e., SSPP FR with a contaminated biometric enrolment database (SSPP-ce FR), where the SSPP-based enrolment database is contaminated by nuisance facial variations in the wild, such as poor lightings, expression change, and disguises (e.g., wearing sunglasses, hat, and scarf). In SSPP-ce FR, the most popular generic learning methods will suffer serious performance degradation because the prototype plus variation (P+V) model used in these methods is no longer suitable in such scenarios. The reasons are twofold. First, the contaminated enrolment samples could yield bad prototypes to represent the persons. Second, the generated variation dictionary is simply based on the subtraction of the average face from generic samples of the same person and cannot well depict the intrapersonal variations. To address the SSPP-ce FR problem, we propose a novel iterative dynamic generic learning (IDGL) method, where the labeled enrolment database and the unlabeled query set are fed into a dynamic label feedback network for learning. Specifically, IDGL first recovers the prototypes for the contaminated enrolment samples via a semisupervised low-rank representation (SSLRR) framework and learns a representative variation dictionary by extracting the "sample-specific" corruptions from an auxiliary generic set. Then, it puts them into the P+V model to estimate labels for query samples. Subsequently, the estimated labels will be used as feedback to modify the SSLRR, thus updating new prototypes for the next round of P+V-based label estimation. With the dynamic learning network, the accuracy of the estimated labels is improved iteratively by virtue of the steadily enhanced prototypes. Experiments on various benchmark face data sets have demonstrated the superiority of IDGL over state-of-the-art counterparts.

Hierarchical Connectivity-Centered Clustering for Unsupervised Domain Adaptation on Person Re-Identification.

Unsupervised domain adaptation (UDA) on person Re-Identification (ReID) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. Recent works mainly optimize the ReID models with pseudo labels generated by unsupervised clustering on the target domain. However, the pseudo labels generated by the unsupervised clustering methods are often unreliable, due to the severe intra-person variations and complicated cluster structures in the practical application scenarios. In this work, to handle the complicated cluster structures, we propose a novel learnable Hierarchical Connectivity-Centered (HCC) clustering scheme by Graph Convolutional Networks (GCNs) to generate more reliable pseudo labels. Our HCC scheme learns the complicated cluster structure by hierarchically estimating the connectivity among samples from the vertex level to cluster level in a graph representation, and thereby progressively refines the pseudo labels. Additionally, to handle the intra-person variations in clustering, we propose a novel relation feature for HCC clustering, which exploits the identities from the source domain as references to represent target domain samples. Experiments demonstrate that our method is able to achieve state-of-the art performance on three challenging benchmarks.

Modifications to the PREEMPT Protocol for OnabotulinumtoxinA Injections for Chronic Migraine in Clinical Practice.

To assess the PREEMPT protocol modifications that have developed in clinical practice over time. The United States Food and Drug Administration approved the 155-unit fixed-dose, fixed-site PREEMPT protocol of onabotulinumtoxinA (BoNT-A) injections for migraine prevention 9 years ago. This is an anonymous survey with free text response options of Headache Medicine clinicians. Out of the 878 contacted Headache Medicine clinicians, 182 (20.7%) completed the survey. Of the 182 respondents, 141 (77.5%) reported that they did not always follow the PREEMPT protocol. Of the 182 respondents, 128 (70%) changed the number of injections, 115 (63%) changed the total units of BoNT-A injected, 105 (57.7%) altered the location of injection sites (58%); 101 (55.5%) do not aspirate to ensure the absence of blood return; 22 (12.1%) changed the dilution; and 4 (2.2%) added lidocaine. The main reported reasons for changes in number, dose, and location of injections included adapting to the patients' pain, anatomy, and preferences. The wide inter- and intra-personal variations in BoNT-A injections for chronic migraine prevention seen in this survey raise concerns about the standardization of the procedure and suggest that an advisory protocol containing more evidence and discussion of the reasoning behind the recommendations might be more helpful than the current prescriptive protocol.

Inferring Respiratory Minute Volume from Wrist Motion.

Exposure to air pollutants poses major health risk for patients with chronic pulmonary diseases such as asthma, bronchitis, and emphysema. Such risk can be mitigated by continuous exposure tracking. The effective dose of exposure is directly proportional to the respiratory minute volume, aka minute ventilation (VE). Till date, the clinical standard for measuring VE is Spirometry, a highly invasive and cumbersome modality, which is not suitable for continuous day-to-day use. This paper presents a novel non-invasive method toward continuous assessment of VE using a wrist-mount wearable motion sensor. Data from 25 healthy subjects were collected while they performed ambulatory and sedentary activities and physical exercises. Noise and artifacts of the motion signal are removed and the processed signal is used to extract explanatory features. The features are used to train and evaluate multiple regression models, among which, the probabilistic Gaussian process regression achieves the best performance in inferring VE from the wearable motion signal. The effects of inter- and intra-personal variations are explored to demonstrate the potential of the proposed method for continuously monitoring pollutant exposure risk in respiratory health applications.

Deep-feature encoding-based discriminative model for age-invariant face recognition

Facial aging variation is a major problem for face recognition systems due to large intra-personal variations caused by age progression. A major challenge is to develop an efficient, discriminative feature representation and matching framework, which is robust to facial aging variations. In this paper, we propose a robust deep-feature encoding-based discriminative model for age-invariant face recognition. Our method learns high-level deep features using a pre-trained deep-CNN model. These features are then encoded by learning a codebook, which converts each of the features into a discriminant S-dimensional codeword for image representation. By incorporating the locality information in the whole learning process, a closed-form solution is obtained for both the codebook-updating and encoding stages. As the features of the same person at different ages should have certain correlations, canonical correlation analysis is utilized to fuse the pair of training features, for two different ages, to make the codebook discriminative in terms of age progression. In the testing stage, the gallery and query image's features are encoded using the learned codebook. Then, linear mapping based on linear regression is employed for face matching. We evaluate our method on three publicly available challenging facial aging datasets, FGNET, MORPH Album 2, and Large Age-Gap (LAG). Experimental results show that our proposed method outperforms various state-of-the-art age-invariant face recognition methods, in terms of the rank-1 recognition accuracy.

A robust multi-level sparse classifier with multi-modal feature extraction for face recognition

In the past few years, face recognition based on sparse representation is providing satisfactory classification accuracy. Face images involved in real life applications usually exhibit considerable pose, lighting, and expression variations, resulting in significant performance degradation of traditional sparse-based algorithms. In this paper, a novel face recognition method is developed as multi-level sparse (MLS) classifier with multi-modal feature extraction, which integrates benefits of sparse representation manifolds. In MLS classifier, sparse representation-based classification is performed at multiple levels to extract the hierarchical relationship information between training and testing images, which not only improves classification accuracy but also makes the system scalable. Also, the use of multi-modal feature in MLS classifier makes it discriminative to face changes while robust to intra-personal variations. To highlight the competency of proposed method, results are compared with sparse representation and other existing state-of-art methods in terms of mean classification error. An investigation on classification accuracy is performed to showcase the reliability of proposed method.

Age invariant face recognition: a survey on facial aging databases, techniques and effect of aging

Age invariant face recognition (AIFR) is highly required in many applications like law enforcement, national databases and security. Recognizing faces across aging is difficult even for humans; hence, it presents a unique challenge for computer vision systems. Face recognition under various intra-person variations such as expression, pose and occlusion has been an intensively researched field. However, age invariant face recognition still faces many challenges due to age related biological transformations in presence of the other appearance variations. In this paper, we present a comprehensive review of literature on cross age face recognition. Starting with the biological effects of aging, this paper presents a survey of techniques, effects of aging on performance analysis and facial aging databases. The published AIFR techniques are reviewed and categorized into generative, discriminative and deep learning methods on the basis of face representation and learning techniques. Analysis of the effect of aging on the performance of age-invariant face recognition system is an important dimension. Hence, such analysis is reviewed and summarized. In addition, important facial aging databases are briefly described in terms of the number of subjects and images per subject along with their age ranges. We finally present discussions on the findings, conclusions and future directions for new researchers.