Age-based Classification Research Articles

Multifractal Analysis in Age-Based Classification for COVID-19 Patients’ CT-Scan Images with Different Noise Levels

Recently, many number of people various countries contracted the COVID-19 (C-19) disease. The identification of this harmful disease and other pneumonia diseases is an important process in the medical world. CT-Scan is mainly prescribed to predict the severity of lung diseases affected by C-19 and other pneumonia diseases. Generally, a CT-Scan image is represented as a grayscale image (GI). Biomedical-oriented GIs are more complex and they are too difficult to recognize the status of lung diseases directly from the experimental images. To address this type of medical problem, a multifractal approach can be applied to analyze and illustrate the GIs in detail. Therefore, the multifractal dimensional analysis is used to diagnose and explore the vehemence of the contamination levels in the lungs. In this study, the complexity of CT-Scan images of C-19 patients is analyzed in order to perform a comparison in terms of age and noise levels. It was also discovered that the intricate of CT-Scan images is considerably varied for patients aged 50 and above when compared to younger subjects. This comparative study indicates that the deadly virus affects elderly persons compared to youngsters. The proposed age-based classification is supported by image processing techniques, qualitative measures, and statistical tools. The obtained results are demonstrated graphically by Generalized Fractal Dimensions (GFD) spectrum, 3D visualization, ANOVA table, and box plots to expose the rate of discrimination of complexity in CT-Scan lung images.

Z Generation: Living in Society 5.0 Or Learning Religious Science in Pesantren

In the contemporary landscape, the age-based classification of individuals has gained prominence, with a focus on Generation Z, encompassing those aged between 11 and 28 years. This Generation, shaped by rapid technological advancements, particularly embraces social media as a pivotal digital platform. This research aims to investigate whether Generation Z prioritizes Society 5.0 ideals or emphasizes religious knowledge within the constraints of pesantren, where social media use is restricted. Employing a quantitative survey design with purposive sampling, the study included 100 respondents, conducting a comparative analysis between public school and pesantren students. The findings, presented through tables and graphs, underscore Generation Z's open-mindedness and their inclination towards socializing through social media to connect with their community. Public school students perceive social media as beneficial, acknowledging its negative impacts. Conversely, pesantren students prioritize religious education for their future. Despite limited social media access, they express a desire to utilize it to stay informed about technological developments in Society 5.0. The results indicate that 80% of Generation considers social media indispensable, irrespective of their educational setting. This study provides valuable insights for students, parents, and educational practitioners, highlighting the intricate relationship between Generation Z, social media, and its multifaceted impacts on their future. It suggests the need for a balanced approach in addressing the diverse needs and preferences of this Generation in different educational environments.

Motivation for professional activity in extreme conditions in Antarctica among winterers of different age groups

Professional activities in the extreme conditions of Antarctica and the Arctic are primarily associated with major risks to life and health, including psychological risks, for the personnel of polar stations. The relevance of this study is driven by the fact that in extreme forms of professional activity, such as expeditions to the Earth’s poles, extreme sports, military operations, etc., one of the main criteria for selecting and forming effective and cohesive teams or groups is understanding the primary motives behind their actions. Motivated individuals tend to perform better and achieve greater success. The purpose of the study is to identify the leading motivations for professional activity in extreme conditions among winterers of Ukrainian Antarctic expeditions across different age groups. To achieve this purpose, methods of theoretical and empirical analysis, observation, generalisation, and systematisation of age groups were chosen. The paper defines and establishes an age-based classification of participants in annual expeditions to the Ukrainian Antarctic station “Academic Vernadsky”. Through surveys, the study measures the level of motivation among individuals towards professional activities in Antarctica within three age groups: younger, middle-aged, and older. Three leading motives are identified: spiritual, social, and material. The analysis of the obtained data concerning the level of these leading motivational factors in polar station environments provides insights into the main reasons driving motivation for prolonged professional activity and helps in adapting to the extreme conditions of the surrounding environment. In addition, age-related differences in motivational processes among different age groups of winterers are presented, allowing for the formation of well-coordinated teams of polar expedition participants of varying age categories. The findings can be used in planning activities in long-term polar expeditions, in military and sports psychology.

Characterizing bourbon whiskey via the combination of LC-MS and GC–MS based molecular fingerprinting

To discover molecular fingerprints that vary by bourbon's age or color, 121 commercial bourbon samples were analyzed using both gas chromatography and liquid chromatography combined with mass spectrometry. In total, 81 non-volatile compounds and 102 volatile compounds were identified. A subset of samples containing age information was divided into training (n = 70) and testing (n = 12) groups to build and validate the Partial Least Square – Discriminant Analysis models for age-based classification. After internal and external validation, our models showed that 8 molecular markers unitedly provided up to 100% accuracy of prediction in distinguishing unaged (0 years), younger (0–4 years), and older (>4 years) bourbons. Additional efforts were also made to detect molecular signatures that contributed to the color differences in bourbon. Overall, our study provided a powerful strategy for the confident identification of high-quality products and robust evaluation of their commercial values.

A Dimension Centric Proximate Attention Network and Swin Transformer for Age-Based Classification of Mild Cognitive Impairment From Brain MRI

A Dimension Centric Proximate Attention Network and Swin Transformer for Age-Based Classification of Mild Cognitive Impairment From Brain MRI

The changing context of age-based classification and policy research in the age of subscription-video-on-demand

This article draws on a history of media classification in Australia to consider how this field is developing. The focus is on age-based classification of commercially and professionally produced content, specifically made available through streaming and subscription-video-on-demand (SVOD) platforms. As platform company Netflix steps into the terrain of regulation, this environment is changing quite dramatically. The Netflix tool emerges in a governmental space characterized by new and emerging transnational governance and monitoring Boards, ghost work and moral panics in the form of online firestorms. Questions developed in the time of legacy media that consider human and machine, and industry and government as working separately, are confronted by new practices and points of inquiry with impacts broader than Australian media consumption.

Simple action for depression detection: using kinect-recorded human kinematic skeletal data

BackgroundDepression, a common worldwide mental disorder, which brings huge challenges to family and social burden around the world is different from fluctuant emotion and psychological pressure in their daily life. Although body signs have been shown to present manifestations of depression in general, few researches focus on whole body kinematic cues with the help of machine learning methods to aid depression recognition. Using the Kinect V2 device to record participants’ simple kinematic skeleton data of the participant’s body joints, the presented spatial features and low-level features is directly extracted from the record original Kinect-3D coordinates. This research aimed to constructed machine learning model with the preprocessed data importing, which could be used for depression automatic classification.MethodsConsidering some patients’ conditions and current status and refer to psychiatrists’ advices, simple and significant designed stimulus task will lead human skeleton data collection job. With original Kinect skeleton data extracting and preprocessing, the proposed experiment demonstrated four strong machine learning tools: Support Vector Machine, Logistic Regression, Random Forest and Gradient Boosting. Using the precision, recall, sensitivity, specificity, roc-curve, confusion matrix et.al, indicators were calculated as the measurement of methods, which were commonly used to evaluate classification methodologies.ResultsAcross screened 64 pairs with age and gender totally matching in depression and control group, and Gradient Boosting achieved the best performance with the prediction accuracy of 76.92%. Sorted by female (54.69%) and male for the gender-based depression recognition, we applied best performance classifier Gradient Boosting got prediction accuracy of 66.67% in the male group, and 71.73% in the female group. Utilizing the best model Gradient Boosting for age-based classification, prediction accuracy got 76.92% in the older group (age >40, 50% of total) and 53.85% accuracy in the younger group (age <= 40).ConclusionThe depression and non-depression individuals can be well classified by computational models using Kinect captured skeletal data. The Gradient Boosting, an excellent machine learning tool, get the performance in the four methods we demonstrated. Meanwhile, in the gender-based depression classification also gets reasonable accuracy. In particular, the recognition results of the old group are significantly better than that of the young group. All these findings suggest that kinematic skeletal data based depression recognition can be applied as an effective tool for assisting in depression analysis.

Rethinking Measures of Functional Connectivity via Feature Extraction

Functional magnetic resonance imaging (fMRI)-based functional connectivity (FC) commonly characterizes the functional connections in the brain. Conventional quantification of FC by Pearson's correlation captures linear, time-domain dependencies among blood-oxygen-level-dependent (BOLD) signals. We examined measures to quantify FC by investigating: (i) Is Pearson's correlation sufficient to characterize FC? (ii) Can alternative measures better quantify FC? (iii) What are the implications of using alternative FC measures? FMRI analysis in healthy adult population suggested that: (i) Pearson's correlation cannot comprehensively capture BOLD inter-dependencies. (ii) Eight alternative FC measures were similarly consistent between task and resting-state fMRI, improved age-based classification and provided better association with behavioral outcomes. (iii) Formulated hypotheses were: first, in lieu of Pearson’s correlation, an augmented, composite and multi-metric definition of FC is more appropriate; second, canonical large-scale brain networks may depend on the chosen FC measure. A thorough notion of FC promises better understanding of variations within a given population.

Comparison of plasma adipocytokines & C-reactive protein levels in healthy schoolgoing adolescents from private & government-funded schools of Delhi, India.

Background & objectives:Obesity-mediated chronic inflammatory state is primarily governed by lifestyle and food habits in adolescents and marked by alterations in the level of various inflammatory markers. This cross-sectional study was aimed to compare the inflammatory status of healthy Indian adolescents vis-à-vis their obesity profile. The inflammatory state of urban adolescents attending private and government-funded schools, and the relationship between inflammatory marker levels and anthropometric indices in the study participants from both groups were examined.Methods:A total of 4438 study participants (10-17 yr) were chosen from various schools of Delhi, India, and their anthropometric parameters were measured. Plasma adipocytokines (adiponectin, leptin and resistin) of the study participants were measured by enzyme-linked immunosorbent assay, and plasma C-reactive protein (CRP) levels were assayed by a biochemical analyzer. Metabolic syndrome-related risk factors such as waist circumference, hip circumference (HC), fasting glucose, fasting insulin, Homeostatic Model Assessment of Insulin Resistance, total cholesterol, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol and triglycerides of normal-weight adolescents were also evaluated.Results:The level of leptin and CRP increased with increasing adiposity, whereas adiponectin levels were found to be negatively related to obesity. All plasma cytokine levels (adiponectin, leptin and resistin) were significantly elevated in female than male adolescents. Age-based classification revealed a distinct trend of variability in the levels of all the inflammatory markers among adolescents of varying age groups. Significant differences were observed between private and government schoolgoing adolescents in terms of anthropometric and inflammatory parameters, with higher adiposity indices in the former group. The relationship of plasma adipokine and CRP levels with various adiposity indices was found to be distinctly different between private and government schoolgoing students.Interpretation & conclusions:Inflammatory markers were significantly elevated in overweight/obese adolescents. The socio-economic condition of urban Indian schoolgoing adolescents reflecting lifestyle transition has profound effects on their adiposity indices and inflammatory states. Longitudinal studies in different regions of the country need to be done to further confirm the findings.

Gender and age classification using a new Poincare section-based feature set of ECG

In the medicine, the implication of individual differences has frequently been emphasized. Gender- and age-related differences can be mentioned as the most important individual parameters. On the other hand, electrocardiogram (ECG) signals are the subject of these differences. However, limited information is available regarding these individual dissimilarities in ECG dynamics. This study was aimed to evaluate gender and age differences by means of novel Poincare section indices. Our focus was to detect and classify dynamical behaviors of the ECG trajectories using three binary classification strategies: (1) gender-, (2) age-, (3) gender- and age-based classification. After constructing the 2D phase space of ECG, linear Poincare sections in distinct angles were developed and some geometric indices were extracted. The effect of delayed phase space on ECG measures was also inspected. We tested our algorithm on 79 healthy subjects. Using support vector machine, the maximum correct rate of 93.33% was achieved for the gender- and age-based classification strategies. Considering the information of both age and gender, the highest rate was 94.66%. The best results were achieved with delays of 5 and 6. In conclusion, our results showed that basin geometry of the ECG phase states is affected by individual differences.

Agresores sexuales juveniles: tipología y perfil psicosocial en función de la edad de sus víctimas

In order to improve the understanding of risk factors involved in sexual crimes committed by adolescents, data was compiled about 63 young offenders who had committed at least one sexual offense and had served an internment judicial measure in Madrid Region for this crime between 2006 and 2015. To explore possible differences, two groups were established according to an age-based classification of the victim, distinguishing between adolescents who sexually offend children (AM) and adolescents who sexually assault peers or older victims (AI). The results indicate that the AM group presents a profile characterized by a higher victimization and social isolation, while the AI group is characterized by a greater presence of other antisocial and aggressive behaviors. Finally, the implications of the results for therapeutic intervention in these adolescents are discussed.

Age-based classification of arecanut crops: a case study of Channagiri, Karnataka, India

Arecanut is one of the predominant plantation crop grown in India. Yield of this crop depends upon age of the crop and there is no information on the spectral behaviour of arecanut crops across its ages. In this study popular supervised classification algorithms were utilized for age discrimination of arecanut crops using Hyperion imagery. Arecanut plantations selected for the study are located in Channagiri Taluk, Davanagere district of Karnataka state, India. Ground truth information collected involves: (i) GPS coordinates of selected plots, (ii) spectral reflectance of arecanut crops with age ranging from 1 to 50 years, using handheld spectroradiometer with 1 nm spectral resolution. These spectral measurements were made close in time to the acquisition of Hyperion imagery to build age-based spectral library. It is observed from the analysis that crops of ages below 3, 3–7, 8–15 and above 15 years were showing distinct spectral behaviour. Accordingly, crops age ranging from 1 to 50 were grouped into four classes. Classification of arecanut crops based on age groups was performed using methods like spectral angle mapper, support vector machine and minimum distance classifier, and were compared to find the most suitable method. Among the classification methods adopted, support vector machine with linear kernel function resulted in most accurate classification method with overall accuracy of 72% for within class seperability. Individual age group classification producer’s accuracy varied minimum of 12.5% for 3–7 years age group and maximum of 86.25% for above 15 years age group. It may be concluded that, not only age- based arecanut crop classification is possible, but also it is possible to develop age-based spectral library for plantation crop like arecanut.

Mapping forest growth and degradation stage in the Brigalow Belt Bioregion of Australia through integration of ALOS PALSAR and Landsat-derived foliage projective cover data

Differentiation of forest growth stages through classification of single date or time-series of Landsat sensor data is limited because of insensitivity to their three-dimensional structure. This study therefore evaluated the benefits of integrating the Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) L-band HH and HV polarisation response from the woody components of vegetation with Landsat-derived foliage projective cover (FPC). Focus was on 12 regional ecosystems (REs) distributed across the Brigalow Belt Bioregion (BRB) of Queensland, Australia, where different stages of growth dominated by brigalow (Acacia harpophylla) were widespread. From remnant areas of brigalow-dominated forests mapped previously for each RE by the Queensland Herbarium through field visits and interpretations of aerial imagery, frequency distributions of all three channels were extracted and compared to those of image segments generated using FPC and PALSAR data. For woody vegetation (with an FPC threshold of ≥9%) outside of the remnant areas, mature (non-remnant) forests were associated with segments where the HH and HV backscatter thresholds were within one standard deviation of the mean extracted for remnant forest. Early-stage regrowth was differentiated using an L-band HH threshold of <−14dB, common for all REs because of similarities in structure at this stage. The early-stage included forests regrowing over several decades and often occurred in areas recovering from recent clearing events. Objects falling between the early and mature stages were considered to be intermediate regrowth and/or degraded forest. All areas with an FPC <9% were mapped as non-forest.Within the BRB, the Queensland Herbarium established that forests with brigalow as a dominant or subdominant component originally occupied over 7.3 million ha but were reduced to 586,364ha by 2009, with 460,499ha (78.5%) having brigalow as the dominant component. Using the Landsat FPC and ALOS PALSAR data, an additional 722,686ha of brigalow-dominated regrowth forest were identified giving a total forested area (brigalow-dominated remnant and secondary forest) of 1,183,185ha or 17.2% of the area of the 12 REs. Within this area, the greater proportion of regrowth (368,473ha or 31.1%) was mapped as early stage primarily because of recovery following recent clearance events. 230,551 (19.5%) ha and 123,662ha (10.5%) were mapped as intermediate and mature (non-remnant) stages respectively and the remainder (38.9%) was remnant forest. Users’ and producers’ accuracies were, respectively, 81% and 69% for early regrowth and 71% and 89% for mature and intermediate stage forests combined. The mapping, which used Queensland Herbarium’s RE data to delineate brigalow extent, provided a structural, rather than age-based classification of growth stage, as is typically retrieved using time-series comparison of optical imagery. The regional estimates of growth/degradation stage generated for the BRB provide a basis for optimising the use and recovery of these threatened brigalow ecosystems with benefits for biodiversity and carbon sequestration.

Age-related activation of the muscles during upright standing in patients with haemophilia

Thehaemophilicarthropathyaffects thefunction of theknee joint muscles. The aim of thisstudywas to investigatethe myoelectrical signal ofknee jointmusclesin different agestages during upright standing. Surface EMG (SEMG) amplitudes of quadriceps, hamstrings and gastrocnemii were measured in 191 patients with severe haemophilia A (n=164) and B (n=27) while standing on an even surface. After an age-based classification of patients into the subgroups H(A): 17-29 (n = 37), H(B): 30-39 (n = 50), HC: 40-49 (n = 61), H(D): 50-70 in years (n = 43) the clinical WFH score for the ankle and knee joint was determined. To normalize the SEMG values amplitude ratios (percentage of cumulated activity) were calculated with respect to the specific limb. With increasing age, the patient showed descriptively a deterioration of the joint situation. The extensors of the knee joint reached significantly higher absolute and percentage levels in the muscle activity with increasing age (p < 0.05). The absolute amplitude values of the Mm. gastrocnemii showed no differences in the age groups while the relative levels were decreased. The present study shows that patients with increasing age and degree of haemophilic arthropathy develop a modified control strategy during upright standing, in the form of a shift from the plantar flexors to the extensors of the knee joint.

The Depressed Child and Adolescent, second edition • Anxiety Disorders in Children and Adolescents: Research, Assessment, and Intervention

The Depressed Child and Adolescent, second edition • Anxiety Disorders in Children and Adolescents: Research, Assessment, and Intervention

Liver transplantation in the first three months of life.

Pediatric liver transplant recipients have traditionally been grouped according to age. Age-based classification schemes are useful in identifying clinical problems in selected age groups and also for developing solutions to these problems. Although infants in the first 3 months of life have not traditionally been considered a distinct age group, several features of these infants may distinguish them from other pediatric liver transplant recipients. The experience with liver transplantation in infants during the first 3 months of life in three large pediatric liver transplant programs (University of Chicago, Stanford University, and UCLA) was analyzed in order to characterize this group. A total of 23 liver transplants were performed at these three centers in children younger than 3 months of age. This group of patients comprised approximately 37% of the U.S. experience between 1988 and 1994 according to United Network for Organ Sharing statistics. Age distribution at the time of transplantation included the following: <1 month, 28%; 1-2 months, 35%; and 2-3 months, 36%. Median age at the time of transplantation was 37 days (range, 7-90 days), and mean age was 57+/-30 days. Mean weight at the time of transplantation was 3.8+/-1.0 kg. Etiology of liver disease included idiopathic hepatitis, 52%; iron storage disease, 17%; and other causes, 31%. Types of liver allografts used included cadaveric, 85% (reduced size, 60%, and full-size, 25%); living donor, 15%; ABO-identical, 65%; and ABO-compatible, 35%. Actuarial patient and graft survival rates were 60% and 60% at 1 year and 60% and 42% at 2 years, respectively. Median follow-up was 1.5 years. Rejection occurred in 42% of patients, with a median time to first rejection of 13 days. Of these patients, 28% required steroids only and 14% required OKT3. Three patients (14%) were retransplanted at a median time to retransplantation of 1.6 years. Vascular thrombosis occurred in three patients (14%). Liver transplantation performed in infants younger than 3 months of age (1) provides acceptable short- and long-term patient and graft survival, (2) is associated with significant rates of rejection, and (3) is not associated with excessive rates of vascular thrombosis. The etiology of end-stage liver disease occurring in the first 3 months of life is distinct from that in other pediatric liver transplant recipient age groups. These infants should be referred promptly for liver transplantation as reasonable survival can be expected.