Analysis Of Variability Research Articles

Analysis of spatial variability of smog episodes over National Capital Delhi during (2013–2017)

Air pollution is a pressing issue in Delhi, with smog occurrences causing reduced visibility and various respiratory problems. A series of severe SMOG (smoke + fog) episodes between 2013 and 2017 with reduced visibility and exceptionally high PM2.5 concentrations have been reported in Delhi especially around Diwali festival (October–November). The Smog of 2016 is referred as Great Smog of Delhi. This study examined remote sensing data from 2013 to 2017 to investigate smog episodes in Delhi during pre-Diwali, post-Diwali, and Diwali. Satellite-derived parameters viz absorbing aerosol index (AAI), aerosol optical depth (AOD), and ozone monitoring instrument (OMI) along with air pollution data and climatic parameters were used to analyze smog episodes. The results showed that during smog episodes, AOD, AAI and PM2.5 concentrations exceeded permissible limits significantly at all stations across Delhi during the Diwali festival. The ground-based observations at different locations across Delhi and satellite data-derived datasets confirmed the severity of smog episodes. The findings indicate that burning of fire crackers coupled with agriculture stubble burning and subsequent transport of the smoke from North Western states through the Capital had a greater impact on deteriorating air quality in Delhi than local pollution, especially during unfavorable weather conditions associated with high humidity and weaker winds. The outcomes highlight the significance of remotely sensed information in identifying smog episodes and their severity in Delhi. It also underlines the necessity for efficient interventions to control air pollution, particularly amid festivals like Diwali.

Pengaruh Motivasi Kerja dan Disiplin Kerja terhadap Prestasi Kerja Pegawai di Kelurahan Bojong Rawalumbu 2024

This research aims to determine and analyze the influence of work motivation and work discipline on employee work performance in Bojong Rawalumbu Subdistrict. This type of research is quantitative. The sample used was 56 respondents from Bojong Rawalumbu Village employees. To test and prove the research hypothesis using the SPSS version 26 application. The results of the research that has been carried out are that the independent variables have a positive and significant effect on the dependent variable. Based on the test results and data analysis for the Work Motivation variable, it was obtained that the t-value was 2.677 > 1.674 (t-table) with a sig of 0.010 < 0.05, meaning that Work Motivation (X1) had a positive and significant effect on Work Performance (Y). Then the Work Discipline variable obtained a t-count value of 5.873 > 1.674 (t-table) with a sig of 0.000 < 0.05, which means that Work Discipline (X2) has a positive and significant effect on Work Performance (Y) and it is known that the f-count is 33.151 > 3.172 (Ftable) with a sig value of 0.000 < 0.05 that simultaneously Work Motivation (X1) and Work Discipline (X2) have a positive and significant effect on Work Performance (Y).

Structural Insights into the Dynamic Assembly of a YFV sNS1 Tetramer.

Yellow fever virus (YFV) infections can cause severe diseases in humans, resulting in mass casualties in Africa and the Americas each year. Secretory NS1 (sNS1) is thought to be used as a diagnostic marker of flavivirus infections, playing an essential role in the flavivirus life cycle, but little is known about the composition and structure of YFV sNS1. Here, we present that the recombinant YFV sNS1 exists in a heterogeneous mixture of oligomerizations, predominantly in the tetrameric form. The cryoEM structures show that the YFV tetramer of sNS1 is stacked by the hydrophobic interaction between β-roll domains and greasy fingers. According to the 3D variability analysis, the tetramer is in a semi-stable state that may contain multiple conformations with dynamic changes. We believe that our study provides critical insights into the oligomerization of NS1 and will aid the development of NS1-based diagnoses and therapies.

Decadal increases in carbon uptake offset by respiratory losses across northern permafrost ecosystems

Tundra and boreal ecosystems encompass the northern circumpolar permafrost region and are experiencing rapid environmental change with important implications for the global carbon (C) budget. We analysed multi-decadal time series containing 302 annual estimates of carbon dioxide (CO2) flux across 70 permafrost and non-permafrost ecosystems, and 672 estimates of summer CO2 flux across 181 ecosystems. We find an increase in the annual CO2 sink across non-permafrost ecosystems but not permafrost ecosystems, despite similar increases in summer uptake. Thus, recent non-growing-season CO2 losses have substantially impacted the CO2 balance of permafrost ecosystems. Furthermore, analysis of interannual variability reveals warmer summers amplify the C cycle (increase productivity and respiration) at putatively nitrogen-limited sites and at sites less reliant on summer precipitation for water use. Our findings suggest that water and nutrient availability will be important predictors of the C-cycle response of these ecosystems to future warming.

Influence of stop and gate voltage on resistive switching of 1T1R HfO2-based memristors, a modeling and variability analysis

Memristive devices, particularly resistive random access memory (RRAM) cells based on hafnium oxide (HfO₂) dielectrics, exhibit promising characteristics for a wide range of applications. In spite of their potential, issues related to intrinsic variability and the need for precise simulation tools and modeling methods remain a medium-term hurdle. This study addresses these challenges by investigating the resistive switching (RS) behavior of different 1T1R HfO₂-based memristive devices under various experimental conditions. Through a comprehensive experimental analysis, we extract RS parameters using different numerical techniques to understand the cycle-to-cycle (C2C) and device-to-device (D2D) variability. Additionally, we employ advanced simulation methodologies, including circuit breaker-based 3D simulation, to shed light on our experimental findings and provide a theoretical framework to disentangle the switching phenomena. Our results offer valuable insights into the RS mechanisms and variability, contributing to the improvement of robust parameter extraction methods, which are essential for the industrial application of memristive devices.

Studies on Genetic Variability, Heritability, Genetic Advance and Correlation Analysis in Marigold (Tagetes spp.)

Studies on Genetic Variability, Heritability, Genetic Advance and Correlation Analysis in Marigold (Tagetes spp.)

Microclimatic Zonation and Climatic Variability of Sikkim Himalaya

The Köppen classification of climate integrates precipitation and temperature information with natural vegetation patterns to create a precise representation of any particular region's climate. This integration depends on the empirical relationship of climate and vegetation, which indicates that distinct locations in the same class have similar vegetation attributes. Köppen climatic classification factors are explained and Sikkim's climate characteristics are regionalized based on it. The method for making representations of air temperatures and precipitation has been described, and an illustration of Sikkim's climatic zones with variability is generated as a result of these changes. The geographic pattern of climatic types and subtypes in Sikkim has been briefly addressed using an available high-resolution gridded dataset (ERA5-Land). This is described that the constraints of microclimatic zonation emanate from the empirical prerequisite of climate classifications, as well as the nature of data selection and the methodologies employed for climate variability analysis and classification. Based on the Köppen classification for the long term (1980–2021), the Sikkim Himalaya contains three primary climatic classes, particularly ETc (cold, tundra, and cool summer), Cfc (moderately warm, humid, and cool summer), and Cfb (moderately warm, humid, and warm summer). Climate variability on the basis of temperature and precipitation change with respect to 1980–2021 over the entire Sikkim Himalaya concludes that the climatic pattern of the Sikkim has been changed from cold-dry to warm-wet. The alteration in the corresponding climatic pattern is further supported by changes in LULC and NDVI. The results suggest that the precipitation change in the past two decades (1980–2000) is negative, while a significant positive change has been noticed in the recent two decades (2001–2021). Subsequently, the number of extremely wet days decreases in the entire ETc and Cfc climate zones. Regardless, the southern part of the Cfb climatic zone has experienced an increase in extremely wet days. This study's findings will significantly contribute to the development of future policies and initiatives by providing insights crucial for achieving sustainable development, environmental protection, and climate change adaptation.

Phenotypic and genetic characterization of a near-isogenic line pair: insights into flowering time in chickpea

BackgroundCicer arietinum is a significant legume crop cultivated mainly in short-season environments, where early-flowering is a desirable trait to overcome terminal constraints. Despite its agricultural significance, the genetic control of flowering time in chickpea is not fully understood. In this study, we developed, phenotyped, re-sequenced and genetically characterized a pair of near-isogenic lines (NILs) with contrasting days to flowering to identify candidate gene variants potentially associated with flowering time.ResultsIn addition to days to flowering, noticeable differences in multiple shoot architecture traits were observed between the NILs. The resequencing data confirms that the NILs developed in this study serve as appropriate plant materials, effectively constraining genetic variation to specific regions and thereby establishing a valuable resource for future genetic and functional investigations in chickpea research. Leveraging bioinformatics tools and public genomic datasets, we identified homologs of flowering-related genes from Arabidopsis thaliana, including ELF3 and, for the first time in chickpea, MED16 and STO/BBX24, with variants among the NILs. Analysis of the allelic distribution of these genes revealed their preservation within chickpea diversity and their potential association with flowering time. Variants were also identified in members of the ERF and ARF gene families. Furthermore, in silico expression analysis was conducted elucidating their putative roles in flowering.ConclusionsWhile the gene CaELF3a is identified as a prominent candidate, this study also exposes new targets in chickpea, such as CaMED16b and LOC101499101 (BBX24-like), homologs of flowering-related genes in Arabidopsis, as well as ERF12 and ARF2. The in silico expression characterization and genetic variability analysis performed could contribute to their use as specific markers for chickpea breeding programs. This study lays the groundwork for future investigations utilizing this plant material, promising further insights into the complex mechanisms governing flowering time in chickpea.

Characterising the killing of girls and women in urban settings in Latin America, 2000–2019: an analysis of variability and time trends using mortality data from vital registration systems

IntroductionLatin America is burdened by high levels of violence. Although boys and men often experience more violence and fatalities, girls and women face a greater risk of being killed by...

Eye Movements and Autonomic Regulation of Cognitive Activity during Reading in Adolescence. Part I. Functional “Cost” of Cognitive Activity when Reading Text from the Screen in Adolescence

Reading from the screen of an electronic device (ED) is a significant cognitive activity for adolescences, and its complexity affects visceral functions. We conducted an analysis of the heart rate variability (HRV) and eye movements (EM) in adolescents while they were reading complex text on an ED screen. The aim was to assess the functional state characteristics under these conditions and reveal the intensity (“cost”) associated with this activity The study involved 22 adolescences with an average age of 15 years (М = 15.46, SD = 0.44). Reading text from an ED screen in adolescents was associated with a high functional “cost,” characterized by a decrease in overall HRV, an increase in the tension index and heart rate. These changes indicate the functional tension of regulatory systems during cognitive activity. The study revealed varying levels of parafoveal processing involvement. For the majority of adolescents (86.4%), word-by-word reading and a low percentage of regressions (12.0%) were observed, suggesting developed average reading skill. However, a qualitative analysis of individual EM tracks indicated varying level of reading skill development among adolescents, possibly due to a limited vocabulary and a lack of understanding of syntax. Additionally, 40.9% of adolescents demonstrated poor text comprehension. Our results showed that reading remains a complex cognitive task for adolescents, despite the expectation that their reading skills should be well-developed and automated by this age. Individual analysis of HRV and EM in adolescents with varying levels of text comprehension during reading demonstrated different strategies of adaptive behavior and autonomic reactions when performing a complex cognitive task. The functional “cost” of information processing when reading text from the ED screen results from a combination of age-related and individual adaptation characteristics, language competence and the psycholinguistic complexity of the text.

Analysis of heart rate variability during emergency flight simulator missions in fighter pilots

IntroductionManaging emergency situations in different simulated flight segments can entail a workload that could affect the performance of military pilots. The aim was to analyse the modifications in neurovegetative balance...

Cryo-EM architecture of a near-native stretch-sensitive membrane microdomain

Biological membranes are partitioned into functional zones termed membrane microdomains, which contain specific lipids and proteins1–3. The composition and organization of membrane microdomains remain controversial because few techniques are available that allow the visualization of lipids in situ without disrupting their native behaviour3,4. The yeast eisosome, composed of the BAR-domain proteins Pil1 and Lsp1 (hereafter, Pil1/Lsp1), scaffolds a membrane compartment that senses and responds to mechanical stress by flattening and releasing sequestered factors5–9. Here we isolated near-native eisosomes as helical tubules made up of a lattice of Pil1/Lsp1 bound to plasma membrane lipids, and solved their structures by helical reconstruction. Our structures reveal a striking organization of membrane lipids, and, using in vitro reconstitutions and molecular dynamics simulations, we confirmed the positioning of individual PI(4,5)P2, phosphatidylserine and sterol molecules sequestered beneath the Pil1/Lsp1 coat. Three-dimensional variability analysis of the native-source eisosomes revealed a dynamic stretching of the Pil1/Lsp1 lattice that affects the sequestration of these lipids. Collectively, our results support a mechanism in which stretching of the Pil1/Lsp1 lattice liberates lipids that would otherwise be anchored by the Pil1/Lsp1 coat, and thus provide mechanistic insight into how eisosome BAR-domain proteins create a mechanosensitive membrane microdomain.

Effects of Surface Area and Water Level Variability on Fisheries Production in Lake Naivasha, Kenya

Lake Naivasha is a freshwater, a Ramsar site and an Important Bird Area. It provides multiple services to the surrounding communities such as Fishing, Tourism and Agriculture. Lake Naivasha has been experiencing a water level variation cycle over the years as a result of it being shallow and experiencing high evaporation. Variation in water levels leads to flooding, and receding which disrupts the Lake ecosystem thus causing direct and indirect effects on fishing. The study was conducted to establish the relationship between Surface area and water level variability and fish yield and revenue in Lake Naivasha. Primary data was collected using a semi-structured questionnaire, and Key informant interviews. Secondary data was collected using Landsat images that provided the temporal and spatial water level variability and documentary analysis between 1989 to 2022. Both descriptive statistics and inferential statistics were used for data analysis. The results show a positive correlation between Surface area Variability and fish yield (r= 0.6261, R2= 0.3920) and fish revenue (r= 0.5219, R2= 0.2724). Surface area variability accounts for 39% of the variation in total fish yield, and 27% of the fish revenue. The study recommends continuous monitoring and surveillance of the lake level, and water quality by the relevant government agencies, as well as watershed management and conservation measures for the sustainability of the Lake’s fishery.

Multi-resolution assessment of heart rate variability signals during yogic and normal breathing using machine learning modules

In recent times, yoga has gained global prominence due to its wide-ranging health benefits. Heart rate variability (HRV) analysis has become a widely embraced tool to assess the impact of yoga and meditation on individuals. The effects of yoga can vary significantly among individuals based on their prior expertise, skills, and the interplay of external and internal dynamics. This variability complicates the task of extracting distinctive characteristics from raw data to categorize yogic and pre-yogic states. This paper introduces a new approach that enhances the screening of yogic states through multi-resolution analysis of HRV data combined with a machine learning module. The empirical Fourier decomposition (EFD) technique is harnessed to decompose input data into distinct multi-resolution modes. Various statistical characteristics are then extracted from these modes, allowing for a comprehensive analysis of the immediate effects of yoga breathing on HRV signals. To discern between yogic and pre-yogic HRV signals, a selection of significant features is ranked using a chi-square score and subsequently inputted into machine-learning modules. Notably, employing normalized features with the decision medium tree classifier yields an area under the curve (AUC) value of 0.99, while using direct features with an ensemble bagged tree classifier results in an AUC value of 0.82. Furthermore, this research underscores the importance of assessing outcomes from various metrics to compute the underlying dynamics of HRV data during breathing practices. This approach facilitates precise monitoring, personalized feedback, and intervention in yoga machine interfacing systems, ultimately enriching the overall experience and well-being of yoga practitioners.

Exploring the Real-Time Variability and Complexity of Sitting Patterns in Office Workers with Non-Specific Chronic Spinal Pain and Pain-Free Individuals.

Chronic spinal pain (CSP) is a prevalent condition, and prolonged sitting at work can contribute to it. Ergonomic factors like this can cause changes in motor variability. Variability analysis is a useful method to measure changes in motor performance over time. When performing the same task multiple times, different performance patterns can be observed. This variability is intrinsic to all biological systems and is noticeable in human movement. This study aims to examine whether changes in movement variability and complexity during real-time office work are influenced by CSP. The hypothesis is that individuals with and without pain will have different responses to office work tasks. Six office workers without pain and ten with CSP participated in this study. Participant's trunk movements were recorded during work for an entire week. Linear and nonlinear measures of trunk kinematic displacement were used to assess movement variability and complexity. A mixed ANOVA was utilized to compare changes in movement variability and complexity between the two groups. The effects indicate that pain-free participants showed more complex and less predictable trunk movements with a lower degree of structure and variability when compared to the participants suffering from CSP. The differences were particularly noticeable in fine movements.

Clinical Validation of a Deep Learning Algorithm for Automated Coronary Artery Disease Detection and Classification Using a Heterogeneous Multivendor Coronary Computed Tomography Angiography Data Set.

We sought to clinically validate a fully automated deep learning (DL) algorithm for coronary artery disease (CAD) detection and classification in a heterogeneous multivendor cardiac computed tomography angiography data set. In this single-centre retrospective study, we included patients who underwent cardiac computed tomography angiography scans between 2010 and 2020 with scanners from 4 vendors (Siemens Healthineers, Philips, General Electrics, and Canon). Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification was performed by a DL algorithm and by an expert reader (reader 1, R1), the gold standard. Variability analysis was performed with a second reader (reader 2, R2) and the radiologic reports on a subset of cases. Statistical analysis was performed stratifying patients according to the presence of CAD (CAD-RADS >0) and obstructive CAD (CAD-RADS ≥3). Two hundred ninety-six patients (average age: 53.66 ± 13.65, 169 males) were enrolled. For the detection of CAD only, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 95.3%, 79.7%, 87.5%, and 87.5%, respectively. For the detection of obstructive CAD, the DL algorithm showed sensitivity, specificity, accuracy, and area under the curve of 89.4%, 92.8%, 92.2%, and 91.1%, respectively. The variability analysis for the detection of obstructive CAD showed an accuracy of 92.5% comparing the DL algorithm with R1, and 96.2% comparing R1 with R2 and radiology reports. The time of analysis was lower using the DL algorithm compared with R1 (P < 0.001). The DL algorithm demonstrated robust performance and excellent agreement with the expert readers' analysis for the evaluation of CAD, which also corresponded with significantly reduced image analysis time.

Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level.

Forensic Characterization, Genomic Variability and Ancestry Analysis of Six Populations from Odisha Using mtDNA SNPs and Autosomal STRs.

Located on India's eastern coast, Odisha is known for its diverse tribes and castes. In the early days of genome sequencing technology, researchers primarily studied the Austroasiatic communities inhabiting this region to reconstruct the ancient origins and dispersal of this broad linguistic group. However, current research has shifted towards identifying population and individual-specific genome variation for forensic applications. This study aims to analyze the forensic efficiency and ancestry of six populations from Odisha. We assessed the SF mtDNA-SNP60™ PCR Amplification Kit by comparing it with PowerPlex® Fusion 6C System, a widely used autosomal STR (aSTR) kit, in an Indian cohort. Although the mtDNA SNP kit showed low discriminating power for individuals of a diverse population, it could identify deep lineage divergence. Also, we utilized mitochondrial and autosomal variation information to analyze the ancestry of six endogamous ethnic groups in Odisha. We observe two extremities-populations with higher West Asian affinity and those with East Asian affinity. This observation is in congruence with the existing information of their tribal and non-tribal affiliation. When compared with neighbouring populations from Central and Eastern India, multivariate analysis showed that the Brahmins clustered separately or with the Gopala, Kaibarta appeared as an intermediate, Pana and Kandha clustered with the Gonds, and Savara with the Munda tribes. Our findings indicate significant deep lineage stratification in the ethnic populations of Odisha and a gene flow from West and East Asia. The artefacts of unique deep lineage in such a diverse population will help in improving forensic identification. In addition, we conclude that the SF mtDNA-SNP60 PCR Amplification Kit may be used only as a supplementary tool for forensic analysis.

Acceleration of skeletal maturation in Central Europe over the last two decades: insights from two cohorts of healthy children

BackgroundDeviations between the determination of bone age (BA) according to Greulich and Pyle (G&P) and chronological age (CA) are common in Caucasians. Assessing these discrepancies in a population over time requires analysis of large samples and low intra-observer variability in BA estimation, both can be achieved with artificial intelligence-based software. The latest software-based reference curve contrasting the BA determined by G&P to the CA of Central European children dates back over two decades.ObjectiveTo examine whether the reference curve from a historical cohort from the Netherlands (Rotterdam cohort) between BA determined by G&P and CA still applies to a current Central European cohort and derive a current reference curve.Materials and methodsThis retrospective single-center study included 1,653 children and adolescents (aged 3–17 years) who had received a radiograph of the hand following trauma. The G&P BA estimated using artificial intelligence-based software was contrasted with the CA, and the deviations were compared with the Rotterdam cohort.ResultsAmong the participants, the mean absolute error between BA and CA was 0.92 years for girls and 0.97 years for boys. For the ages of 8 years (boys) and 11 years (girls) and upward, the mean deviation was significantly greater in the current cohort than in the Rotterdam cohort. The reference curves of both cohorts also differed significantly from each other (P < 0.001 for both boys and girls).ConclusionThe BA of the current Central European population and that of the curve from the Rotterdam cohort from over two decades ago differ. Whether this effect can be attributed to accelerated bone maturation needs further evaluation.

STUDY AND ANALYSIS OF FETAL HEART RATE MONITORING SYSTEM

Analysis of FHR variability, together with uterine muscle contractions and fetal movement activity, subject to cardiotocographic monitoring, currently play a fundamental role in fetal assessment. The strength of cardiotocography lies in that normal ranges of the results of the FHR signal analysis almost always (&gt;95%) confirm fetal wellbeing. Unfortunately, questionable or abnormal signal features may indicate both fetal distress and its absence. The classic fetal heart rate variability analysis consists in determining the FHR baseline and then in identifying bradycardia/tachycardia, acceleration/deceleration patterns as well as the type and amplitude of oscillations. A more advanced FHR analysis is aimed at evaluating instantaneous FHR variability, especially with beat-to-beat approach, which is very important in fetal condition assessment. The most common technique of recording the FHR signal is the Doppler ultrasound method, which makes it difficult to correctly estimate the instantaneous heart rate variability because the measurements are averaged. On the other hand, instantaneous heart rate variability plays a crucial role in the diagnosis of fetal condition [1]. The increased interest in monitoring the fetal cardiac bioelectrical activity is due to the fact that the analysis of the fetal electrocardiogram ensures high efficiency and accuracy of the heart rate measurement even on a beat-to-beat level. The first fetal electrocardiogram was recorded as early as in the 1960-ies by means of a special spiral electrode enabling direct signal registration from a fetal head. Due to the way the electrode is placed, its use is limited only to the advanced labour stage [1]..