Normal Frequency Distribution Research Articles

Genetic studies and mapping of quantitative trait loci (QTL) for yield and yield-related traits in vegetable and seed soybean (Glycine max L.) -derived populations

Understanding the genetic control of seed yield and yield-related traits is essential for its targeted manipulation and genetic improvement. In this study, vegetable soybean (Glycine max L.) genotype- AGS457, and seed soybean genotype- SKAF148 derived F2 and F2:3 populations were phenotyped in the field for yield and yield-related traits and were genotyped with SSR markers for understanding genetic control and mapping of the yield and yield related traits. Normal frequency distribution of the traits indicated polygenic inheritance of yield and yield-related traits under study. The genetic map constructed with 105 polymorphic SSR markers covered 2005.7cM across 20 chromosomes of soybean. In the F2 generation of the crosses, 11 distinct QTLs were mapped for the yield and yield-related traits with any single QTL explaining 4.9 to 27.93% phenotypic variations of the trait concerned. The 11 QTLs were mapped on 9 different chromosomes viz., 5, 6, 7, 9, 11, 13, 16, 18 and 19. Similarly, 19 QTLs were mapped in the F2:3 population with phenotypic variation explained (PVE) by any single QTL ranging from 3.27 to 23.14%. The QTLs in the F2:3 population were mapped on 12 different chromosomes viz., 1, 3, 4, 6, 7, 10, 11, 12, 13, 16, 18 and 20. Three QTLs, one each for plant height at maturity (qPHM11-1), two-seeded pod length (qTSPL16-1) and seed weight per plant (qSWPP6-1) were consistent across F2 and F2:3 generations. Most of the 19 QTLs mapped in this study were novel ones. The knowledge of genetic control of the target traits and their mapping with SSR markers would facilitate the deployment of these QTLs for enhancing yield and other related traits in soybean.

Exploring GWAS and genomic prediction to improve Septoria tritici blotch resistance in wheat

Septoria tritici blotch (STB) is a destructive foliar diseases threatening wheat grain yield. Wheat breeding for STB disease resistance has been identified as the most sustainable and environment-friendly approach. In this work, a panel of 316 winter wheat breeding lines from a commercial breeding program were evaluated for STB resistance at the seedling stage under controlled conditions followed by genome-wide association study (GWAS) and genomic prediction (GP). The study revealed a significant genotypic variation for STB seedling resistance, while disease severity scores exhibited a normal frequency distribution. Moreover, we calculated a broad-sense heritability of 0.62 for the trait. Nine single- and multi-locus GWAS models identified 24 marker-trait associations grouped into 20 quantitative trait loci (QTLs) for STB seedling-stage resistance. The seven QTLs located on chromosomes 1B, 2A, 2B, 5B (two), 7A, and 7D are reported for the first time and could potentially be novel. The GP cross-validation analysis in the RR-BLUP model estimated the genomic-estimated breeding values (GEBVs) of STB resistance with a prediction accuracy of 0.49. Meanwhile, the GWAS assisted wRR-BLUP model improved the accuracy to 0.58. The identified QTLs can be used for marker-assisted backcrossing against STB in winter wheat. Moreover, the higher prediction accuracy recorded from the GWAS-assisted GP analysis implies its power to successfully select superior candidate lines based on their GEBVs for STB resistance.

Clinical report and genetic analysis of a Chinese neonate with craniofacial microsomia caused by a splicing variant of the splicing factor 3b subunit 2 gene.

Craniofacial microsomia (CFM) is a common congenital malformation with unknown pathogenesis. Although few cases have been reported, it is suggested that variants of the SF3B2 gene may lead to CFM. We herein report the case of a neonate with CFM exhibiting rare features of airway obstruction. Trio whole-exome sequencing and Sanger validation were performed on the proband and her parents. Candidate gene mutations were analyzed using the Genome Aggregation Database (gnomAD) for normal frequency distributions. The Human Splicing Finder (HSF) and Rare Disease Data Center (RDDC) RNA splicer algorithms predicted the variant's harmfulness, verified by a Minigene assay. The proband had a heterozygous SF3B2 variant, NM_006842.3:c.777+1G>A. The patient's father also carried this variant and exhibited facial abnormalities. The variant was not in gnomAD, and HSF and RDDC RNA splicers indicated donor site disruption. The minigene assay suggested that two mRNA products were produced, leading to a premature termination codon. For this family, the pathogenesis of CFM may have been caused by an SF3B2 splicing variant. Affected family members exhibited varying degrees of malformation, indicating that CFM has phenotypic heterogeneity. This finding expands the phenotype and variant spectrum of SF3B2, enriches neonatal CFM research, and provides a possible guide to genetic counseling.

Morphological Diversity of Edible Yam (Dioscorea spp) in Tablas, Island, Romblon, Philippines

Yam plants (Dioscorea spp), either wild or cultivated, propagate naturally and are highly productive crops even in marginal areas adapted to diverse conditions. This crop plays a vital role in the lives of many people, as a staple food, feedstuff to livestock/poultry and medicine. However, limited study was conducted on the morphological characterization of edible yams in Tablas Island, Romblon. This study was conducted to determine the morphological characteristics of the existing yam in the province. The different accessions from seven species were collected from cultivated and wild species and were assessed using the descriptors for yams proposed by International Plant Genetic Resources Institute and the National Plant Genetic Resources Laboratory to characterize the yam. Data on morphological diversity were analyzed using descriptive statistics such as percentage and frequency. Eighty-six morphological characters were used in morphological characterization and in assessing the genetic diversity of accessions using the Shannon-Weaver Index. Frequency distribution per trait was determined for the calculation of diversity. The mean H’ of the qualitative and quantitative traits was high, H=0.62 and H=0.83, respectively. The qualitative and quantitative traits have shown normal frequency distribution indicating a wide variation of yam accessions in Tablas Island.

Prevalence of Burnout in Administrative and Industrial Sectors

Burnout is one of the most common psychological conditions that modern society is increasingly facing with, as it influences health status and can mediate the relationship between workload and mental disorders. The aim of this study was to investigate the prevalence of burnout with its dimensions among the specified groups of workers. A self-administered questionnaire was completed by a sample of three groups of workers to assess burnout with its three dimensions (Maslach Burnout Inventory–General Survey). Pearson correlation coefficient and Spearman rank correlation coefficient were used to determine the relationship between numerical variables with normal/not normal frequency distribution. The total burnout score was 2.55 ± 1.08, which corresponded to a moderate degree of this dimension. Of the total sample 18.1% were with a severe degree of burnout while 68.9% were with moderate. Significantly the worst was the situation in the industrial respondents, 25% were severely and 69.3% were moderately burned out, followed by the private and public groups. Our study indicated a high prevalence of burnout, especially in the industrial sectors. The use of accurate results could prove vital in the early detection and management of affected individuals, especially in high-risk professions. Burnout prevention and burnout reduction interventions should be made to highlight the increasing need for the development and deployment of mental health institutions and professionals around the country, in the efforts to pay more attention to mental health.

Mapping of QTLs for morphophysiological and yield traits under water-deficit stress and well-watered conditions in maize.

Maize productivity is significantly impacted by drought; therefore, improvement of drought tolerance is a critical goal in maize breeding. To achieve this, a better understanding of the genetic basis of drought tolerance is necessary. Our study aimed to identify genomic regions associated with drought tolerance-related traits by phenotyping a mapping population of recombinant inbred lines (RILs) for two seasons under well-watered (WW) and water-deficit (WD) conditions. We also used single nucleotide polymorphism (SNP) genotyping through genotyping-by-sequencing to map these regions and attempted to identify candidate genes responsible for the observed phenotypic variation. Phenotyping of the RILs population revealed significant variability in most of the traits, with normal frequency distributions, indicating their polygenic nature. We generated a linkage map using 1,241 polymorphic SNPs distributed over 10 chromosomes (chrs), covering a total genetic distance of 5,471.55cM. We identified 27 quantitative trait loci (QTLs) associated with various morphophysiological and yield-related traits, with 13 QTLs identified under WW conditions and 12 under WD conditions. We found one common major QTL (qCW2-1) for cob weight and a minor QTL (qCH1-1) for cob height that were consistently identified under both water regimes. We also detected one major and one minor QTL for the Normalized Difference Vegetation Index (NDVI) trait under WD conditions on chr 2, bin 2.10. Furthermore, we identified one major QTL (qCH1-2) and one minor QTL (qCH1-1) on chr 1 that were located at different genomic positions to those identified in earlier studies. We found co-localized QTLs for stomatal conductance and grain yield on chr 6 (qgs6-2 and qGY6-1), while co-localized QTLs for stomatal conductance and transpiration rate were identified on chr 7 (qgs7-1 and qTR7-1). We also attempted to identify the candidate genes responsible for the observed phenotypic variation; our analysis revealed that the major candidate genes associated with QTLs detected under water deficit conditions were related to growth and development, senescence, abscisic acid (ABA) signaling, signal transduction, and transporter activity in stress tolerance. The QTL regions identified in this study may be useful in designing markers that can be utilized in marker-assisted selection breeding. In addition, the putative candidate genes can be isolated and functionally characterized so that their role in imparting drought tolerance can be more fully understood.

Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models that can result in limited observations. An alternative to this task is using time series forecasting methods that seek to generalize system characteristics based on past information. However, the analysis of these techniques on large diesel/HFO engines used in Brazilian power plants under the dispatch regime has not yet been well-explored. Therefore, given the complex characteristics of engine fuel consumption during power generation, this work aimed to investigate patterns generalization abilities when linear and nonlinear univariate forecasting models are used on a representative database related to an engine-driven generator used in a TPP located in Pernambuco, Brazil. Fuel consumption predictions based on artificial neural networks were directly compared to XGBoost regressor adaptation to perform this task as an alternative with lower computational cost. AR and ARIMA linear models were applied as a benchmark, and the PSO optimizer was used as an alternative during model adjustment. In summary, it was possible to observe that AR and ARIMA-PSO had similar performances in operations and lower error distributions during full-load power output with normal error frequency distribution of −0.03 ± 3.55 and 0.03 ± 3.78 kg/h, respectively. Despite their similarities, ARIMA-PSO achieved better adherence in capturing load adjustment periods. On the other hand, the nonlinear approaches NAR and XGBoost showed significantly better performance, achieving mean absolute error reductions of 42.37% and 30.30%, respectively, when compared with the best linear model. XGBoost modeling was 8.7 times computationally faster than NAR during training. The nonlinear models were better at capturing disturbances related to fuel consumption ramp, shut-down, and sudden fluctuations steps, despite being inferior in forecasting at full-load, especially XGBoost due to its high sensitivity with slight fuel consumption variations.

Bubbles in biomass fluidized bed gasifiers: A phenomenological probabilistic predictive model

Bubble properties in fluidized beds including size, velocity, and path, vary randomly. This article proposes a new PPPM (Phenomenological Probabilistic Predictive Model) l, that allows one to establish a vrb-BAC prediction band, to describe bubble data, obtained in a lab scale biomass-sand fluidized bed, by using CREC Optiprobes. The PPPM, represents a significant improvement from an earlier Probabilistic Predictive Model (PPM) reported by Torres and de Lasa in 2021, with the new PPPM being based on a balance of forces including gravity, drag, buoyancy, and minimum fluidization excess velocity. The resulting PPPM bubble rise velocity (vrb) is obtained as a function of the bubble axial chord (BAC) and is specifically evaluated by studying bubbles in a high-density sand (above 2500 kg/m3) fluidized bed. The PPPM leads to a behavioral band showing a normal bubble frequency distribution that becomes increasingly skewed as the gas superficial velocity increases. The proposed PPPM is shown to be applicable to single bubbles in a sand bed at incipient fluidization and to multiple bubbles in a biomass-sand bubbling bed. The PPPM is validated in the present study, by comparing it against 46,000 bubbles, under different gas superficial velocities and biomass loadings. By using the PPPM, it is shown 91% of all bubbles studied fall within the PPPM prediction band. Given the demonstrated ability of the PPPM, it is anticipated that this model will significantly contribute to the development of large-scale multiphase biomass gasifier models.

Investigating a Variant in the ACE Gene in Ahvaz Male and Female Athletes Compared to the Control Group with Normal Body Mass

Background: The ACE (angiotensin-converting enzyme) gene is located on chromosome 17 and encodes a metallopeptidase dependent on zinc (Zn) and is widely found on the surface of endothelial and epithelial cells. Objectives: This study compares the relationship between ACE enzyme activity and I/D genotype in male and female bodybuilders and normal aerobic male and female athletes. Methods: For this purpose, the blood of 200 people, who were divided into two groups of athletes with body mass and normal people, was collected and extracted, and the genotype distribution and allele frequency in both groups were checked by the PCR method. The data obtained from examining the D/I variant in the ACE gene in Ahvaz male and female athletes compared to the control group with normal body mass was evaluated using the chi-square test and GraphPad Prism 6 software. Results: Findings of the study showed that 28% of athletes had the D/D genotype, 24% had the I/D genotype, and 48% had the I/I genotype. The normal allele frequency distribution in the athlete population was 40% D allele and 60% I allele. Normal people had 42% D/D genotype, 29% I/D genotype, and 29% I/I genotype. Normal distribution in the control group was 57% D allele and 43% I allele. Conclusions: Findings of the study showed a higher chance of strength and sports ability in people with the I/I genotype. It seems that the activity of ACE among this genotype indicated an increase in activity in the I/I group compared to all the samples selected in the athlete group. The I/D genotype is associated with an increase in physical strength and also increases the activity of ACE, and there is a significant relationship between the activity of ACE compared to athletes and the control group.

Single object profiles regression analysis (SOPRA): a novel method for analyzing high-content cell-based screens

BackgroundHigh-content screening (HCS) experiments generate complex data from multiple object features for each cell within a treated population. Usually, these data are analyzed by using population-averaged values of the features of interest, increasing the amount of false positives and the need for intensive follow-up validation. Therefore, there is a strong need for novel approaches with reproducible hit prediction by identifying significantly altered cell populations.ResultsHere we describe SOPRA, a workflow for analyzing image-based HCS data based on regression analysis of non-averaged object features from cell populations, which can be run on hundreds of samples using different cell features. Following plate-wise normalization, the values are counted within predetermined binning intervals, generating unique frequency distribution profiles (histograms) for each population, which are then normalized to control populations (control-based normalization). These control-normalized frequency distribution profiles are analyzed using the Bioconductor R-package maSigPro, originally developed to analyze time profiles. However, statistically significant altered frequency distributions are also identified by maSigPro when integrating it into the SOPRA workflow. Finally, significantly changed profiles can be used to generate a heatmap from which altered cell populations with similar phenotypes can be identified, enabling the detection of siRNAs and compounds with the same ‘on-target’ profile and reducing the number of false positive hits.ConclusionsSOPRA is a novel analysis workflow for the detection of statistically significant normalized frequency distribution profiles of cellular features generated in high-throughput RNAi screens. For the validation of the SOPRA software workflow, a screen for cell cycle progression was used. We were able to identify such profiles for siRNA-mediated gene perturbations and chemical inhibitors of different cell cycle stages. The SOPRA software is freely available from Github.

Standardization of the JobMatch Loqiq (JMLQ) Instrument to IQ-Scores

In the present study, standardized IQ-score distributions were constructed (M=100, SD=15) for the norm-group and for each of the JMLQ scales, including the General factor and the JMLQ scales (Math, Numerical, Logical 1 & 2, Speed). Subsequently, the influence of levels of education, i.e., elementary school, upper secondary school, post-secondary and university education, and occupational specialization, including advanced tasks, leadership, administration, practical skills, communication, practical work, upon IQ-scoring were assessed. It was found that normal frequency distributions of standardized IQ-scores were obtained for the General (N=1017), Numeric (N= 1004), Math (N=1001), Logic (N=1005), Logic2 (N=998), Complex (N=990), and Speed (N=1013) JMLQ-scales. Furthermore, it was observed that the standardized IQ-scores for the General scale over the four educational levels, elementary school, upper secondary, post-secondary and university, IQ-levels increased incrementally from the former, elementary and secondary school levels to post-secondary and university levels of education. Finally, the standardized IQ-scores for the six occupational sub-norm-groups, advanced tasks, leadership, administration, practical skills, communication, practical work, presented higher scores among the former three groupings, Advanced tasks, Leadership and Administration, than the latter three groups, Practical skills, Communication, Practical work presented. These results, consistent with those results presented in studies utilizing metaanalysis, intelligence testing and life-span analysis, underline advantages of higher academic preponderance and job specialization for the maintenance of higher intellectual and neurocognitive functioning.

Multiple Ensembles of the Hydrogen-bonded Network in Ethylammonium Nitrate versus Water from Vibrational Spectral Dynamics of SCN- Probe.

We performed classical molecular dynamics simulations to monitor the structural interactions and ultrafast dynamical and spectral response in the protic ionic liquid, ethylammonium nitrate (EAN) and water using the nitrile stretching mode of thiocyanate ion (SCN- ) as the vibrational probe. The normalized frequency distribution of the nitrile stretch in SCN- attains an asymmetric shape in EAN, indicating the existence of more than one hydrogen-bonding environment in EAN. Further, we computed the 2D IR spectrum from classical trajectories, applying the response function formalism. Spectral diffusion dynamics in EAN undergo an initial rattling of the SCN- inside the local ion-cage occurring at a timescale of 0.10 ps, followed by the breakup of the ion-cage activating molecular diffusion at 7.86 ps timescale. In contrast, the dynamics of structural reorganization occur at a timescale of 0.58 ps in H2 O. Hence, the time dependence of the frequency-frequency correlation function decay hints at the local molecular structure and ultrafast ion dynamics of the SCN- probe. The loss of frequency correlation read from the peak shape changes in the 2D correlation spectrum as a function of waiting time is faster in H2 O than in EAN due to the enhanced structural ordering and higher viscosity of the latter. We provide an atomic-level interpretation of the solvation environment around SCN- in EAN and water, which indicates multiple ensembles of the hydrogen bond network in EAN.

EXAMINATION OF OSTRACISM AND INTERNET ADDICTION IN GIFTED STUDENTS ACCORDING TO MULTIPLE VARIABLES

In this study, the aim is to examine the ostracism and internet addiction of gifted high school students according to multiple variables. The research group of this study constitutes 190 gifted high school students aged between 14 and 18 students in 21 provinces of Turkey in the 2021-2022 Autumn term selected through purposive sampling. In this study, the correlational survey model, which is a quantitative research method, was used. Data was gathered with the use of the Ostracism Experience Scale for Adolescents (Mercan, 2016) and the Internet Addiction Scale for Adolescents. The data was analyzed using SPSS 25.0. A test of normalcy was implemented on the data. In data showing normal distribution frequency distributions, t-tests and Anova tests were used, and to determine the significance of the significant results Cohen d and Eta squared significance tests were used. As a result of the study the ostracism and internet addiction of gifted students revealed no significance between owning a smartphone, education, mother-father work conditions but showed medium level significance in age, gender, mother-father education levels.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0917/a.php" alt="Hit counter" /></p>

Finite Mixture of the Hidden Markov Model for Driving Style Analysis

Analyzing driving style is useful for developing intelligent vehicles. Previous studies usually consider the statistical features (e.g., the means and standard deviations of brake pressure) of the measured driving data or manually define the number of patterns divided by behavior semantics to characterize driving styles. In this paper, we propose a driving style analysis to describe the personalized driving styles from time-series driving data without specifying the levels in advance but by estimating them from the data. First, range, range rate, and acceleration are selected as three feature variables to describe car-following scenarios. Then, the car-following data are normalized to reduce the scale influence of different variables on the segmentation results. The hidden Markov model (HMM) and the finite mixture of the hidden Markov model (MHMM) are adopted to extract behavior semantics. Compared with the HMM, the MHMM can identify the heterogeneity of data and then provide more reasonable primitive driving patterns. Based on the results, this study uses the K-means clustering to label all the driving patterns semantically and identifies a total of 75 different driving patterns. We use the normalized frequency distributions to describe personalized driving behavior characteristics, and similarity evaluations of driving styles are applied using the Kolmogorov–Smirnov test. The proposed approach in this paper is useful for exploring the characteristics of driving habits.

Revisiting OD-stretching dynamics of methanol‑d4, ethanol-d6 and dilute HOD/H2O mixture with predefined potentials and wavelet transform spectra

• The stretching profiles of OD probes were obtained from classical trajectories. • The normalized frequency distribution and rotational anisotropy are reported. • An accurate and efficient method was reported for the spectral profiles. • The reported results are in an excellent agreement with that of the experiment. Classical molecular dynamics simulations were performed to explore the stretching profiles of hydrogen-bonded liquids using the wavelet transform of classical trajectories to calculate the time-resolved vibrational stretching frequencies of the probes. This method considers the instantaneous distance fluctuations of the unit vector and the momentum along the corresponding bond to calculate the instantaneous stretching frequencies and equilibrium distribution. In this work, we calculated the vibrational spectral properties of the O-D stretch modes in hydrogen-bonding liquids, water, methanol, and ethanol. The time-varying vibrational stretching frequencies of the probes were obtained from the simulated trajectory accurately as compared to other methods. The normalized frequency distribution, time-dependent decay of frequency fluctuations, rotational anisotropy calculations are reported, and relative comparison is made with the existing results. The results of the isotopically diluted HOD mixture and deuterated methanol molecules are in excellent agreement with the experiment, and the ethanol results are satisfactory. This computational method of determining the stretching frequency distribution comparable to the reported vibrational spectrum is one of the complementary accurate theoretical models for the ultrafast vibrational echo spectroscopy experiments. Further, the method can also be adapted to calculate the instantaneous stretching frequency of any probe of interest in a complicated molecular environment.

Effect of Zone and Climate on Yields, Quality and Ripening Characteristics of Montecristo Bananas Grown in Puerto Rico

A study was conducted to determine the effect of zone and climate on yields, ripening characteristics and quality of Montecristo bananas. Experimental plots were established at the Fortuna Substation on the southern coast of Puerto Rico at sea level, and the Adjuntas Substation located in the humid mountainous area at an elevation of 1,800 feet. In plots planted monthly throughout the year, the interval from planting to flowering ranged at the Adjuntas Substation from 371 to 448 days and from 319 to 388 days at the Fortuna Substation. At Fortuna, the length of the flowering period ranged from 120 to 147 days, while at Adjuntas it ranged from 107 to 200 days. A direct relationship was observed at Adjuntas between the length of the flowering period and time of planting. The flowering pattern of the combined monthly plots was different for the two zones. Two flowering cycles were observed at Adjuntas, with the highest percentage of plants flowering from June to December. Although three flowering cycles were observed at Fortuna, the distribution was similar to a normal frequency distribution. Bunch development was much slower at Adjuntas than at Fortuna. The age for harvesting at a given stage of development varied with the time of flowering, but this relationship was more marked at Adjuntas than at Fortuna. The fruit harvested at Adjuntas was quite different in characteristics from the fruit harvested at Fortuna. The bunches were heavier and the fruit weighed more. The bunches also had more hands and more fruit than those harvested at Fortuna. At Adjuntas, the weight of the bunch remained fairly constant throughout the year, while at Adjuntas the weight of the bunch varied with the month of flowering. The weight of the fingers at both locations varied with the time of flowering while the number of fruit per bunch at Fortuna varied only slightly throughout the year. Because the seed material used in these studies was obtained from two farms in the Adjuntas region, another set of experiments was conducted to study the behavior of the Montecristo bananas produced from seed from the experimental plots from each Substation when planted on the other. The results from these experiment showed that the seed material used in our previous studies were of uniform character and that differences observed in flowering patterns, yields, fruit quality and other characteristics were due to the effect of ecological conditions and not to clonal variation. The fruit from both locations when processed into purees gave products of good quality. Fruit from Adjuntas produced purees of a more pronounced yellow color and the flavor was better than fruit from Fortuna. From the standpoint of fresh fruit quality, however, the fruit from Fortuna was better than the fruit from Adjuntas. The fruit from Fortuna had better ripening characteristics, ripening more evenly and with a more attractive yellow color, than the fruit from Adjuntas.

Performance of dense coding and teleportation for random states: Augmentation via preprocessing

In order to understand the resourcefulness of a natural quantum system in quantum communication tasks, we study the dense coding capacity (DCC) and teleportation fidelity of Haar uniformly generated random multipartite states of various ranks. We prove that when a rank-2 two-qubit state, a Werner state, and a pure state possess the same amount of entanglement, the DCC of a rank-2 state belongs to the envelope made by pure and Werner states. In a similar way, we obtain an upper bound via the generalized Greenberger-Horne-Zeilinger state for rank-2 three-qubit states when the dense coding with two senders and a single receiver is performed and entanglement is measured in the senders:receiver bipartition. The normalized frequency distributions of DCC for randomly generated two-, three-, and four-qubit density matrices with global as well as local decodings at the receiver's end are reported. The estimation of mean DCC for two-qubit states is found to be in good agreement with the numerical simulations. Universally, we observe that the performance of random states for dense coding as well as teleportation decreases with the increase of the rank of states, which we have shown to be surmounted by the local preprocessing operations performed on the shared states before starting the protocols, irrespective of the rank of the states. The local preprocessing employed here is based on positive operator valued measurements along with classical communication and we show that unlike dense coding with two-qubit random states, the senders' operations are always helpful to probabilistically enhance the capabilities of implementing dense coding as well as teleportation.

Investigating the Effect of Women’s Position on Advancement in the Banking Sector of Bangladesh

The paper aimed at investigating the existing status and the impact of giving promotion on women’s position in the banking industry. The stud was used purposive sampling on 250 employees serving the public, private and specialized banks. Normal frequency distribution as well as inferential statistics (χ2-test) was applied to analyze data. For examining the effect of the number of promotion received by employees on gender position they hold chi-square test statistic was used. The study reveals that there was 85% of male and 15% of female employees worked in the banking industry. It was observed that female employees worked 16.7% of state-owned commercial (SOBs), 13.5% of private commercial (PCBs), and 13.08% of specialized (SPBs) banks (shown in Table 5). Consequently, a few numbers of female employees were at the senior level. The entry level female employees were 25% of female observed. The study also found that there was a strong association between the women’s position and the number of promotion they up gradated. The study has suggested for managerial as well as policy implications the study suggested for further study to treat equally sustaining in the competitive field of financial institutions.
 Int. J. Soc. Sc. Manage. 7(4): 191-197.

Mapping QTLs underpin nutrition components in aromatic rice germplasm

As rice is an important staple food globally, research for development and enhancement of its nutritional value it is an imperative task. Identification of nutrient enriched rice germplasm and exploiting them for breeding programme is the easiest way to develop better quality rice. In this study, we analyzed 113 aromatic rice germplasm in order to identify quantitative trait loci (QTL) underpinning nutrition components and determined by measuring the normal frequency distribution for Fe, Zn, amylose, and protein content in those rice germplasm. Comparatively, the germplasm Radhuni pagal, Kalobakri, Thakurbhog (26.6 ppm) and Hatisail exhibited the highest mean values for Fe (16.9 ppm), Zn (34.1 ppm), amylose (26.6 ppm) and protein content (11.0 ppm), respectively. Moreover, a significant linear relationship (R2 = 0.693) was observed between Fe and Zn contents. Cluster analysis based on Mahalanobis D2 distances revealed four major clusters of 113 rice germplasm, with cluster III containing a maximum 37 germplasm and a maximum inter-cluster distance between clusters III and IV. The 45 polymorphic SSRs and four trait associations exhibited eight significant quantitative trait loci (QTL) located on eight different chromosomes using composite interval mapping (CIM). The highly significant QTL (variance 7.89%, LOD 2.02) for protein content (QTL.pro.1) was observed on chromosome 1 at 94.9cM position. Also, four QTLs for amylose content were observed with the highly significant QTL.amy.8 located on chromosome 8 exhibiting 7.2% variance with LOD 1.83. Only one QTL (QTL.Fe.9) for Fe content was located on chromosome 9 (LOD 1.24), and two (QTL.Zn.4 and QTL.Zn.5) for Zn on chromosome 4 (LOD 1.71) and 5 (LOD 1.18), respectively. Overall, germplasm from clusters III and IV might offer higher heterotic response with the identified QTLs playing a significant role in any rice biofortification breeding program and released with development of new varieties.

A Statistical Approach to Detect Land Cover Changes in Mediterranean Ecosystems Using Multi-Temporal Landsat Data: The Case Study of Pianosa Island, Italy

The normalized difference vegetation index (NDVI) is commonly used to detect spatiotemporal changes of vegetation cover. This study modeled the spatiotemporal changes of land cover on Pianosa Island, Italy, in the period 1999–2015, using the multi-temporal Landsat images. Since the end of the 1990s, the natural vegetation has been re-colonizing an area of abandoned agricultural land and the island is undergoing a process of re-naturalization in harsh (drought and hot) environmental conditions. Hence, it is an ideal test site to monitor the effects of anthropogenic and climatic stressors on vegetation dynamics under Mediterranean climate. In this work, we proposed a new statistical approach based on a pixel-by-pixel analysis of multi-temporal Landsat images. Mean (µ) and standard deviation (σ) values of the NDVI images taken in 2015 were used for the determination of the pixel thresholds (µ ± 3σ). The evaluation of land cover change was carried out by comparing the µ value of a single NDVI pixel for 2015 with the same pixel of different years of the study period. The results indicate that surface reflectance (SR) Landsat images are more suitable in detecting the vegetation dynamics on the island than the top of atmosphere (TOA) ones and highlight an increasing trend of vegetation cover on Pianosa Island, mainly during the early seven years following the land abandonment in all the main land cover classes: abandoned crops and pastures, Mediterranean macchia, and woodland. However, the abandoned agricultural and pasture areas showed a higher increase in the vegetation cover and a shift in the shape of the normalized frequency distribution of the SR NDVI data during the study period, suggesting that a colonization process from other vegetation classes is occurring (i.e., Mediterranean macchia and trees are colonizing the abandoned land, partly replacing herbaceous species). Our data highlight that the statistical approach applied in this study is suitable for detecting vegetation cover changes associated with anthropogenic and climatic drivers in a typical Mediterranean environment and could be proposed as a new methodological approach in several other land monitoring studies.