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Developing and identifying high-yielding genotypes with shoot fly resistance in sorghum is a complex task that requires a detailed examination of genetic variability and relationships between numerous component traits related to grain yield and pest resistance. The purpose of this study was to use 15 morphological traits to assess genetic variability in 64 sorghum genotypes, which were analysed using principal component analysis (PCA) and the box plot technique. PCA identified seven components that account for almost 74% of the total variability in grain yield and shoot fly resistance. Two of the most reliable components (PC1 and PC2) were strongly correlated with a number of traits, including deadhearts per cent (21 and 28 DAE), seedling vigour, number of eggs per plant and trichome density on upper and lower surfaces, fodder yield per plot, fodder yield per plant, days to maturity and days to 50% flowering. PCA biplots identify groups of genotypes that can be suitable for specific breeding strategies. These include genotype clusters that combine grain yielding ability with resistance to shoot flies. According to the results of the box plot analysis, most of the traits showed greater variation towards grain yield and shoot fly resistance. This research has provided useful information on sorghum genotype genetic variability and its potential use in sorghum development programmes. [ABSTRACT FROM AUTHOR]

The histogram remains a widely used tool for visualization of the distribution of a continuous variable, despite the disruption of binning the underlying continuity into somewhat arbitrarily sized discrete intervals imposed by the simplicity of its pre-computer origins. Alternatives include three visualizations, namely a smoothed density distribution such as a violin plot, a box plot, and the direct visualization of the individual data values as a one-dimensional scatter plot. To promote ease of use, the plotting function discussed in this work, Plot(x), automatically integrates these three visualizations of a continuous variable x into what is called a VBS plot here, tuning the resulting plot to the sample size and discreteness of the data. This integration complements the information derived from the histogram well and more easily generalizes to a multi-panel presentation at each level of a second categorical variable.

Datashak (Amaranthus lividus) are climate-smart, stress-resistant, C4 leafy vegetables. Fourteen datashak genotypes were evaluated in three replicates during the summer growing season at Bangabandhu Shiekh Mujibur Rahman Agricultural University. The results revealed highly significant differences among the genotypes, indicating a wide range of variability. By considering the genetic parameters, selection was performed based on the total biomass per plant (TBPP), shoot weight (SW), stem weight (StW), and shoot length to improve the biological yield (BY) of the genotypes. The correlation results revealed that almost all the features showed a significant increase in the BY of datashak. The StW, root length, leaf weight, and SW demonstrated a strong direct and positive effect on and a noteworthy genotypic association with the BY, indicating that direct choice depending on these traits will be useful for enhancing the BY of datashak. The datashak accessions were divided into four clusters based on the Euclidean distance matrix using Ward's statistics method. Clusters II and III datashak might be selected for the next breeding programs based on the mean cluster values and distances within the clusters and between clusters since these two clusters had superior mean values for the majority of the characteristics. Redtower and Data (cross) could be selected as multitrait high-performance accessions based on the multitrait genotype-ideotype distance index (MGIDI), as these datashak displayed balanced traits related to the SW, StW, TBPP, and BY without assigning weights, and were free from multicollinearity. Lalgolapi, Lolita, and BARI lalshak-1 were more promising than the others due to their strong positive contributions; therefore, choosing these datashak accessions would be better in terms of the yield, according to the principal component analysis, heatmap, and cluster dendrogram. These datashak accessions could be considered high-yielding, promising varieties for future breeding activities. [ABSTRACT FROM AUTHOR]

The histogram remains a widely used tool for visualization of the distribution of a continuous variable, despite the disruption of binning the underlying continuity into somewhat arbitrarily sized discrete intervals imposed by the simplicity of its pre-computer origins. Alternatives include three visualizations, namely a smoothed density distribution such as a violin plot, a box plot, and the direct visualization of the individual data values as a one-dimensional scatter plot. To promote ease of use, the plotting function discussed in this work, Plot(x), automatically integrates these three visualizations of a continuous variable x into what is called a VBS plot here, tuning the resulting plot to the sample size and discreteness of the data. This integration complements the information derived from the histogram well and more easily generalizes to a multi-panel presentation at each level of a second categorical variable. [ABSTRACT FROM AUTHOR]

In the field of small-sample domains, since its introduction, the effectiveness and practicality of the megatrend diffusion (MTD) method have been demonstrated in various studies. Recently, with the popularity of generative deep learning, researchers have integrated Wasserstein generative adversarial networks (WGANs) with the MTD method and proposed a novel framework called WGAN-MTD for virtual data generation. It uses the MTD for producing estimates, which restricts the generative model's output value range and generates effective synthetic samples. However, the validity of developing virtual samples using real-world data containing outliers remains controversial, and the weight clipping method in WGAN has been shown to affect the stability of model training. In this study, we propose an advanced framework in which the boxplot is integrated with a penalization term to limit the effect of outliers, especially from small samples. The proposed framework considers the convolutional layers to capture local information features and lower the complexity of the model by reducing the number of parameters between the input and output layers. Additionally, we adopt a WGAN with the gradient penalty (GP) method instead of WGAN alone to improve the training stability and precision in the generative model. Experimental results demonstrate that both the boxplot and the penalization term enhance the accuracy of the generative models for small datasets containing outliers. [ABSTRACT FROM AUTHOR]

This study is a component of a larger initiative that involves the assessment and screening of pharmaceutical and chemical factories that produce medical substances, particularly in Asia and export them to poor nations. The present study concentrated on the inactive pharmaceutical ingredients of a frequently used excipient in pharmaceutical products made of amylopectin and amylose, named Amylum Maydis by the International Union of Pure and Applied Chemistry (IUPAC) nomenclature. This compound has the chemical formula C6H10O5. Manufacturers asserted that all raw ingredients complied with the British Pharmacopoeia (BP), harmonizing requirements and analytical criteria in the process. As a result, every test complied with the official standard procedures described in the raw material testing monograph. The chosen tests included oxidizing agents, sulfated ash, and loss on drying (LOD). Software for statistical process control, or SPC, was used to collect and handle datasets. Preliminary data examination was done using box plots and three variable visualization techniques associated with the correlation matrix. All results showed that improvements of the inspection characteristics records are mandated to show stable variations even if there was no out-of-specification detected. Accordingly, the output of the tests should be investigated to correct for the assignable causes of the variations. It should be noted that the present data did not follow specific distributions, especially with the presence of aberrant values. Furthermore, it was found that there were several out-of-control points even in cases where there was no deviation from the specification, highlighting the need for suitable inquiry and correction for assignable reasons of variances among batches. Government enforcement of industrial SPC regulations is necessary to ensure the safety and quality of produced medical substances. [ABSTRACT FROM AUTHOR]

In this work, we considered the parametric estimation of the characteristics of the M / M / 1 / N waiting model with Bernoulli feedback. Through a Monte-Carlo simulation study, we have illustrated the effect of the estimation of the starting parameters of the considered waiting system on the statistical properties of its performance measures estimates, when these latter are obtained using the plug-in method. In addition, several types of convergence (bias, variance, MSE, in law) of these performance measure estimators have also been showed by simulation. [ABSTRACT FROM AUTHOR]

Crop evapotranspiration is essential for planning and designing an efficient irrigation system. The present investigation assessed the capability of four machine learning algorithms, namely, XGBoost linear regression (XGBoost Linear), XGBoost Ensemble Tree, Polynomial Regression (Polynomial Regr), and Isotonic Regression (Isotonic Regr) in modeling daily reference evapotranspiration (ETo) at IARI, New Delhi. The models were developed considering full and limited dataset scenarios. The efficacy of the constructed models was assessed against the Penman–Monteith (PM56) model estimated daily ETo. Results revealed the under full and limited dataset conditions, XGBoost Ensemble Tree gave the best results for daily ETo modeling during the model training period, while in the testing period under scenarios S1(Tmax) and S2 (Tmax, and Tmin), the Isotonic Regr models yielded superior results over other models. In addition, the XGBoost Ensemble Tree models outperformed others for the rest of the input data scenarios. The XGBoost Ensemble Tree algorithms reported the best values of correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE). Thus, we recommend applying the XGBoost Ensemble Tree algorithm for precisely modeling daily ETo in semi-arid climatic conditions. HIGHLIGHTS Four machine learning algorithms were tested for daily ETo modeling.; Five input scenarios were developed using cross-correlation analysis.; The performance was assessed using five statistical performance indicators.; The XGBoost Ensemble Tree algorithm is found best in daily ETo modeling.; The weighted score-based model ranking was done.;

The collar rot disease caused by the soil-borne fungus Sclerotium rolfsii (Sacc.) is a major constraint of chili (Capsicum annum L.) production in Pakistan. Considering the fact that biological control has low efficacy in field conditions, their encapsulation into biopolymers like alginate meets the essential criteria for bacteria viability, effectiveness, shelf life, stability, and controlled release. The current research was conducted to develop stable alginate beads of biocontrol bacteria (Ochrobactrum ciceri) and to check their efficacy in managing collar rot disease in C. annum. In vitro, antifungal bioassays indicated that increasing concentrations (16, 24, and 64%) of culture filtrate or the cell pellets significantly decreased fungal growth and enzyme activity (catalase: CAT, peroxidase: POX, polyphenol oxidase: PPO, and phenylalanine ammonia-lyase: PAL), hence increased offshoots hyphae, and caused distortion in sclerotia and hyphae. O. ciceri successfully developed stable alginate beads (AlgB-OC) as indicated by FTIR analysis, encapsulation efficiency (91.15%), moisture content (99.19%), swelling ratio (130%), particle size (wet: 2.11 mm; dry: 1.15 mm), film-forming time (48 h), and slow-release of entrapped bacteria till 30 days. AlgB-OC exhibited 76% antifungal potential in vitro and managed 70% of the collar rot disease in vivo. Moreover, the application of AlgB-OC significantly improved plant growth (length and biomass) and physio-chemical attributes (photosynthetic pigments, total protein content, activity of CAT, POX, PPO, and PAL), along with more lignin, phenolics, gel, and starch accumulation in roots. Multivariate analysis based on a total of 13 morpho-physiological indices of chili plants also identified AlgB-OC as the most effective treatment for disease management. It was concluded that the formulation of O. ciceri into alginate could be an effective alternative for managing collar rot disease in chili plants and obtaining better plant growth. Antifungal potential of alginate beads of Ochrobactrum ciceri (AlgB-OC) against Sclerotium rolfsii (SR). In vitro antifungal assay (A), FTIR of the AlgB-OC (B), Effect of AlgB-OC on collar rot disease in chili caused by SR (C) and PCA-based analysis on disease, biophysical, and biochemical attributes of chili (D). [Display omitted] • Ochrobactrum ciceri was capable of suppressing the growth of Sclerotium rolfsii by suppressing the sclerotial formation and deteriorating hypha on agar plates and broth medium. • Stable alginate beads of O. ciceri (AlgB-OC) prepared by the ionic gelation method and in vitro exhibited 76% antifungal activity against S. rolfsii. • Plants' morpho-physiological and histo-chemical responses improved by AlgB-OC due to the management of 70% of the collar rot disease in vivo. • Formulation of O. ciceri into alginate could be an effective alternative for managing collar rot disease in chili plants and obtaining better growth. [ABSTRACT FROM AUTHOR]

Eight parents and the 28 crosses of Indian Mustard (Brassica juncea (L.) Czern & Coss.) in two segregating generations, viz., F2 and F3 were studied for their genetic variability during the rabi seasons of 2020-21 and 2021-22, respectively. Observations were recorded for six morphological characters such as plant height (cm), primary branches per plant, secondary branches per plant, siliquae per plant, 1000 seed weight (g) and seed yield per plant (g). The PCV was found to be greater than the GCV and the difference between them was high in all the characters in both F2 and F3 generations. Most of the characters revealed a medium range of GCV and PCV. Moderate heritability was expressed by all the characters except primary branches per plant in F3 generation. The Genetic Advance as a percentage of Mean (GAM) was higher in most characters except in plant height. The character plant height was found to be positively correlated with seed yield per plant in both F2 and F3 generations, with secondary branches per plant in the F3 generation and negatively correlated with primary branches per plant in F3 generation. A high direct effect on seed yield per plant was exhibited by plant height and 1000 seed weight in both F2 and F3 generations and by Secondary branches per plant in the F2 generation and Siliquae per plant in the F3 generation.

The survival of mankind cannot be imagined without air. Consistent developments in almost all realms of modern human society affected the health of the air adversely. Daily industrial, transport, and domestic activities are stirring hazardous pollutants in our environment. Monitoring and predicting air quality have become essentially important in this era, especially in developing countries like India. In contrast to the traditional methods, the prediction technologies based on machine learning techniques are proved to be the most efficient tools to study such modern hazards. The present work investigates six years of air pollution data from 23 Indian cities for air quality analysis and prediction. The dataset is well preprocessed and key features are selected through the correlation analysis. An exploratory data analysis is exercised to develop insights into various hidden patterns in the dataset and pollutants directly affecting the air quality index are identified. A significant fall in almost all pollutants is observed in the pandemic year, 2020. The data imbalance problem is solved with a resampling technique and five machine learning models are employed to predict air quality. The results of these models are compared with the standard metrics. The Gaussian Naive Bayes model achieves the highest accuracy while the Support Vector Machine model exhibits the lowest accuracy. The performances of these models are evaluated and compared through established performance parameters. The XGBoost model performed the best among the other models and gets the highest linearity between the predicted and actual data. [ABSTRACT FROM AUTHOR]

Diagnosis is a measurement, and so is intelligence. We present a novel visual method to analyze diagnoses. The concept of country health was introduced in a trial to compare the efficiency of two treatments: box plot and pull anti. We found that the pull anti performs better in both accuracy and extent. The box plot is a diagnosis of abnormality using simple up–down symmetry; however, when abnormalities occur in the probability, the symmetry of the structure may be negatively affected. The pull anti checks the asymmetry of the left and right and therefore results in a better analysis. Furthermore, we designed another trial to test the sampling bias and found that an insensible disturbance might lead to statistical self-significance. We thus suggest that extended observations towards certainty are necessary to obtain better diagnoses or intelligence. [ABSTRACT FROM AUTHOR]

Background: Hierarchical composite endpoints are complex endpoints combining outcomes of different types and different clinical importance into an ordinal outcome that prioritizes the clinically most important (e.g. most severe) event of a patient. Hierarchical composite endpoint can be analysed with the win odds, an adaptation of win ratio to include ties. One of the difficulties in interpreting hierarchical composite endpoint is the lack of proper tools for visualizing the treatment effect captured by hierarchical composite endpoint, given the complex nature of the endpoint which combines events of different types. Methods: Hierarchical composite endpoints usually combine time-to-event outcomes and continuous outcomes into a composite; hence, it is important to capture not only the shift from more severe categories to less severe categories in the active group in comparison to the control group (as in any ordinal endpoint), but also changes occurring within each category. We introduce the novel maraca plot which combines violin plots (with nested box plots) to visualize the density of the distribution of the continuous outcome and Kaplan–Meier plots for time-to-event outcomes into a comprehensive visualization. Conclusion: The novel maraca plot is suggested for visualization of hierarchical composite endpoints consisting of several time-to-event outcomes and a continuous outcome. It has a very simple structure and therefore easily communicates both the overall treatment effect and the effect on individual components. [ABSTRACT FROM AUTHOR]

With the continuous development of technologies such as the Internet of Things (IoT) and cloud computing, sensors collect and store large amounts of sensory data, realizing real-time recording and perception of the environment. Due to the open characteristics of WSN, the security risks during information transmission are prominent, and network attack or intrusion is likely to occur. Therefore, effective anomaly detection is vital for IoT systems to keep the system safe. The original Isolation Forest algorithm is an anomaly detection algorithm with linear time complexity and has a better detection effect on perceptual data. However, there are also disadvantages such as strong randomness, low generalization performance, and insufficient stability. This paper proposes a data anomaly detection method named BS-iForest (box plot-sampled iForest) for wireless sensor networks based on a variant of Isolation Forest to address the problems. This method first uses the sub-dataset filtered by the box graph to train and construct trees. Then, isolation trees with higher accuracy are selected in the training set to form a base forest anomaly detector. Next, the base forest anomaly detector uses anomaly detection to judge data outliers during the next period. These experiments were performed on datasets collected from sensors deployed in a data center of a university, and the Breast Wisconsin (BreastW) dataset, showing the performance of the variant of the Isolation Forest algorithm. Compared with the traditional isolation forest, the area under the curve (AUC) increased by 1.5% and 7.7%, which verified that the proposed method outperforms the standard Isolation Forest algorithm with the two datasets we chose. [ABSTRACT FROM AUTHOR]

Association rule mining is typically used to uncover the enthralling interdependencies between the set of variables and reveals the hidden pattern within the dataset. The associations are identified based on co-occurring variables with high frequencies. These associations can be positive (A→B) or negative (A→⌐B). The number of these association rules in larger databases are considerably higher which restricted the extraction of valuable insights from the dataset. Some rule pruning strategies are used to reduce the number of rules that can sometimes miss an important, or include an unimportant rule into the final rule set because of not considering the context of the rule. Context-based positive and negative association rule mining (CBPNARM) for the first time included context variable in the algorithms of association rule mining for selection/ de-selection of such rules. In CBPNARM, the selection of context variable and its range of values are done by the user/expert of the system which demands unwanted user interaction and may add some bias to the results. This paper proposes a method to automate the selection of context variable and selection of its value range. The context variable is chosen by using the diversity index and chi-square test, and the range of values for the context variable is set by using box plot analysis. The proposed method on top of it added conditional-probability increment ratio (CPIR) for further pruning uninteresting rules. Experiments show the system can select the context variable automatically and set the right range for the selected context variable. The performance of the proposed method is compared with CBPNARM and other state of the art methods. [ABSTRACT FROM AUTHOR]

The concentration of nine heavy metals in Oreochromis niloticus inhabiting Agodi reservoir, Nigeria, was investigated. Samples were collected at the inlet, centre and outlet of the reservoir. The concentration of the metals was determined with Atomic Absorption Spectrophotometer. Ecological Risk Quotient (ERQ) was used to numerically quantify the ecological risks associated with the mineral, while Box Plot was used to evaluate the tolerance of the fish to heavy metals. The ERQ, which was in the order of Mn > Fe > Zn > Cu > Cd > Pb > Cr > Co > Ni, showed that Ni, Co, Cr and Pb had values of less than one, implying that they did not constitute an ecological threat to the environment, while Mn, Fe, Zn, Cu and Cd were of ecological risk. Results revealed that the concentration of Cd, Co and Ni exceeded the bio‐tolerance of the fish at the inlet, while Cd, Pb, Cu and Ni were beyond the tolerability of the fish at the centre. Similarly, the concentration of Co, Cu, Cr, Pb, Zn and Ni was above the tolerance limit of the fish. This showed that Fe and Mn were within the tolerance range of the fish and that the tolerability of the fish in relation to the location was inlet < Centre < Outlet. [ABSTRACT FROM AUTHOR]

An investigation was conducted with 217 germplasm lines of rice to estimate potential variation among rice genotypes. Multivariate analyses viz., PCA and cluster analysis to assess genetic diversity were performed on seven agronomic traits. For the evaluated traits in the lines of germplasm, box plots and normal probability plots displayed substantial estimates of variability. PCA showed that PC1 and PC2 represented 46.15 per cent of variation. PC1 was responsible for the most variance (24%) for four characters, followed by PC2 (22.15%) for three parameters. Filled grain number per panicle, single plant yield, plant height and thousand grain weight were identified as vital traits contributing to variability. Based on agglomerative hierarchical cluster analysis, the germplasm lines were grouped into five clusters, which explained a lot of variation in the traits. The study identified that plant height and 1000 grain weight have the greatest impact on variation. The genotypes viz., WGL 1063, RNR 26085, OR 2511-3, Swarna, KNM 7123, WGL 1063, MGD-101, IVT MS-6130, IVT IM-4231, IVT IME-3948 and IVT NPT-6303 were found to be the best performing genotypes for the traits panicle length, plant height and single plant yield, which can be used in hybridisation programme to improve these traits. [ABSTRACT FROM AUTHOR]

This study was conducted for extensive and systemic investigation of anatomical, palynological and seed morphological properties of two endemic Noccaea species, naturally growing in Turkey. Independent sample T-test and box plot were carried out using quantitative characters of the studied species. The anatomical results showed that the species had similar characteristics, though there were significant differences in root cortex cells and trachea; stem epidermis, cortex cell and endodermis; leaf upper and lower epidermis, lower cuticle, mesophyll and palisade parenchyma. Pollen grains of two endemic species were observed as radially symmetric, isopolar, with tricolpate aperture, prolate pollen shape and had small size. Pollen surface ornamentation was micro-reticulate in both species. Considering palynological characters, equatorial axis, AMB exine and intine have taxonomic importance. Seeds of N. birolmutlui were ovate to orbicular in shape and orange-brown in color, with colliculate ornamentation; however, the seeds of N. camlikensis were ovate to oblong in shape and brown and shiny in color, with colliculate-reticulate ornamentation.

The hygrothermal ageing of epoxy resins and epoxy matrix composite materials has been studied many times in the literature. Models have been developed to represent the diffusion behaviour of the materials. For reversible diffusions, Fick, Dual–Fick and Carter a n d Kibler models are widely used. Many parameters, correlated or not, have been identified. The objectives of this review by statistical analysis are to confirm or infirm these correlations, to highlight other correlations if they exist, and to establish which are the most important to study. This study focuses on the parameters of the Fick, Dual–Fick and Carter a n d Kibler models. For this purpose, statistical analyses are performed on data extracted and calculated from individuals described in the literature. Box plot and PCA analyses were chosen. Differences are then noticeable according to the different qualitative parameters chosen in the study. Moreover, correlations, already observed in the literature for quantitative variables, are confirmed. On the other hand, differences appear which may suggest that the models used are inappropriate for certain materials. [ABSTRACT FROM AUTHOR]

As the core component of an electric vehicle, the health of the traction battery closely affects the safety performance of the electric vehicle. If the sub-health state cannot be identified and dealt with in time, it may cause traction battery failure, pose a safety hazard, and cause property damage to the driver and passengers. This study used data-driven methods to identify the two typical types of sub-health state. For the first type of sub-health state, the interclass correlation coefficient (ICC) method was used to determine whether there was an inconsistency between the voltage of a single battery and the overall voltage of the battery pack. In order to determine the threshold, the ICC value of each vehicle under different working conditions was analyzed using box plots, and a statistical ICC threshold of 0.805 was used as the standard to determine the first sub-health type. For the second type of sub-health state, the Z-score and the differential area method were combined to determine whether the single cell voltage deviated from the overall battery pack voltage. A battery whose voltage differential area exceeds the range of u ± 3σ is regarded as having a sub-health state. The results show that both methods can accurately judge the sub-health state type of a single battery. Furthermore, combined with the one-month operation data of the vehicle, we could calculate the sub-health state frequency of each single battery and take single batteries with a high frequency as the key object of attention in future vehicle operations. [ABSTRACT FROM AUTHOR]

The goal of the study is to analyse the outcomes of a questionnaire survey which concerns under standability of military language used on former Afghanistan missions. Two groups of respondents took part in the survey – 50 soldiers with experience from a foreign mission, 50 soldiers without such experience. The data have indicated that when it comes to decoding randomly generated expressions, an important role is mainly played by the soldiers' other foreign missions, professional specialisations, length of service, and close contact with the participants of the Afghan missions. [ABSTRACT FROM AUTHOR]

Synchrophasor technology improves power grid visibility by installing phasor measurement units (PMUs) over a wide area in the power system. Big data received from PMUs contains important information about grid behavior. This information is useful in monitoring the safety and security of the grid. An extensive state-of-the-art review of big data analytics and its prime applications in power systems are expressed in this paper. It presents, a general background in data analysis techniques such as exploratory data analysis, statistical data analysis, and unsupervised data mining techniques like clustering. Two 400 kV transmission line tripping events are analyzed from the data recorded by the PMUs installed in the western part of the Indian grid i.e., Maharashtra State Electricity Transmission Company Limited (MSETCL) grid. The box plots, Correlogram, and the formation of clusters carried out for the PMU data recorded under ambient and disturbance events. This provides insights on how effective big data helps to make the right decision at right time for effective management of the power grid under normal and contingency conditions.

Introduction: Oral health is an integral component of general health. Many oral systemic diseases manifest orally, and general medical practitioners are the primary health-care providers in society, so understanding oral health and its significance is critical for medical students and practitioners. The present study was conducted to assess the oral health awareness among MBBS students and interns. Materials and Methods: After obtaining institutional ethical committee approval, the present cross-sectional study was conducted among 318 subjects who were second, third, and final year MBBS students and interns of ESIC medical college and PGIMSR. A validated, self-structured questionnaire comprising of 20 questions pertaining to oral health awareness were prepared using Google forms and was sent to the study participants tthrough WhatsApp and the findings were analysed using proper statistical methods. Results: In the present study, 19% of the study population had good oral health awareness, 49% and 32% had fair and poor oral health awareness, respectively. The final year students had a good oral health awareness compared to the other years of students and interns. Oral health awareness scores did not differ significantly between male and female study participants. Conclusion: The present study found a fair oral health awareness amongst the study population. Clinical Significance: A proper knowledge of oral health is very essential among the medical students as they would be approached by the general population for most of the primary health-care needs. [ABSTRACT FROM AUTHOR]

Macrophominaphaseolina, is widely considered to be amongst the most important threatening fungal pathogen of over 750 plant species including mungbean. Synthetic chemical fungicides are not an appropriate option due to the robust nature of the fungal sclerotia. Zinc (Zn) is indispensable for the healthy development of plants, nontoxic in appropriate amount and display a broad-spectrum of strong antifungal activity at low concentration. In the current study, antimycotic and disease managing potential of ZnSO4 was assessed in vitro and in vivo. Results indicated that in vitro, the fungal activity as growth, reproduction, biochemical, metal accumulation, and motility attributes were negatively affected by high levels of Zn ions (2.50–8.50 mM) in the growth medium. Complete growth inhibition was achieved at 16.50 mM. In planta, the pathogen caused 100% charcoal rot disease index, considerably reduced biophysical (growth and dry biomass) and biochemical traits (photosynthetic pigment, total protein content, catalase/CAT, peroxidase/POX, polyphenol oxidase/PPO and phenylalanine ammonia lyase /PAL) in the mungbean plants. However, the disease was noticeably suppressed (65, 40 and 15%) when exposed in vivo to foliar Zn concentrations (0.013, 0.025 and 0.037 Mm, respectively), along with noticeable improvement in the plant's biophysical and biochemical traits. It was concluded that mungbean is metal non-accumulator species, where non-phytotoxic Zn concentration induced positive impact on plant resistance to M.phaseolina infection, hence well-qualify Zn to be applied directly to accomplish significant disease management in mungbean. [ABSTRACT FROM AUTHOR]

Introduction: Concerns about the potential effects of climate change on agricultural products have prompted significant research in the past decade. Cereals are the most important food in the world population and among the various cereals, wheat plays the most important role. In our country, wheat is the most important crop in the country and has a significant role in feeding people. Due to the importance of this plant in providing food security, this study was conducted to investigate the increase in the concentration of carbon dioxide on the yield of wheat. For this purpose, meta-analysis method was used to quantitatively compare the effect of CO2 on wheat crop yield. Material and methods: The purpose of meta-analysis is to obtain more information than available information. In order to obtain the necessary data for the present study, the printed sources review method was used. 75 articles were extracted on the effect of increasing carbon dioxide concentration on wheat yield, then duplicate articles and articles that lacked the desired data were removed and among the remaining articles CO2 information, sample size, average and standard deviation was extracted. In the next step, these values were recorded in Excel software and finally, using Stata 16 software, the necessary forest plot and funnel plot were drawn, and to investigate the publication bias among the studies using the trim and fill method and drawing its graphs, this was investigated. Results and discussion: The results of the forest plot showed that after deleting the outlier data, the two groups T2 (15-25) and T3 (35_25) have a greater final size effect (about 1.6), which indicates that with increasing temperature up to Wheat plant yield increased by 35 ° C. Also, group T0, which is not mentioned in the articles of this group, has the lowest size effect (0.38). So, it can be inferred that the yield of wheat plant will increase with increasing temperature between 15 to 35 ° C and with increasing CO2 concentration in this temperature range. Examination of the funnel plot showed that most studies had accumulated at the top of the diagram. These studies have smaller standard error, larger sample size and higher accuracy. Publication bias was also observed in a positive direction. After drawing the funnel plot, the trim and fill method was used to estimate the potential missing studies due to the biased dissemination in the funnel plot and the adjustment of the estimate of their overall effect. After performing the trim and fill method, 6 dots marked in orange are added. These points are missing studies that need to be placed to create symmetry in the graph. This indicates that previous studies have been positive. Conclusion: The results of this study showed that the studies conducted in recent years have more reliable results (due to the larger sample size and greater accuracy in the results of these studies). Also, considering that increasing the concentration of CO2 can also cause an increase in temperature, it is suggested that in future studies, studies that have examined the interaction between increasing the concentration of CO2 and increasing the temperature simultaneously on important crops, using the meta-analysis method should be examined.

Aiming at the problem of end-to-end user perception evaluation method for mobile internet media streams was still vacant,a method based on entropy and fuzzy hierarchy was proposed to evaluate media streams.Firstly,XDR data indicators were processed by box-line graph method.Then,entropy method and fuzzy hierarchy method were used to revise the evaluation.The whole network process of the whole video service was modified from multi-dimension to realize the perception evaluation of the use of mobile internet video users.At the same time,the end-to-end health evaluation of the network was also realized.The results of a series of simulation experiments show that the proposed method can improve the accuracy of media stream evaluation while avoiding one-sided evaluation of a single index.It provids a method and solution for the optimization of the overall internet quality and multi-dimensional network fault location.

Objective: In the diabetes treatment policy after the Kumamoto Declaration 2013, it is difficult to accurately predict the incidence of complications in patients using the JJ risk engine. This study was conducted to develop a prediction equation suitable for the current diabetes treatment policy using patient data from Kitasato University Kitasato Institute Hospital (Hospital A) and to externally validate the developed equation using patient data from Kitasato University Hospital (Hospital B). Outlier tests were performed on the patient data from Hospital A to exclude the outliers. Prediction equation was developed using the patient data excluding the outliers and was subjected to external validation. Results: By excluding outlier data, we could develop a new prediction equation for the incidence of coronary heart disease (CHD) as a complication of type 2 diabetes, incorporating the use of antidiabetic drugs with a high risk of hypoglycemia. This is the first prediction equation in Japan that incorporates the use of antidiabetic drugs. We believe that it will be useful in preventive medicine for treatment for people at high risk of CHD as a complication of diabetes or other diseases. In the future, we would like to confirm the accuracy of this equation at other facilities. [ABSTRACT FROM AUTHOR]

With the continuous development of technologies such as the Internet of Things (IoT) and cloud computing, sensors collect and store large amounts of sensory data, realizing real-time recording and perception of the environment. Due to the open characteristics of WSN, the security risks during information transmission are prominent, and network attack or intrusion is likely to occur. Therefore, effective anomaly detection is vital for IoT systems to keep the system safe. The original Isolation Forest algorithm is an anomaly detection algorithm with linear time complexity and has a better detection effect on perceptual data. However, there are also disadvantages such as strong randomness, low generalization performance, and insufficient stability. This paper proposes a data anomaly detection method named BS-iForest (box plot-sampled iForest) for wireless sensor networks based on a variant of Isolation Forest to address the problems. This method first uses the sub-dataset filtered by the box graph to train and construct trees. Then, isolation trees with higher accuracy are selected in the training set to form a base forest anomaly detector. Next, the base forest anomaly detector uses anomaly detection to judge data outliers during the next period. These experiments were performed on datasets collected from sensors deployed in a data center of a university, and the Breast Wisconsin (BreastW) dataset, showing the performance of the variant of the Isolation Forest algorithm. Compared with the traditional isolation forest, the area under the curve (AUC) increased by 1.5% and 7.7%, which verified that the proposed method outperforms the standard Isolation Forest algorithm with the two datasets we chose.

Control Charts are one of the most powerful tools used to detect aberrant behavior in industrial processes. A valid performance measure for a control chart is the average run length (ARL); which is the expected number of runs to get an out of control signal. At the same time, robust estimators are of vital importance in order to estimate population parameters. Median absolute deviation (MAD) and quantiles are such estimators for population standard deviation. In this study, alternative control charts to the Tukey control chart based on the robust estimators are proposed. To monitor the control chart's performance, the ARL values are compare for many symmetric and skewed distributions. The simulation results show that the in-control ARL values of proposed control charts are higher than Tukey's control chart in all cases and more efficient to detect the process mean. However, the out- of- control ARL values for the all control charts are worse when the probability distribution is non-normal. As a result, it is recommended to use control chart based on the estimator Qn for the process monitoring performance when data are from normal or non-normal distribution. An application example using real-life data is provided to illustrate the proposed control charts, which also supported the results of the simulation study to some extent.

As the core component of an electric vehicle, the health of the traction battery closely affects the safety performance of the electric vehicle. If the sub-health state cannot be identified and dealt with in time, it may cause traction battery failure, pose a safety hazard, and cause property damage to the driver and passengers. This study used data-driven methods to identify the two typical types of sub-health state. For the first type of sub-health state, the interclass correlation coefficient (ICC) method was used to determine whether there was an inconsistency between the voltage of a single battery and the overall voltage of the battery pack. In order to determine the threshold, the ICC value of each vehicle under different working conditions was analyzed using box plots, and a statistical ICC threshold of 0.805 was used as the standard to determine the first sub-health type. For the second type of sub-health state, the Z-score and the differential area method were combined to determine whether the single cell voltage deviated from the overall battery pack voltage. A battery whose voltage differential area exceeds the range of u ± 3σ is regarded as having a sub-health state. The results show that both methods can accurately judge the sub-health state type of a single battery. Furthermore, combined with the one-month operation data of the vehicle, we could calculate the sub-health state frequency of each single battery and take single batteries with a high frequency as the key object of attention in future vehicle operations.

The Center for Biologics Evaluation and Research (CBER) at the US Food and Drug Administration (FDA) regulates gene therapies, among other products. The approval of four gene therapy products since 2017 represents a significant milestone for a new class of treatments with the potential to treat or cure diseases, particularly rare diseases, that were previously considered incurable. Several factors have contributed to the recent rapid development of gene therapies including advances in genetics to facilitate target-detection, advances in vectors, and regulatory incentives such as breakthrough therapy designation, priority review and market exclusivity. The patient population affected by a rare disease is typically small, heterogeneous and geographically dispersed. As a result, clinical trials on a rare disease have unique features in terms of study design, subject enrollment, data analyses and interpretation of study results. Given that the patient population affected is small for rare diseases, providing substantial evidence of effectiveness and evidence of safety in trials for rare disease presents challenges. In this paper, we share our experiences in the statistical review of three gene therapy products that have been approved by FDA CBER. Our motivation in writing this paper is to encourage the use of appropriate analysis strategies for other similar small trials, with a focus on data visualization strategies. [ABSTRACT FROM AUTHOR]

Forests play a vital role in the regulation of climate, absorption of carbon dioxide, hydrological cycle, conservation of water, soil and biodiversity and help mitigate natural disasters. With the help of various remote sensors, high-resolution satellite images are being collected nowadays, which helps in tackling the global challenges of forest mapping in remote areas. Each landscape will grow different types of trees and in turn substantiate a part of the country's economy. This paper uses visualization and machine learning (ML) processes to classify the forest land on the terrain dataset composed of the advanced spaceborne thermal emission and reflection radiometer (ASTER) imaging instrument to get the insight of the cumulated data by using Box Plot and Heat Map. The accuracy obtained by utilizing different machine learning techniques like Support Vector Machine (SVM) gives 95.4%, Logistic Regression (LR) gives 94.5%, K-Nearest Neighbor (K-NN) gives 93.7%, Decision Tree (DT) with 89.5%, Stochastic Gradient Descendent (SGD) with 92.4% and CN2 Rule Induction (RI) gives 85.3% are allied which gives appreciable results in forest mapping substantiated the same with confusion matrix and ROC. We also obtained the DT and rules for the considered dataset. [ABSTRACT FROM AUTHOR]