Utilization of multivariate data analysis in catalysis research has extraordinary importance. The aim of the MIRA21 (MIskolc RAnking 21) model is to characterize heterogeneous catalysts with bias-free quantifiable data from 15 different variables to standardize catalyst characterization and provide an easy tool to compare, rank, and classify catalysts. The present work introduces and mathematically validates the MIRA21 model by identifying fundamentals affecting catalyst comparison and provides support for catalyst design. Literature data of 2,4-dinitrotoluene hydrogenation catalysts for toluene diamine synthesis were analyzed by using the descriptor system of MIRA21. In this study, exploratory data analysis (EDA) has been used to understand the relationships between individual variables such as catalyst performance, reaction conditions, catalyst compositions, and sustainable parameters. The results will be applicable in catalyst design, and using machine learning tools will also be possible.

BACKGROUND: Electronic paper (E-paper) screens use electrophoretic ink to provide paper-like low-power displays with advanced networking capabilities that may potentially serve as an alternative to traditional whiteboards and television display screens in hospital settings. E-paper may be leveraged in the emergency department (ED) to facilitate communication. Providing ED patient status updates on E-paper screens could improve patient satisfaction and overall experience and provide more equitable access to their health information.
OBJECTIVE: We aimed to pilot a patient-facing digital whiteboard using E-paper to display relevant orienting and clinical information in real time to ED patients. We also sought to assess patients\' satisfaction after our intervention and understand our patients\' overall perception of the impact of the digital whiteboards on their stay.
METHODS: We deployed a 41-inch E-paper digital whiteboard in 4 rooms in an urban, tertiary care, and academic ED and enrolled 110 patients to understand and evaluate their experience. Participants completed a modified Hospital Consumer Assessment of Health Care Provider and Systems satisfaction questionnaire about their ED stay. We compared responses to a matched control group of patients triaged to ED rooms without digital whiteboards. We designed the digital whiteboard based on iterative feedback from various departmental stakeholders. After establishing IT infrastructure to support the project, we enrolled patients on a convenience basis into a control and an intervention (digital whiteboard) group. Enrollees were given a baseline survey to evaluate their comfort with technology and an exit survey to evaluate their opinions of the digital whiteboard and overall ED satisfaction. Statistical analysis was performed to compare baseline characteristics as well as satisfaction.
RESULTS: After the successful prototyping and implementation of 4 digital whiteboards, we screened 471 patients for inclusion. We enrolled 110 patients, and 50 patients in each group (control and intervention) completed the study protocol. Age, gender, and racial and ethnic composition were similar between groups. We saw significant increases in satisfaction on postvisit surveys when patients were asked about communication regarding delays (P=.03) and what to do after discharge (P=.02). We found that patients in the intervention group were more likely to recommend the facility to family and friends (P=.04). Additionally, 96% (48/50) stated that they preferred a room with a digital whiteboard, and 70% (35/50) found the intervention \"quite a bit\" or \"extremely\" helpful in understanding their ED stay.
CONCLUSIONS: Digital whiteboards are a feasible and acceptable method of displaying patient-facing data in the ED. Our pilot suggested that E-paper screens coupled with relevant, real-time clinical data and packaged together as a digital whiteboard may positively impact patient satisfaction and the perception of the facility during ED visits. Further study is needed to fully understand the impact on patient satisfaction and experience.
BACKGROUND: ClinicalTrials.gov NCT04497922; https://clinicaltrials.gov/ct2/show/NCT04497922.

As universities are the change agent of society, institutions from all nations set their goals to transform the world by exploring various societal challenges that humans are facing. Together, the higher education systems across the world developing strategies based on the United Nations\' Sustainable Development Goals (SDGs). The current study aimed to provide policymakers, academics, and researchers an insight on the influence of 16 SDGs on each other paving the way for the universities to set a clear goal in attaining Sustainable Development goals by 2030. To analyze the SDGs\' interactions towards each other, 201,844 research publications from India during five years on 16 SDGs are retrieved from the Scopus database. Spearman Rank Correlation is applied to understand the correlation of each SDG towards one another. We could observe converging results out of the interactions among the SDGs. A significant positive and moderately positive correlation between pairs of SDGs are identified. While a significant number of negative correlations is also classified which need deep thinking among researchers to develop healthy relationships. The most frequent interactions between SDGs is a positive sign for any university in strategizing the goal towards SDGs. The association of all university stakeholders and some constitutional and cultural changes are necessary to put SDGs at the core of the management of the university. Embracing this task by researchers will improve the overall performance of universities. The analysis presented in the present study is useful for academics, governments, funding agencies, researchers, and policy-makers.

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.

BACKGROUND: Schizophrenia (SCZ) presents complex challenges related to diagnosis and clinical monitoring. The study of conditions associated with SCZ can be facilitated by using potential markers and patterns that provide information to support the diagnosis and oral health.
METHODS: The salivary composition of patients diagnosed with SCZ (n = 50) was evaluated and compared to the control (n = 50). Saliva samples from male patients were collected and clinical parameters were evaluated. The concentration of total proteins and amylase were determined and salivary macro- and microelements were quantified by ICP OES and ICP-MS. Exploratory data analysis based on artificial intelligence tools was used in the investigation.
RESULTS: There was a significant increase in the salivary concentrations of Al, Fe, Li, Mg, Na, and V, higher prevalence of caries (p < 0.001), periodontal disease (p < 0.001), and reduced salivary flow rate (p = 0.019) in SCZ patients. Also, samples were grouped into six clusters. As, Co, Cr, Cu, Mn, Mo, Ni, Se, and Sr were correlated with each other, while Fe, K, Li, Ti, and V showed the highest concentrations in the samples distributed in the clusters with the highest association between SZC patients and controls.
CONCLUSIONS: The results obtained indicate changes in salivary flow, organic composition, and levels of macro- and microelements in SCZ patients. Salivary concentrations of Fe, Mg, and Na may be related to oral conditions, higher prevalence of caries, and periodontal disease. The exploratory analysis showed different patterns in the salivary composition of SCZ patients impacted by associations between oral health conditions and the use of medications. Future studies are encouraged to confirm the results investigated in this study.

Sensor technologies allow ethologists to continuously monitor the behaviors of large numbers of animals over extended periods of time. This creates new opportunities to study livestock behavior in commercial settings, but also new methodological challenges. Densely sampled behavioral data from large heterogeneous groups can contain a range of complex patterns and stochastic structures that may be difficult to visualize using conventional exploratory data analysis techniques. The goal of this research was to assess the efficacy of unsupervised machine learning tools in recovering complex behavioral patterns from such datasets to better inform subsequent statistical modeling. This methodological case study was carried out using records on milking order, or the sequence in which cows arrange themselves as they enter the milking parlor. Data was collected over a 6-month period from a closed group of 200 mixed-parity Holstein cattle on an organic dairy. Cows at the front and rear of the queue proved more consistent in their entry position than animals at the center of the queue, a systematic pattern of heterogeneity more clearly visualized using entropy estimates, a scale and distribution-free alternative to variance robust to outliers. Dimension reduction techniques were then used to visualize relationships between cows. No evidence of social cohesion was recovered, but Diffusion Map embeddings proved more adept than PCA at revealing the underlying linear geometry of this data. Median parlor entry positions from the pre- and post-pasture subperiods were highly correlated (R = 0.91), suggesting a surprising degree of temporal stationarity. Data Mechanics visualizations, however, revealed heterogeneous non-stationary among subgroups of animals in the center of the group and herd-level temporal outliers. A repeated measures model recovered inconsistent evidence of a relationships between entry position and cow attributes. Mutual conditional entropy tests, a permutation-based approach to assessing bivariate correlations robust to non-independence, confirmed a significant but non-linear association with peak milk yield, but revealed the age effect to be potentially confounded by health status. Finally, queueing records were related back to behaviors recorded via ear tag accelerometers using linear models and mutual conditional entropy tests. Both approaches recovered consistent evidence of differences in home pen behaviors across subsections of the queue.

The European Food Safety Authority (EFSA) guidance (EFSA, 2017) for dermal absorption (DA) studies recommends stringent mass balance (MB) limits of 95-105%. EFSA suggested that test material can be lost after penetration and requires that for chemicals with <5% absorption the non-recovered material must be added to the absorbed dose if MB is <95%. This has huge consequences for low absorption pesticides. Indeed, one third of the MBs in the EFSA DA database are outside the refined criteria. This is also true for DA data generated by Cosmetics Europe (Gregoire et al., 2019), indicating that this criterion is often not achieved even when using highly standardized protocols. While EFSA hypothesizes that modern analytical and pipetting techniques would enable to achieve this criterion, no scientific basis was provided. We describe how protocol procedures impact MB and evaluate the EFSA DA database to demonstrate that MB is subject to random variation. Generic application of \"the addition rule\" skews the measured data and increases the DA estimate, which results in unnecessary risk assessment failure. In conclusion, \"missing material\" is just a random negative deviation to the nominal dose. We propose a data-driven MB criterion of 90-110%, fully in line with OECD recommendations.

Estimates from individual studies included in a meta-analysis often are not in agreement, giving rise to statistical heterogeneity. In such cases, exploration of the causes of heterogeneity can advance knowledge by formulating novel hypotheses. We present a new method for visualizing between-study heterogeneity using combinatorial meta-analysis. The method is based on performing separate meta-analyses on all possible subsets of studies in a meta-analysis. We use the summary effect sizes and other statistics produced by the all-subsets meta-analyses to generate graphs that can be used to investigate heterogeneity, identify influential studies, and explore subgroup effects. This graphical approach complements alternative graphical explorations of data. We apply the method to numerous biomedical examples, to allow readers to develop intuition on the interpretation of the all-subsets graphical display. The proposed graphical approach may be useful for exploratory data analysis in systematic reviews. Copyright © 2012 John Wiley & Sons, Ltd.