Statistical Interpretation Research Articles

Guidelines for balancing the number of trials and the number of subjects to ensure the statistical power to detect variability – Implication for gait studies

Variability is one of the most crucial outcomes in human movement studies: variance and standard deviation of various parameters have been reported in numerous studies. However, in many of these studies, the numbers of trials and subjects have been intuitively determined and not justified with statistical considerations. Here, we investigated the impact of the numbers of trials and subjects on statistical power, based on the assumption that results per trial follow a normal distribution, using mathematical analysis and numerical simulation. An inverse-like relationship was observed between the number of trials and subjects required to ensure the statistical power for detecting differences in variance between subject groups or conditions. For instance, assuming a 1.2-times difference in population variance between pre-and post-training sessions as an alternative hypothesis, our simulation demonstrated that combinations of the number of subjects and trials, such as measuring 100 trials from each of 12 subjects under each condition, or measuring 20 trials from each of 60 subjects, can guarantee an 80 % of statistical power. Planning research based on such mathematical considerations will enable meaningful statistical interpretations in studies focusing on movement variability, such as gait studies.

Clinical Biochemistry of Serum Troponin.

Accurate measurement and interpretation of serum levels of troponin (Tn) is a central part of the clinical workup of a patient presenting with chest pain suspicious for acute coronary syndrome (ACS). Knowledge of the molecular characteristics of the troponin complex and test characteristics of troponin measurement assays allows for a deeper understanding of causes of false positive and false negative test results in myocardial injury. In this review, we discuss the molecular structure and functions of the constituent proteins of the troponin complex (TnT, TnC, and TnI); review the different isoforms of Tn and where they are from; survey the evolution of clinical Tn assays, ranging from first-generation to high-sensitivity (hs); provide a primer on statistical interpretation of assay results based on different clinical settings; and discuss potential causes of false results. We also summarize the advances in technologies that may lead to the development of future Tn assays, including the development of point of care assays and wearable Tn sensors for real-time continuous measurement.

Reconstructing the unknown: an integrated and multisource description of Albanian landscape transformations since the 1850s

This paper aims to reconstruct the Albanian rural landscape through qualitative, diachronic, and multisource interpretations from the mid-19th century until 2022. For the first time, the data suitable for studying and describing landscape changes have been outlined to understand them over time, combining literature, historical maps, paintings, photo collections, movies, land art, and statistical dataset interpretations. By describing the changes in the Albanian landscape and interpretation of agricultural statistics, the research has confirmed the land-use dynamicity of its rural landscape. These considerations call for a more contemporary approach to landscape planning, stressing the need for the State Party to sign the Council of Europe Landscape Convention. In addition, strategies and actions should valorise both the ordinary landscape and the community involvement in its proactive management.

Spin Practices and Low Reporting Quality in Studies on Prediction Model of Diagnosis Based on TCM Information: A Systematic Review and Evidence Mapping

Background: Despite the abundance of published studies on prediction models for diagnosing Traditional Chinese Medicine (TCM), there remains a lack of comprehensive assessment regarding reporting and methodological quality, as well as an absence of examination into the objectivity of linguistic aspects within these studies. Methods: The PubMed, Cochrane Library, Web of Science, CNKI, VIP, and WANFANG databases were systematically searched from inception to October 30th, 2023, to identify studies developing and/or validating diagnostic and prognostic TCM prediction models using supervised machine learning. PROBAST and TRIPOD were employed to assess the reporting and methodological quality of identified studies. A previous article about spin in prognostic factor studies already identified several practices, which we modified for our data extraction the present study was registered on PROSPERO with the registration number CRD42023450907. Results: 35 and 19 eligible studies published in Chinese and English were identified respectively from 1746 records. The clinical diseases with the most publications were diabetes (n = 7, 14.8%), coronary heart disease (n = 6, 11.1%), and lung cancer (n = 5, 9.26%). Primary analysis and sensitivity analysis confirmed that the reporting and methodological quality of included studies were correlated (rs = 0.504, p < 0.001). The quality of the CM prediction model requires improvement by including a structured title, participants and predictor’s selection, statistical analysis methods, model performance and interpretation. Two studies (4.55%) recommended the model to be used in daily practice lacked any external validation of the developed models. Six studies (13.63%) made recommendations for clinical use in their main text without any external validation. Reporting guidelines were only cited in one study (1.85%). Conclusion: The available evidence indicated TCM information can provide predict information for different diseases, but the scientific quality of published studies needs to be improved.

Is Quantization a Co-Operative Phenomenon? On the Ensemble Interpretation of Quantum Physics

A fresh look at Planck's quantum, h, re-interprets action as the companion of power in a dynamic interplay of energies in a multi particle system of the micro world. An exchange of energy, δE, requires a characteristic duration, δτ, with δE and δτ tied together in a natural way. Action, the product δEδτ, must have a nonzero lower limit, h, as a classical necessity everywhere in physics. Zero h would constitute a singularity. The veritable quantum is action, not energy. Black body radiation formulas (Wien, Planck, and Rayleigh) all require this non zero h. Planck's thermodynamic approach was not consistently developed further. Einstein in his 1905 description of the photo effect chose the single "quantum of light" as statistically independent actor in radiation. The resulting discussions about the wave-particle dualism, the meaning of uncertainty, the collapse of wave packets when observed, self-interference, and related problems can be avoided by the consistent view of the ensemble interpretation. In agreement with action defined as δEδτ only co-operative phenomena produce the effects we observe. In particular, this concerns the photon, directly associated with h, as naturally interactive quantum. Careful experiments on low intensity interference suggest to take the photon out of the isolation, thus changing the general view of quantum physics with surprising consequences. Do photons transport energy or action? To understand where 20th century physics has gone a glance at the historical background is in order.

Skewness and kurtosis of solar wind proton distribution functions: The normal inverse-Gaussian model and its implications

Context.In the solar wind (SW), the particle distribution functions are generally not Gaussian. They present nonthermal features that are related to underlying acceleration and heating processes. These processes are critical in the overall dynamics of this expanding astrophysical fluid.Aims.The Proton Alpha Sensor (PAS) on board Solar Orbiter commonly observes skewed proton distributions, with a more populated high-energy side in the magnetic field direction than the Gaussian distribution. Our objectives are: (1) to identify a theoretical statistical function that adequately models the observed distributions and (2) to use its statistical interpretation to constrain the acceleration and heating processes.Methods.We analyzed the 3D velocity distribution functions (VDFs) measured by PAS and compared them to model statistical functions.Results.We show that the normal inverse Gaussian (NIG), a type of hyperbolic statistical distribution, provides excellent fits of skewed and leptokurtic proton distributions. NIG can model both the core distribution and the beam, if present. We propose an interpretation that is inspired by the mathematical formulation of the NIG. It assumes that the acceleration or heating mechanism can be modeled as a drifting diffusion process in velocity space, controlled (or subordinated) by the time of interaction of the particles with “accelerating structures”. The probability function of the interaction time is an inverse Gaussian (IG), obtained by considering a random drift across structures of a given size. The control of the diffusion by interaction times that follow an IG probability function formally defines the NIG distribution. Following this model, we show that skewness and kurtosis can be used to estimate the kinetic and thermal energy gains provided by the interaction with structures. For example, in the case studies presented here, the analyzed populations would have gained kinetic energy representing approximately two to four times their thermal energy, with an increase in velocity – due to acceleration – of from one-tenth to one-third of the observed flow velocity. We also show that the model constrains the initial temperature of the populations.Conclusions.Overall, the NIG model offers excellent fits of the observed proton distributions. Combining the skewness and the kurtosis, it also leads to constraints in the part of acceleration and heating due to the interactions with structures in the formation of the proton populations. We suggest that these effects add to the classical thermal evolution of the bulk velocity and temperature resulting from SW expansion.

Streamlining Randomized Clinical Trials for Device Therapies in Heart Failure: Bayesian Borrowing of External Data.

The Breakthrough Devices Program of the US Food and Drug Administration has accelerated the development and evaluation of medical devices for patients with heart failure. One such device is the Optimizer Smart System, which the US Food and Drug Administration approved in 2019. The Optimizer device was evaluated in a pivotal randomized clinical trial (FIX-HF-5C [Confirmatory Randomized Trial Evaluating the Optimizer System]) that leveraged Bayesian borrowing of external data to reduce the sample size and determine therapeutic device benefit versus continued medical therapy. Bayesian borrowing is explained in the context of the FIX-HF-5C trial, including an overview of the statistical methodologies, regulatory considerations, and interpretations of trial results. The US Food and Drug Administration Breakthrough Devices Program and novel Bayesian statistical methodology accelerated the path to regulatory approval and patient access to a potentially lifesaving device and may serve as a model for future clinical trials.

Microbial water quality investigation through flow cytometry fingerprinting: from source to tap

Abstract Ensuring the quality of treated drinking water is crucial for preventing potential health impacts, regulatory fines, and reputation damage. Traditional culture-based microbiological methods often fail to capture the heterogeneity of the bacterial communities in drinking water. This study employed daily interstage monitoring and flow cytometry (FCM) analysis over a period of one year to investigate the dynamics of water treatment processes and service reservoirs. The objective of this study was to test the utility of FCM fingerprints for aiding in microbial event detection. We found that the chlorine concentration contact time was pivotal for microbial log reduction across the treatment works. FCM fingerprints exhibited significant deviations during operational events, such as process interruptions, but did not correlate with the presence of bacterial indicator organisms in the distributed and tap water. Furthermore, the diversity of bacterial fingerprints, quantified by the Bray–Curtis dissimilarity index, served as an indicator for identifying potentially poor microbial water quality. In chlorinated waters with low cell counts, the background signal shows potential as a metric to differentiate between different water sources, thereby offering the possibility to characterize breakthrough events in these circumstances that challenge most other microbial analytical methods. Interestingly, groundwater from simpler treatment works showed a higher occurrence of bacterial indicators, whereas surface water works had a lower incidence. These findings underpin the importance of appropriate disinfection even for “low-risk” source waters and the added value that the statistical interpretation of FCM data can offer objective decision making.

Fluctuations in the entropy of Hawking radiation

We use the gravitational path integral (GPI) to compute the fluctuations of the Hawking radiation entropy around the Page curve in a two-dimensional model introduced by Penington Before the Page time, we find that δS=e−S/2, where S is the black hole entropy. This result agrees with the Haar-averaged entropy fluctuations in a bipartite system. After the Page time, we find that δS∼e−S, up to a prefactor that depends logarithmically on the width of the microcanonical energy window. This is not symmetric under exchange of subsystem sizes and so does not agree with the Haar average for a subsystem of fixed Hilbert space dimension. The discrepancy can be attributed to the fact that the black hole Hilbert space dimension is not fixed by the state preparation: even in a microcanonical ensemble with a top-hat smearing function, the GPI yields an additive fluctuation in the number of black hole states. This result, and the fact that the Page curve computed by the GPI is smooth, all point towards an ensemble interpretation of the GPI. Published by the American Physical Society 2024

Characteristics of Forest Windthrow Produced in Eastern Carpathians in February 2020

Windthrow is a phenomenon that causes major changes to tree stand evolution by blowing down or breaking either isolated trees or entire tree stands, with a strong ecological, social and economic impact. Both scattered and large-scale windthrow occurred in spruce (Picea abies (L.) Karst.) tree stands of Romania. They affected surfaces of various dimensions from harvestable forests. Such a phenomenon took place in the Curvature Carpathians in February 2020. Large-scale windthrow occurred in this area in 1995 as well, in the upper watershed of Bâsca river. Using the climate data from February 2020, this paper aims to identify the manner in which factors such as climate and site conditions together with tree stand characteristic and the anthropogenic factor impacted and influenced the occurrence of windthrow. The results showed that the intensity of this phenomenon had maximum effects when the wind coming from north/northeast reached the maximum speed of 32 m·s−1. Pure spruce tree stands situated on slopes with an inclination between 16 and 30° were mainly affected. Their position was counter to the wind direction, at an altitude between 1300 and 1500 m, on cambisols and spodosols. The analysis and statistical interpretation of data in the case of scattered and large-scale windthrow from the two management units showed that the same factors studied influence the variation of windthrow intensity in a different manner, or sometimes they do not influence it at all or they can only account for a small part of this variation.

Scientific-methodological knowledge and its development in the educational process

Introduction. For students, mastering the methodological foundations of scientific research plays an important role in professional education, providing scientifically proven support for professional thinking and readiness to scientifically substantiate the practical activities of a specialist to form a mechanism for intellectual and personal self-development. Today, numerous educational literature is being published, and educational disciplines devoted to the methodology of science and scientific research are being introduced. However, in most of the educational and scientific works created by students (course projects, final qualification papers, etc.), methodological provisions are formulated with errors; descriptions of the organisation of research, interpretation and design of their results are unsatisfactory. Therefore, there is the problem of finding ways to improve methodological literacy, methodological competence, and methodological culture of students. The relevance of such research is conditioned by the need to study the causes of such a situation, to develop ways for improving student methodological competence, to highlight and summarise the experience of teaching methodological knowledge, and to suggest ways for improving the educational process. The solution to this problem is especially significant for the humanities specialists (in particular, for educators and psychologists), who deal with the influence on the personality and human consciousness included in the social regulation of human behaviour. Aim. The present research aims to: 1) substantiate the theoretical approach to the scientific and methodological support of the process of mastering the methodological foundations of scientific research by students as to the development of scientific-methodological knowledge (SMK) based on the analysis of scientific and educational literature, the practice of teaching disciplines aimed at studying the methodological aspects of psychological and pedagogical research; 2) to identify the main guidelines for improving the ways to support the formation of SMK using examples from empirical studies of its development by students in the educational process, from the generalisation of the practical experience of the authors. Methodology and research methods. The research methodological framework was based on the approaches of Russian scientists to the methodological foundations of science and scientific research as a holistic knowledge, in which principles, norms, methods and scientific and cognitive activity are developed at the philosophical, general scientific, concrete scientific, methodical levels. Analysis, generalisation and systematisation were used as theoretical research methods; testing, survey, included observation and generalisation of experience were applied as empirical methods. To prove the propositions put forward by the authors, four empirically verifiable hypotheses were formulated. Two of them were confirmed: one was partially confirmed; the second one (about the absence of a connection between direct and remote forms with the formation of SMK) was not confirmed. In the mathematical and statistical interpretation of individual research results (correlation and variance types of analysis), the IBM SPSS Statistics 23 was employed. Results. The guidelines for supporting the development of SMK by students in the educational process were outlined: reliance on the creation of a research project by the trainee; placing the trainees in the position of an independent researcher; developing an understanding of the content and scope of the key concepts of research, its unity and integrity; paying special attention to the transformation of information and its presentation in various forms. Scientific novelty. The authors proposed and substantiated the idea of the study of the research methodological foundations as the development of SMK, which equips the researcher with an arsenal of guidelines, means and methods of action, providing the closest possible approach to the truth in the search for new scientific knowledge. Practical significance. The proposed approaches to the development of SMK can be used in the practice of studying disciplines devoted to the research methodological foundations in the social sciences and humanities, in support of students’ course projects and final qualifying works.

Analysis of Technical Condition of Cars in Western Poland: A Study Based on Selected Indicators

(1) Background: Ensuring road user safety relies on the optimal technical condition of cars, addressing both active and passive safety measures. In Poland, vehicle regulations, articulated in the Minister of Infrastructure’s decree of 31 December 2002, establish technical prerequisites and necessary equipment. For this purpose, the main question was: What is the current technical condition of cars on the road in Western Poland? (2) Methods: A total of 1067 vehicles were tested, reflecting a maximum error of 3% in a population of 20 million cars. Tests were conducted at the diagnostic station from 1 October 2022 to 30 September 2023. Statistical analysis was conducted using STATISTICA software. (3) Results: Periodic technical tests yield insights into passenger car safety standards in western Poland. The application of formulated characteristics allows a comprehensive evaluation, providing valuable information on the overall safety condition of inspected vehicles. The vehicles in Poland have an average age exceeding 14 years, and their average mileage is 168,000 km. (4) Conclusions: The examination uncovered various technical defects and provided statistical interpretations, unequivocally demonstrating that these identified issues have the potential to impact traffic safety. Such studies act as a reference point for other researchers addressing the broader issue of road traffic.

Occurrence of Imipenem-Resistant Uropathogenic Escherichia coli in Pregnant Women: An Insight into Their Virulence Profile and Clonal Structure.

Uropathogenic Escherichia coli (UPEC) is the predominant pathogen in Urinary Tract Infection (UTI) in pregnant and non-pregnant women. Limited studies were initiated to explore UPEC from pregnant women with respect to imipenem resistance, pathogenicity, and their clonal lineage. In this study, imipenem resistance, phylogenetic background, virulence-associated genes, and clonal characteristics in UPECs isolated from pregnant and non-pregnant cohorts were investigated. E. coli was identified biochemically from urine culture-positive samples from pregnant and non-pregnant women. Carbapenem (meropenem, ertapenem, imipenem) susceptibility was determined by Kirby-Bauer disk diffusion test. The pathogenic determinants were identified by PCR. MEGA 11 was used to interpret clonal lineages from MLST. GraphPad Prism 8.0 and SPSS 26.0 were used for statistical interpretation. Results indicated highest resistance against imipenem compared to meropenem and ertapenem in UPECs isolated from pregnant (UPECp; 63.89%) and non-pregnant (UPECnp; 87.88%) women. Although phylogroup E was predominant in both imipenem-resistant isolates, acquisition of virulence factors was higher among UPECnp than UPECp. Akin to this observation, the presence of PAI III536 and PAI IV536 was statistically significant (p < 0.05) in the former. MLST analysis revealed similar clonal lineages between UPECnp and UPECp, which showed an overall occurrence of ST405 followed by ST101, ST410, ST131, and ST1195 in UPECnp and ST167 in UPECp, respectively, with frequent occurrence of CC131, CC405. Therefore, imipenem-resistant UPECp although discrete with respect to their virulence determinants when compared to UPECnp shared similar STs and CCs, which implied common evolutionary history. Thus, empiric treatment must be restricted in UTIs to especially protect maternal and fetal health.

SAFE-MIL: a statistically interpretable framework for screening potential targeted therapy patients based on risk estimation.

Patients with the target gene mutation frequently derive significant clinical benefits from target therapy. However, differences in the abundance level of mutations among patients resulted in varying survival benefits, even among patients with the same target gene mutations. Currently, there is a lack of rational and interpretable models to assess the risk of treatment failure. In this study, we investigated the underlying coupled factors contributing to variations in medication sensitivity and established a statistically interpretable framework, named SAFE-MIL, for risk estimation. We first constructed an effectiveness label for each patient from the perspective of exploring the optimal grouping of patients' positive judgment values and sampled patients into 600 and 1,000 groups, respectively, based on multi-instance learning (MIL). A novel and interpretable loss function was further designed based on the Hosmer-Lemeshow test for this framework. By integrating multi-instance learning with the Hosmer-Lemeshow test, SAFE-MIL is capable of accurately estimating the risk of drug treatment failure across diverse patient cohorts and providing the optimal threshold for assessing the risk stratification simultaneously. We conducted a comprehensive case study involving 457 non-small cell lung cancer patients with EGFR mutations treated with EGFR tyrosine kinase inhibitors. Results demonstrate that SAFE-MIL outperforms traditional regression methods with higher accuracy and can accurately assess patients' risk stratification. This underscores its ability to accurately capture inter-patient variability in risk while providing statistical interpretability. SAFE-MIL is able to effectively guide clinical decision-making regarding the use of drugs in targeted therapy and provides an interpretable computational framework for other patient stratification problems. The SAFE-MIL framework has proven its effectiveness in capturing inter-patient variability in risk and providing statistical interpretability. It outperforms traditional regression methods and can effectively guide clinical decision-making in the use of drugs for targeted therapy. SAFE-MIL offers a valuable interpretable computational framework that can be applied to other patient stratification problems, enhancing the precision of risk assessment in personalized medicine. The source code for SAFE-MIL is available for further exploration and application at https://github.com/Nevermore233/SAFE-MIL.

Proposal for a new Green Red Water Index for geo-environmental surface water monitoring

One of challenges of today’s world is the long-term geo-monitoring of phenomena and processes that affect our environment after the closure of mining activities. Water resources are one of the components affected by post-mining processes. Moreover, land subsidence can be observed both during and after the cessation of mining activities. This phenomenon has an impact on the entire water management of a region. So far, radar or multispectral images have been used to identify water surfaces. This paper will present a methodology for using drones to detect water surfaces using vegetation indices such as NDVI, GRNDVI and NDWI. During their research, the authors modified the GRNDVI indicator by including the Red Edge band in the calculations. The newly developed Green Red Water Index – GRWI, makes it possible to identify water surfaces. This is important, because the change of water conditions makes it important focus more on the water supply and availability. However, analysis of the spectral bands of the different land-use classes in the Ruhr study area shown that the spectral profiles for water, soils, road and street surfaces have similar spectral characteristics and therefore difficulties may arise in distinguishing between the land-use classes shown. In this article a comparison of the indicators will be presented: NDVI, GRNDVI, NDWI and GRWI together with their statistical interpretation.

Bioinformatics Methods for Transcriptome Analysis on Teratogenesis Testing.

Teratogenesis testing can be challenging due to the limitations of both in vitro and in vivo models. Test-systems, based especially on human embryonic cells, have been helping to overcome the difficulties when allied to omics strategies, such as transcriptomics. In these test-systems, cells exposed to different compounds are then analyzed in microarray or RNA-seq platforms regarding the impacts of the potential teratogens in the gene expression. Nevertheless, microarray and RNA-seq dataset processing requires computational resources and bioinformatics knowledge. Here, a pipeline for microarray and RNA-seq processing is presented, aiming to help researchers from any field to interpret the main transcriptome results, such as differential gene expression, enrichment analysis, and statistical interpretation. This chapter also discusses the main difficulties that can be encountered in a transcriptome analysis and the better alternatives to overcome these issues, describing both programming codes and user-friendly tools. Finally, specific issues in the teratogenesis field, such as time-course analysis, are also described, demonstrating how the pipeline can be applied in these studies.

Domino: A new framework for the automated identification of weather event precursors, demonstrated for European extreme rainfall

AbstractA number of studies have investigated the large‐scale drivers and upstream precursors of extreme weather events, making it clear that the earliest warning signs of extreme events can be remote from the impacted region in both time and space. Integrating and leveraging our understanding of dynamical precursors provides a new perspective on ensemble forecasting for extreme events, focused on building storylines of possible event evolution. This then acts as a tool for raising awareness of the conditions conducive to high‐impact weather and providing early warning of their possible development. However, operational applications of this developing knowledge base are limited, perhaps for want of a clear framework for doing so. Here, we present such a framework, supported by open software tools, designed for identifying large‐scale precursors of categorical weather events in an automated fashion and reducing them to scalar indices suitable for statistical prediction, forecast interpretation, and model validation. We demonstrate this framework by systematically analysing the precursor circulations of daily rainfall extremes across 18 regional‐ to national‐scale European domains. We discuss the precursor rainfall dynamics for three disparate regions, and show our findings are consistent with, and extend, previous work. We provide an estimate of the predictive utility of these precursors across Europe based on logistic regression, and show that large‐scale precursors can usefully predict heavy rainfall between two and six days ahead, depending on region and season. We further show how, for more continental‐scale applications, the regionally specific precursors can be synthesised into a minimal set of indices that drive heavy precipitation. We then provide comments and guidance for generalisation and application of our demonstrated approach to new variables, timescales, and regions.

Terapias alternativas para infecções virais causadas pelo SARS-Cov-2

Abstract Introduction The relevance of the studied topic lies in the complexity of the treatment of infection caused by SARS-CoV-2. Objective To discuss the potential advantages and disadvantages of alternative treatments compared to conventional medical approaches, and to highlight the importance of collaborative communication between patients and healthcare providers in making informed decisions about alternative treatments of the SARS-CoV-2 virus. Methods The research methodology employed literature analysis methods, including bibliographic and bibliosemantic approaches. The study used theoretical, systematic, and statistical methods, including analysis, synthesis, generalization, interpretation, classification, and meta-analysis to explore alternative treatments for SARS-CoV-2 infections, their interrelationships, and statistical trends in incidence. Results The study identifies diverse alternative therapies for treating SARS-CoV-2 infections, highlighting herbal medicine, acupuncture, reflexology, biohacking, homoeopathy, and magnetotherapy. It underscores the potential benefits of herbal remedies like garlic, ginger, chamomile, and honeysuckle, as well as vitamins (C, D, B12) and minerals (zinc, selenium) in managing COVID-19 symptoms. Conclusion While of-fering holistic benefits, these therapies warrant cautious consideration due to limited scientific backing and potential interactions. Cultural understanding, patient-provider dialogue, and informed choices are key in harnessing the potential of alternative medicine along-side conventional approaches for managing COVID-19 challenges.

Explaining Black Boxes With a SMILE: Statistical Model-Agnostic Interpretability With Local Explanations

Explaining Black Boxes With a SMILE: Statistical Model-Agnostic Interpretability With Local Explanations

Chemical assessment and spatial distribution of selected elements in aquatic sediments in the region of Peja (Kosovo)

In Kosovo, the supply of potable water from water sources is represented by about 40%. This study represents the geochemical analyses of sediments, spatial and statistical analyses of major elements in sediments (fraction &lt; 63 µm) of water sources in the Drini i Bardhë River basin. For a better understanding of the geochemical consistency of sediments, inductively coupled plasma-mass spectrometry (ICP-MS) was used and the amounts of P and S were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES). By comparing the concentrations of Ba, Se, and P with the existing criteria, it was found that 26 of 50 sample points exceed recommended norms, which cause low/significant toxic effects and 23 of 50 sample points include anomalies of the analyzed elements. In terms of statistical interpretation, the average concentration of the studied elements decreased in such order: Ca &gt; Al &gt; Mg &gt; K &gt; S &gt; P &gt; Na &gt; Ba &gt; Sr &gt; Li &gt; Rb &gt; B &gt; Ga &gt; Cs &gt; Be &gt; Se. In terms of correlation analyses, elements such as Li, Be, Mg, P, and Ba did not show a significant positive correlation with any element. Cluster analysis of R-modality shows a branch of dendrogram linked with Ca, Al, Mg, S, K, P, Na, Ba, Sr, Cs, B, Ga, Se, Be, Rb, and Li. Spatial distribution maps of major elements show that high concentrations of Li, Ca, and Ba were located mostly in the northern part, high concentrations of B, Na, Mg, Sr, and Cs were located in the north-east part, high concentrations of Be and Al were located in the north and south part and high concentrations of P, S, K and Rb were located mostly in the south-east part of the catchment basin. This high concentration of elements is impacted by the lithology of rocks and by the minor influence of human activity (the case of P).