Modeling of the 1783 Tsunami Event in Scilla Generated by Landslide

In 1783, an event that has gone down in history as the great seismic crisis in Calabria began, during which two major earthquakes occurred, affecting the Calabrian ridge from the Strait to the north. Between 6 and 7 February in Scilla a tsunami occurred that caused the greatest number of victims in Italy: 1500 people. The mechanism that triggered the tsunami was the detachment of a ridge of rock due to a violent earthquake that affected the area; this detachment caused a subaerial landslide which, by sliding, then deposited the rock on the seabed a few kilometers from the coast, immediately generating the tsunami event. The objective of this study is to perform numerical simulations for tsunami events that occurred in history and use models that perform the propagation of a tsunami, using the best possible bathymetric and topographic data and the historical data to compare the validity of the results. In this way, one can obtain the validation of a model that can be used to simulate possible events of this magnitude on the Calabrian coasts and therefore be able to develop a reliable early warning tsunami system; it also has the advantage of perfectly combining computational burdens and the validity of results.

Self-Monitoring of Urinary Hormones in Combination with Telemedicine — a Timely Review and Opinion Piece in Medically Assisted Reproduction

Cycle monitoring via ultrasound and serum-based hormonal assays during medically assisted reproduction (MAR) can provide information on ovarian response and assist in optimizing treatment strategies in addition to reducing complications such as ovarian hyperstimulation syndrome (OHSS). Two surveys conducted in 2019 and 2020, including overall 24 fertility specialists from Europe, Asia and Latin America, confirmed that the majority of fertility practitioners routinely conduct hormone monitoring during MAR. However, blood tests may cause inconvenience to patients. The reported drawbacks of blood tests identified by the survey included the validity of results from different service providers, long waiting times and discomfort to patients due to travelling to clinics for tests and repeated venepunctures. Historically, urine-based assays were used by fertility specialists in clinics but were subsequently replaced by more practical and automated serum-based assays. A remote urine-based hormonal assay could be an alternative to current serum-based testing at clinics, reducing the inconvenience of blood tests and the frequency of appointments, waiting times and patient burden. Here we provide an overview of the current standard of care for cycle monitoring and review the literature to assess the correlation between urine-based hormonal assays and serum-based hormonal assays during MAR. In addition, in this review, we discuss the evidence supporting the introduction of remote urine-based hormonal monitoring as part of a novel digital health solution that includes remote ultrasound and tele-counselling to link clinics and patients at home.

Revisiting Gaussian copulas to handle endogenous regressors

Marketing researchers are increasingly taking advantage of the instrumental variable (IV)-free Gaussian copula approach. They use this method to identify and correct endogeneity when estimating regression models with non-experimental data. The Gaussian copula approach’s original presentation and performance demonstration via a series of simulation studies focused primarily on regression models without intercept. However, marketing and other disciplines’ researchers mainly use regression models with intercept. This research expands our knowledge of the Gaussian copula approach to regression models with intercept and to multilevel models. The results of our simulation studies reveal a fundamental bias and concerns about statistical power at smaller sample sizes and when the approach’s primary assumptions are not fully met. This key finding opposes the method’s potential advantages and raises concerns about its appropriate use in prior studies. As a remedy, we derive boundary conditions and guidelines that contribute to the Gaussian copula approach’s proper use. Thereby, this research contributes to ensuring the validity of results and conclusions of empirical research applying the Gaussian copula approach.

Measuring Involvement for Organic Food Products: Testing and Identifying the Variables Affecting Consumer Involvement in Selected Urban Dwellings of Gujarat

The main aim of this research was to first design, test and validate a structured tool to measure the construct of consumer involvement for organic food products. To do this, three most populated urban dwellings in the state of Gujarat, India were surveyed. A total sample of 200 respondents was deemed appropriate in terms of the validity of results as well as resources at hand. The three urban dwellings covered under this study were Ahmedabad, Surat and Vadodara. Out of the total sample size, 80 valid responses were collected from Ahmedabad, 70 from Surat and 50 from Vadodara. Initially, a structured tool was developed keeping in mind four basic dimensions which were, Information Search, Affection, Importance and Purchase. The tool had twenty statements asking for respondents’ opinion on a five-point Likert scale ranging from ‘Strongly Agree’ to ‘Strongly Disagree’. Apart from this the questionnaire collected demographic data of the respondents. After collecting data, using factor analysis, four antecedents of involvement were validated since the Eigenvalues for each of them were above 1. Overall, these four antecedents or factors explained 65.71% of the total variance. After statistically validating the tool, consumer involvement was measured and results showed moderately higher involvement. Further analysis was carried out to understand the inter-relationship between the antecedents inter-alia and consumer involvement. Correlation analysis confirmed strong positive correlation between all the antecedents as well as between consumer involvement and its antecedents which further confirmed the validity of this tool. Since correlation was found to be highly positive and significant, it was considered appropriate to establish and test this construct using regression analysis. Regression analysis revealed that all the antecedents had more or less similar impact on consumer involvement for organic food products.

CONFORMAL PREDICTION USAGE FOR THE ACUTE PANCREATITIS OUTCOMES FORECAST

Конформное прогнозирование и его частный случай Мондриановская конформная классификация является новым методом, позволяющим повысить валидность результатов. Конформное прогнозирование удобно в применении и позволяет использовать большинство алгоритмов машинного обучения. Одним из отличий от классических методов является то, что алгоритм конформного прогнозирования позволяет использовать скошенные выборки без предварительной корректировки. В работе рассматривается возможность применения данного метода к задаче прогнозирования течения острого панкреатита на небольшой выборке (91 запись). Рассматривается возможность прогнозирования возникновения инфекционного осложнения и летального исхода. Используется реализация метода в KNIME Analytics Platform, выполненная компанией Redfield AB, Швеция, показывается порядок применения узлов, описываются критерии качества для конформной классификации (эффективность и валидность). Результат позволяет говорить о хороших показателях при применении метода в условиях скошенности классов, в случае прогнозирования исхода, без предварительной корректировки выборки и возможности его применения к небольшим выборкам. Для прогноза исхода эффективность составила 0.67 для класса «Выжил» и 0.14 для класса «Летальный исход», валидность составила 1 и 0.86 соответственно, для прогноза инфекционных осложнений эффективность составила 0.86 для класса «Нет инфекционных осложнений» и 0.43 для «Инфекционные осложнения», валидность 0.71 и о.79 соответственно Conformal prediction and its special case Mondrian conformal classification is a new method that can improve the validity of results significantly. One of the interesting differences from the classical methods is that the conformal prediction algorithm allows using skewed samples without preliminary preprocessing. Possibility of applying this method to the problem of predicting the outcomes of acute pancreatitis on a small sample is studied in the paper (91 records). The implementation of the method in the KNIME Analytics Platform, made by Redfield AB, Sweden, is used, the order of the nodes usage is shown, the quality criteria for the conformal classification (efficiency and validity) are described. The results are allowed to say about good results of applying the method on the sample with skewed classes without preliminary adjusting the sample and the possibility of applying it to small samples. For the “Outcome” variable, the effectiveness was 0.67 for the class "Survived" and 0.14 for the class "Fatal outcome", the validity was 1 and 0.86, respectively, for the prognosis of infectious complications, the effectiveness was 0.86 for the class "No infectious complications" and 0.43 for the class "Infectious complications", validity 0.71 and o.79, respectively

Why most Principal Component Analyses (PCA) in population genetic studies are wrong

Principal Component Analysis (PCA) is a multivariate analysis that allows reduction of the complexity of datasets while preserving data's covariance and visualizing the information on colorful scatterplots, ideally with only a minimal loss of information. PCA applications are extensively used as the foremost analyses in population genetics and related fields (e.g., animal and plant or medical genetics), implemented in well-cited packages like EIGENSOFT and PLINK. PCA outcomes are used to shape study design, identify and characterize individuals and populations, and draw historical and ethnobiological conclusions on origins, evolution, whereabouts, and relatedness. The replicability crisis in science has prompted us to evaluate whether PCA results are reliable, robust, and replicable. We employed an intuitive color-based model alongside human population data for eleven common test cases. We demonstrate that PCA results are artifacts of the data and that they can be easily manipulated to generate desired outcomes. PCA results may not be reliable, robust, or replicable as the field assumes. Our findings raise concerns on the validity of results reported in the literature of population genetics and related fields that place a disproportionate reliance upon PCA outcomes and the insights derived from them. We conclude that PCA may have a biasing role in genetic investigations. An alternative mixed-admixture population genetic model is discussed.

Public attitudes towards COVID-19 and data sharing: A survey-based study to understand the security and privacy concerns over the use of identifiable health data in light of COVID-19 (Preprint)

BACKGROUND The COVID-19 pandemic stimulated the availability and use of population and individual health data to optimise tracking and analysis of the spread of the virus. Many health care services have had to rapidly digitalise in order to maintain the continuity of care provision. Data collection and dissemination have provided critical support to defence against the spread of the virus since the beginning of the pandemic; however, little is known about public perceptions of and attitudes towards the use, privacy and security of data. OBJECTIVE The goal of this study is to better understand people’s willingness to share data in the context of COVID-19. METHODS A web-based survey was conducted, looking at individuals’ use of and attitudes toward health data for those 18 years and older, and in particular, those with a reported diagnosis of a chronic health condition placing them most ‘at risk’ of severe COVID-19. RESULTS In total, 4,764 individuals responded to this web-based survey. 4,674 (98.1%) reported a medical diagnosis of at least 1 health condition (average 4 per person), with type 2 diabetes (2,974, 62.7%), hypertension (2,147, 45.2%) and type 1 diabetes (1,299, 27.4%) being most prominent in our sample. In general, more people are comfortable with sharing anonymised data than personally identifiable data. People reported feeling comfortable sharing data that were able to benefit others; 66% (3,121 respondents) would share personal identifiable data if its primary purpose was deemed beneficial for the health of others. Almost two-thirds (3,026; 63.9%) would consent to sharing personal, sensitive health data with government or health authority organisations. Conversely, over a quarter of respondents (1,297, 27.8%) stated they did not trust any organisation to protect their data and 54% (2,528) reported concerns about the implications of sharing personal information. Almost two-thirds (3,054, 65%) of respondents were concerned around the provisions of appropriate legislation that seeks to prevent data misuse and hold organisations accountable in the case of data misuse. CONCLUSIONS Although our survey focused mainly on the views of those living with chronic health conditions, the results indicate that data sensitivity is highly contextual. More people are comfortable with sharing anonymised data than personally identifiable data. Willingness to share data also depended on the receiving body, highlighting trust as a key theme, in particular who may have access to shared personal health data and how they may be used in the future. This in turn suggests that anonymisation’s disadvantages (in terms of confirming data and correlating shared with other data) might be offset by better (wider, deeper, more accurate) sampling leading to greater validity of results. Further evidence comes from the interaction (or correlation) between these attitudinal responses and other characteristics, meaning that non-anonymised collection might lead to biased results. The nascency of legal guidance in this area suggests a need for humanitarian guidelines for data responsibility during disaster relief operations such as pandemics, and for involving the public in their development. CLINICALTRIAL Ethics approval was obtained from the Human Research Ethics Committee (HREC) of the University of Warwick (BSREC 144/19-20).

Comparative Analysis of the Normalization Techniques in the Context of MCDM Problems

NormNormalization is an essential step in data analysis and for MCDM methods. This study aims to outline the positive and negative features of the normalization techniques that can be used in MCDM problems. In order to compare the different normalization techniques, fourteen sets representing different scenarios of decision problems were used. According to the results, if the decision-maker chooses to take the alternative with the highest value in the criteria and avoid the one with the lowest value, or vice versa, optimization-based normalization techniques should be preferred, whereas the reference-based normalization techniques are considered appropriate for situations where there are ideal values determined by the decision-maker for each criterion. However, if the decision-maker believes that the values in the criteria do not represent the monotonous increasing or decreasing benefit/cost, then non-linear normalization techniques should be used. Also, in the event of a change in the conditions mentioned above, the decision maker may opt for mixed normalization techniques. However, some data structures, such as the presence of zero, and negative values in the decision matrix, can prevent the use of some normalization techniques. The choice of the normalization technique may also be affected by the problem of rank reversal, the range of normalized values, obtaining the same optimization aspect for all criteria, and the validity of results.

Instability modulation properties of the (2 + 1)-dimensional Kundu–Mukherjee–Naskar model in travelling wave solutions

This paper focuses on the instability modulation and new travelling wave solutions of the (2 + 1)-dimensional Kundu–Mukherjee–Naskar equation via the tanh function method. Dark, mixed dark–bright, complex solitons and periodic wave solutions are archived. Strain conditions for the validity of results are also reported. Instability modulation properties of the governing model are also extracted. Various wave simulations in 2D, 3D and contour graphs under the strain conditions are presented.