Statistical Interpretation Research Articles

A codifiable methodology for estimating pallet sliding displacements on steel racking systems

AbstractTo facilitate the logistic processes within a modern warehouse, the contents of steel racking systems are not mechanically connected to the supporting beams, allowing a content‐sliding mechanism to develop when static friction is exceeded. Given that the mass of contents is dominant, this is beneficial for limiting the apparent inertia. On the other hand, pallet fall‐off may occur during strong seismic excitations, which is a failure mode that is not addressed by current seismic design processes and guidelines for racks. Along these lines, a codifiable methodology is proposed for estimating sliding displacements on steel racking systems, based on the statistical interpretation of a large set of response history analyses, using different rack configurations and ground motions. Firstly, a multi‐parametric analysis is conducted using simplified rack models to (i) select the intensity measure and engineering demand parameter that can best describe the problem of pallet sliding and (ii) identify the salient rack characteristics that dominate sliding behavior. Thereafter, a series of multi‐stripe analyses are performed using 180 rack realizations with different feature combinations to derive a so‐called, Empirical Sliding Prediction Equation (ESPE) by following a three‐step procedure: (a) perform regression on maximum sliding, (b) perform regression on the normalized sliding profile, and (c) combine steps a‐b to derive the denormalized profile at a given confidence level. The proposed empirical relationships are then validated through a comparison between the observed and fitted sliding displacements in three rack case studies.

Beyond the Surface: Investigating Clinical and Magnetic Resonance Imaging Dynamics in Knee Injuries: A Cross-sectional Study

Abstract Context: Orthopedic surgeons frequently encounter knee soft tissue injuries such as meniscal and ligament injuries. This study seeks to explore the diagnostic efficacy of clinical examination, magnetic resonance imaging (MRI), and arthroscopy in identifying knee injuries. Subjects and Methods: This prospective observational study enrolled 120 patients with knee injuries slated for arthroscopic surgery based on clinical and MRI assessments. The study assessed and compared the diagnostic accuracy of clinical and MRI findings. Statistical Analysis Used: Statistical analysis was performed using SPSS software (SPSS Inc., Chicago, IL, USA) for the Windows program (21.0 version). The Spearman correlation analysis was done to assess the relationship between the various parameters. The level of agreement was assessed using Cohen’s kappa statistics. Interpretation of kappa statistics is as follows: 0 to 0.20-Slight agreement; 0.1 to 0.39-Fair; 0.40 to 0.59-Moderate; 0.60 to 0.79- Substantial and 0.80 to 0.90-Almost perfect agreement. Sensitivity, specificity, accuracy, positive predictive value and negative predictive value were calculated and observed by using 2 × 2 contingency table. P < 0.05 was considered statistically significant. Results: When comparing MRI and arthroscopy findings, the anterior cruciate ligament (ACL) tear exhibited the highest sensitivity and specificity at 100.00% and 91.84%, respectively, while the medial meniscus tear had the lowest sensitivity and specificity at 80.23% and 61.76%. Regarding the comparison between clinical examination and arthroscopy, the Lachman test displayed the highest sensitivity at 95.92%, whereas McMurray’s test (Lateral Meniscus) had the lowest sensitivity at 54.55%, and the posterior Drawer’s test showed the lowest specificity at 60.00%. Lachman also demonstrated the highest diagnostic accuracy at 98.33%, while the posterior Drawer’s test had the lowest diagnostic accuracy at 70.00%. Conclusions: Despite a few limitations, clinical examination and MRI currently stand out as the most precise non-invasive methods for detecting abnormalities in the menisci and the ACL. These diagnostic approaches provide valuable insights into the structural integrity of these knee components, allowing for a comprehensive assessment without the need for invasive procedures.

The identity of religion always comes up: the war, the mass media and the reproduction of Muslim identity in the west

Islamophobia in the media occurs when media sources present hostile, inaccurate, and/or deceptive stories about Islam, Muslims, or Arabs. Islamophobia is the term used to describe strong antipathy or prejudice toward Muslims, intense hatred or fear of Islam, particularly as a political force, and the study of how and to what extent the media promotes There has been a lot of scholarly and political discourse on Islamophobia. The discourse around Islamophobia in the media often focuses on the rhetorical strategies utilized by individual media outlets or by the mass media of a given nation or region, such the US or Europe. A few instances of this are when Islam is portrayed in a poor light relative to other religions, when Muslims are linked to terrorism, when Muslims and their followers are portrayed as aggressive or primitive, and when Muslim viewpoints are excluded from discussions on politics and academia, among other subjects. On the other hand, reactions to talks about Islamophobia in the media frequently cast doubt on what actually qualifies as Islamophobia, how frequently, how severe, and how effective Islamophobic language is, as well as the political reasons for such talks. This study aims to determine whether or not there was obvious inherent bias by examining the tone, structure, and topics offered by the print media prior to a terrorist event. This study examines how the world press has connected Islam to terrorism and emphasizes how subliminal print messages might contribute to Muslim racial profiling and the spread of Islamophobia. The editorials of six prominent international newspapers between 2014 and 2017 provided the data for this study, which created strata of ten significant worldwide terrorism incidents at random. The investigator examined editorials for a month following the event. The researcher’s results and conclusions are obtained by statistical interpretation following data analysis using event methodology and content analysis.

CyCadas: accelerating interactive annotation and analysis of clustered cytometry data.

Single cell profiling by cytometry has emerged as a key technology in biology, immunology and clinical-translational medicine. The correct annotation, which refers to the identification of clusters as specific cell populations based on their marker expression, of clustered high-dimensional cytometry data, is a critical step of the analysis. Its accuracy determines the correct interpretation of the biological data. Despite the progress in various clustering algorithms, the annotation of clustered data still remains a manual, time consuming and error-prone task. We developed a user-friendly cluster annotation and differential abundance detection tool that can be applied on data generated with Self Organizing Map clustering algorithms, thus simplifying the annotation process of datasets that consist of hundreds or thousands of clusters. We present Cytometry Cluster Annotation and Differential Abundance Suite (CyCadas), a semi-automated software tool that facilitates cluster annotation in cytometry data by offering both visual and computational guidance. CyCadas addresses the critical need for efficient and accurate annotation of high-resolution clustered cytometry data, significantly reducing the time needed to perform the analysis compared to both manual gating approaches and manual annotation of clustered data. The tool features a user-friendly interface, visual tools enabling data exploration and automated threshold estimation to separate negative and positive marker expression. It facilitates the definition and annotation of cell phenotypes among multiple clusters in a tree-based data structure. Finally, it calculates the abundance of various cell populations across the conditions with statistical interpretation. It is an ideal resource for researchers aiming to streamline their cytometry workflow. CyCadas is available as open source at: https://github.com/DII-LIH-Luxembourg/cycadas.

8737 Primary Hyperparathyroidism (PHPT) “Phenotypes” and the Results of Parathyroid [99mTc]Tc-Sestamibi SPECT/CT Scintigraphy: Minimizing the Risk of Negative Imaging Results

Abstract Disclosure: K. Kucharczyk: None. A. Drynda: None. J. Podlewski: None. M. Trofimiuk-Muldner: None. A. Hubalewska-Dydejczyk: None. Introduction. Primary hyperparathyroidism (PHPT) is a prevalent endocrinopathy typically identified through biochemical testing. PHPT is characterized by hypercalcemia accompanied by increased or inappropriately normal plasma parathyroid hormone (PTH) concentrations. Preoperative imaging is conducted when surgery is indicated to pinpoint parathyroid adenomas. [99mTc]Tc-Sestamibi scintigraphy is highly specific and sensitive in diagnosing PHPT, with a higher adenoma detection rate demonstrated for patients with larger lesions or higher blood calcium concentrations. The study aimed to investigate the relationship between the results of biochemical tests and clinical manifestations (PHPT “phenotype”) and the rate of positive results of parathyroid [99mTc]Tc-Sestamibi scintigraphy. Materials and methods. This was a retrospective study of patients suspected of PHPT who underwent parathyroid [99mTc]Tc-Sestamibi SPECT/CT scintigraphy between 2010 and 2022. Patients’ cluster analysis was conducted based on PTH, serum calcium (Ca), and phosphate (Pi) concentrations to find distinct patterns in these parameters. Furthermore, we compared the distributions of additional patients’ attributes, including gender, lesion size, serum vitamin D, ALP concentrations, urinary calcium, urolithiasis prevalence, as well as parathyroid scintigraphy results. Results. 513 patients were divided into 5 clusters using different algorithms. Finally, clusters from K-means methods were selected based on statistical metrics and expert interpretation. Those were organized in ascending order of positive scintigraphy results, with Cluster 5 reaching a 79.5% positivity rate and median values of PTH - 315.5 pmol/L, Ca - 2.96 mmol/L, and Pi - 0.7 mmol/L. As the proportion of positive imaging outcomes declined, median PTH and calcium concentrations decreased while phosphate concentrations increased. In Cluster 1, with 30% of positive imaging results, medians were as follows: PTH - 79.06 pmol/L, Ca - 2.55 mmol/L and Pi - 1.1 mmol/L. A comparative analysis of various clinical and biochemical parameters revealed statistically significant differences between clusters. Patients from Clusters with a higher frequency of positive scintigraphy were significantly more likely to be males, vitamin D deficient, with higher ALP concentrations, diagnosed with urolithiasis, and harbouring larger lesions. Conclusions. PHPT “phenotypes” correlated with disease severity are evidently linked to parathyroid scintigraphy detection rate. Therefore, it is crucial to know that false-negative imaging is more likely in PHPT patients with minor deviations in laboratory tests. Judicious assessment of biochemical and clinical features may limit the number of redundant parathyroid scintigraphies and, in consequence, reduce healthcare costs and unnecessary radiation exposure. Presentation: 6/2/2024

Mastering meta-analysis in Microsoft Excel with MetaXL add-in: A comprehensive tutorial and guide to meta-analysis.

Meta-analysis, a powerful technique for combining effect estimates from multiple studies, enhances statistical power and precision. However, its adoption can be hindered by challenges in statistical interpretation and the complexity of specialized software. MetaXL, a freely available Microsoft Excel add-in, aims to mitigate these barriers by providing comprehensive support and facilitating seamless integration of meta-analytical results into research publications. This tutorial illustrates the practical application of MetaXL for synthesizing meta-analytical evidence, with a focus on common effect sizes and their presentation. This paper reintroduce MetaXL's functions and provide concise explanations of common effect sizes employed in meta-analysis. The tutorial delves into fundamental concepts such as the selection of appropriate effect sizes for pooling and the choice of meta-analytical models. Eight illustrative examples are presented, incorporating diverse effect sizes and data formats, including scenarios involving incidence rate ratios, weighted and standardized mean differences, hazard ratios, and prevalence. Additionally, key concepts in network meta-analysis are discussed, along with their implementation in MetaXL. MetaXL provides convenient access to data formatting templates tailored to various data types and effect sizes encountered in included studies. This tutorial offers researchers, particularly those with limited resources, detailed explanations and insights into commonly used methodologies for pooling effect sizes. Furthermore, it introduces the new Excel functions that comes with the MetaXL add-in. Accurate population of this function and adherence to the correct format are essential to ensure error-free analyzes.

Overcoming the pitfalls of categorizing continuous variables in ecology, evolution and behaviour.

Many variables in biological research-from body size to life-history timing to environmental characteristics-are measured continuously (e.g. body mass in kilograms) but analysed as categories (e.g. large versus small), which can lower statistical power and change interpretation. We conducted a mini-review of 72 recent publications in six popular ecology, evolution and behaviour journals to quantify the prevalence of categorization. We then summarized commonly categorized metrics and simulated a dataset to demonstrate the drawbacks of categorization using common variables and realistic examples. We show that categorizing continuous variables is common (31% of publications reviewed). We also underscore that predictor variables can and should be collected and analysed continuously. Finally, we provide recommendations on how to keep variables continuous throughout the entire scientific process. Together, these pieces comprise an actionable guide to increasing statistical power and facilitating large synthesis studies by simply leaving continuous variables alone. Overcoming the pitfalls of categorizing continuous variables will allow ecologists, ethologists and evolutionary biologists to continue making trustworthy conclusions about natural processes, along with predictions about their responses to climate change and other environmental contexts.

20 years of monitoring of a steel jacket offshore platform: variability of environmental and modal parameters

The fixed steel offshore platform VEGA-A has been operating in the Sicily channel since 1988. The platform, one of the largest in the Mediterranean Sea, is equipped with a monitoring system that collects environmental (by means of a current meter, a depth gauge, and a system for detecting wind speed and direction) and structural (by means of nine accelerometers) data. After a description of the recording system, this paper provides a statistical interpretation of the structural response of the platform over the last 20 years. The structural monitoring system is aimed at performing dynamic identification (i.e., evaluating main frequencies and mode shapes), and part of the acquired data is directly processed on the platform in order to constantly check their representativeness in relation to the structural control of the platform. The possible variation of main frequencies and mode shapes over time may be due to both changes in environmental loads/conditions and/or structural damage; in this respect, a data-driven approach capable of recognizing anomalies in the dynamic behavior was adopted here to assess the performances of the platform over the 20 years monitoring period.

Numerical illustration using finite difference method for the transient flow through porous microchannel and statistical interpretation of entropy using response surface methodology

The current article discloses the influence of the hyperbolic tangent nanofluid on time dependent flow through a microchannel when a magnetic field is applied. The porous medium was incorporated using the Darcy–Forchheimer model. The chemical reaction is explained by Arrhenius activation energy. Temperature is determined by convective boundary conditions. The irreversibility occurring in the flow is analyzed. The modeled problem gives rise to partial differential equations, which are computed by finite difference method. Response surface methodology, an optimization technique, is used to attain the optimal conditions for entropy generated for the flow of fluid. Results of the analysis reveal that concentration decreases with the rise in reaction rate parameter and increases with activation energy parameter. Prandtl and Eckert numbers, with their increase, enhance entropy, and fluid friction irreversibility is at its highest. Perfect co-relation is attained for the model by the response surface methodology, with a co-relation coefficient of 100 %. The Weissenberg number is highly sensitive to change in the present modeling, followed by Darcy and Reynolds numbers. The Reynolds number and Darcy number show positive sensitivity, while the Weissenberg number shows negative sensitivity to the entropy generated.

Design of integrated evolutionary finite differences for nonlinear electrohydrodynamics ion drag flow in cylindrical conduit model

This research implements an evolutionary optimized finite differences scheme (FDS) for nonlinear electrohydrodynamics ion drag flow dynamics in a cylindrical conduit (EHD-IDFCC). In the scheme, finite differences are exploited to discretize governing expressions of EHD-IDFCC, represented with a nonlinear singular seconder order differential equation, into a nonlinear equations-based system. The fitness function based on residual error is constructed for the FDS-based discretized EHD-IDFCC model. Its optimization is conducted with the global search capability of genetic algorithms (GAs) aided by the local search efficacy of the interior-point method (IPM), i.e., FDS-GA-IPM. The performance of the designed stochastic numerical solver FDS-GA-IPM is evaluated for a variant of the EHD-IDFCC model for different scenarios to measure the effect of axial flow velocity by varying the electric Hartmann numbers, as well as the nonlinearity factor. Statistical interpretations based on histogram illustrations, probability plots, and boxplots in terms of the cost function, mean absolute error (MAE), Thai inequality coefficients (TIC), and coefficient of determination metrics (R2) are used to endorse the accuracy, convergence, stability, and strength of the FDS-GA-IPM.

Random measurement and prediction errors limit the practical relevance of two velocity sensors to estimate the 1RM back squat.

While maximum strength diagnostics are applied in several sports and rehabilitative settings, dynamic strength capacity has been determined via the one-repetition maximum (1RM) testing for decades. Because the literature concerned several limitations, such as injury risk and limited practical applicability in large populations (e.g., athletic training groups), the strength prediction via the velocity profile has received increasing attention recently. Referring to relative reliability coefficients and inappropriate interpretation of agreement statistics, several previous recommendations neglected systematic and random measurement bias. This article explored the random measurement error arising from repeated testing (repeatability) and the agreement between two common sensors (vMaxPro and TENDO) within one repetition, using minimal velocity thresholds as well as the velocity = 0m/s method. Furthermore, agreement analyses were applied to the estimated and measured 1RM in 25 young elite male soccer athletes. The results reported repeatability values with an intraclass correlation coefficient (ICC) = 0.66-0.80, which was accompanied by mean absolute (percentage) errors (MAE and MAPE) of up to 0.04-0.22m/s and ≤7.5%. Agreement between the two sensors within one repetition showed a systematic lower velocity for the vMaxPro device than the Tendo, with ICCs ranging from 0.28 to 0.88, which were accompanied by an MAE/MAPE of ≤0.13m/s (11%). Almost all estimations systematically over/ underestimated the measured 1RM, with a random scattering between 4.12% and 71.6%, depending on the velocity threshold used. In agreement with most actual reviews, the presented results call for caution when using velocity profiles to estimate strength. Further approaches must be explored to minimize especially the random scattering.

Effect of secondary processes on the petrophysical properties of sand rock of the tanopchin formation of the Tambey field (Yamalo-Nenets autonomous okrug)

Relevance. Finding the correlation between filtration-capacitive properties and secondary processes helps to identify missed oil and gas deposits. Traditional methods of processing geophysical information are becoming less and less effective, and accordingly there is a need to introduce new techniques for detecting missed hydrocarbon deposits. Aim. To study the effect of secondary processes on the filtration and reservoir properties of rocks in the productive lower strata of the tanopchin formation of the North Tambey license area, located within the Tambey field, using materials from geophysical surveys of wells and core data. Methods. The statistical-correlation interpretation method represents a significant breakthrough in the field of interpretation of well logging data. It allows for a more accurate interpretation of data from existing reservoir studies, which makes it possible to identify productive sections of wells where hydrocarbons could not be detected by traditional methods. Statistical correlation interpretation is carried out on the basis of data from secondary geochemical processes occurring in the formation. The scientific novelty. Identification of the relationship between the intensity of secondary processes and the petrophysical properties of the rocks of the Lower Cretaceous productive horizon of the Tambey field (Yamalo-Nenets autonomous okrug). Results and conclusions. The author has determined the dependence of the filtration-capacitive properties of rocks on the intensity of secondary geochemical processes. It was established that secondary geochemical processes such as carbonatization and pyritization worsen reservoir properties, while kaolinization and pelitization lead to an improvement in the filtration and reservoir properties of reservoir rocks. The paper explains the mechanism of effect of secondary processes on filtration-capacitive properties. Pelitization of potassium feldspars leads to the formation of three-layer clay minerals, which have the largest sorption surface area and cation exchange capacity; as a result, the reservoir capacity increases, which leads to an increase in permeability several times.

Mapping Geospatial AI Flood Risk in National Road Networks

Previous studies have utilized machine learning algorithms that incorporate topographic and geological characteristics to model flood susceptibility, resulting in comprehensive flood maps. This study introduces an innovative integration of geospatial artificial intelligence for hazard mapping to assess flood risks on road networks within Portuguese municipalities. Additionally, it incorporates OpenStreetMap’s road network data to study vulnerability, offering a descriptive statistical interpretation. Through spatial overlay techniques, road segments are evaluated for flood risk based on their proximity to identified hazard zones. This method facilitates the detailed mapping of flood-impacted road networks, providing essential insights for infrastructure planning, emergency preparedness, and mitigation strategies. The study emphasizes the importance of integrating geospatial analysis tools with open data to enhance the resilience of critical infrastructure against natural hazards. The resulting maps are instrumental for understanding the impact of floods on transportation infrastructures and aiding informed decision-making for policymakers, the insurance industry, and road infrastructure asset managers.

BURNOUT SYNDROME AMONG NURSES OF ELBASAN REGIONAL HOSPITAL "XHAFERR KONGOLI"

Introduction: Burnout syndrome is the result of chronic stress in the workplace, which has not been successfully managed. Burnout is not a medical condition, but an occupational phenomenon (according to WHO) and nurses are one of the populations most predisposed to be affected by this syndrome. Purpose: The purpose of this study is to evaluate the Burnout syndrome among the nurses of the various services of the Elbasan Regional Hospital "Xhaferr Kongoli" and the connection of its level with the associated factors in order to guide methods for the prevention of this syndrome. Methods: This cross-sectional studywas performed in different wards such as: surgery, emergency, infectious, neurology, palliative etc. during the period January-February 2024. The data analysis was done through the distribution of a questionnaire prepared by us of 20 questions via google forms against a population of n=50 nurses and their statistical interpretation. Results: According to the study population (80% of nurses are women and 20% are men), 20% of them work in the neurology ward, 20% in surgery, 20% in emergency, 20% in pediatrics, 10% in infectious, 6 % in maternity and 4% in palliative health, 82% of nurses appear emotionally overworked; 34% of them with signs of depression; 34% of nurses have symptoms such as dyspnea, weight gain and headache; 14% of them have gastrointestinal problems and 48% of nurses think about leaving work. Conclusion: The level of burnout among the nurses of the Elbasan Regional Hospital "Xhaferr Kongoli" is at significant levels; age, gender do not have major impacts on Burnout, but work experience has an impact on Burnout. Burnout causes increased risk for various pathologies, mainly gastrointestinal and neurological ones. Physical exercises reduce the level of Burnout.

Forensic Science and How Statistics Can Help It

ABSTRACTThe persistent issue of wrongful convictions in the United States emphasizes the need for scrutiny and improvement of the criminal justice system. While statistical methods for the evaluation of forensic evidence, including glass, fingerprints, and deoxyribonucleic acid, have significantly contributed to solving intricate crimes, there is a notable lack of national‐level standards to ensure the appropriate application of statistics in forensic investigations. We discuss the obstacles in the application of statistics in court and emphasize the importance of making statistical interpretation accessible to non‐statisticians, especially those who make decisions about potentially innocent individuals. We investigate the use and misuse of statistical methods in crime investigations, in particular the likelihood ratio approach. We further describe the use of graphical models, where hypotheses and evidence can be represented as nodes connected by arrows signifying association or causality. We emphasize the advantages of special graph structures, such as object‐oriented Bayesian networks and chain event graphs, which allow for the concurrent examination of evidence of various nature.

Adolescents' accessibility and use of sexual and reproductive health services in Kaolack and Gossas, Senegal.

In Senegal, the needs for care related to sexual and reproductive health and rights (SRHR) among adolescents are significant. This study evaluates the accessibility and use of gender-related SRHR services by adolescents in the municipalities of Kaolack and Gossas. It is based on quantitative and qualitative data from 2,263 adolescents in the 2 sites, 84 in-depth individual interviews, 12 focus groups with adolescents and sexual reproductive Health actors, and 4 interviews with key informants. Quantitative analysis consisted of the interpretation of univariate statistics and bivariate analyses, while qualitative analysis relied on the coding and thematic analysis of verbatim statements. The results show low use of health structures (3% and 0.4% in Gossas and Kaolack respectively). This was attributed to the perceptions that services are not suitable to the needs of adolescents, and also because of socio-cultural constraints in the two sites. Additionally, the distribution of access to reproductive health services by sex shows gaps between boys and girls. We conclude that efforts should be made to tailor the sexual and reproductive health services offered to adolescents to their needs and social circumstances.

Range expansions across landscapes with quenched noise

When biological populations expand into new territory, the evolutionary outcomes can be strongly influenced by genetic drift, the random fluctuations in allele frequencies. Meanwhile, spatial variability in the environment can also significantly influence the competition between subpopulations vying for space. Little is known about the interplay of these intrinsic and extrinsic sources of noise in population dynamics: When does environmental heterogeneity dominate over genetic drift or vice versa, and what distinguishes their population genetics signatures? Here, in the context of neutral evolution, we examine the interplay between a population's intrinsic, demographic noise and an extrinsic, quenched random noise provided by a heterogeneous environment. Using a multispecies Eden model, we simulate a population expanding over a landscape with random variations in local growth rates and measure how this variability affects genealogical tree structure, and thus genetic diversity. We find that, for strong heterogeneity, the genetic makeup of the expansion front is to a great extent predetermined by the set of fastest paths through the environment. The landscape-dependent statistics of these optimal paths then supersede those of the population's intrinsic noise as the main determinant of evolutionary dynamics. Remarkably, the statistics for coalescence of genealogical lineages, derived from those deterministic paths, strongly resemble the statistics emerging from demographic noise alone in uniform landscapes. This cautions interpretations of coalescence statistics and raises new challenges for inferring past population dynamics.

Interpretable prediction model for decoupling hot rough rolling camber-process parameters

The presence of asymmetrical camber defects in reversible hot rough rolling processes significantly impacts the quality of plate shapes. Understanding and quantifying the intricate relationship between process parameters and camber is essential for developing cost-effective strategies to rectify plate shape defects. Previous research predominantly concentrated on specific couplings between process variables and camber, employing simplified mathematical models with physical mechanisms. However, these approaches may overlook critical factors inherent in the entire rough rolling process, which can substantially influence plate shape deformation. Furthermore, heavy reliance on expertise within the rolling domain may limit the capability and accuracy of coupling analysis models. In this paper, we introduce an Interpretable Prediction Model for Decoupling Camber-process Parameters (called IP-DCP), to investigate the process coupling within the entire rough rolling process. IP-DCP employs conditional modeling to integrate the intricate dependencies of process parameters during rough rolling, dynamically shaping composite features that enhance accurate camber predictions. Additionally, we introduce statistical interpretations for camber predictions using SHapley Additive exPlanation (SHAP) models. This enables a comprehensive comparison of critical process factors influencing camber and coupling analysis in various rolling scenarios, including the overall rough-rolled slab and specific steel grades such as Q335B and ST12. Extensive experiments have been conducted on actual rough roughing datasets, demonstrating the superior performance of our camber prediction model compared to baselines. Furthermore, we have conducted a quantitative analysis of their overall effect, main effect, and interaction effect. These coupling analysis results consistently align with established theoretical insights, showing their substantial significance in advancing precise process optimization strategies and cost-efficient measures for controlling camber.

A Computer Laboratory for the Calculation of the Heat Capacity of a Peptide Unfolding Transition

ABSTRACT We present the implementation of a computer simulation laboratory for the calculation of the heat capacity of the folding-unfolding pseudo phase transition of a polyalanine Ala30 α-helix, using molecular dynamics simulations. Unlike conventional calorimetry experiments, which provide a macroscopic interpretation of the heat capacity, the computer laboratory additionally offers a molecular perspective of the energy exchange processes that lead to the peak of the heat capacity at the phase transition, as well as the statistical mechanical interpretation of the phase transition. The exercise is targeted toward 1st- or 2nd-year students of biophysics in the context of an undergraduate physical chemistry course. We use a graphical web interface to overcome the steep learning barriers associated with command line interactions in commonly used molecular dynamics protocols. The web interface (VIKING) also handles communication with computing hardware in the background, so that students with limited or no programming experience can launch and analyze the simulations easily. A collaboration between the VIKING support team and responsible teachers is required to set up VIKING. We also present a solution in which simulations can be launched on students’ own computers, if some command line knowledge can be expected. The laboratory can also be repurposed for students of various backgrounds (physics, chemistry, biology, or engineering) and scholastic levels (undergraduate or graduate).

Datacube segmentation via deep spectral clustering

Extended vision techniques are ubiquitous in physics. However, the data cubes steaming from such analysis often pose a challenge in their interpretation, due to the intrinsic difficulty in discerning the relevant information from the spectra composing the data cube. Furthermore, the huge dimensionality of data cube spectra poses a complex task in its statistical interpretation; nevertheless, this complexity contains a massive amount of statistical information that can be exploited in an unsupervised manner to outline some essential properties of the case study at hand, e.g. it is possible to obtain an image segmentation via (deep) clustering of data-cube’s spectra, performed in a suitably defined low-dimensional embedding space. To tackle this topic, we explore the possibility of applying unsupervised clustering methods in encoded space, i.e. perform deep clustering on the spectral properties of datacube pixels. A statistical dimensional reduction is performed by an ad hoc trained (variational) AutoEncoder, in charge of mapping spectra into lower dimensional metric spaces, while the clustering process is performed by a (learnable) iterative K-means clustering algorithm. We apply this technique to two different use cases, of different physical origins: a set of macro mapping x-ray fluorescence (MA-XRF) synthetic data on pictorial artworks, and a dataset of simulated astrophysical observations.