High Deviation Research Articles

Exploring B-cell Epitope Conservation and Antigenicity Shift in Current COVID-19 Variants: Analyzing Spike-Antibody Interactions for Therapeutic Uses

SARS-CoV-2, responsible for the global COVID-19 pandemic, has undergone significant genetic changes, leading to various variants impacting transmissibility, severity, and vaccine efficacy. The methodology involved evaluating SARS-CoV-2 variants designated by WHO as Variants of Interest (VOIs) and Variants Under Monitoring (VUMs). Several noteworthy mutations including G446S, K417N, T478K, E484A, N501Y, and Y505H. exhibit a strong pattern of convergent evolution across all these variants, particularly at antigenic sites within the spike protein. Conformational epitopes mapping and antigenicity shift analyses implicated epitope changes which were compared for therapeutic purposes. VUMs BA.2.86 and XBB.2.3 show significant antigenicity changes and epitope dynamics, correlating with high root mean square deviation values and epitope expansions or contractions. Nonsynonymous mutations are predominant in all variants, suggesting functional changes affecting transmissibility and immune evasion. VOIs XBB.1.5, BA.2.86, and CH.1.1 show high solvent-accessible surface area and radius of gyration, indicating structural expansion and increased epitope availability. In contrast, stable VOI EG.5.1 displays minimal structural changes and moderate epitope expansions. We evaluated two classes of antibodies for their effectiveness in neutralizing SARS-CoV-2 variants. Antibodies CC12.1 and P4A1 from Class I, alongside CV07-250, P5A-2G9, and MW05 from Class II, display strong binding across multiple variants, indicating broad neutralizing capabilities. Specifically, P4A1 shows the highest affinity for EG.5 and EG.5.1, while MW05 exhibits the strongest binding to XBB.1.5, CH.1.1, and XBB.2.3, highlighting their potent neutralization potential. This study aims to elucidate epitope variations in evolving SARS-CoV-2 strains, offering critical insights for developing targeted interventions against current challenges posed by the virus.

Performance and Robustness of Physiological Controllers With Integrated Pressure Sensors on Inflow Cannula.

The control of left ventricular assist devices (LVADs) requires sensors and/or estimators to account for the physiological state of the patient and apply advanced controllers. Sensor characteristics are a challenge when using implantable pressure sensors because they influence the quality of physiological control and the robustness of the controlled system. The objective of this work is to investigate the performance and robustness of LVAD controllers that operate based on LVAD integrated pressure sensors. Four pressure-based LVAD controllers are tested with a HeartMate 3 that has an integrated pressure sensor. Controller sensitivity as well as robustness to sensor drift and noise are evaluated based on controller response to large changes in preload and afterload. A fail-safe strategy for sensor failure used also to investigate the reliable operation of the LVAD in realistic conditions. All tested controllers are sensitive to drifting pressure signals, which can lead to unstable behavior. The results show that two controllers are robust to noise. The other two controllers show high deviation and oscillation in cardiac output even for small noise. Additionally, a noticeable difference of the controller's response between simulated and measured pressure input was observed, indicating the need for a robust controller design. Reliable operation even in the event of a sensor failure was achieved on the basis of a fail-safe control system.

Study of the Technical and Operational Parameters of Injectors Using Biogas Fuel

Using biogas fuel in a modern internal combustion engine equipped with gas equipment of the fourth and fifth generations can create several difficulties. This is due to the low heat of combustion of untreated biogas, the presence of moisture, and the specifics of the injectors. The main problem of the studies we considered is that there are no data on the operating parameters of biogas fuel injectors. Studies on the parameters of the Matrix, Barracuda, Valtek, Hana, and Keihin injectors in relation to biogas fuel were carried out according to performance indicators, the linearity of operation, the resistance of the injectors, the ability to maintain factory parameters, and service life. According to the indicators of performance and linearity of work, Valtek injectors have the highest deviation in productivity and linearity of work, with an average of 38.8%, and the lowest deviation of Barracuda injectors is 7.5%. Keihin (15.3%) and Hana injectors (19.1%) also showed good performance indicators, and therefore can be used effectively for biogas fuel systems. As a result of research on the response time of the injectors, it was established that the best indicators were found for Hana (1.75 ms) and Keihin (1.99 ms) injectors. Valtek injectors showed good response rates (2.07 ms), as did Barracuda injectors (2.19 ms), but the highest response time was found in Matrix injectors, with 2.44 ms. Keihin injectors had the lowest average resistance value of 1.25 ohms, and Valtek injectors had the highest resistance value of 3 ohms. According to the research results, Keihin, Matrix, and Barracuda injectors provide the best ability to maintain factory performance when using biogas fuel at 2 to 5%, and Valtek had the worst performance up to 20%. Keihin, Barracuda, and Hana experimental injectors had the highest service life, which is from 200 to 250 thousand km of car mileage. The lowest indicators were found for Valtek and Matrix injectors, the service life of which varies from 70 to 100 thousand km of mileage.

Effect of build orientation on the fabrication trueness of additively manufactured implant-supported complete arch interim fixed prostheses.

To evaluate how build orientation affects the fabrication trueness of additively manufactured implant-supported complete arch prostheses by comparing them to subtractively manufactured high-impact polymer-based prostheses. An edentulous maxillary model with four implants at canine and first molar regions bilaterally was digitized (ATOS Core 80 5MP) to design a reference implant-supported complete arch prosthesis standard tessellation language file (RF-STL). The STL file was used to manufacture prostheses additively in five different orientations according to the build platform (AM-0, 0-degree; AM-15, 15-degree; AM-30, 30-degree; AM-45, 45-degree; AM-90, 90-degree) or subtractively (SM-HIP, control) (n = 10). The prostheses were digitized with an intraoral scanner (Trios 3) to generate their STLs (TF-STL). After superimposing TF-STLs over the RF-STL with a metrology-grade analysis software program (Geomagic Control X), surface deviations at four regions (overall, occlusal, overall without occlusal, and abutments), linear deviations at each abutment site, and interimplant distance deviations (canine-to-molar, canine-to-canine, and molar-to-molar) were calculated. One-way analysis of variance and Tukey HSD tests were used for the statistical analyses (α = 0.05). AM-90 mostly had the highest surface deviations, while AM-0 had the lowest overall, and lowest overall without occlusal region deviations (p ≤ 0.022). SM-HIP had the lowest occlusal region deviations (p < 0.001). AM-90 had the highest linear deviations (p < 0.001). AM-15 had higher canine-to-molar deviations than SM-HIP (p = 0.042). SM-HIP had the highest canine-to-canine deviations, while AM-90 had higher deviations than AM-0 and AM-30 (p ≤ 0.026). AM-45 and AM-90 had the highest and AM-0 had the lowest molar-to-molar deviations (p ≤ 0.013). AM-0 mostly had higher trueness and AM-90 mostly had lower trueness within tested outcomes. Additively manufactured prostheses mostly had lower canine-to-molar distances and higher molar-to-molar distances, whereas SM-HIP prostheses consistently had higher distances than the design file.

A “Delve” into the evidence of AI in production of academic business literature

This study tests the hypothesis that generative AI is impacting the volume of academic publications in the business subject disciplines. The author performed a t-test using the average growth rates of articles published in the database ProQuest ABI/INFORM Global containing keywords or phrases purported to be commonly used in content generated by AI during the years before and after common generative AI availability. Results show evidence that publication rates after generative AI availability experienced an improbably high deviation from the norm. This indicates the potential of AI impacting the scholarly output of academic literature in the business subject discipline.

Digitally Controllable Multifrequency Impedance Emulator for Bioimpedance Hardware Validation

ABSTRACTThe accurate emulation of the electrical impedance of biological tissues is crucial for the development and validation of bioimpedance measurement devices and algorithms. This paper describes a digitally controllable impedance emulator capable of reproducing values representative of tissue bioimpedance in user‐specified resistance, reactance, and frequency ranges up to 1 MHz. The presented solution uses a 2R‐1C impedance model to emulate the impedance characteristics of a biological tissue. Specific selection of each element value in this model is achieved using analog multiplexers with low resistance. A MATLAB algorithm was developed for value estimation using target impedance requirements. An example design to emulate impedance from 1 kHz to 1 MHz with 10 to 400 resistance and maximum reactance is provided. The nonideal behavior of this design was evaluated and compared against experimentally collected impedance measurements. Deviations of &lt;1% were observed between experimental and theoretical resistances for values up to 100 kHz (with approximately 5% deviations up to 1 MHz) and reactance deviations were also &lt;1% up to 10 kHz. High frequency deviations are attributed to the parasitic capacitance in the realization of the design. The experimental results validate the design approach and realization for low frequencies. Overall, the innovation of the proposed approach is the control of both resistance and reactance for emulating electrical impedance representative of biological tissues.

A Validated Unsteady Turbofan Engine Performance Simulation Using Pseudo Bond Graph Approach

Abstract In this paper, the unsteady performance of a turbofan engine is illustrated and modeled using the pseudo-bond graph approach. The pseudo-bond graph method allows multiphysical and interdisciplinary systems to be described analytically and comprehensibly across disciplines. In this study, a high bypass ratio turbofan engine is modeled using a pseudo-bond graph model that can be adapted for hybrid electric propulsion systems. The development of the model is followed by a full-scale test run of the in-house V2500-A1 turbofan engine. The highly instrumented turbofan engine enables the time-resolved recording of performance-specific variables, thus facilitating the validation of the numerical model. Slam acceleration and deceleration are performed to analyze the unsteady performance. It is shown that the pseudo-bond graph model is able to predict the dynamics of the turbofan engine. High deviations are explained by the incomplete modeling of the control system.

Resting Energy Expenditure in Patients with Extreme Obesity: Comparison of the Harris-Benedict Equation with Indirect Calorimetry.

Background: The main objective of the work was the analysis and description of data on body composition and resting energy expenditure (REE) values of selected groups of patients with obesity whose REE measurement results using indirect calorimetry reached a level below 95% of the predicted REE calculated using the Harris-Benedict (H-B) equation. The sub-goals were to describe the dependence of body composition on the size of the REE and to find out if the deviations between the number of the total measured REE and the REE calculated using H-B in the adapted group (patients with altered REE values, lower than expected caused by long caloric restriction) are significant. Methods: For the research, 71 (39 women and 32 men) patients treated in obesitology were selected. Patients underwent the measurement of resting metabolism using indirect calorimetry (IC) and body composition measurement on the bioimpedance device and, at the same time, the value of resting metabolism was calculated for everyone using the H-B equation. The whole group was divided into five groups according to the deviation of the measurement using IC and the calculation of the H-B equation. Results: In the total set of examined individuals, there were 32.4% with a reduced REE value compared to the REE calculation according to the H-B equation, which corresponds to 23 individuals. In the adapted group, the average measured REE was 2242 ± 616 kcal compared to the H-B calculation of 2638 ± 713 kcal. Statistically, these results were not significant, but a high case-to-case variation was found. The highest deviation from the H-B predictive calculation was -42% and +43% in the whole research group. The amount of muscle tissue in the adapted group averaged 44.3 ± 11.9 kg and the amount of fat-free mass (FFM) 77.9 ± 20.1 kg. When statistically testing the dependence of REE on FFM and muscle tissue in the adapted group, a strong correlation was found. Conclusions: The H-B equation alone is not suitable for setting a suitable diet therapy for an individual with obesity. In order to select and characterize a group of adapted individuals, it will be necessary to use other methods or a larger research sample, and preferably examine and divide patients with specific comorbidities or include their health status.

A novel technique to resolve directional ambiguity for Particle Streak Velocimetry

In this study, a novel streak direction-resolving algorithm is introduced to determine the direction of the flow field for a single image frame with multiple streaks. The streak direction was resolved by varying its intensity from 100% to 50% of the total intensity of the streak image. This technique was applied to two main types of flows parallel and Hill’s vortex flows, parallel flows were divided into further two types constant velocity parallel flow and accelerating parallel flow. The purpose of using different types of flows was to test the robustness of the algorithm with easy and complex flows. The performance of the algorithm was checked by the angular deviation between the true and least-square fitted velocity vectors. The number of correct synthetic streak directions was measured with the success rate in percentage. A high success rate means low angular deviation and a high number of velocity vectors with correct direction was obtained. In this research, four different types of image formats were considered DP (double precision), 16-bit (without noise), 16-bit (1.0% noise), 16-bit (5.0% noise), and 8-bit (without noise) and the best results were obtained for DP and 16-bit image formats. The results of the parallel flows indicated a 100% streak direction success rate, Hill’s vortex was a type of complex flow therefore, the algorithm hard to resolve some streak directions due to very low velocities (less than 0.1 px (pixel)/interval) and very high-velocity gradients (greater than 52 px/interval). The observation shows that Hill’s vortex synthetic streak images were resolved with a success rate of 92.76% which means that the majority of the synthetic streaks were resolved. Experimental analysis was also done by using the PDMS microchannel setup. Intensity variation streaks were recorded with long camera exposure time and for maintaining the intensity variation, as the LED switched on maximum illumination was started, and continuous decrement in illumination was set by switching off at 50% of the total illumination intensity. The best results were achieved with a 94% success rate. Therefore, the proposed novel approach can be used with less expensive hardware for image processing with a single image frame and is useful for multiple applications.

Choline chloride and amino acid solutions taste and hydration behavior with experimental thermodynamic properties and COSMO-PC-SAFT calculation

Aqueous amino acid solutions have been introduced as dietary supplements for both animals and humans. This study investigates the physicochemical properties of the solutions containing amino acids (l-glycine, d,l-alanine, l-proline), choline chloride, and water at temperature range of 288.15 to 318.15 K. The results show that increasing concentrations of amino acids and choline chloride lead to higher solution densities. Analysis of apparent molar volume (Vφ) and apparent molar isentropic compressibility (κφ) reveals that Vφ values increase with choline chloride concentration and temperature, indicating enhanced solute–solvent interactions, while κφ values decrease, suggesting increased solution compression. Thermodynamic analysis using the Redlich-Mayer model and COSMO-based modeling provides insights into molecular interactions. However, COSMO-based parameters show high average relative deviation percentage (ARD %) values, indicating poor predictive performance for the density of these systems. In contrast, the ePC-SAFT equation of state effectively predicts the densities, particularly for l-proline-based solutions, which show very low ARD % values, indicating high accuracy. The ePC-SAFT model also performs reasonably well for l-glycine solutions but shows poorer results for d,l-alanine-based solutions. The study also examines the sweetness and saltiness criteria (ASV and ASIC) of these solutions. The ASV values, which serve as a sweetness criterion, are higher than the ideal range of 0.5 < ASV < 0.7, suggesting an overly sweet taste. The ASIC values follow a similar trend, indicating increased saltiness. To achieve an appropriate grade of sweetness and saltiness, dilution to lower concentrations of the solution is recommended. Furthermore, the use of choline chloride is found to increase salt intake and enhance the taste of salt, which can be beneficial in amino acid supplements used in animal food.

Assessment of IRI2020 model accuracy in predicting ionospheric parameters: Insights from multiple ionosonde stations

This research assesses the performance of the IRI2020 model in forecasting the ionospheric critical frequency of the F2 layer (foF2) and Maximum Usable Frequency (MUF) of the ionosphere from 2020 to 2023 across multiple ionosonde stations. The primary objective is to analyse the characteristics of foF2 and MUF across various longitudinal sectors during the solar minimum to ascending phase of solar cycle 25 and to evaluate the IRI2020 model’s performance in replicating observed data. Additionally, the study explores the influence of various space weather parameters on the variability of these ionospheric parameters. The findings reveal that the IRI2020 model overestimates the daily mean values of both foF2 and MUF(3000)F2 across the stations over the three years. While exhibiting good performance in capturing the pattern of hourly predictions, the model tends to overestimate foF2 more than MUF(3000)F2, with better predictions observed for MUF(3000)F2. However, hourly estimations display a mix of underestimation and overestimation, predominantly overestimating values. Correlations ranged from 0.58 to 0.92 for foF2 and 0.72 to 0.91 for MUF(3000)F2, showing variability across stations. As solar activity increases, seasonal variations become more evident, especially in stations located in the northern hemisphere.In contrast, stations in the southern hemisphere exhibit less pronounced seasonal variations, suggesting a notable hemispheric asymmetry. Although the IRI2020 better captured the general trend of foF2 and MUF(3000)F2 in our investigation, the percentage deviation for MUF(3000)F2 ranges from 1 % to 15.1 %, which can result in a significant range reduction for practical applications like HF communications. Stations closer to the geomagnetic equatorial line exhibit stronger seasonal asymmetry and higher deviations. Some key space weather parameters, including sunspot number R, F10.7 and R/F10.7 ratio, were found to significantly influence ionospheric variability, with sunspot number R and R/F10.7 showing a stronger correlation with foF2 and MUF(3000)F2. These suggest that the variability of the solar active region parameters primarily controls ionospheric foF2 and MUF(3000)F2.

Intelligent joint communication and computation scheme of UAV-assisted Offloading in high speed rail scenarios

With the continuous development of communication and computation technologies, large bandwidth services place higher demands on computing capacity and throughput in High-Speed Rail (HSR) scenarios. On the one hand, Millimetre-Wave enables high data transmission rates, but leads to high Doppler frequency deviation as well as large path loss. On the other hand, the development of Mobile Edge Computing greatly alleviates user computing congestion. In this paper, we propose the Adaptive Joint Communication and Computation Resource Allocation scheme to solve the energy optimization problem of a dual-band UAV and Mobile Relay relay-assisted HSR offloading system. This scheme works well to optimize the performance of the system through resource allocation. In addition, since the optimization problem is modeled as a Mixed-Integer Nonlinear Program, we propose the Pre- and Post-state connected Parameterized Deep Q-Network algorithm, which is based on Deep Reinforcement Learning approach, for offloading decision and bandwidth resource allocation. Simulation results show that the proposed algorithms result in lower system energy consumption, while ensuring a high task completion rate as well.

AI-Based Prediction of Ultrasonic Vibration-Assisted Milling Performance.

The current study aims to evaluate the performance of the ultrasonic vibration-assisted milling (USVAM) process when machining two different materials with high deviations in mechanical properties, specifically 7075 aluminium alloy and Ti-6Al-4V titanium alloy. Additionally, this study seeks to develop an AI-based model to predict the process performance based on experimental data for the different workpiece characteristics. In this regard, an ultrasonic vibratory setup was designed to provide vibration oscillations at 28 kHz frequency and 8 µm amplitude in the cutting feed direction for the two characterised materials of 7075 aluminium alloy (150 BHN) and Ti-6Al-4V titanium alloy (350 BHN) workpieces. A series of slotting experiments were conducted using both conventional milling (CM) and USVAM techniques. The axial cutting force and machined slot surface roughness were evaluated for each method. Subsequently, Support Vector Regression (SVR) and artificial neural network (ANN) models were built, tested and compared. AI-based models were developed to analyse the experimental results and predict the process performance for both workpieces. The experiments demonstrated a significant reduction in cutting force by up to 30% and an improvement in surface roughness by approximately four times when using USVAM compared to CM for both materials. Validated by the experimental findings, the ANN model accurately and better predicted the performance metrics with RMSE = 0.11 µm and 0.12 N for Al surface roughness and cutting force. Regarding Ti, surface roughness and cutting force were predicted with RMSE of 0.12 µm and 0.14 N, respectively. The results indicate that USVAM significantly enhances milling performance in terms of a reduced cutting force and improved surface roughness for both 7075 aluminium alloy and Ti-6Al-4V titanium alloy. The ANN model proved to be an effective tool for predicting the outcomes of the USVAM process, offering valuable insights for optimising milling operations across different materials.

Optimization of Desired Multiple Resonant Modes of Compliant Parallel Mechanism Using Specific Frequency Range and Targeted Ratios

In this paper, a dynamic optimization method capable of optimizing the dynamic responses of a compliant parallel mechanism (CPM), in terms of its multiple primary resonant modes, is presented. A novel two-term objective function is formulated based on the specific frequency range and targeted ratios. The first term of the function is used to optimize the first resonant mode of the CPM, within a specific frequency range. The obtained frequency value of the first mode is used in the second term to define the remaining resonant modes to be optimized in terms of targeted ratios. Using the proposed objective function, the resonant modes of a CPM can be customized for a specific purpose, overcoming the limitations of existing methods. A 6-degree-of-freedom (DoF) CPM with decoupled motion is synthesized, monolithically prototyped, and investigated experimentally to demonstrate the effectiveness of the proposed function. The experimental results showed that the objective function is capable of optimizing the six resonant modes within the desired frequency range and the targeted ratios. The highest deviation between the experimental results and the predictions among the six resonant modes is found to be 9.42%, while the highest deviation in the compliances is 10.77%. The ranges of motions are found to be 10.0 mm in the translations, and 10.8° in the rotations.

Comparative analysis of delivered and planned doses in target volumes for lung stereotactic ablative radiotherapy

BackgroundAdaptive therapy has been enormously improved based on the art of generating adaptive computed tomography (ACT) from planning CT (PCT) and the on-board image used for the patient setup. Exploiting the ACT, this study evaluated the dose delivered to patients with non-small-cell lung cancer (NSCLC) patients treated with stereotactic ablative radiotherapy (SABR) and derived relationship between the delivered dose and the parameters obtained through the evaluation procedure.MethodsSABR treatment records of 72 patients with NSCLC who were prescribed a dose of 60 Gy (Dprescribed) to the 95% volume of the planning target volume (PTV) in four fractions were analysed in this retrospective study; 288 ACTs were generated by rigid and deformable registration of a PCT to a cone-beam computed tomography (CBCT) per fraction. Each ACT was sent to the treatment planning system (TPS) and treated as an individual PCT to calculate the dose. Delivered dose to a patient was estimated by averaging four doses calculated from four ACTs per treatment. Through the process, each ACT provided the geometric parameters, such as mean displacement of the deformed PTV voxels (Warpmean) and Dice similarity coefficient (DSC) from deformation vector field, and dosimetric parameters, e.g. difference of homogeneity index (ΔHI, HI defined as (D2%-D98%)/Dprescribed*100) and mean delivered dose to the PTV (Dmean), obtained from the dose statistics in the TPS. Those parameters were analyzed using multiple linear regression and one-way-ANOVA of SPSS® (version 27).ResultsThe prescribed dose was confirmed to be fully delivered to internal target volume (ITV) within maximum difference of 1%, and the difference between the planned and delivered doses to the PTV was agreed within 6% for more than 95% of the ACT cases. Volume changes of the ITV during the treatment course were observed to be minor in comparison of their standard deviations. Multiple linear regression analysis between the obtained parameters and the dose delivered to 95% volume of the PTV (D95%) revealed four PTV parameters [Warpmean, DSC, ΔHI between the PCT and ACT, Dmean] and the PTV D95% to be significantly related with P-values < 0.05. The ACT cases of high ΔHI were caused by higher values of the Warpmean and DSC from the deformable image registration, resulting in lower PTV D95% delivered. The mean values of PTV D95% and Warpmean showed significant differences depending on the lung lobe where the tumour was located.ConclusionsEvaluation of the dose delivered to patients with NSCLC treated with SABR using ACTs confirmed that the prescribed dose was accurately delivered to the ITV. However, for the PTV, certain ACT cases characterised by high HI deviations from the original plan demonstrated variations in the delivered dose. These variations may potentially arise from factors such as patient setup during treatment, as suggested by the statistical analyses of the parameters obtained from the dose evaluation process.

Near-Infrared Spectroscopic Determination of Pentacyclic Triterpenoid Concentrations in Additives for Animal Food.

The nutritional composition of food for animal production can be enhanced using olive tree and plant by-products due to their high content of bioactive compounds such as pentacyclic triterpenes. Here, we present a novel application of near-infrared spectroscopy (NIRS) for the prediction of the total or individual [maslinic acid (MA), oleanolic acid (OA), and uvaol (UO)] pentacyclic triterpene concentrations in a feed additive obtained from a plant mixture. The oxygen radical absorbance capacity of these types of samples demonstrated the existence of a high antioxidant capacity. The conventional determination methods of pentacyclic triterpene concentration are costly, labor-intensive, and not practical for analyzing several lines within a limited timeframe at the factory level. The optimal regression model developed in our work demonstrated high correlation values for the calibration and validation sets, along with a high residual prediction deviation value. We used 63 samples for the development of the model. The NIRS method can be applied directly to dried powder and makes extraction and high-performance liquid chromatography (HPLC) analysis unnecessary. Our results also demonstrate that NIRS can accurately quantify pentacyclic triterpenes even at low concentrations in food additives. It can be used at the factory level to directly determine the pentacyclic triterpene concentrations in the additive powder at the same time that the powder is produced.

Introducing Attribute Association Graphs to Facilitate Medical Data Exploration: Development and Evaluation Using Epidemiological Study Data.

Interpretability and intuitive visualization facilitate medical knowledge generation through big data. In addition, robustness to high-dimensional and missing data is a requirement for statistical approaches in the medical domain. A method tailored to the needs of physicians must meet all the abovementioned criteria. This study aims to develop an accessible tool for visual data exploration without the need for programming knowledge, adjusting complex parameterizations, or handling missing data. We sought to use statistical analysis using the setting of disease and control cohorts familiar to clinical researchers. We aimed to guide the user by identifying and highlighting data patterns associated with disease and reveal relations between attributes within the data set. We introduce the attribute association graph, a novel graph structure designed for visual data exploration using robust statistical metrics. The nodes capture frequencies of participant attributes in disease and control cohorts as well as deviations between groups. The edges represent conditional relations between attributes. The graph is visualized using the Neo4j (Neo4j, Inc) data platform and can be interactively explored without the need for technical knowledge. Nodes with high deviations between cohorts and edges of noticeable conditional relationship are highlighted to guide the user during the exploration. The graph is accompanied by a dashboard visualizing variable distributions. For evaluation, we applied the graph and dashboard to the Hamburg City Health Study data set, a large cohort study conducted in the city of Hamburg, Germany. All data structures can be accessed freely by researchers, physicians, and patients. In addition, we developed a user test conducted with physicians incorporating the System Usability Scale, individual questions, and user tasks. We evaluated the attribute association graph and dashboard through an exemplary data analysis of participants with a general cardiovascular disease in the Hamburg City Health Study data set. All results extracted from the graph structure and dashboard are in accordance with findings from the literature, except for unusually low cholesterol levels in participants with cardiovascular disease, which could be induced by medication. In addition, 95% CIs of Pearson correlation coefficients were calculated for all associations identified during the data analysis, confirming the results. In addition, a user test with 10 physicians assessing the usability of the proposed methods was conducted. A System Usability Scale score of 70.5% and average successful task completion of 81.4% were reported. The proposed attribute association graph and dashboard enable intuitive visual data exploration. They are robust to high-dimensional as well as missing data and require no parameterization. The usability for clinicians was confirmed via a user test, and the validity of the statistical results was confirmed by associations known from literature and standard statistical inference.

Assessment of coffee leaves nutritive value via portable X-ray fluorescence spectrometry and machine learning algorithms

Portable X-ray fluorescence (pXRF) spectrometry is a non-destructive technique that has been successfully used to analyze different matrices. Foliar analysis is challenging because some plant nutrients cannot be detected by pXRF. Even so, the uptake interactions among nutrients which reflect different concentrations of macro- and micronutrients can be accessed via pXRF, constituting a basis to obtain predictive models of plant nutrients. The objective of this work was to compare and assess the accuracy of linear regression and non-linear models (support vector machine – SVM; and random forest – RF) to predict the actual concentration of macro- (N, P, K, Ca, Mg, and S) and micronutrients (B, Cu, Fe, Zn, and Mn) in coffee leaves. A greenhouse experiment was conducted using Hoagland and Arnon solution to obtain leaves with contrasting elemental composition. Ground and oven-dried leaf samples were analyzed via pXRF using: i) a manufactured pXRF calibration developed for general earth-materials (Geoexploration mode); ii) the Spectrometer mode with varying voltage and current (15 kV and 25 μA; 10 kV and 10 μA). The same samples were also analyzed via conventional acid digestion and quantified via inductively coupled plasma-optical emission spectroscopy (ICP-OES). The best predictions were obtained using SVM and RF algorithms, with high R2 (0.82 to 0.99) and high residual prediction deviation (RPD) (2.35 to 9.34) values. However, some elements (e.g., K, Ca, Cu, Mn) were successfully predicted using linear models (LR and MLR). Even elements not detected (N and B) by pXRF were accurately predicted using the RF model. The pXRF operational conditions influenced the performance of the models. However, by parsimony, Geoexploration mode provided data for accurate prediction of macro- and micronutrients. This comprehensive study can potentially spark further investigations into examining coffee leaves from plants cultivated under various environmental and management conditions. Additionally, the methodological framework outlined here holds promise for ongoing experimentation across diverse crops, offering a streamlined, non-invasive, eco-friendly, and rapid approach for foliar analysis.

Quantum phase transitions in one-dimensional nanostructures: a comparison between DFT and DMRG methodologies.

In the realm of quantum chemistry, the accurate prediction of electronic structure and properties of nanostructures remains a formidable challenge. Density functional theory (DFT) and density matrix renormalization group (DMRG) have emerged as two powerful computational methods for addressing electronic correlation effects in diverse molecular systems. We compare ground-state energies ( ), density profiles ( ), and average entanglement entropies ( ) in metals, insulators and at the transition from metal to insulator, in homogeneous, superlattices, and harmonically confined chains described by the fermionic one-dimensional Hubbard model. While for the homogeneous systems, there is a clear hierarchy between the deviations, , and all the deviations decrease with the chain size; for superlattices and harmonic confinement, the relation among the deviations is less trivial and strongly dependent on the superlattice structure and the confinement strength considered. For the superlattices, in general, increasing the number of impurities in the unit cell represents lower precision in the DFT calculations. For the confined chains, DFT performs better for metallic phases, while the highest deviations appear for the Mott and band-insulator phases. This work provides a comprehensive comparative analysis of these methodologies, shedding light on their respective strengths, limitations, and applications. The DFT calculations were performed using the standard Kohn-Sham scheme within the BALDA approach. It integrated the numerical Bethe-Ansatz (BA) solution of the Hubbard model as the homogeneous density functional within a local-density approximation (LDA) for the exchange-correlation energy. The DMRG algorithms were implemented using the ITensor library, which is based on the matrix product states (MPS) ansatz. The calculations were performed until the energy reaches convergence of at least .

Trueness and fit of complete-arch implant-supported frameworks in new-generation additively and subtractively manufactured polymers: An in-vitro study.

There is limited knowledge on the fabrication trueness and fit of additively or subtractively manufactured complete-arch implant-supported frameworks in recently introduced polymers. To evaluate the trueness and marginal fit of additively or subtractively manufactured polymer-based complete-arch implant-supported frameworks, comparing with those of strength gradient zirconia frameworks. A typodont model with 4 implants (left first molar (abutment 1), left canine (abutment 2), right canine (abutment 3), and right first molar (abutment 4)) was digitized (ATOS Core 80 5MP) and an implant-supported complete-arch framework was designed. This design file was used to fabricate frameworks from 5 different materials: strength gradient zirconia (SM-ZR), high impact polymer composite (SM-CR), nanographene-reinforced PMMA (SM-GR), PMMA (SM-PM), and additively manufactured temporary resin (AM) (n = 10). These frameworks were digitized and each scan file was virtually segmented into 4 regions (abutments, occlusal, overall without occlusal, and overall). The surface deviations at these regions, and linear and interimplant distance deviations were evaluated (Geomagic Control X). Marginal gaps were evaluated according to triple-scan protocol after seating frameworks on the model with the 1-screw test. Data were statistically analyzed (α = 0.05). Surface deviations of all regions differed among tested materials (p ≤ 0.001). AM frameworks mostly had surface deviations that were similar to or lower than those of other materials (p ≤ 0.031), except for the occlusal surface, where it mostly had higher deviations (p ≤ 0.013). Abutment 4 of SM-CR had higher linear deviations than abutment 2 (p = 0.025), and material type did not affect the linear deviations within abutments (p ≥ 0.171). Interimplant distance deviations differed within and among materials (p ≤ 0.017), except for those between abutments 1 and 2 among materials (p = 0.387). Marginal gaps of subtractively manufactured materials differed among abutments, while those of abutments 3 and 4 differed among materials (p ≤ 0.003). AM frameworks mostly had lower marginal gaps at abutments 3 and 4 (p ≤ 0.048). Although there was no clear trend among tested materials for measured deviations, marginal gaps of additively manufactured resin were mostly lower than those of subtractively manufactured materials and did not differ among abutment sites. Nevertheless, the differences in measured deviations among materials were small and marginal gaps were within the previously reported acceptability thresholds.