Quantitative Interpretation Research Articles

Theoretical spectroscopy for unraveling the intensity mechanism of the optical and photoluminescent spectra of chiral Re(I) transition metal complexes.

In this work, we present a computational study that is able to predict the optical absorption and photoluminescent properties of the chiral Re(I) family of complexes [fac-ReX(CO)3L], where X is either Cl or I and L is N-heterocyclic carbene extended with π-conjugated [5]-helicenic unit. The computational strategy is based on carefully calibrated time dependent density functional theory calculations and operates in conjunction with an excited state dynamics approach to treat in addition to absorption (ABS) and photoluminescence (PL), electronic circular dichroism (ECD), and circularly polarized luminescence (CPL) spectroscopies, respectively. The employed computational approach provides, an addition, access to the computation of phosphorescence rates in terms of radiative and non-radiative relaxation processes. The chosen molecules consist of representative examples of non-helicenic (NHC) and helicenic diastereomers. The agreement between theoretical and experimental spectra, including absorption (ABS, ECD) and emission (PL, CPL), is excellent, validating a quantitative interpretation of the spectral features on the basis of natural transition orbitals and TheoDore analyses. It is demonstrated that across the set of studied Re(I) diastereomers, the emission process in the case of NHC diastereomers is metal to ligand charge transfer in nature and is dominated by the easy-axis anisotropy of the emissive excited multiplet. On the contrary, in the cases of the helicenic diastereomers, the emission process is intra ligand charge transfer in nature and is dominated by the respective easy-plane anisotropy of the emissive excited multiplet. This affects remarkably the photoluminescent properties of the molecules in terms of PL and CPL spectral band shapes, spin-vibronic coupling, relaxation times, and the respective quantum yields. Spin-vibronic coupling effects are investigated at the level of the state-average complete active space self-consistent field in conjunction with quasi-degenerate second order perturbation theory. It is in fact demonstrated that a spin-vibronic coupling mechanism controls the observed photophysics of this class of Re(I) complexes.

Accelerating the Discovery of Hybrid Perovskites with Targeted Band Gaps via Interpretable Machine Learning.

The band gap of hybrid organic-inorganic perovskites (HOIPs) is a key factor affecting the light absorption characteristics and thus the performance of perovskite solar cells (PSCs). However, band gap engineering, using experimental trial and error and high-throughput density functional theory calculations, is blind and costly. Therefore, it is critical to statistically identify the multiple factors influencing band gaps and to rationally design perovskites with targeted band gaps. This study combined feature engineering, the gradient-boosted regression tree (GBRT) algorithm, and the genetic algorithm-based symbolic regression (GASR) algorithm to develop an interpretable machine learning (ML) strategy for predicting the band gap of HOIPs accurately and quantitatively interpreting the factors affecting the band gap. Seven best physical features were selected to construct a GBRT model with a root-mean-square error of less than 0.060 eV, and the most important feature is the electronegativity difference between the B-site and the X-site (χB-X). Further, a mathematical formula (Eg = χB-X2 + 0.881χB-X) was constructed with GASR for a quantitative interpretation of the band gap influence patterns. According to the ML model, the HOIP MA0.23FA0.02Cs0.75Pb0.59Sn0.41Br0.24I2.76 was obtained, with a suitable band gap of 1.39 eV. Our proposed interpretable ML strategy provides an effective approach for developing HOIP structures with targeted band gaps, which can also be applied to other material fields.

Isotope Ratio – Discharge Relationships of Solutes Derived From Weathering Reactions

To date, the vast majority of studies seeking to link discharge to solute concentrations have been based on representations of fluid age distributions in watersheds that are time-invariant. As increasingly detailed spatial and temporal datasets become available for weathering-derived riverine solute concentrations, the capacity to link this mass flux to transient routing of reactive fluids through Critical Zone environments is vital to quantitative interpretation. Relationships between fluid age distributions and the stable isotope ratios of these geogenic solutes are even less developed, yet these signatures are vital to parsing the suite of water-rock-life interactions that create concentration-discharge relationships. Here we offer the first merging of a hydrological model featuring time-variant fluid age distributions with a geochemical model for isotopically fractionating weathering reactions. Using SiO2(aq) and the corresponding silicon isotope ratio δ30Si as an example, we show that the stable isotope signatures of riverine solutes produced by weathering reactions reflect a component of the fluid age distribution that is unique to the corresponding solute concentrations. This distinct sensitivity is the result of a stronger link between isotope ratios and the age distribution parameters describing a given watershed. This novel modeling framework is used to provide a quantitative basis for the interpretation of SiO2(aq) and δ30Si in six low-order streams spread across a diversity of climates, geologies, and ecosystems. To our knowledge, this is the first forward and process-based model to describe the isotopic signatures of solutes derived from weathering reactions in watersheds subject to time-varying discharge.

Insight into ADHD diagnosis with deep learning on Actimetry: Quantitative interpretation of occlusion maps in age and gender subgroups

Attention Deficit/Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder in childhood that often persists into adulthood. Objectively diagnosing ADHD can be challenging due to the reliance on subjective questionnaires in clinical assessment. Fortunately, recent advancements in artificial intelligence (AI) have shown promise in providing objective diagnoses through the analysis of medical images or activity recordings. These AI-based techniques have demonstrated accurate ADHD diagnosis; however, the growing complexity of deep learning models has introduced a lack of interpretability. These models often function as black boxes, unable to offer meaningful insights into the data patterns that characterize ADHD. Objective:This paper proposes a methodology to interpret the output of an AI-based diagnosis system for combined ADHD in age and gender-stratified populations. Methods:Our system is based on the analysis of 24 hour-long activity records using Convolutional Neural Networks (CNNs) to classify spectrograms of activity windows. These windows are interpreted using occlusion maps to highlight the time–frequency patterns explaining ADHD activity. Results:Significant differences in the frequency patterns between ADHD and controls both in diurnal and nocturnal activity were found for all the populations. Temporal dispersion also presented differences in the male population. Conclusion:The proposed interpretation techniques for CNNs highlighted gender- and age-related differences between ADHD patients and controls. Leveraging these differences could potentially lead to improved diagnostic accuracy, especially if a larger and more balanced dataset is utilized. Significance:Our findings pave the way for the development of an AI-based diagnosis system for ADHD that offers interpretability, thereby providing valuable insights into the underlying etiology of the disease.

Integrating seismic attributes, post-stack seismic inversion, and static modeling for assessing the Pliocene-Pleistocene hydrocarbon potentialities, Offshore North Sinai Field, Egypt

Optimal gas production from marginal fields vulnerable to tidal energetic effects is exceedingly challenging. Conventional seismic analysis is insufficient for a significant understanding of the geomorphological description, volumetric calculations, and microfacies analysis of the anticipated heterogeneous gas-bearing sands. New insights into the offshore North Sinai (ONS) Field based on quantitative and geostatistical interpretations integrated with physical and geometrical attribute analyses are boosted for reconstructing diverse-scale reservoir internal architecture.Deterministic band-limited inversion using acoustic impedance logs contributed to the initial knowledge of facies migration during tidal-dominated delta progradation and revealed four 2nd-order sequence boundaries dependent on P-velocity variations with depth as a function of rock elasticity, low impedance gas content of 2500–3000 kPa s/m and overburden compressional factors. Gradual boundaries confining reservoir bases due to spatiotemporal facies discrepancies triggered the implementation of stochastic algorism as a model norm for tracing thin-bedded and internal heterogeneous reservoirs with no predictable gas-water contacts across subtle dismantled areas. Stochastic inversion with intensive layering below −1 ms along with static property reservoir modeling allowed the evaluation of both fine and coarse-scale reservoirs discovered as extensions of injector-controlled stacked mouth bars, fan-lobes, and distributary channels crosscutting tidal mudflats of delta top facies associations within a system of spatially migrated clinoforms to the north. Stream avulsion-induced and accommodation space inconsistencies across syn-sedimentary faults caused the shifting of the Late Plio-Pleistocene clinoform depocenter northward and deltaic facies variation inside each individual sand system, not eustatic sea level fall.After calculating the biogenic gas reserves occupying each reservoir, the present strategy offers an additional seven broad gas prospects within four dismantled blocks, raising the ONS initial gas in place to 967.95 Bcf. In addition, it depicts five gas shoals emplaced at shallow depths, posing considerable drilling hazards. This paper workflow is the first of its kind in the Neogene section of the OSN Basin.

Improved Electrical Impedance Tomography Reconstruction via a Bayesian Approach With an Anatomical Statistical Shape Model.

electrical impedance tomography (EIT) is a promising technique for rapid and continuous bedside monitoring of lung function. Accurate and reliable EIT reconstruction of ventilation requires patient-specific shape information. However, this shape information is often not available and current EIT reconstruction methods typically have limited spatial fidelity. This study sought to develop a statistical shape model (SSM) of the torso and lungs and evaluate whether patient-specific predictions of torso and lung shape could enhance EIT reconstructions in a Bayesian framework. torso and lung finite element surface meshes were fitted to computed tomography data from 81 participants, and a SSM was generated using principal component analysis and regression analyses. Predicted shapes were implemented in a Bayesian EIT framework and were quantitatively compared to generic reconstruction methods. Five principal shape modes explained 38% of the cohort variance in lung and torso geometry, and regression analysis yielded nine total anthropometrics and pulmonary function metrics that significantly predicted these shape modes. Incorporation of SSM-derived structural information enhanced the accuracy and reliability of the EIT reconstruction as compared to generic reconstructions, demonstrated by reduced relative error, total variation, and Mahalanobis distance. As compared to deterministic approaches, Bayesian EIT afforded more reliable quantitative and visual interpretation of the reconstructed ventilation distribution. However, no conclusive improvement of reconstruction performance using patient specific structural information was observed as compared to the mean shape of the SSM. The presented Bayesian framework builds towards a more accurate and reliable method for ventilation monitoring via EIT.

An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.

To determine whether the texture feature analysis of multi-phase abdominal CT can provide a robust prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. A total of 1051 participants with renal tumor were split into the internal cohort (850 patients from four different hospitals) and the external testing cohort (201 patients from another local hospital). The proposed framework comprised a 3D-kidney and tumor segmentation model by 3D-UNet, a feature extractor for the regions of interest based on radiomics and image dimension reduction, and the six classifiers by XGBoost. A quantitative model interpretation method called SHAP was usedto explore the contribution of each feature. The proposed multi-phase abdominal CT model provides robust prediction for benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in the internal validation set, with the AUROC values of 0.88 ± 0.1, 0.90 ± 0.1, 0.91 ± 0.1, 0.89 ± 0.1, 0.84 ± 0.1, and 0.88 ± 0.1, respectively. The external testing set also showed impressive results, with AUROC values of 0.83 ± 0.1, 0.83 ± 0.1, 0.85 ± 0.1, 0.81 ± 0.1, 0.79 ± 0.1, and 0.81 ± 0.1, respectively. The radiomics feature including the first-order statistics, the tumor size-related morphology, and the shape-related tumor features contributed most to the model predictions. Automatic texture feature analysis of abdominal multi-phase CT provides reliable predictions for multi-tasks, suggesting the potential usage of clinical application. The automatic texture feature analysis framework, based on multi-phase abdominal CT, provides robust and reliable predictions for multi-tasks. These valuable insights can serve as a guiding tool for clinical diagnosis and treatment, making medical imaging an essential component in the process. • The automatic texture feature analysis framework based on multi-phase abdominal CT can provide more accurate prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. • The quantitative decomposition of the prediction model was conducted to explore the contribution of the extracted feature. • The study involving 1051 patients from 5 medical centers, along with a heterogeneous external data testing strategy, can be seamlessly transferred to various tasks involving new datasets.

Применение ситуации критической самооценки в процессе профессиональной подготовки сотрудников органов внутренних дел, впервые принятых на службу, в качестве участковых уполномоченных

The presented article actualizes the problems of improving the process of professional training of employees of the internal affairs bodies who were first hired as district commissioners. The subject of this study is the analysis of the possibilities of implementing the situation of critical self-assessment in the process of professional training of employees of the internal affairs bodies who were first recruited as district commissioners. The purpose of the study is aimed at identifying problematic factors that hinder the process of further improving the professional training of employees of internal affairs bodies who were first hired as district commissioners, and searching for ways to overcome them using the method of critical self-assessment on the example of the Barnaul Law Institute of the Ministry of Internal Affairs of the Russian Federation. The leading methodological basis of this work was the theory and technology of the motivational program-target approach. The methods used to achieve this goal were methods of analyzing theoretical sources on the studied issues, survey and analysis, and quantitative interpretation of the results of practical activities.
 The respondents in this study were 89 district commissioners who underwent initial professional training at the base (BYU) for six months of 2022. The results of the work. The conducted research made it possible to determine the prevailing intrapersonal, motivational nature of the hindering factors and determined the choice of an adequate tool for overcoming them. Such a tool in this work is the method of creating a situation of critical self-assessment, taking into account the specifics of the category of students and the scope of their activities, which allows for a corrective psychological impact on the individual by referring to the intrapersonal mechanisms of self-management of their behavior. The vast majority of listeners noted that as a result of introspection and subsequent work, they were able to realize the difficulties and determine the further line of their behavior, lower or increase the level of claims, concentrating on the tasks set, and not on past negative experiences. These results allow us to conclude about the effectiveness of the activities carried out. The novelty of the study may lie in the fact that for the first time the situation of critical self-assessment was used as a tool to overcome the motivational problems of professional training of employees of internal affairs bodies who were first recruited as a district commissioner. Conclusions. The preliminary results obtained suggest the conclusion that it is necessary to continue the research that has begun, which implies its further development from the technological and substantive side, as well as the expansion of the composition of groups of employees of internal affairs bodies, taking into account the peculiarities and specifics of service for each of the categories.

Influence of partial saturation on bulk modulus and attenuation: the experiment and the theoretical model

SUMMARY Modelling the dispersion and attenuation of seismic waves in partially saturated rocks is important for quantitative interpretation of seismic data. To observe the partial saturation effects on modulus and attenuation, three sandstone samples with different porosities were used in the laboratory experiment. The samples were measured by ultrasonic testing system under different water saturation using drainage process. The experimental results show that the bulk modulus more rapidly increased at higher saturations (>80 per cent), and attenuation peaks could be observed. Then we develop a new partial saturation model based on Chapman’s theory and poroelastic theory. The new model is consistent with the Gassmann equation when the water saturation is 0 or 100 per cent. By analysing the characteristic frequency, the new model can be concluded in the form of a standard linear solid model. A comparison of the new model with the White model and the measured data is conducted. The results show that the model performed well at predicting the effective bulk modulus and attenuation of the rock with different water saturation. Finally, we discussed the impact of frequency, fluids and pore structures on the new model. The results reveal that the new model will be helpful in discussing partial saturation in rocks.

Azimuthal Prestack Seismic Anisotropic Inversion on a Deep and Tight Carbonate Reservoir From the North Potwar Basin of Pakistan

Summary Quantitative characterization of deep, tight, and heterogeneous reservoirs plays an important role in identifying hydrocarbon pathways for effective and optimal reservoir field development. In this case study, we used an azimuthal prestack seismic anisotropic inversion approach to estimate attributes of horizontal transverse isotropy (HTI) caused by a set of vertical fractures, oriented cracks, and stress. Anisotropic inversion facilitated the conversion of interface properties to the corresponding layer-based properties, which led to the quantitative interpretation of reservoir properties related to azimuthal variation in seismic amplitudes. To estimate the anisotropy magnitude and the direction of the isotropy axis in HTI media, elastic properties (P-impedance and Vp/Vs) obtained from prestack seismic inversion (using six azimuth × four angle stack) served as inputs. The isotropic low-frequency model (LFM) is used as the foundation of the inversion for all azimuths, and the anisotropy effects are later added by updating the model along the azimuths. The direction of the isotropy plane resulting from the anisotropic inversion is determined by using the maximum horizontal stress as a prior constraint, which eliminates any inherent uncertainty. The workflow used effectively characterized the orientation and density of fractures from the recently discovered oilfield reservoirs of the Paleocene (Lockhart) formation located in Pakistan’s north Potwar Basin. It also helped improve the prediction accuracy for fractures in the study area. According to the observations (fractures) from the exploratory drilled well (D1) in the tight carbonate (Lockhart) reservoir, a significant amount of anisotropy magnitude is observed. This provides the basis for hydrocarbon exploration, field development, and reliable drilling decisions.

Quantification of freeze–thaw hysteresis of unfrozen water content and electrical resistivity from time lapse measurements in the active layer and permafrost

AbstractWe quantified seasonal, in‐situ variation in ground electrical resistivity, unfrozen water content, and ground temperature using daily and sub‐daily measurements in active layer and in permafrost on a high‐latitude monitoring site in West Greenland. The in‐situ unfrozen water content in a clayey soil undergoing phase change was 10% higher during freezing than during thawing at the same ground temperature, thus shifting the freezing curve parameterization seasonally. The freeze–thaw unfrozen water content hysteresis was a measurable part of the energy balance of the active layer and corresponds to 12.1% of the total yearly enthalpy change for our field site. We also observed and quantified hysteresis in the relationships between ground electrical resistivity and unfrozen water content, where the inverted resistivities during freezing in the temperature range from 0°C to approximately −4°C were up to one order of magnitude higher than during thawing at the same unfrozen water content. As a result, the petro‐physical models that uniquely derive soil unfrozen water content from bulk soil electrical resistivity measured from ground surface do not fully capture the complexity of the in‐situ processes. The hysteresis should therefore be considered in quantitative interpretation of ground resistivity changes in terms of ground temperature and ice content changes.

An FFT-based framework for predicting corrosion-driven damage in fractal porous media

Understanding fracture in cementitious materials caused by the deposition and growth of corrosion products requires scale-bridging approaches due to the large length-scale difference between the micro-pores, where deposition occurs, and the structure, where deterioration manifests. Cementitious materials bear a highly heterogeneous micro-structure owing to the fractal nature of micro-pores. Simultaneously, a corrosion-driven fracture is a multi-physics problem involving ionic diffusion, chemical reactions, and stress development. This multi-scale and multi-physical character makes scale-bridging studies computationally costly, often leading to the use of simplified fractal porous media, which has important consequences for the quantitative interpretation of the results. Recent advances in homogenization approaches using Fast-Fourier-Transform (FFT) based methods have raised interest due to their ease of implementation and low computational cost. This paper presents an FFT-based framework for solving corrosion-driven fractures within fractal porous media. We demonstrate the effectiveness of the Fourier-based spectral method in resolving the multiple corrosion-driven mechanisms such as ionic diffusion, stress development, and damage within a fractal porous microstructure. Based on the presented methodology, we analyse the impact of simplifying fractal porous media with simple Euclidean geometry on corrosion-driven fracture. Our results demonstrate the importance of preserving both the porosity and fractal nature of pores for precise and reliable modelling of corrosion-driven failure mechanisms.

Impact of electrode orientation, myocardial wall thickness, and myofiber direction on intracardiac electrograms: numerical modeling and analytical solutions.

Intracardiac electrograms (iEGMs) are time traces of the electrical potential recorded close to the heart muscle. We calculate unipolar and bipolar iEGMs analytically for a myocardial slab with parallel myofibers and validate them against numerical bidomain simulations. The analytical solution obtained via the method of mirrors is an infinite series of arctangents. It goes beyond the solid angle theory and is in good agreement with the simulations, even though bath loading effects were not accounted for in the analytical calculation. At a large distance from the myocardium, iEGMs decay as 1/R (unipolar), 1/R 2 (bipolar and parallel), and 1/R 3 (bipolar and perpendicular to the endocardium). At the endocardial surface, there is a mathematical branch cut. Here, we show how a thicker myocardium generates iEGMs with larger amplitudes and how anisotropy affects the iEGM width and amplitude. If only the leading-order term of our expansion is retained, it can be determined how the conductivities of the bath, torso, myocardium, and myofiber direction together determine the iEGM amplitude. Our results will be useful in the quantitative interpretation of iEGMs, the selection of thresholds to characterize viable tissues, and for future inferences of tissue parameters.

Analysis of Legal Risk in Farms of Intensive Chicken Production - The Case of Kosovo

The intensive poultry industry in Kosovo fulfills a significant portion of the local demand for eggs. Considering this context, the sustainable development of this industry necessitates specific attention due to potential risks and threats. This paper aims to identify and evaluate legal risk events associated with the industry. We created a questionnaire with eight questions, using information from previous research and considering the actual conditions of the intensive poultry industry in Kosovo. Through face-to-face interviews with farmers and agricultural economists, we empirically assessed the likelihood and impact of each legal risk event. We set using a Likert scale ranging from 1 (very low) to 5 (very high). Both qualitative and quantitative methods were employed to evaluate the risk level of each event. The qualitative analysis and interpretation of the results emphasized the risk factors, which were categorized based on severity. The findings indicate that two events exhibit a mouse-like level of aggressiveness; one mirrors the aggressiveness of a rabbit, another resembles that of a shark, and four display the hostility of a lion. The quantitative analysis and interpretation of the results revealed a relatively high distribution of 75%, with a standard deviation of 9,608 euros and a considerably high coefficient of variation (95%) if these events were to occur. To mitigate the adverse impact of legal risk events, we recommend that farmers seek additional information and consult with professionals such as economists, veterinarians, animal husbandry experts, and lawyers.

Rheological study of highly concentrated aqueous graphene oxide suspensions: the effects of concentration, particle lateral dimensions and number of layers per particle

Aqueous graphene oxide (GO) suspensions have attracted considerable attention due to their unique properties, which can find numerous applications in various fields, from industry to biomedicine. When designing a GO hydrogel for a specific application, a thorough understanding of the effects of appropriate starting material properties and its concentration range on the suspension stability, structural and rheological properties is of essential importance. The present work is concerned with high solids content (from 1.00 to 2.00 w/v%) GO aqueous suspensions exhibiting marked elastic properties. The effects of the GO particles lateral dimensions and the number of GO layers per particle on the flow and viscoelastic properties are investigated. Rheological parameters useful for quantitative interpretation of the results were obtained by fitting the experimental data using Cross, Herschel-Bulkley, and generalized Maxwell models. All GO suspensions showed a marked shear thinning and thixotropic behaviour with a significant yield stress. Their mechanical spectra confirmed viscoelastic behaviour typical for a suspension in an arrested state. Observed differences in values of rheological parameters and their variations with concentration are explained by particle properties and corresponding rotational restrictions, electrostatic attraction/repulsion, nematic phase formation, and particle agglomeration. The research presents fundamental insights into the rheological properties of GO suspensions, which can be manipulated by varying particle geometry and concentration.

Benchmarks and explanations for deep learning estimates of X-ray galaxy cluster masses

ABSTRACTWe evaluate the effectiveness of deep learning (DL) models for reconstructing the masses of galaxy clusters using X-ray photometry data from next-generation surveys. We establish these constraints using a catalogue of realistic mock eROSITA X-ray observations which use hydrodynamical simulations to model realistic cluster morphology, background emission, telescope response, and active galactic nucleus (AGN) sources. Using bolometric X-ray photon maps as input, DL models achieve a predictive mass scatter of $\sigma _{\ln M_\mathrm{500c}} = 17.8~{{\ \rm per\ cent}}$, a factor of two improvements on scalar observables such as richness Ngal, 1D velocity dispersion σv,1D, and photon count Nphot as well as a 32 per cent improvement upon idealized, volume-integrated measurements of the bolometric X-ray luminosity LX. We then show that extending this model to handle multichannel X-ray photon maps, separated in low, medium, and high energy bands, further reduces the mass scatter to 16.2 per cent. We also tested a multimodal DL model incorporating both dynamical and X-ray cluster probes and achieved marginal gains at a mass scatter of 15.9 per cent. Finally, we conduct a quantitative interpretability study of our DL models and find that they greatly down-weight the importance of pixels in the centres of clusters and at the location of AGN sources, validating previous claims of DL modelling improvements and suggesting practical and theoretical benefits for using DL in X-ray mass inference.

Genotoxicity assessment: opportunities, challenges and perspectives for quantitative evaluations of dose-response data.

Genotoxicity data are mainly interpreted in a qualitative way, which typically results in a binary classification of chemical entities. For more than a decade, there has been a discussion about the need for a paradigm shift in this regard. Here, we review current opportunities, challenges and perspectives for a more quantitative approach to genotoxicity assessment. Currently discussed opportunities mainly include the determination of a reference point (e.g., a benchmark dose) from genetic toxicity dose-response data, followed by calculation of a margin of exposure (MOE) or derivation of a health-based guidance value (HBGV). In addition to new opportunities, major challenges emerge with the quantitative interpretation of genotoxicity data. These are mainly rooted in the limited capability of standard in vivo genotoxicity testing methods to detect different types of genetic damage in multiple target tissues and the unknown quantitative relationships between measurable genotoxic effects and the probability of experiencing an adverse health outcome. In addition, with respect to DNA-reactive mutagens, the question arises whether the widely accepted assumption of a non-threshold dose-response relationship is at all compatible with the derivation of a HBGV. Therefore, at present, any quantitative genotoxicity assessment approach remains to be evaluated case-by-case. The quantitative interpretation of in vivo genotoxicity data for prioritization purposes, e.g., in connection with the MOE approach, could be seen as a promising opportunity for routine application. However, additional research is needed to assess whether it is possible to define a genotoxicity-derived MOE that can be considered indicative of a low level of concern. To further advance quantitative genotoxicity assessment, priority should be given to the development of new experimental methods to provide a deeper mechanistic understanding and a more comprehensive basis for the analysis of dose-response relationships.

Dukungan Keluarga Pada Ibu Menjelang Menopause

ABSTRACT
 Family support is the support that individuals receive from people in the family environment such as husbands, children, and parents, so that individuals feel cared for, valued, and loved. Support can serve as a preventive strategy to reduce stress. The purpose of the study was to determine family support for mothers approaching menopause in Bendo Kidul Hamlet, Bendo Village, Pare District, Kediri Regency
 The research design used was descriptive with a population of 65 people and a sample of 58 people. The sampling technique used was purposive sampling. The research time is from 4 to 9 January 2021.The research instrument uses a questionnaire, the research variable is family support for the mother before menopause. Data analysis uses percentages and quantitative data interpretation.
 The results obtained from 58 respondents, most of the respondents have good support for as many as 40 respondents (69%). Almost half of the respondents have sufficient support as many as 17 respondents (29.3) and a small proportion of respondents have less support as much as 1 respondent (1.7%).
 Factors that can influence include age, education level, status in the family, occupation, never getting information, media information. It is hoped that the family will provide support to the mother before menopause, so that the mother can go through the premenopause period well.
 
 Keywords: Family Support, Mothers Approaching Menopause.

Seeing through the CO2plume: Joint inversion-segmentation of the Sleipner 4D seismic data set

Time-lapse (4D) seismic inversion is the leading method to quantitatively monitor fluid-flow dynamics in the subsurface, with applications ranging from enhanced oil recovery to subsurface CO2storage. The process of inverting 4D seismic data for reservoir properties is a notoriously ill-posed inverse problem due to the band-limited and noisy nature of seismic data and inaccuracies in the repeatability of 4D acquisition surveys. Consequently, ad-hoc regularization strategies are essential for the 4D seismic inverse problem to obtain geologically meaningful subsurface models and associated 4D changes. Motivated by recent advances in the field of convex optimization, we propose a joint inversion-segmentation algorithm for 4D seismic inversion that integrates total variation and segmentation priors as a way to counteract missing frequencies and present noise in 4D seismic data. The proposed inversion framework is designed for poststack seismic data and applied to a pair of seismic volumes from the open Sleipner 4D seismic data set. Our method has three main advantages over state-of-the-art least-squares inversion methods. First, it produces high-resolution baseline and monitor acoustic models. Second, it mitigates nonrepeatable noise and better highlights real 4D changes by leveraging similarities between multiple data. Finally, it provides a volumetric classification of the acoustic impedance 4D difference model (4D changes) based on user-defined classes (i.e., percentages of speedup or slowdown in the subsurface). Such advantages may enable more robust stratigraphic/structural and quantitative 4D seismic interpretation and provide more accurate inputs for dynamic reservoir simulations. Alongside presenting our novel inversion method, we introduce a streamlined data preprocessing sequence for the 4D Sleipner poststack seismic data set that includes time-shift estimation and well-to-seismic tie. Finally, we provide insights into the open-source framework for large-scale optimization that we used to implement the proposed algorithm in an efficient and scalable manner.

Reactivity of New Monomers of the Polyurethanes Green Chemistry, the Reaction Mechanism, and the Medium Effect

The influence of the substituents inductive effect and the proton-donor OH group in the substituted cyclocarbonates differing in the alkyl chain length on the activation barrier of their aminolysis reaction, which underlies the process of urethane formation without the participation of isocyanates, has been studied. Account for the solvent molecules has allowed quantitative interpretation of the process regularities. Kinetics of the model aminolysis reaction of a series of monomers in DMSO has been investigated.