Separate Images Research Articles

Shale Characterization Using T1-T2* Magnetic Resonance Relaxation Correlation Measurement at Low and High Magnetic Fields

Shale characterization is complicated by low porosity and low permeability. Nano-porosity and a high degree of heterogeneity present further difficulties. 1H magnetic resonance (MR) methods have great potential to provide quantitative and spatially resolved information on fluids present in porous rocks. The shale MR response, however, is challenging to interpret due to short-lived signals that complicate quantitative signal detection and imaging. Multicomponent signals require high-resolution methods for adequate signal differentiation. MR methods must cope with low measurement sensitivity at low field. In this paper, T1-T2* and Look-Locker T1*-T2* methods were employed to resolve the shale signal for water, oil, and kerogen at high and low field. This permits fluid quantification and kerogen assessment. The T1-T2* measurement was employed to understand and control contrast in the single-point ramped imaging with T1 enhancement (SPRITE) imaging method. This permitted imaging that gave separate images of water and oil. Water absorption/desorption, evaporation, step pyrolysis, and water uptake experiments were monitored using T1-T2* measurement and MR imaging. The results showed (i) the capability of the T1-T2* measurement to differentiate and quantify kerogen, oil, and water in shales, (ii) the characterization of shale heterogeneity on the core plug scale, and (iii) demonstrated the key role of wettability in determining the spatial distribution of water in shales.

Lossless Reversible Data Hiding in Encrypted Image for Multiple Data Hiders Based on Pixel Value Order and Secret Sharing

Reversible data hiding in encrypted images (RDH-EI) is instrumental in image privacy protection and data embedding. However, conventional RDH-EI models, involving image providers, data hiders, and receivers, limit the number of data hiders to one, which restricts its applicability in scenarios requiring multiple data embedders. Therefore, the need for an RDH-EI accommodating multiple data hiders, especially for copyright protection, has become crucial. Addressing this, we introduce the application of Pixel Value Order (PVO) technology to encrypted reversible data hiding, combined with the secret image sharing (SIS) scheme. This creates a novel scheme, PVO, Chaotic System, Secret Sharing-based Reversible Data Hiding in Encrypted Image (PCSRDH-EI), which satisfies the (k,n) threshold property. An image is partitioned into N shadow images, and reconstruction is feasible when at least k shadow images are available. This method enables separate data extraction and image decryption. Our scheme combines stream encryption, based on chaotic systems, with secret sharing, underpinned by the Chinese remainder theorem (CRT), ensuring secure secret sharing. Empirical tests show that PCSRDH-EI can reach a maximum embedding rate of 5.706 bpp, outperforming the state-of-the-art and demonstrating superior encryption effects.

Preoperative visualization of midline-crossing subcutaneous arteries in transverse abdominal flaps using photoacoustic tomography

Photoacoustic tomography is a noninvasive vascular imaging modality that uses near-infrared pulsed laser light and ultrasound to visualize vessels. We previously demonstrated the utility of photoacoustic tomography for anterolateral thigh flap surgery involving body-attachable vascular mapping sheets. However, it was not possible to obtain clear separate images of arteries and veins. In this study, we tried to visualize subcutaneous arteries that cross the midline of the abdomen, since these arteries are known to be important for obtaining large perfusion areas in transverse abdominal flaps. Four patients scheduled to undergo breast reconstruction with abdominal flaps were examined. Photoacoustic tomography was performed preoperatively. The tentative arteries and veins were traced according to the S-factor, an approximate hemoglobin oxygen saturation parameter calculated using 2 laser excitation wavelengths (756 and 797nm). Intraoperatively, arterial-phase indocyanine green (ICG) angiography was performed after abdominal flap elevation. Images of vessels speculated to be arteries by preoperative photoacoustic tomography were merged with those of intraoperative ICG angiography and analyzed in an 8×4-cm2 area below the umbilical region. The S-factor was used to visualize the midline-crossing subcutaneous arteries in all 4 patients. A matching analysis compared preoperative tentative arteries according to photoacoustic tomography with ICG angiography results in the 8×4-cm2 area below the umbilical region and indicated a 71.3-82.1% match (average: 76.9% match). This study demonstrates that the S-factor, a noninvasive, label-free imaging modality, can be used to successfully visualize subcutaneous arteries. This information can aid in selecting perforators for abdominal flap surgery.

ANALYSIS OF THE SITUATION ON THE OBSERVED SCENE CONTAINING MANY MOVING OBJECTS

Abstract. In many processes, control is based on the classification of the current states of objects of interest or situations and the selection of appropriate control actions. In the presented work, the task of classifying dynamic situations based on the analysis of the received video sequence is considered, for example, when monitoring traffic, observing the behavior of a crowd of people or animals, etc. abnormal, dangerous, etc.). At the same time, the classification of situations by separate static images of similar observed scenes can be difficult, physically unrealizable or impractical. The aim of the study is to increase the efficiency of process management by using some features (signs) of dynamic situations for classification. Automatic classification of current dynamic situations in such processes will allow timely organization of measures to correct undesirable development of situations and/or reduce possible predicted losses. A technique for classifying dynamic situations based on the analysis of motion parameters identified by the transformer network and subsequent classification implemented by the perceptron is presented. As a demonstration example, the classification of street situations determined by people's behavior is investigated. Examples of distinguished classes of situations are given, confirming the possibility of implementing the proposed methodology.

Compressed sensing of low-rank plus sparse matrices

Expressing a matrix as the sum of a low-rank matrix plus a sparse matrix is a flexible model capturing global and local features in data. This model is the foundation of robust principle component analysis (Candes et al., 2011) (Chandrasekaran et al., 2009), and popularized by dynamic-foreground/static-background separation (Bouwmans et al., 2016) amongst other applications. Compressed sensing, matrix completion, and their variants (Eldar and Kutyniok, 2012) (Foucart and Rauhut, 2013) have established that data satisfying low complexity models can be efficiently measured and recovered from a number of measurements proportional to the model complexity rather than the ambient dimension. This manuscript develops similar guarantees showing that $m\times n$ matrices that can be expressed as the sum of a rank-$r$ matrix and a $s$-sparse matrix can be recovered by computationally tractable methods from $\mathcal{O}(r(m+n-r)+s)\log(mn/s)$ linear measurements. More specifically, we establish that the restricted isometry constants for the aforementioned matrices remain bounded independent of problem size provided $p/mn$, $s/p$, and $r(m+n-r)/p$ reman fixed. Additionally, we show that semidefinite programming and two hard threshold gradient descent algorithms, NIHT and NAHT, converge to the measured matrix provided the measurement operator's RIC's are sufficiently small. Numerical experiments illustrating these results are shown for synthetic problems, dynamic-foreground/static-background separation, and multispectral imaging.

Pengaruh Persepsi Label Halal Terhadap Keputusan Pembelian Produk Wardah Pada Masyarakat Desa Gandu

Wardah is the originator of the trend for halal cosmetics in Indonesia. Wardah always carries a "halal" strategy in its advertising and marketing, so that Wardah is the most well-known halal cosmetic product in the community, because "halal" has become a separate brand image for Wardah. This study aims to determine the effect of perceptions of the halal label on purchasing decisions for Wardah products in the people of Gandu Village.The method used in this research is a quantitative method. Test techniques and data analysis include validity test, reliability test, percentage data description test, normality test, simple linear regression, Pearson correlation, t test and coefficient of determination (r2) using SPSS version 22.The results showed that the value of r2 was 0.293 which implies that the influence of the perceived halal label variable (X) on the purchase decision variable (Y) was 29.3% and the remaining 70.7% was influenced by other variables. The results of hypothesis testing using simple linear regression analysis show that perceptions of halal labels have a significant effect on purchasing decisions.The conclusion of the research results shows that the variable perception of the halal label and the purchase decision has a unidirectional relationship (b = 0.647), the level of closeness is moderate (r = 0.541), and the variable perception of the halal label contributes to the purchase decision variable by 29.3% with low criteria. Furthermore, based on the results of hypothesis testing (t test), it shows that the hypothesis (H1) is accepted, meaning that the perception variable of the halal label influences the purchasing decision variable.

Identifying covariate-related subnetworks for whole-brain connectome analysis.

Whole-brain connectome data characterize the connections among distributed neural populations as a set of edges in a large network, and neuroscience research aims to systematically investigate associations between brain connectome and clinical or experimental conditions as covariates. A covariate is often related to a number of edges connecting multiple brain areas in an organized structure. However, in practice, neither the covariate-related edges nor the structure is known. Therefore, the understanding of underlying neural mechanisms relies on statistical methods that are capable of simultaneously identifying covariate-related connections and recognizing their network topological structures. The task can be challenging because of false-positive noise and almost infinite possibilities of edges combining into subnetworks. To address these challenges, we propose a new statistical approach to handle multivariate edge variables as outcomes and output covariate-related subnetworks. We first study the graph properties of covariate-related subnetworks from a graph and combinatorics perspective and accordingly bridge the inference for individual connectome edges and covariate-related subnetworks. Next, we develop efficient algorithms to exact covariate-related subnetworks from the whole-brain connectome data with an $\ell_0$ norm penalty. We validate the proposed methods based on an extensive simulation study, and we benchmark our performance against existing methods. Using our proposed method, we analyze two separate resting-state functional magnetic resonance imaging data sets for schizophrenia research and obtain highly replicable disease-related subnetworks.

Approaches for Three Material Decomposition using a Triple-Layer Flat-Panel Detector.

X-ray spectral imaging has been increasingly investigated in radiography and interventional imaging. Flat-panel detectors with more than one detection layer have demonstrated advantages in providing separate soft tissue and bone images. Current dual-layer flat-panel detectors (DL-FPD) have limited capability to further differentiate between iodinated contrast agent and bony/calcified structures. In this work, we investigate a triple-layer flat-panel detector (TL-FPD) and the feasibility of three-material (water/calcium/iodine) decomposition. A physical model of TL-FPD, including system geometry, spectrum sensitivities, blur and noise models was developed. Using simulated triple-layer projections, three-material decompositions were performed using three different processing methods: polynomial-based, model-based, and a machine learning-based method (ResUnet). We find that the polynomial-based method leads to very noisy images with poor differentiation between calcium and iodine maps. The model-based method achieved much lower noise levels than the polynomial-based method but exhibited residual errors between the iodine and calcium channels. The ResUnet method offered the best decompositions among the investigated methods in terms of root mean square error from the ground truth and noise in the material maps. These preliminary results demonstrate the feasibility of three-material decomposition using TL-FPD and suggest a path for clinical translation of single-shot contrast/iodine differentiation in radiography and fluoroscopy.

PD22-07 A MULTICENTER STUDY PRESENTING AN EXPLAINABLE AI APPROACH TO AUTOMATIC SEGMENTATION OF RENAL TUMORS ON COMPUTED TOMOGRAPHY

You have accessJournal of UrologyCME1 Apr 2023PD22-07 A MULTICENTER STUDY PRESENTING AN EXPLAINABLE AI APPROACH TO AUTOMATIC SEGMENTATION OF RENAL TUMORS ON COMPUTED TOMOGRAPHY Annemarie Uhlig, Sophie Bachanek, Paul Romario Würzberg, Manuel Nietert, Lutz Trojan, Joachim Lotz, and Johannes Uhlig Annemarie UhligAnnemarie Uhlig More articles by this author , Sophie BachanekSophie Bachanek More articles by this author , Paul Romario WürzbergPaul Romario Würzberg More articles by this author , Manuel NietertManuel Nietert More articles by this author , Lutz TrojanLutz Trojan More articles by this author , Joachim LotzJoachim Lotz More articles by this author , and Johannes UhligJohannes Uhlig More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003295.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Automatic segmentation of renal tumors is a cornerstone for Artificial Intelligence (AI) based tumor diagnostics. This study presents an automatic segmentation algorithm and visualization method using CT-studies from renal tumor patients recruited in a multicenter setting. METHODS: Renal tumor patients diagnosed between 2018 and 2021 were retrospectively assessed for this study. Inclusion criteria were CT-imaging of renal tumors in corticomedullary (CM) or nephrogenic (NG) contrast media phase. Patients aged <18years, and those with cystic or infiltrative renal tumors (i.e. lymphomas) were excluded. Manual segmentation of the renal tumors was performed by a GU-radiologist on all axial CT slices. A convolutional neural network (UNET) was trained based on the radiologists´ manual segmentations. In an independent validation dataset, the accuracy of the UNETs predictions of renal tumor contours was compared to the references standard of manual segmentations and quantified using the DICE score. RESULTS: A total of n=394/ n=350 patients with renal tumors imaged in CM /NG phase, respectively, were included (median age 66y; 35% female; median tumor diameter 5.4cm). CT-studies from >20 radiological imaging centers were included with different imaging protocols and slice thickness. The UNET was trained on n=316 CM and n=294 NG contrast phase patients (n=7019 / n=6859 separate CT images). In the independent validation dataset (n=78 / n=56 patients with 1713 / 1298 CT images), the UNET achieved a DICE score of 0.88 and 0.90 for the corticomedullary and nephrogenic CM phase, respectively. The UNET predictions were visualized using a tile-based approach with color-coding and contour-lines that could be overlaid on CT-images to depict varying levels of prediction confidence. CONCLUSIONS: A UNET AI-approach yields a robust automatic delineation of renal tumors on CT-images acquired in clinical routine, irrespective of the contrast media phase. Using color-coding and contour-lines that could be overlaid on original CT-images provides an explainable approach to the UNETs predictions and might improve AI-acceptance in clinical practice. Source of Funding: This study received funding by the 2022 ESR Research Seed Grant. The 2022 ESR Research Seed Grants were kindly supported by an unrestricted, non-exclusive grant from GE Healthcare © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e667 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Annemarie Uhlig More articles by this author Sophie Bachanek More articles by this author Paul Romario Würzberg More articles by this author Manuel Nietert More articles by this author Lutz Trojan More articles by this author Joachim Lotz More articles by this author Johannes Uhlig More articles by this author Expand All Advertisement PDF downloadLoading ...

A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program

The conditions under which galactic nuclear regions become active are largely unknown, although it has been hypothesized that secular processes related to galaxy morphology could play a significant role. We investigate this question using optical i-band images of 3096 SDSS quasars and galaxies at 0.3 < z < 0.6 from the Hyper Suprime-Cam Subaru Strategic Program, which possesses a unique combination of area, depth, and resolution, allowing the use of residual images, after removal of the quasar and smooth galaxy model, to investigate internal structural features. We employ a variational auto-encoder, which is a generative model that acts as a form of dimensionality reduction. We analyze the lower-dimensional latent space in search of features that correlate with nuclear activity. We find that the latent space does separate images based on the presence of nuclear activity, which appears to be associated with more pronounced components (i.e., arcs, rings, and bars) as compared to a matched control sample of inactive galaxies. These results suggest the importance of secular processes and possibly mergers (by their remnant features) in activating or sustaining black hole growth. Our study highlights the breadth of information available in ground-based imaging taken under optimal seeing conditions and having an accurate characterization of the point-spread function (PSF), thus demonstrating future science to come from the Rubin Observatory.

Dual-Tracer PET Image Separation by Deep Learning: A Simulation Study

Multiplexed positron emission tomography (PET) imaging provides perfectly registered simultaneous functional and molecular imaging of more than one biomarker. However, the separation of the multiplexed PET signals within a single PET scan is challenging due to the fact that all PET tracers emit positrons, which, after annihilating with a nearby electron, give rise to 511 keV photon pairs that are detected in coincidence. Compartment modelling can separate single-tracer PET signals from multiplexed signals based on the differences in bio-distribution kinetics and radioactive decay. However, the compartment-modelling-based method requires staggered injections and assumes that each tracer’s input function is known. In this paper, we propose a deep-learning-based method to simultaneously separate dual-tracer PET signals without explicitly knowing the input functions. We evaluate the proposed deep-learning-based separation method on dual-tracer [18F]FDG and [11C]MET PET simulations and compare its separation performance to that of the compartment-modelling-based method, assessing performance dependence on the time interval between tracer injections as well as on the amount of training data. It is shown that the proposed method implicitly denoises the separated images and offers reduced variance in the separated images compared to compartment modelling.

Flame structure and broadening in turbulent premixed jet flames

High-resolution and high signal-to-noise (SNR) planar Rayleigh scattering thermometry measurements are performed to investigate preheat and reaction zone broadening, turbulent transport of high-temperature gases, and the internal flame structure in a series of highly turbulent, lean (ϕ = 0.75), premixed methane-air turbulent jet flames. These flames are characterized by turbulence intensities ranging from u′/SL of 35 to 150 and turbulent Reynolds numbers approaching 104, as determined from separate particle image velocimetry (PIV) measurements. Average flame profiles obtained using a gradient-based flame reconstruction method and probability density functions (PDFs) of the preheat, reaction, and thermal layer thicknesses show substantial broadening of the preheat and thermal layers. Instantaneous preheat zones are broadened by as much as a factor of 30 compared to laminar flames, with an average broadening approaching a factor of 14 for the most turbulent case. Moderate broadening of the reaction layer is observed with up to a factor of eight times that of a laminar premixed flame for instantaneous realizations and an average broadening of four times that of a laminar flame for the most turbulent case. Conditional statistics of temperature obtained at isodistance contours within and upstream of the preheat zone show large shifts towards significantly higher temperatures compared to laminar flame values. In addition, broad temperature distributions and reduced thermal gradients at each position provide strong evidence of persistent turbulent transport of high-temperature gases ahead of the preheat zone. Finally, a detailed analysis of scalar dissipation rate statistics and dissipation layer topology was conducted. The conditional mean scalar dissipation rates for the turbulent flames show significant reductions compared to calculated laminar flame values and a shift towards higher values of the reaction progress variable (CT) for increasing turbulence levels. PDFs show a transition from near-Gaussian to highly negatively skewed with increasing turbulence levels, where the most probable values of the scalar dissipation rate shift towards higher values of CT, indicating that the primary reaction zone and flame front are subjected to increased strain. In terms of structure, no significant broadening of the small-scale dissipation layers was observed with increasing turbulence intensity. However, as turbulence intensity increases, there is a transition from a sparse number of continuous layers to a large number of shorter, ”broken”, nearly straight segments. The reduction of individual layer lengths (i.e., ”breaking”) is favored over increases in dissipation layer wrinkling, where the sharp decreases in the local dissipation rate values are highly correlated with regions of high curvature. A final notable outcome of this work is that there were negligible differences in all statistical and topological results when moving between the two highest turbulent cases, even though the turbulence intensity, turbulent Reynolds number, and the turbulence Karlovitz number increased substantially (70 - 120%). This implies that there may be an asymptotic limit in which turbulence affects flame behavior for the current configuration.

Abstract P6-04-12: Differential diagnoses of breast biopsies by spatial parametric modeling of histological structures and explainable AI

Abstract Background: Pathologists typically diagnose the breast tissue slides under a microscope by examining: (i) lumen and ductal morphology, (ii) nuclei size, shape, and spatial arrangement and their combinations, (iii) intraductal architecture, and (iv) textural properties. These features may be subtle and can overlap between diagnoses which contribute to inter- and intra-observer variability. We aim to mitigate this arbitrary nature of breast diagnoses with an exemplar-driven precision pathology pipeline based on spatial parametric modeling of histological structures. Methods: For our study, we consider a broad spectrum of breast biopsies including: (i) invasive breast cancer, (ii) three high-risk benign lesions: ductal carcinoma in-situ, atypical ductal hyperplasia (ADH), flat epithelial atypia (FEA), and (iii) three low-risk benign lesions: usual ductal hyperplasia, columnar cell change and Normal; where the risk is indicated by the relative chance of developing breast cancer. We build spatial parametric models for a dictionary of histological structures that pathologists frequently use (also documented in the standard reference book from WHO on the classification of tumors) in making complex diagnostic decisions. These models enable our precision pathology pipeline to simultaneously identify distinct exemplar images to account for inter-class heterogeneity, and learn the relative importance of lumen/ductal morphology (LD), intraductal structures including nuclei morphology and spatial arrangements (ID) and textural features (T) from automatically identified exemplar images in assigning diagnostic labels. In doing so, we assert that the assignment of relative importances to LD, ID, and T features is driven by similar looking ducts (‘exemplars’) which were previously encountered during pathology training or clinical practice. Results: We evaluated the inferential power of our exemplar-driven precision pathology pipeline on two separate breast core biopsy image datasets, i) dataset containing 4539 regions of interest (ROIs) images extracted from 387 whole slide images (WSIs, 40x), and ii) dataset containing 1237 ROI images extracted from 93 WSIs (20x). Our precision pathology pipeline shows significant improvement (~20%) in the overall classification performance compared to state-of-the-art black box deep learning methods (e.g., graphical neural networks) on both datasets. In particular, while our performance in detecting invasive lesions is comparable to baseline methods, we show a significant improvement (p&amp;lt; 0.01) in detecting diagnostically important high-risk ADH and FEA ROIs compared to the baseline methods, where inter- and intra-observer variability is a problem. Conclusions: A key highlight of our method is in its ability to provide pathologist friendly diagnostic explanations without largely compromising on the classification performance. The strategy outlined in this work can be generalized to other tissue histologies from other organs as defined in the WHO Classification of Tumors books. Further, our approach can facilitate a communication platform between pathologists and computational scientists to interact and develop AI-driven algorithmic tools that can enhance patient care in a clinical setting. Our framework provides pathologist-friendly explanations paving the way for better, transparent, and trustworthy diagnostic tools. Differential diagnoses of breast biopsies Differential diagnoses of breast biopsies The precision pathology pipeline optimizes the identification of a broad spectrum of breast biopsies (invasive, DCIS, benign), including difficult borderline cases (e.g., ADH, FEA, etc.). It provides pathologist-friendly explanations integrated into a clinical workflow for better, transparent, and trustworthy diagnostic aid. This approach sddresses the limitations of standard black-box AI in building trust with pathologists. Citation Format: Akif Burak Tosun, S. Chakra Chennubhotla. Differential diagnoses of breast biopsies by spatial parametric modeling of histological structures and explainable AI [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-04-12.

Fine spectral tuning of a flavin-binding fluorescent protein for multicolor imaging

Flavin-binding fluorescent proteins are promising genetically encoded tags for microscopy. However, spectral properties of their chromophores (riboflavin, flavin mononucleotide, and flavin adenine dinucleotide) are notoriously similar even between different protein families, which limits applications of flavoproteins in multicolor imaging. Here, we present a palette of 22 finely tuned fluorescent tags based on the thermostable LOV domain from Chloroflexus aggregans. We performed site saturation mutagenesis of three amino acid positions in the flavin-binding pocket, including the photoactive cysteine, to obtain variants with fluorescence emission maxima uniformly covering the wavelength range from 486 to 512nm. We demonstrate three-color imaging based on spectral separation and two-color fluorescence lifetime imaging of bacteria, as well as two-color imaging of mammalian cells (HEK293T), using the proteins from the palette. These results highlight the possibility of fine spectral tuning of flavoproteins and pave the way for further applications of flavin-binding fluorescent proteins in fluorescence microscopy.

Minor Prophets

Minor Prophets Christopher T. Begg, Fred W. Guyette, and Richard A. Taylor 738. [Spirit, Kingship, and Inner-Biblical Allusion in the Book of the Twelve and the NT] Beth M. Stovell, "Spirit, Kingship, and Inner-Biblical Allusion in the Book of the Twelve and the New Testament," The Spirit Says, 39-60 [see #910]. This chapter has explored how conceptions of God's spirit and presence join with conceptions of God's kingship in the Twelve and continue into the NT via use of inner-biblical allusion. A few key insights have emerged from this analysis. First, the depictions of the spirit as water and the spirit as oil serve key roles in joining the depictions of God's spirit in Joel and Zechariah to their interpretations in Acts and John. … Second, the conceptual understanding of the spirit in relation to God's kingship is based repeatedly on iterations of inner-biblical allusion. Such allusion may come in the form of direct or indirect citation, but in both cases, the purpose is to draw the hearer back [End Page 250] to earlier events and experiences of the spirit that shed light on the conceptualizations of the spirit and kingship. Joel and Zechariah did not come to their understandings of God's spirit and kingship entirely on their own, but are grounded in their predecessors' conceptions of God's spirit and kingship. Joel uses the Exodus account to draw links between the spirit's revelation given to all peoples with the experience of God as Divine Warrior who calls out from Zion. Joel's linking of God as king and the spirit's outpouring upon the people are picked up in the NT by Acts to illustrate the actions of the Holy Spirit in relation to the Father and to Jesus the Son who is Lord and Messiah, the anointed king. Besides the Divine Warrior-king metaphor linked with the spirit in the Twelve, the shepherd king metaphor is also linked with depictions of God's spirit and presence in the Twelve in passages such as Zechariah 9–12. Building on allusions to the Psalter and Ezekiel, Zechariah 9–12 depicts God's recurring presence to protect and fight for his people as their king and the spirit's outpouring on David's house. The interplay of spirit and kingship in these texts enables John the Evangelist … to incorporate the outpouring of the Holy Spirit as oil and as water into his depictions of Jesus' kingship in John 12 and 19. … [Adapted from author's conclusion, pp. 59-60—C.T.B.] *739. [Weather Imagery in Hosea] Szabolcs Ferencz Kato, "Rain, Dew, Flourishing Plants as Images of Monolatry and Righteousness: The Weather Imagery and Its Meaning in Hosea," HTS 77 (4, 2021) 1-7 [see Editor's Notes, p. 367]. Weather imagery plays a major role in the Book of Hosea. The prophet wants us to see an interconnectedness between faithfulness and the fruitfulness of the land. Since Israel has been unfaithful to God, the land is suffering from drought and agriculture is failing (Hosea 2). There is no bread or water, no wool, no flax, and no cooking oil. Hos 4:2-3 describes the withering of the land. In 6:3; 10:12; 14:6, several types of precipitation serve to draw attention to the utterances of Yhwh and the need for righteousness. In 9:10, 13, 16; 10:1; 13:5; and 14:6, 8, Israel is symbolized by different plants that blossom and wither, depending on their relation to Yhwh. Hosea also uses weather imagery to describe the political consequences of idolatry and injustice. Those behaviors cause drought and famine in a literal sense, but they also cause the withering of Israel in a figurative sense. The remedy for the land and for God's people?: Genuine repentance and renewed trust in Yhwh.—F.W.G. 740. [Hosea 7] Kurtis Peters, "Theology in Knead of Language: Bread, Ovens, and the Politics of Hosea 7," From Words to Meanings, 107-17 [see #911]. Hosea 7 contains two separate but related enigmatic cooking images. The first is that of a heated oven...

Commentary: Astronomy from space after the JWST

Commentary: Astronomy from space after the <i>JWST</i>

Diagnostic value of 3D T1-weighted gradient-echo and 2D T1-weighted in-phase and out-of-phase gradient-echo sequences for appendicitis diagnosis in pregnant women.

This study compared the diagnostic value of 3D T1-weighted (T1W) gradient-echo (GRE) and 2D T1W in-phase and out-of-phase GRE sequences for appendicitis diagnosis in pregnant women. This retrospective study included 25 pregnant patients with suspected appendicitis who underwent 1.5 Tesla abdominal magnetic resonance imaging and had definitive diagnoses. Four doctors approached four separate imaging groups: A (only T2-weighted image [T2WI] sequences), B (T2WI and 3D T1W GRE sequences), C (T2WI and T1W in-phase and out-of-phase GRE sequences), and D (T2WI, 3D T1W GRE, and T1W in-phase and out-of-phase GRE sequences). The kappa (κ) index was used to compare the appendicitis diagnostic results between groups. The diagnostic value of these sequences in the diagnosis of pregnancy appendicitis was also evaluated. Groups A and C had average consistency with definitive diagnosis (κ = 0.6), lower than Groups B and D (κ = 0.865), indicating a high definite diagnosis consistency. Groups B and D had similarly high sensitivity (80%), specificity (100%), positive predictive value (100%), negative predictive value (95.2%), and accuracy (ACC) (96%), higher than Groups A and C (60%, 95%, 75%, 90.5%, and 88%, respectively). 3D T1W-GRE sequences improve appendicitis diagnosis in pregnancy compared to T2W sequences alone. Adding in and out phase GRE sequences do not increase diagnostic ACC.

Камерно-вокальное творчество Йозефа Гайдна. Часть 2

The purpose of this research paper is devoted to the still little-studied problem of identifying the relations between the vocal and instrumental factors in Haydn’s music. In the paper the vocal cycle «VI ORIGINAL CANZONETTAS» is analyzed. It comprises the combination of independent and finished by the form of songs subordinate to the same artistic design. The composer follows the basic principle of the integration of parts into a cycle – the principle of music character contrast – and dramatically unites separate sketches, moods and images with the same author’s point of view. The success of this cycle was due to lyrics chosen by the composer. The novelty of the research lies in the determination of Haydn’s vocal music specificity characterized by rather complex relations of text and music in terms of both content and expression. As a result, it is proved that the maturity of expression appeared in Haydn’s style under the influence of fresh London impressions and is clearly observed in English songs. This is evident in the use of the musical language for composing the works of the other genres in London (primarily, symphonies). So the composer raises the vocal genre to a qualitatively different stage leading directly to Schubert.

An Image Construction Based on the VTLF Fusion Technique for Oil–Water Two-Phase Flow of Noncontact Electrical Impedance Tomography

Electrical tomography (ET) has attracted extensive attention in the petroleum multiphase flow field due to its advantages of nonintrusion, nonradiation, and simple equipment structure. Some contactless impedance imaging methods, such as capacitively coupled electrical resistance tomography (CCERT) and phase-based dielectric spectroscopy tomography, or displacement current phase tomography (DCPT), have been studied for the parameter measurement of two-phase flow. The mentioned research mainly focuses on the separate imaging by real part, imaginary part, or phase information of the voltage/current. However, the distribution change of media in the region of interest (RoI), especially the presence of high permittivity and conductivity materials, involves the distribution change of conductivity, permittivity, and loss factor. Developing a proper image fusion technique based on the three components can provide more comprehensive image information for tomographic imaging. This article proposed an improved logic filter image fusion algorithm based on a variable threshold (VTLF), and a noncontact electrical impedance tomography (NEIT) system for imaging water–oil two-phase flow is carried out. Image reconstruction is implemented by the VTLF image fusion algorithm under different media distributions, conductivities, and excitation frequencies, and the image fusion algorithms of a weighted average (WA) and logic filter (LF) are also used for comparison. The simulation results show that the reconstructed images by the proposed fusion method of VTLF had the lowest artifacts and the highest correlation coefficient (above 0.7) compared with other methods. Also, in the experiment, the VTLF also got the best performance, so it is effective to improve the reconstruction accuracy.

Diffraction Separation and Least-Squares Imaging Based on Multiscale and Multidirectional Wavefield and Image Decomposition

Diffraction Separation and Least-Squares Imaging Based on Multiscale and Multidirectional Wavefield and Image Decomposition