Reservoir connectivity in fluvio-deltaic systems depends on the spatial arrangement of sand bodies and shale barriers. This study examines the potential static geological connectivity of the C2000–D1000 and D1000–D2000 intervals in the Chinak Field, onshore Niger Delta, using well logs, seismic interpretation, structural modelling, variogram analysis, and Sequential Indicator Simulation. The available modelled net-to-gross (NTG) ranges are 0.53882–0.95224 for C2000–D1000 and 0.47117463–0.99902231 for D1000–D2000. Fitted horizontal variogram ranges of 1,234 m and a vertical range of 3.9 m give a horizontal-to-vertical correlation-range ratio of approximately 316.4. The facies-model illustrations suggest potentially greater lateral sand-body continuity in C2000–D1000 and more channel-scale variability and possible compartmentalization in D1000–D2000. These are qualitative interpretations of potential static connectivity, not measurements of connected sand volumes or evidence of dynamic fluid communication. The absence of cell-level facies-grid exports prevented calculation of component counts, largest connected components, connected sand fractions, and uncertainty across realizations.
Mammography is the primary imaging modality utilizes low energy characteristics radiation for detection of abnormal growth in breasts; and its effectiveness in breast cancer diagnosis has been widely reported. Most manufacturers of mammography machine do rely on the dance formular for calculating the Mean Glandular Dose (MGD) values on the Digital Information for Communication (DICOM) header of mammography machines. The aim of this study was to check the reliability of the MGD values estimated using the Dance formula using an ionization chamber. Five mammography machines from five tertiaries medical centres within Southwest Nigeria were considered for this research work. Direct measurement was carried out using the Ionization Chamber of the RadCal Accu-gold detector. The entrance surface dose was measured and thereafter converted to MGD using the recommendation of the American College of Radiology. The Ionization chamber recorded higher values for MGD per patients, ranging from 2.49±1.32 mGy to 6.99±3.88 mGy for CC and MLO views respectively. The MGD values per patient estimated by the detector ranged from 1.98±0.84 mGy to 5.94±2.78 mGy for CC and MLO views respectively. The MGD values measured by the Accu-Gold Ionization chamber were relatively higher when compare to the estimated MGD values from the DICOM header of the mammography machines. This may be due to the fact that some exposure factors like the compression contributed to the low dose recorded by the mammogram machine in the DICOM header. The study established the reliability of the MGD values estimated using the Dance formula
Early detection of breast cancer plays a crucial role in its diagnosis and treatment. This research presents an edge-enhanced Convolutional Neural Network (CNN) model for breast cancer detection and classification using Magnetic Resonance Imaging (MRI) scans. A dataset of 1480 breast MRI scans (comprising of healthy and diseased MRI breast scans) was sourced from Kaggle, an open repository for medical images. The images were pre-processed via resizing, de-noising, normalization, and contrast enhanced using contrast limited adaptive histogram equalization (CLAHE) to obtain uniform and standard images. Pre-processed images were then split into training, testing and validation sets in the ratio 60:20:20. To enhance the structural descriptors, the grayscale MRI images were further processed by means of Canny edge- and Harris corner- detection algorithms, leading to generation of additional two versions of each of the images: Canny edge map and Harris corner mask. These two versions together with the grayscale version constitute the three inputs that were fed into the CNN model. The CNN model uses three convolutional blocks with rectified linear unit activation, max-pooling, dropout regularization, a sigmoid output, binary cross-entropy loss with Adam optimizer for 15 epochs and batch size of 16. The model achieves an accuracy, precision, recall, and F1-score of 97%, 99%, 95%, and 97%, respectively. The results indicate that incorporating explicit edge and structural information into the CNN input can enhance the aid detection of breast cancer in MRI images
Zirconium based chalcogenide perovskites, AZrS3 (A = Ca, Sr, Ba), are lead free semiconductors composed of earth abundant elements and are of interest for photovoltaics and optoelectronics. The evidence base is, however, strongly unbalanced. Experimental work concerns mainly BaZrS3, which is orthorhombic (Pnma) at room temperature, has a measured optical gap of about 1.75–1.8 eV and shows band edge absorption coefficients above 105 cm−1. CaZrS3 remains largely computational, and SrZrS3 must be specified by polymorph because it also forms a non-perovskite, needle-like phase. This narrative critical review, based on a documented literature search, appraises experimental and first-principles studies of crystal chemistry, phase behavior, synthesis, electronic and optical properties, stability, defects and device relevance, and labels each benchmark as experimental, computed or simulated. Three evidence limitations are emphasized. First, the cubic (Pm3̄m) phase widely used in calculations is predicted by simulation to form only above about 880 K, whereas high temperature diffraction of BaZrS3 has so far revealed orthorhombic and tetragonal phases. Second, the reduction of the BaZrS3 gap from 1.78 to 1.51 eV by 4 at. % Ti alloying is experimental, whereas the reported photovoltaic efficiencies of 28–33% are device simulations, not measured devices. Third, inconsistent functionals and structural models prevent quantitative comparison across the Ca–Sr–Ba series. A minimum computational protocol and a staged assessment framework are proposed to support evidence-based comparison of the series.
.Existing electric-vehicle battery-management studies often treat CCCV charging, cell balancing, and mobile monitoring separately; few provide a reproducible simulation framework that couples SoC/capacity-aware discharge allocation with real-time Android monitoring. This paper presents a MATLAB/Simulink BMS for a multi-cell lithium-ion pack integrating CCCV charging, passive cell balancing, and a responsibility-based discharge allocator. Each cell is represented by an OCV-Rint/Thevenin equivalent circuit; SoC is updated by coulomb counting, and hybrid pulse power characterization (HPPC) pulses are used to verify the OCV–SoC relationship. In discharge mode, load current is distributed among cell-level DC–DC converters using weights derived from each cell’s usable energy, , subject to Kirchhoff’s current law, current limits, and SoC limits; this addresses capacity differences that SoC-only allocation ignores. The model was compared with uniform discharge under identical initial SoC, 30 A load, and cutoff conditions. The proposed method extended discharge duration from below 1,000 s for uniform discharge to 4,507.93 s, with SoC equalization at 706.82 s, 2,147.73 s, and 2,315.91 s for initial SoCs of 30%, 50%, and 90%, i.e. at least a 351% increase in runtime under the stated fixed-load simulation. A Jetpack Compose Android app using MQTT and Room/SQLite provided real-time SoC, range, energy, and runtime monitoring. Limitations include fixed-load simulation, no thermal/aging model, and the need for cell-level power converters to realize independent cell currents; experimental validation is required
