[Inter-Korea] :
Kim Yo-jong, the powerful sister of North Korean leader Kim Jong-un, declared that the regime will continue to bolster its nuclear capabilities in protest of criticisms from the international community. In a statement carried by the North's Korean Central News Agency(KCNA) on Saturday, Kim ...

[more...]

[International] :
The operator of Japan’s Fukushima Daiichi nuclear power plant said it retrieved a small amount of melted fuel from one of the reactors for the first time since a major earthquake and tsunami crippled the nuclear facility in 2011. According to Japanese media outlets on Thursday, the Tokyo Electric Power ...

[more...]

The English language is challenging due to complicated grammar, inconsistent sentence structure and colloquial idioms that it doesn't share with related languages. However, English is a target language that sees significantly more resources and opportunities for immersion than many other languages.

How does a hybrid car improve your gas mileage? And more importantly, does it pollute less just because it gets better gas mileage? Learn how hybrids work, plus get tips on how to drive a hybrid car for maximum efficiency.

Fall is the season of festivals and baseball here in Korea. What are some interesting festivals taking place around the country? What is the deal with the first pitch by celebrities at the baseball...

[more...]

[Economy] :
A large delegation representing Czech power authorities will make a two-week visit to South Korea for working-level negotiations ahead of the conclusion of a final contract for the Czech nuclear power plant project.  Korea Hydro and Nuclear Power(KHNP), which was selected in July as the preferred bidder ...

[more...]

Small-angle X-ray scattering (SAXS) is widely used to analyze the shape and size of nanoparticles in solution. A multitude of models, describing the SAXS intensity resulting from nanoparticles of various shapes, have been developed by the scientific community and are used for data analysis. Choosing the optimal model is a crucial step in data analysis, which can be difficult and time-consuming, especially for non-expert users. An algorithm is proposed, based on machine learning, representation learning and SAXS-specific preprocessing methods, which instantly selects the nanoparticle model best suited to describe SAXS data. The different algorithms compared are trained and evaluated on a simulated database. This database includes 75 000 scattering spectra from nine nanoparticle models, and realistically simulates two distinct device configurations. It will be made freely available to serve as a basis of comparison for future work. Deploying a universal solution for automatic nanoparticle model selection is a challenge made more difficult by the diversity of SAXS instruments and their flexible settings. The poor transferability of classification rules learned on one device configuration to another is highlighted. It is shown that training on several device configurations enables the algorithm to be generalized, without degrading performance compared with configuration-specific training. Finally, the classification algorithm is evaluated on a real data set obtained by performing SAXS experiments on nanoparticles for each of the instrumental configurations, which have been characterized by transmission electron microscopy. This data set, although very limited, allows estimation of the transferability of the classification rules learned on simulated data to real data.

Proliferating cell nuclear antigen (PCNA) plays a critical role in DNA replication by enhancing the activity of various proteins involved in replication. In this study, the crystal structure of ApePCNA1, one of three PCNAs from the thermophilic archaeon Aeropyrum pernix, was elucidated. ApePCNA1 was cloned and expressed in Escherichia coli and the protein was purified and crystallized. The resulting crystal structure determined at 2.00 Å resolution revealed that ApePCNA1 does not form a trimeric ring, unlike PCNAs from other domains of life. It has unique structural features, including a long interdomain-connecting loop and a PIP-box-like sequence at the N-terminus, indicating potential interactions with other proteins. These findings provide insights into the functional mechanisms of PCNAs in archaea and their evolutionary conservation across different domains of life. A modified medium and protocol were used to express recombinant protein containing the lac operon. The expression of the target protein increased and the total incubation time decreased when using this system compared with those of previous expression protocols.

New software capabilities in RMCProfile allow researchers to study the structure of materials by combining machine learning interatomic potentials and reverse Monte Carlo.

In the title compound, [Cu2(L)2]·2CH2Cl2, the CuII ions coordinate two (S,O)-chelating aroyl­thio­urea moieties of doubly deprotonated furan-2,5-di­carbonyl­bis­(N,N-di­ethyl­thio­urea) (H2L) ligands. The coordination geometry of the metal centers is best described as a flat isosceles trapezoid with a cis arrangement of the donor atoms.

Structure refinement with reverse Monte Carlo (RMC) is a powerful tool for interpreting experimental diffraction data. To ensure that the under-constrained RMC algorithm yields reasonable results, the hybrid RMC approach applies interatomic potentials to obtain solutions that are both physically sensible and in agreement with experiment. To expand the range of materials that can be studied with hybrid RMC, we have implemented a new interatomic potential constraint in RMCProfile that grants flexibility to apply potentials supported by the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) molecular dynamics code. This includes machine learning interatomic potentials, which provide a pathway to applying hybrid RMC to materials without currently available interatomic potentials. To this end, we present a methodology to use RMC to train machine learning interatomic potentials for hybrid RMC applications.

Small-angle X-ray scattering (SAXS) is a widely used method for nanoparticle characterization. A common approach to analysing nanoparticles in solution by SAXS involves fitting the curve using a parametric model that relates real-space parameters, such as nanoparticle size and electron density, to intensity values in reciprocal space. Selecting the optimal model is a crucial step in terms of analysis quality and can be time-consuming and complex. Several studies have proposed effective methods, based on machine learning, to automate the model selection step. Deploying these methods in software intended for both researchers and industry raises several issues. The diversity of SAXS instrumentation requires assessment of the robustness of these methods on data from various machine configurations, involving significant variations in the q-space ranges and highly variable signal-to-noise ratios (SNR) from one data set to another. In the case of laboratory instrumentation, data acquisition can be time-consuming and there is no universal criterion for defining an optimal acquisition time. This paper presents an approach that revisits the nanoparticle model selection method proposed by Monge et al. [Acta Cryst. (2024), A80, 202–212], evaluating and enhancing its robustness on data from device configurations not seen during training, by expanding the data set used for training. The influence of SNR on predictor robustness is then assessed, improved, and used to propose a stopping criterion for optimizing the trade-off between exposure time and data quality.

Reaction between equimolar amounts of 3,3,3',3'-tetraethyl-1,1'-(furan-2,5-dicarbonyl)bis(thiourea) (H2L) and CuCl2·2H2O in methanol in the presence of the supporting base Et3N gave rise to a neutral dinuclear complex bis[μ-3,3,3',3'-tetraethyl-1,1'-(furan-2,5-dicarbonyl)bis(thioureato)]dicopper(II) dichloromethane disolvate, [Cu2(C16H22N4O3S2)2]·2CH2Cl2 or [Cu2(L)2]·2CH2Cl2. The aroylbis(thioureas) are doubly deprotonated and the resulting anions {L2–} bond to metal ions through (S,O)-chelating moieties. The copper atoms adopt a virtually cis-square-planar environment. In the crystal, adjacent [Cu2(L)2]·2CH2Cl2 units are linked into polymeric chains along the a-axis direction by intermolecular coordinative Cu...S interactions. The co-crystallized solvent molecules play a vital role in the crystal packing. In particular, weak C—Hfuran...Cl and C—Hethyl...Cl contacts consolidate the three-dimensional supramolecular architecture.

Bone material contains a hierarchical network of micro- and nano-cavities and channels, known as the lacuna-canalicular network (LCN), that is thought to play an important role in mechanobiology and turnover. The LCN comprises micrometer-sized lacunae, voids that house osteocytes, and submicrometer-sized canaliculi that connect bone cells. Characterization of this network in three dimensions is crucial for many bone studies. To quantify X-ray Zernike phase-contrast nanotomography data, deep learning is used to isolate and assess porosity in artifact-laden tomographies of zebrafish bones. A technical solution is proposed to overcome the halo and shade-off domains in order to reliably obtain the distribution and morphology of the LCN in the tomographic data. Convolutional neural network (CNN) models are utilized with increasing numbers of images, repeatedly validated by `error loss' and `accuracy' metrics. U-Net and Sensor3D CNN models were trained on data obtained from two different synchrotron Zernike phase-contrast transmission X-ray microscopes, the ANATOMIX beamline at SOLEIL (Paris, France) and the P05 beamline at PETRA III (Hamburg, Germany). The Sensor3D CNN model with a smaller batch size of 32 and a training data size of 70 images showed the best performance (accuracy 0.983 and error loss 0.032). The analysis procedures, validated by comparison with human-identified ground-truth images, correctly identified the voids within the bone matrix. This proposed approach may have further application to classify structures in volumetric images that contain non-linear artifacts that degrade image quality and hinder feature identification.

During beam time at a research facility, alignment and optimization of instrumentation, such as spectrometers, is a time-intensive task and often needs to be performed multiple times throughout the operation of an experiment. Despite the motorization of individual components, automated alignment solutions are not always available. In this study, a novel approach that combines optimisers with neural network surrogate models to significantly reduce the alignment overhead for a mobile soft X-ray spectrometer is proposed. Neural networks were trained exclusively using simulated ray-tracing data, and the disparity between experiment and simulation was obtained through parameter optimization. Real-time validation of this process was performed using experimental data collected at the beamline. The results demonstrate the ability to reduce alignment time from one hour to approximately five minutes. This method can also be generalized beyond spectrometers, for example, towards the alignment of optical elements at beamlines, making it applicable to a broad spectrum of research facilities.

Transition-metal nitro­gen-doped carbons (TM-N-C) are emerging as a highly promising catalyst class for several important electrocatalytic processes, including the electrocatalytic CO2 reduction reaction (CO2RR). The unique local environment around the singly dispersed metal site in TM-N-C catalysts is likely to be responsible for their catalytic properties, which differ significantly from those of bulk or nanostructured catalysts. However, the identification of the actual working structure of the main active units in TM-N-C remains a challenging task due to the fluctional, dynamic nature of these catalysts, and scarcity of experimental techniques that could probe the structure of these materials under realistic working conditions. This issue is addressed in this work and the local atomistic and electronic structure of the metal site in a Co–N–C catalyst for CO2RR is investigated by employing time-resolved operando X-ray absorption spectroscopy (XAS) combined with advanced data analysis techniques. This multi-step approach, based on principal component analysis, spectral decomposition and supervised machine learning methods, allows the contributions of several co-existing species in the working Co–N–C catalysts to be decoupled, and their XAS spectra deciphered, paving the way for understanding the CO2RR mechanisms in the Co–N–C catalysts, and further optimization of this class of electrocatalytic systems.

The phase problem in the context of focusing synchrotron beams with X-ray lenses is addressed. The feasibility of retrieving the surface error of a lens system by using only the intensity of the propagated beam at several distances is demonstrated. A neural network, trained with a few thousand simulations using random errors, can predict accurately the lens error profile that accounts for all aberrations. It demonstrates the feasibility of routinely measuring the aberrations induced by an X-ray lens, or another optical system, using only a few intensity images.

Developing new materials for Li-ion and Na-ion batteries is a high priority in materials science. Such development always includes performance tests and scientific research. Synchrotron radiation techniques provide unique abilities to study batteries. Electrochemical cell design should be optimized for synchrotron studies without losing electrochemical performance. Such design should also be compatible with operando measurement, which is the most appropriate approach to study batteries and provides the most reliable results. The more experimental setups a cell can be adjusted for, the easier and faster the experiments are to carry out and the more reliable the results will be. This requires optimization of window materials and sizes, cell topology, pressure distribution on electrodes etc. to reach a higher efficiency of measurement without losing stability and reproducibility in electrochemical cycling. Here, we present a cell design optimized for nuclear resonance techniques, tested using nuclear forward scattering, synchrotron Mössbauer source and nuclear inelastic scattering.

The synthesis, crystal structure and magnetic properties of an ox­am­ate-con­taining erbium(III) com­plex, namely, tetra­butyl­ammonium aqua­[N-(2,4,6-tri­methyl­phen­yl)oxamato]erbium(III)–di­methyl sulfoxide–water (1/3/1.5), (C16H36N)[Er(C11H12NO3)4(H2O)]·3C2H6OS·1.5H2O or n-Bu4N[Er(Htmpa)4(H2O)]·3DMSO·1.5H2O (1), are reported. The crystal structure of 1 reveals the occurrence of an erbium(III) ion, which is surrounded by four N-phenyl-substituted ox­am­ate ligands and one water mol­ecule in a nine-coordinated environment, together with one tetra­butyl­ammonium cation acting as a counter-ion, and one water and three dimethyl sulfoxide (DMSO) mol­ecules of crystallization. Variable-temperature static (dc) and dynamic (ac) magnetic mea­sure­ments were carried out for this mononuclear com­plex, revealing that it behaves as a field-induced single-ion magnet (SIM) below 5.0 K.

Lanthanide ions have ideal chemical properties for catalysis, such as hard Lewis acidity, fast ligand-exchange kinetics, high coordination-number preferences and low geometric requirements for coordination. As a result, many small-molecule lanthanide catalysts have been described in the literature. Yet, despite the ability of enzymes to catalyse highly stereoselective reactions under gentle conditions, very few lanthanoenzymes have been investigated. In this work, the mononuclear binding of europium(III) and gadolinium(III) to the active site of a mutant of the model enzyme phosphotriesterase are described using X-ray crystallography at 1.78 and 1.61 Å resolution, respectively. It is also shown that despite coordinating a single non-natural metal cation, the PTE-R18 mutant is still able to maintain esterase activity.

When solving a structure of a protein from single-wavelength anomalous diffraction X-ray data, the initial phases obtained by phasing from an anomalously scattering substructure usually need to be improved by an iterated electron-density modification. In this manuscript, the use of convolutional neural networks (CNNs) for segmentation of the initial experimental phasing electron-density maps is proposed. The results reported demonstrate that a CNN with U-net architecture, trained on several thousands of electron-density maps generated mainly using X-ray data from the Protein Data Bank in a supervised learning, can improve current density-modification methods.

During the automatic processing of crystallographic diffraction experiments, beamstop shadows are often unaccounted for or only partially masked. As a result of this, outlier reflection intensities are integrated, which is a known issue. Traditional statistical diagnostics have only limited effectiveness in identifying these outliers, here termed Not-Excluded-unMasked-Outliers (NEMOs). The diagnostic tool AUSPEX allows visual inspection of NEMOs, where they form a typical pattern: clusters at the low-resolution end of the AUSPEX plots of intensities or amplitudes versus resolution. To automate NEMO detection, a new algorithm was developed by combining data statistics with a density-based clustering method. This approach demonstrates a promising performance in detecting NEMOs in merged data sets without disrupting existing data-reduction pipelines. Re-refinement results indicate that excluding the identified NEMOs can effectively enhance the quality of subsequent structure-determination steps. This method offers a prospective automated means to assess the efficacy of a beamstop mask, as well as highlighting the potential of modern pattern-recognition techniques for automating outlier exclusion during data processing, facilitating future adaptation to evolving experimental strategies.

A group of three deep-learning tools, referred to collectively as CHiMP (Crystal Hits in My Plate), were created for analysis of micrographs of protein crystallization experiments at the Diamond Light Source (DLS) synchrotron, UK. The first tool, a classification network, assigns images into categories relating to experimental outcomes. The other two tools are networks that perform both object detection and instance segmentation, resulting in masks of individual crystals in the first case and masks of crystallization droplets in addition to crystals in the second case, allowing the positions and sizes of these entities to be recorded. The creation of these tools used transfer learning, where weights from a pre-trained deep-learning network were used as a starting point and repurposed by further training on a relatively small set of data. Two of the tools are now integrated at the VMXi macromolecular crystallography beamline at DLS, where they have the potential to absolve the need for any user input, both for monitoring crystallization experiments and for triggering in situ data collections. The third is being integrated into the XChem fragment-based drug-discovery screening platform, also at DLS, to allow the automatic targeting of acoustic compound dispensing into crystallization droplets.

Speckle-tracking X-ray imaging is an attractive candidate for dynamic X-ray imaging owing to its flexible setup and simultaneous yields of phase, transmission and scattering images. However, traditional speckle-tracking imaging methods suffer from phase distortion at locations with abrupt changes in density, which is always the case for real samples, limiting the applications of the speckle-tracking X-ray imaging method. In this paper, we report a deep-learning based method which can achieve dynamic X-ray speckle-tracking imaging with high-accuracy phase retrieval. The calibration results of a phantom show that the profile of the retrieved phase is highly consistent with the theoretical one. Experiments of polyurethane foaming demonstrated that the proposed method revealed the evolution of the complicated microstructure of the bubbles accurately. The proposed method is a promising solution for dynamic X-ray imaging with high-accuracy phase retrieval, and has extensive applications in metrology and quantitative analysis of dynamics in material science, physics, chemistry and biomedicine.

This paper uses deep learning to present a proof-of-concept for data-driven chemistry in single-molecule magnets (SMMs). Previous discussions within SMM research have proposed links between molecular structures (crystal structures) and single-molecule magnetic properties; however, these have only interpreted the results. Therefore, this study introduces a data-driven approach to predict the properties of SMM structures using deep learning. The deep-learning model learns the structural features of the SMM molecules by extracting the single-molecule magnetic properties from the 3D coordinates presented in this paper. The model accurately determined whether a molecule was a single-molecule magnet, with an accuracy rate of approximately 70% in predicting the SMM properties. The deep-learning model found SMMs from 20 000 metal complexes extracted from the Cambridge Structural Database. Using deep-learning models for predicting SMM properties and guiding the design of novel molecules is promising.

The accuracy of the information in the Protein Data Bank (PDB) is of great importance for the myriad downstream applications that make use of protein structural information. Despite best efforts, the occasional introduction of errors is inevitable, especially where the experimental data are of limited resolution. A novel protein structure validation approach based on spotting inconsistencies between the residue contacts and distances observed in a structural model and those computationally predicted by methods such as AlphaFold2 has previously been established. It is particularly well suited to the detection of register errors. Importantly, this new approach is orthogonal to traditional methods based on stereochemistry or map–model agreement, and is resolution independent. Here, thousands of likely register errors are identified by scanning 3–5 Å resolution structures in the PDB. Unlike most methods, the application of this approach yields suggested corrections to the register of affected regions, which it is shown, even by limited implementation, lead to improved refinement statistics in the vast majority of cases. A few limitations and confounding factors such as fold-switching proteins are characterized, but this approach is expected to have broad application in spotting potential issues in current accessions and, through its implementation and distribution in CCP4, helping to ensure the accuracy of future depositions.

The mol­ecular and crystal structure of a discrete [Ni8(μ4-OH)6(μ-4-Rpz)12]2− (R = H; pz = pyrazolate anion, C3H3N2−) cluster with an unprecedented, perfectly cubic arrangement of its eight Ni centers is reported, along with its lower-symmetry alkyl-functionalized (R = methyl and n-oct­yl) derivatives. Crystals of the latter two were obtained with two identical counter-ions (Bu4N+), whereas the crystal of the complex with the parent pyrazole ligand has one Me4N+ and one Bu4N+ counter-ion. The methyl derivative incorporates 1,2-di­chloro­ethane solvent mol­ecules in its crystal structure, whereas the other two are solvent-free. The compounds are tetra­butyl­aza­nium tetra­methyl­aza­nium hexa-μ4-hydroxido-dodeca-μ2-pyrazolato-hexa­hedro-octa­nickel, (C16H36N)(C4H12N)[Ni8(C3H3N2)12(OH)6] or (Bu4N)(Me4N)[Ni8(μ4-OH)6(μ-pz)12] (1), bis­(tetra­butyl­aza­nium) hexa-μ4-hydroxido-dodeca-μ2-(4-methyl­pyrazolato)-hexa­hedro-octa­nickel 1,2-di­chloro­ethane 7.196-solvate, (C16H36N)2[Ni8(C4H5N2)12(OH)6]·7.196C2H4Cl2 or (Bu4N)2[Ni8(μ4-OH)6(μ-4-Mepz)12]·7.196(ClCH2CH2Cl) (2), and bis­(tetra­butyl­aza­nium) hexa-μ4-hydroxido-dodeca-μ2-(4-octylpyrazolato)-hexa­hedro-octa­nickel, (C16H36N)2[Ni8(C11H19N2)12(OH)6] or (Bu4N)2[Ni8(μ4-OH)6(μ-4-nOctpz)12] (3). All counter-ions are disordered (with the exception of one Bu4N+ in 3). Some of the octyl chains of 3 (the crystal is twinned by non-merohedry) are also disordered. Various structural features are discussed and contrasted with those of other known [Ni8(μ4-OH)6(μ-4-Rpz)12]2− complexes, including extended three-dimensional metal–organic frameworks. In all three structures, the Ni8 units are lined up in columns.

Reaction of Co(NCS)2 with 4-methyl­pyridine N-oxide in methanol leads to the formation of crystals of the title compound, [Co2(NCS)4(C6H7NO)4(CH4O)2] or Co2(NCS)4(4-methyl­pyridine N-oxide)4(methanol)2. The asymmetric unit consist of one CoII cation, two thio­cyanate anions, two 4-methyl­pyridine N-oxide coligands and one methanol mol­ecule in general positions. The H atoms of one of the methyl groups are disordered and were refined using a split model. The CoII cations octa­hedrally coordinate two terminal N-bonded thio­cyanate anions, three 4-methyl­pyridine N-oxide coligands and one methanol mol­ecule. Each two CoII cations are linked by pairs of μ-1,1(O,O)-bridging 4-methyl­pyridine N-oxide coligands into dinuclear units that are located on centers of inversion. Powder X-ray diffraction (PXRD) investigations prove that the title compound is contaminated with a small amount of Co(NCS)2(4-meth­yl­pyridine N-oxide)3. Thermogravimetric investigations reveal that the methanol mol­ecules are removed in the beginning, leading to a compound with the composition Co(NCS)2(4-methyl­pyridine N-oxide), which has been reported in the literature and which is of poor crystallinity.

A new mononuclear complex, penta­aqua­(cucurbit[6]uril-κ2O,O')(nitrato-κ2O,O')praseodymium(III) dinitrate 9.56-hydrate, [Pr(NO3)(CB6)(H2O)5](NO3)2·9.56H2O (1), was obtained as outcome of the hydro­thermal reaction between the macrocyclic ligand cucurbit[6]uril (CB6, C36H36N24O12) with a tenfold excess of Pr(NO3)3·6H2O. Complex 1 crystallizes in the P21/n space group with two crystallographically independent but chemically identical [Pr(CB6)(NO3)(H2O)5]2+ complex cations, four nitrate counter-anions and 19.12 inter­stitial water mol­ecules per asymmetric unit. The nona­coordinated PrIII in 1 are located in the PrO9 coordination environment formed by two carbonyl O atoms from bidentate cucurbit[6]uril units, two oxygen atoms from the bidentate nitrate anion and five water mol­ecules. Considering the differences in Pr—O bond distances and O—Pr—O angles in the coordination spheres, the coordination polyhedrons of the two PrIII atoms can be described as distorted spherical capped square anti­prismatic and muffin polyhedral.

A new quinoline derivative, namely, 6-(di­ethyl­amino)-4-phenyl-2-(pyridin-2-yl)quinoline, C24H23N3 (QP), and its MnII complex aqua-1κO-di-μ-chlorido-1:2κ4Cl:Cl-di­chlorido-1κCl,2κCl-bis­[6-(di­ethyl­amino)-4-phenyl-2-(pyridin-2-yl)quinoline]-1κ2N1,N2;2κ2N1,N2-dimanganese(II), [Mn2Cl4(C24H23N3)2(H2O)] (MnQP), were synthesized. Their compositions have been determined with ESI-MS, IR, and 1H NMR spectroscopy. The crystal-structure determination of MnQP revealed a dinuclear complex with a central four-membered Mn2Cl2 ring. Both MnII atoms bind to an additional Cl atom and to two N atoms of the QP ligand. One MnII atom expands its coordination sphere with an extra water mol­ecule, resulting in a distorted octa­hedral shape. The second MnII atom shows a distorted trigonal–bipyramidal shape. The UV–vis absorption and emission spectra of the examined compounds were studied. Furthermore, when investigating the aggregation-induced emission (AIE) properties, it was found that the fluorescent color changes from blue to green and eventually becomes yellow as the fraction of water in the THF/water mixture increases from 0% to 99%. In particular, these color and intensity changes are most pronounced at a water fraction of 60%. The crystal structure contains disordered solvent mol­ecules, which could not be modeled. The SQUEEZE procedure [Spek (2015). Acta Cryst. C71, 9–18] was used to obtain information on the type and qu­antity of solvent mol­ecules, which resulted in 44 electrons in a void volume of 274 Å3, corresponding to approximately 1.7 mol­ecules of ethanol in the unit cell. These ethanol mol­ecules are not considered in the given chemical formula and other crystal data.

Two new zinc(II) complexes, tri­ethyl­ammonium di­chlorido­[2-(4-nitro­phen­yl)-4-phenyl­quinolin-8-olato]zinc(II), (C6H16N){Zn(C21H13N2O3)Cl2] (ZnOQ), and bis­(tri­ethyl­ammonium) {2,2'-[1,4-phenyl­enebis(nitrilo­methyl­idyne)]diphenolato}bis­[di­chlorido­zinc(II)], (C6H16N)2[Zn2(C20H14N2O2)Cl4] (ZnBS), were synthesized and their structures were determined using ESI–MS spectrometry, 1H NMR spectroscopy, and single-crystal X-ray diffraction. The results showed that the ligands 2-(4-nitro­phen­yl)-4-phenyl­quinolin-8-ol (HOQ) and N,N'-bis­(2-hy­droxy­benzyl­idene)benzene-1,4-di­amine (H2BS) were deprotonated by tri­ethyl-amine, forming the counter-ion Et3NH+, which inter­acts via an N—H⋯O hydrogen bond with the ligand. The ZnII atoms have a distorted trigonal–pyramidal (ZnOQ) and distorted tetra­hedral (ZnBS) geometries with a coord­ination number of four, coordinating with the ligands via N and O atoms. The N atoms coordinating with ZnII correspond to the heterocyclic nitro­gen for the HOQ ligand, while for the H2BS ligand, it is the nitro­gen of the imine (CH=N). The crystal packing of ZnOQ is characterized by C—H⋯π inter­actions, while that of ZnBS by C—H⋯Cl inter­actions. The emission spectra showed that ZnBS complex exhibits green fluorescence in the solid state with a small band-gap energy, and the ZnOQ complex does exhibit non-fluorescence.

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

Serial crystallography experiments at synchrotron and X-ray free-electron laser (XFEL) sources are producing crystallographic data sets of ever-increasing volume. While these experiments have large data sets and high-frame-rate detectors (around 3520 frames per second), only a small percentage of the data are useful for downstream analysis. Thus, an efficient and real-time data classification pipeline is essential to differentiate reliably between useful and non-useful images, typically known as `hit' and `miss', respectively, and keep only hit images on disk for further analysis such as peak finding and indexing. While feature-point extraction is a key component of modern approaches to image classification, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. This paper proposes a pipeline to categorize the data, consisting of a real-time feature extraction algorithm called modified and parallelized FAST (MP-FAST), an image descriptor and a machine learning classifier. For parallelizing the primary operations of the proposed pipeline, central processing units, graphics processing units and field-programmable gate arrays are implemented and their performances compared. Finally, MP-FAST-based image classification is evaluated using a multi-layer perceptron on various data sets, including both synthetic and experimental data. This approach demonstrates superior performance compared with other feature extractors and classifiers.

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages (Dozor and DIALS), demonstrating that this is an appropriate framework to support future extensions and improvements.

A deep-learning algorithm is proposed for the inpainting of Bragg coherent diffraction imaging (BCDI) patterns affected by detector gaps. These regions of missing intensity can compromise the accuracy of reconstruction algorithms, inducing artefacts in the final result. It is thus desirable to restore the intensity in these regions in order to ensure more reliable reconstructions. The key aspect of the method lies in the choice of training the neural network with cropped sections of diffraction data and subsequently patching the predictions generated by the model along the gap, thus completing the full diffraction peak. This approach enables access to a greater amount of experimental data for training and offers the ability to average overlapping sections during patching. As a result, it produces robust and dependable predictions for experimental data arrays of any size. It is shown that the method is able to remove gap-induced artefacts on the reconstructed objects for both simulated and experimental data, which becomes essential in the case of high-resolution BCDI experiments.

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

Ptychography is a powerful computational imaging technique with microscopic imaging capability and adaptability to various specimens. To obtain an imaging result, it requires a phase-retrieval algorithm whose performance directly determines the imaging quality. Recently, deep neural network (DNN)-based phase retrieval has been proposed to improve the imaging quality from the ordinary model-based iterative algorithms. However, the DNN-based methods have some limitations because of the sensitivity to changes in experimental conditions and the difficulty of collecting enough measured specimen images for training the DNN. To overcome these limitations, a ptychographic phase-retrieval algorithm that combines model-based and DNN-based approaches is proposed. This method exploits a DNN-based denoiser to assist an iterative algorithm like ePIE in finding better reconstruction images. This combination of DNN and iterative algorithms allows the measurement model to be explicitly incorporated into the DNN-based approach, improving its robustness to changes in experimental conditions. Furthermore, to circumvent the difficulty of collecting the training data, it is proposed that the DNN-based denoiser be trained without using actual measured specimen images but using a formula-driven supervised approach that systemically generates synthetic images. In experiments using simulation based on a hard X-ray ptychographic measurement system, the imaging capability of the proposed method was evaluated by comparing it with ePIE and rPIE. These results demonstrated that the proposed method was able to reconstruct higher-spatial-resolution images with half the number of iterations required by ePIE and rPIE, even for data with low illumination intensity. Also, the proposed method was shown to be robust to its hyperparameters. In addition, the proposed method was applied to ptychographic datasets of a Simens star chart and ink toner particles measured at SPring-8 BL24XU, which confirmed that it can successfully reconstruct images from measurement scans with a lower overlap ratio of the illumination regions than is required by ePIE and rPIE.

Source: Streetwise Reports 10/22/2024

The growth of artificial intelligence, the need for more computer data centers, the eventual adoption of electric vehicles (EVs), and the need for more net-zero power means a renaissance in nuclear power is underway.

Just last month, Microsoft Corp. (MSFT:NASDAQ) announced a deal with Constellation Energy Group (CEG:NYSE) to restart and buy all of the power from one of the shut-down reactors at its infamous Three Mile Island plant in Pennsylvania and the Biden administration also announced a plan to restart the Palisades plant in Michigan.

"Biden has called for a tripling of U.S. nuclear power capacity to fuel energy demand that is accelerating in part due to expansion of power-hungry technologies like artificial intelligence and cloud computing," Valerie Volcovici wrote for Reuters on Oct. 8.

The administration also wants to develop small nuclear reactors (SMRs) for certain applications.

All of this is putting the metal needed to power nuclear energy, uranium, front and center. Prices for the element have started rising, with nuclear fuel trading at US$83.30 per pound last Thursday, a level not seen since 2007, according to a report by Daily Finland on Friday.

Uranium prices are expected to move higher by the end of this quarter, when Trading Economics' global macro models and analyses forecast uranium to trade at US$84.15 per pound, Nuclear Newswire reported on Oct. 3. In another year, the site estimates that the metal will trade at US$91.80 per pound.

The Catalyst: Surging Demand

The engine driving the prices is a "fundamental global shortage" of uranium driven by surging demand, said Andre Leibenberg, chief executive officer of Yellow Cake, which is focused on providing exposure to uranium's spot price.

The demand is stemming not only from a growing recognition of nuclear power's role in the future energy mix, but also from its critical importance in supporting the AI boom and the development of data centers, he wrote in a company update last week, according to Mining Weekly.

According to the report, Liebenberg noted that the primary mine supply of 140 million pounds was significantly trailing behind global demand of more than 180 million pounds a year.

In the European Union, a "lack of clarity" about Russian uranium imports is holding back investment in new enrichment plants, according to Reuters.

Russia supplied more than 25% of European and American enriched uranium before the start of the Ukraine war in February 2022, the report said.

Since then, "the U.S. implemented a ban on imports of enriched uranium from Russia in August, with some exemptions, but in Europe, different countries have taken different approaches," muddying the waters.

Complicating matters is a hint in September that Russian President Vladimir Putin might embargo exports of the vital element to the west.

Citi, in a note to clients, said utilities have been stockpiling Russian uranium, but an embargo would make it "hard to replace" supplies of the metal in the next two years.

"Russia supplies close to 12% of U3O8 (known as yellow cake), 25% of UF6 (uranium hexafluoride) and 35% of EUP (enriched uranium product) to international markets," the bank said, according to Forbes. "While the largest share of these supplies goes to China and in supplying nuclear reactors that were built by Russia's Rosatom, we believe that at-risk supplies are exports to the U.S. or Western Europe."

The consequences of what could happen without more nuclear power can be seen in the U.K., where the number of reactors is shrinking. Four of five of them are expected to close in the next couple of years, which could "stretch the grid to the limit."

"As Britain's reactor fleet shrivels, the amount of nuclear capacity will fall from six gigawatts (GW) today to just 1.2 GW by 2028 or soon after," Jonathan Leake and Matt Oliver wrote for The Telegraph last week. "Along with rising demand from power-hungry data centers and technologies of the future, it will make it even harder to keep the lights on when wind and solar generation is low."

Small Nuclear Reactors (SMRs)

SMRs are another possible solution for some medium-sized energy needs. They have been operational for dozens of years in submarines and other long-distance ocean-going craft.

"They can be manufactured in factories and then rapidly erected on-site," Dominic Frisby wrote for his newsletter, The Flying Frisby, on Oct. 13. They are scalable, and that flexibility "aids manufacture, transportation, and installation while reducing construction time and costs."

A 440-megawatt (MW) SMR would produce about 3.5 terawatt hours (TWh) of electricity per year, enough for 1.2 million homes, Frisby noted.

SMRs produce electricity that can easily be adjusted to meet the constant, everyday needs of the grid (baseload), and they can also ramp up or down to follow changes in demand throughout the day, the author wrote. They spin in sync with the grid, so they help keep everything stable.

"When they're running, they act like a steady hand, providing momentum that makes it easier to manage sudden changes in electricity supply or demand," he wrote.

'Bucket Loads of Power' Needed

All of this equates for a bright future for the metal, he said.

"Guess what? AI requires bucket loads of power," Frisby wrote. "That's why Microsoft recently agreed to pay Constellation Energy, the new owner of America's infamous nuclear power station, Three Mile Island, a sizeable premium for its energy. There is cheaper wind and solar power to be had in Pennsylvania, but it isn't as reliable as nuclear 24 hours a day. It's not just AI. The widespread political desire to rid ourselves of fossil fuels means the world needs electricity, and fast."

Chris Temple, publisher of The National Investor, recently noted that with the Three Mile Island deal, "uranium/nuclear power is BACK!"

"I've watched as the news has continued to point to uranium being in the early innings of this new bull market," Temple wrote. "Yet the markets have been yawning . . . until now."

What follows are several uranium explorers and producers that could benefit from this upswing for investors looking to take advantage.

Baselode Energy Corp.

Baselode Energy Corp. (FIND:TSX.V; BSENF:OTCQB) controls 100% of about 273,000 hectares for exploration in the Athabasca Basin area in northern Saskatchewan, Canada.[OWNERSHIP_CHART-10321]

The company said it discovered the ACKIO near-surface, high-grade uranium deposit in September 2021. ACKIO measures greater than 375 meters along strike, greater than 150 meters wide, and is comprised of at least 11 separate zones. Mineralization starts as shallow as 28 meters beneath the surface and continues down to about 300 meters depth beneath the surface, with the bulk of mineralization occurring in the upper 120 meters, Baselode said. ACKIO remains open to the west and south and along the Athabasca sandstone unconformity to the east and south.

Earlier this month, the company reported positive uranium assay results from three drill holes of its 2024 drill program at ACKIO.

Notably, drill hole AK24-119 intersected 0.28% U3O8 over 21.0 meters, including a high-grade section of 1.55% U3O8 over 1.5 meters at a depth of 141 meters. While drill hole AK24-118 returned 0.59% U3O8 over 8.5 meters, including 1.25% U3O8 over 1.5 meters at a depth of 153 meters.

"These results strengthen our confidence in ACKIO," Chief Executive Officer James Sykes said in a release. "It's remarkable that, just over three years after discovering ACKIO, we're still achieving better-than-expected grades and widths."

Baselode expects further assay results from the remaining 40 drill holes to be released after quality review and approval.

David Talbot, Managing Director at Red Cloud Securities, noted in a September 17 report that drilling at ACKIO "continued to expand the mineralized footprint at Pods 1, 6, and 7," highlighting that "thirteen holes reported composite intervals of anomalous radioactivity between 11m and 42m in thickness."

In his report, Talbot rated the stock as a Buy and further projected the potential for "8-10-12 million pounds of U3O8 at a grade of ~0.3% U3O8," which aligns with typical grades found in the southeastern part of the Athabasca Basin.

According to Refinitiv, Baselode has institutions holding 23.26% with Alps Advisors holding the bulk of it with 17.94%, followed by Vident Investment Advisory LLC at 2.97%. Management and Insiders hold 1.59%. The rest is retail.

The company has a market cap of CA$20.05 million, with 131.51 free float shares. It trades in the 52-week range between CA$0.10 and CA$0.61.

Uranium Energy Corp.

According to its website, Uranium Energy Corp. (UEC:NYSE AMERICAN) is America's "largest and fastest growing supplier of uranium."[OWNERSHIP_CHART-402]

The company said it is advancing the next generation of low-cost, environmentally friendly in-situ recovery (ISR) mining uranium projects in the United States and high-grade conventional projects in Canada. It has two production-ready ISR hub and spoke platforms in South Texas and Wyoming.

Additionally, Uranium Energy Corp. said it has diversified uranium holdings with one of the largest physical uranium portfolios of U.S. warehoused U3O8; a major equity stake in Uranium Royalty Corp., the only royalty company in the sector; and a Western Hemisphere pipeline of resource stage uranium projects.

Most recently, the company announced it was expanding its U.S. uranium production capacity by acquiring Rio Tinto Plc.'s Sweetwater Plant and a portfolio of Wyoming uranium assets.

On September 25, Temple of The National Investor noted that UEC was "upgraded back to Buy" following recent uranium market news. He pointed to UEC's acquisition of the Wyoming uranium assets as a catalyst, emphasizing that uranium is "in the early innings of this new bull market."

Jeff Clark of The Gold Advisor, in his September 26 update, called the acquisition a "significant move," noting that it consolidated a large portfolio of uranium assets under UEC's control, positioning the company for rapid growth. He also highlighted the company's strategic advantage with "53,000 additional acres for exploration," reinforcing UEC's potential to ramp up production.

According to Reuters, Uranium Energy has a market cap of US$3.48 billion and 411.41 million shares outstanding. It trades in a 52-week range of US$4.06 and US$8.66.

About 2% of UE is help by management and insiders, Reuters noted. The largest portion, 77.58%, is held by institutional investors. The rest is in retail.

Terra Clean Energy Corp.

Formerly Tisdale Clean Energy Corp., Terra Clean Energy Corp. (TCEC:CSE; TCEFF:OTC; T1KC:FSE), a Canadian-based uranium exploration and development company, is currently developing the South Falcon East uranium project, which holds a 6.96-million-pound inferred uranium resource within the Fraser Lakes Zone B uranium/thorium deposit, located in the Athabasca Basin region of Saskatchewan.[OWNERSHIP_CHART-10935]

Representing a portion of Skyharbour Resources Ltd.'s existing South Falcon Project, Terra Clean Energy's project covers approximately 12,464 hectares and lies 18 kilometers outside the Athabasca Basin, approximately 50 kilometers east of the Key Lake Mine.

Recently, the company announced a comprehensive exploration program set for Winter 2025 at its South Falcon East Uranium Project. The work will focus on extending the mineralized footprint at the Fraser Lakes B Uranium Deposit and includes about 2,000 meters of infill and step-out drilling designed to verify existing mineralized zones and identify additional targets.

In a release, Chief Executive Officer Alex Klenman described the initiative as "a unique setup for a Canadian microcap, offering multiple paths to significant value creation." This US$1.5 million project will involve TerraLogic Exploration Inc., operating out of SkyHarbour's McGowan Lake Camp with helicopter support.

According to Reuters, management and insiders hold 4.62% of Terra Clean Energy. Of those, Alex Klenman holds the most, with 4.37%.

Strategic Investors hold 12.03%, with Planet Ventures Inc holding the most at 7.40%. The rest is retail.

Terra Clean Energy has a market cap of CA$2.98 million and a 52-week range of CA$0.05 to CA$0.22.

North Shore Uranium Ltd.

North Shore Uranium Ltd. (NSU:TSX) said it is working to become a major force in exploration for economic uranium deposits at the eastern margin of the Athabasca Basin.[OWNERSHIP_CHART-10945]

The company said it is running exploration programs at its Falcon and West Bear properties and evaluating opportunities to complement its portfolio of uranium properties.

Falcon consists of 15 mineral claims, the company said. Four of them comprise 12,791 hectares and are 100%-owned by the company. The remaining 11 claims totaling 2,908 hectares are subject to an option agreement with Skyharbour Resources Ltd. Under the terms of the option agreement, North Shore has the option to earn up to 100% interest in the 11 claims by completing certain payments.

Earlier this month, the company announced details of its target generation efforts at its Falcon uranium project at the eastern margin of Saskatchewan's Athabasca Basin. The company said it has identified 36 uranium targets across three zones.

"We have a great pipeline of targets to choose from for our next drill program at Falcon," said President and Chief Executive Officer Brooke Clements. "Our Zone 2 has attracted the interest of uranium explorers in the past, and we believe there is potential to make a significant uranium discovery using new data and interpretation."

Earlier this month, North Shore announced it had received a Crown Land Work permit for the full 55,700-hectare Falcon project. Issued by the Saskatchewan Ministry of Environment, it authorizes the company to conduct mineral exploration activities, including prospecting and ground geophysics, trail and drill site clearing, line cutting, the drilling of up to 75 exploration drill holes, and the storage of drill core. The permit expires in July 2027.

Insiders and founding investors own approximately 45% of the issued and outstanding shares. Clements himself owns 3.6% or 1.33M shares, Director Doris Meyer has 2.11% or 0.78M shares, and Director James Arthur holds 1.58% or 0.58M shares. According to North Shore, 14.92M shares (40.5%) held by six founding investors are subject to a voluntary pooling agreement that restricts the disposition of these shares before October 19, 2026.

Most of the rest is with retail, as the institutional holdings are minor.

North Shore has 36.84M outstanding shares and currently has a market cap of CA$1.47 million. It has traded in the past 52 weeks between CA$0.04 and CA$0.30 per share.

Skyharbour Resources Ltd.

Skyharbour Resources Ltd. (SYH:TSX.V; SYHBF:OTCQX; SC1P:FSE) has an extensive portfolio of uranium exploration projects in Canada's Athabasca Basin, with 29 projects, 10 of which are drill-ready, covering over 1.4 million acres of mineral claims. In addition to being a high-grade uranium exploration company, Skyharbour utilizes a prospect generator strategy by bringing in partner companies to advance its secondary assets.[OWNERSHIP_CHART-6026]

In an updated research note on July 24, Analyst Sid Rajeev of Fundamental Research Corp. wrote that Skyharbour "owns one of the largest portfolios among uranium juniors in the Athabasca Basin."

"Given the highly vulnerable uranium supply chain, we anticipate continued consolidation within the sector," wrote Rajeev, who reiterated the firm's Buy rating and adjusted its fair value estimate from CA$1.16 to CA$1.21 per share. "Additionally, the rapidly growing demand for energy from the AI industry is likely to accelerate the adoption of nuclear power, which should, in turn, spotlight uranium juniors in the coming months."

Skyharbour acquired from Denison Mines, a large strategic shareholder of the company, a 100% interest in the Moore Uranium Project, which is located 15 kilometers east of Denison's Wheeler River project and 39 kilometers south of Cameco's McArthur River uranium mine. Moore is an advanced-stage uranium exploration property with high-grade uranium mineralization at the Maverick Zone, including highlight drill results of 6.0% U3O8 over 5.9 meters, including 20.8% U3O8 over 1.5 meters at a vertical depth of 265 meters.

Adjacent to the Moore Uranium Project is Skyharbour's Russell Lake Uranium Project optioned from Rio Tinto, which hosts historical high-grade drill intercepts over a large property area with robust exploration upside potential. The 73,294-ha Russell Lake Uranium Property is strategically located in the central core of the Eastern Athabasca Basin of northern Saskatchewan. Skyharbour has recently discovered high-grade uranium mineralization in a new zone at Russell and is carrying out an additional 7-8,000-meter drill campaign across both Russell and Moore.

Management, insiders, and close business associates own approximately 5% of Skyharbour.

According to Reuters, President and CEO Trimble owns 1.6%, and Director David Cates owns 0.70%.

Institutional, corporate, and strategic investors own approximately 55% of the company. Denison Mines owns 6.3%, Rio Tinto owns 2.0%, Extract Advisors LLC owns 9%, Alps Advisors Inc. owns 9.91%, Mirae Asset Global Investments (U.S.A) L.L.C. owns 6.29%, Sprott Asset Management L.P. owns 1.5%, and Incrementum AG owns 1.18%, Reuters reported.

There are 182.53 million shares outstanding with 178 million free float traded shares, while the company has a market cap of CA$89.44 million and trades in a 52-week range of CA$0.31 and CA$0.64.

ATHA Energy Corp.

Atha Energy Corp. (SASK:TSX.V; SASKF:OTCMKTS) is a Canadian mineral company engaged in the acquisition, exploration, and development of uranium assets with a portfolio including three 100%-owned post-discovery uranium projects (the Angilak Project located in Nunavut, and CMB Discoveries in Labrador hosting historical resource estimates of 43.3 million pounds and 14.5 million pounds U3O8 respectively, and the newly discovered basement-hosted GMZ high-grade uranium discovery located in the Athabasca Basin).[OWNERSHIP_CHART-11007]

In addition, the company said it holds the largest cumulative prospective exploration land package (more than 8.5 million acres) in two of the world's most prominent basins for uranium discoveries. ATHA also holds a 10% carried interest in key Athabasca Basin exploration projects operated by NexGen Energy Ltd. and IsoEnergy Ltd.

Technical Analyst Maund considers Atha Energy to be "THE top play in the uranium sector" and has an Immediate Strong Buy rating on it, he wrote in the previously mentioned Oct. 17 report.

The company's 3-, 13- and 26-month charts indicate its stock price had been in a bear market since trading began until September, when it had an upwave or preliminary breakout. This, along with other indicators, including positive accumulation-distribution convergence and high volume, suggest another upleg is expected soon, he said.

"Given the outlook for the uranium price and what Atha Energy has going for it, its stock is astoundingly cheap after its persistent downtrend this year," Maund wrote.

According to Refinitiv, 10 management and insiders own 16.44% of Atha Energy. The Top 5 are Timothy Young with 6.32%, Matthew Mason with 5.8%, Atha Chairman Michael Castanho with 1.16%, and Atha Director Sean Kallir with 0.9%.

Seven institutional investors together hold 9.38%. The Top 3 are Alps Advisors Inc. with 6.26%, Sprott Asset Management LP with 1.3%, and Vident Investment Advisory LLC with 0.8%.

The remaining 74.18% of Atha is in retail.

According to the company, it has 277.9M shares outstanding, 14M options, 4M restricted stock units/performance rights, and 10.2M warrants.

Reuters reports Atha's market cap is CA$208.42 million, and its 52-week range is CA$0.46−$1.42 per share.

Sign up for our FREE newsletter at: www.streetwisereports.com/get-news

Important Disclosures:

1. Skyharbour Resources Ltd. and Terra Clean Energy Corp. are billboard sponsors of Streetwise Reports and pay SWR a monthly sponsorship fee between US$4,000 and US$5,000. In addition, Terra Clean Energy has a consulting relationship with Street Smart an affiliate of Streetwise Reports. Street Smart Clients pay a monthly consulting fee between US$8,000 and US$20,000.
2. As of the date of this article, officers and/or employees of Streetwise Reports LLC (including members of their household) own securities of North Shore Uranium Ltd., Uranium Energy Corp., and Terra Clean Energy.
3. Steve Sobek wrote this article for Streetwise Reports LLC and provides services to Streetwise Reports as an employee.
4. This article does not constitute investment advice and is not a solicitation for any investment. Streetwise Reports does not render general or specific investment advice and the information on Streetwise Reports should not be considered a recommendation to buy or sell any security. Each reader is encouraged to consult with his or her personal financial adviser and perform their own comprehensive investment research. By opening this page, each reader accepts and agrees to Streetwise Reports' terms of use and full legal disclaimer. Streetwise Reports does not endorse or recommend the business, products, services or securities of any company.

For additional disclosures, please click here.

( Companies Mentioned: SASK:TSX.V; SASKF:OTCMKTS, FIND:TSX.V; BSENF:OTCQB, NSU:TSX, SYH:TSX.V; SYHBF:OTCQX; SC1P:FSE, TCEC:CSE; TCEFF:OTC; T1KC:FSE, UEC:NYSE AMERICAN, )

Incoming President Biden and Vice President Harris stand with their respective spouses Jill Biden and Doug Emhoff after delivering remarks in Wilmington, Del., on Nov. 7, the day the Democrats were declared the winners in the 2020 election.; Credit: Jim Watson/AFP via Getty Images

We know that President Biden won the 2020 election (regardless of what former President Donald Trump and his allies say). We just haven't had a great picture of how Biden won.

That is until Wednesday, when we got the clearest data yet on how different groups voted, and crucially, how those votes shifted from 2016. The Pew Research Center just released its validated voters' report, considered a more accurate measure of the electorate than exit polls, which have the potential for significant inaccuracies.

The new Pew data shows that shifts among suburban voters, white men and independents helped Biden win in November, even while white women and Hispanics swung toward Trump from 2016 to 2020.

To compile the data, Pew matches up survey respondents with state voter records. Those voter files do not say how a person voted, but they do allow researchers to be sure that a person voted, period. That helps with accuracy, eliminating the possibility of survey respondents overreporting their voting activity. In addition, the Pew study uses large samples of Americans — more than 11,000 people in 2020.

It's a numbers-packed report, but there are some big takeaways about what happened in 2020 (and what it might tell us about 2022 and beyond):

Suburban voters (especially white suburban voters) swung toward Biden

Suburban voters appear to have been a major factor helping Biden win. While Pew found Trump winning the suburbs by 2 points in 2016, Biden won them by 11 points in 2020, a 13-point overall swing. Considering that the suburbs accounted for just over half of all voters, it was a big demographic win for Biden.

That said, Trump gained in both rural and urban areas. He won 65% of rural voters, a 6-point jump from 2016. And while cities were still majority-Democratic, his support there jumped by 9 points, to 33%.

Men (especially white men) swung toward Biden

In 2020, men were nearly evenly split, with 48% choosing Biden to Trump's 50%. That gap shrank considerably from 2016, when Trump won men by 11 points. In addition, this group that swung away from Trump grew as a share of the electorate from 2016 — signaling that in a year with high turnout, men's turnout grew more.

White men were a big part of the swing toward Biden. In 2016, Trump won white men by 30 points. In 2020, he won them again, but by a substantially slimmer 17 points.

In addition, Biden made significant gains among married men and college-educated men. All of these groups overlap, but they help paint a more detailed portrait of the type of men who might have shifted or newly participated in 2020.

However, we can't know from this data what exactly was behind these shifts among men — for example, exactly what share of men might have sat on the sidelines in 2016, as opposed to 2020.

Women (especially white women) swung toward Trump

The idea that a majority of white women voted for Trump quickly became one of the 2016 election's most-cited statistics, as many Hillary Clinton supporters — particularly women — were outraged to see other women support Trump.

While that statistic was repeated over and over, Pew's data ultimately said this wasn't true — they found that in 2016, white women were split 47% to 45%, slightly in Trump's favor but not a majority.

This year, however, it appears that Trump did win a majority of white women. Pew found that 53% of white women chose Trump this year, up by 6 points from 2016.

This support contributed to an overall shift in women's numbers — while Clinton won women of all races by 15 points in 2016, Biden won them by 11 points in 2020. Combined with men's shifts described above, it shrank 2016's historic gender gap.

Notably, the swing in white women's margin (5 points altogether) was significantly smaller than white men's swing toward Biden (13 points altogether).

Hispanic voters swung toward Trump

Trump won 38% of Hispanic voters in 2020, according to Pew, up from 28% in 2016.

That 38% would put Trump near George W. Bush's 40% from 2004 — a recent high-water mark for Republicans with Hispanic voters. That share fell off substantially after 2004, leading some Republican pollsters and strategists to wonder how the party could regain that ground. Trump in 2016 intensified those fears, with his nativist rhetoric and hard-line immigration policies.

There are some important nuances to these Hispanic numbers. Perhaps most notably, there is a sizable education gap. Biden won college-educated Hispanic voters by 39 points, but the Democrat won those with some college education or less by 14 points.

That gap mirrors the education gap regularly seen in the broader voting population.

Unfortunately, Pew's sample sizes from 2016 weren't big enough to break down Hispanic voters by gender that year, so it's impossible to see if this group's gender gap widened.

Nonwhite voters leaned heavily toward Biden

Unlike white and Hispanic voters, Black voters didn't shift significantly from 2016. They remained Democratic stalwarts, with 92% choosing Biden — barely changed from four years earlier.

Nearly three-quarters of Asian voters also voted for Biden, along with 6 in 10 Hispanic voters and 56% of voters who chose "other" as their race. (Those groups' sample sizes also weren't big enough in 2016 to draw a comparison over time.)

2018 trends stuck around ... but diminished

In many of these cases where there were substantial shifts in how different groups voted, they weren't surprising, given how voters in the last midterms voted. For example, white men voted more for Democrats in 2018 than they did in 2016, as did suburban voters.

What it means for 2022

The data signals that Democrats' strength with Hispanic voters has eroded, but that the party succeeded in making further inroads in the suburbs, including among suburban whites.

It suggests that these groups, already major focuses for both parties, will continue to be so in 2022, with Republicans trying to cement their gains among Hispanics (and regain suburban voters), while Democrats do Hispanic outreach and try to hold onto the suburbs.

However, it's hard to project much into the future about what voters will do based on the past two elections because of their unique turnout numbers.

"It's hard to interpret here, because 2018 was such a high turnout midterm election, and then our last data point, 2014, was a historically low turnout midterm election," said Ruth Igielnik, senior researcher at Pew Research Center.

This content is from Southern California Public Radio. View the original story at SCPR.org.

The growth of artificial intelligence, the need for more computer data centers, the eventual adoption of electric vehicles (EVs), and the need for more net-zero power means nuclear power, and the uranium needed to fuel it, is seeing a resurgence. Here are some options to make the situation work for your portfolio.

Approximately 100 community leaders learned about two programs designed to protect area children at the Children's Advocacy and Protection Center's second annual Children's Breakfast.

Many public school students receive little or no exposure to the theory of evolution, the most important concept in understanding biology, says a new guidebook from the National Academy of Sciences (NAS).

American students progress toward proficiency in mathematics requires major changes in instruction, curricula, and assessment in the nations schools, says a new report from the National Research Council of the National Academies.

Focused attention by world leaders is needed to address the substantial challenges posed by disposal of spent nuclear fuel from reactors and high-level radioactive waste from processing such fuel for military or energy purposes.

Each year, tens of millions of Americans, young and old, choose to learn about science in informal ways -- by visiting museums and aquariums, attending after-school programs, pursuing personal hobbies, and watching TV documentaries, for example.

Educational and business leaders want todays students both to master school subjects and to excel in areas such as problem solving, critical thinking, and communication