The shift toward automation has radically changed the infrastructure requirements for food manufacturers.

By embracing TSN, food and beverage companies not only improve their OEE but also set the stage for a future where production lines are not just automated but intelligently interconnected and extremely flexible.

Children's Geographies; 01/13/2022
(AN 154620403); ISSN: 14733285
Academic Search Premier

Health Care Provider Boot Camp Day 7: Certified Workers’ Compensation Health Care Networks

Veracity Trust Network (Veracity) has been awarded the Cybersecurity Co-Innovation and Development Fund (CCDF) CyberCall grant of S$1 million by the Cyber Security Agency Singapore (CSA).

The Co-op Media Network (CMN) is to install 300 new front-of-store digital media screens to turbo-charge its retail media offering, taking the total number of screens to over 9,000 across its store estate.

The hydro­thermal synthesis and structural characterization of four novel 3D pillared-layer metal–organic frameworks are studied, revealing how the malleability of copper coordination geometries drives diverse supramolecular isomerism. The findings provide new insights into designing advanced hybrid materials with tailored properties, emphasizing the significant role of reaction conditions and metal ion flexibility in determining network topologies.

The inherent ambiguity in reconstructed images from coherent diffraction imaging (CDI) poses an intrinsic challenge, as images derived from the same dataset under varying initial conditions often display inconsistencies. This study introduces a method that employs the Noise2Noise approach combined with neural networks to effectively mitigate these ambiguities. We applied this methodology to hundreds of ambiguous reconstructed images retrieved from a single diffraction pattern using a conventional retrieval algorithm. Our results demonstrate that ambiguous features in these reconstructions are effectively treated as inter-reconstruction noise and are significantly reduced. The post-Noise2Noise treated images closely approximate the average and singular value decomposition analysis of various reconstructions, providing consistent and reliable reconstructions.

The well-known copper car­box­yl­ate dimer, with four car­box­yl­ate ligands ex­ten­ding outwards towards the corners of a square, has been employed to generate a series of crystalline com­pounds. In particular, this work centres on the use of the 4-hy­droxy­benzoate anion (Hhba−) and its deprotonated phe­nol­ate form 4-oxidobenzoate (hba2−) to obtain complexes with the general formula [Cu2(Hhba)4–x(hba)xL2–y]x−, where L is an axial coligand (including solvent mol­ecules), x = 0, 1 or 2, and y = 0 or 1. In some cases, short hy­dro­gen bonds result in complexes which may be represented as [Cu2(Hhba)2(H0.5hba)2L2]−. The main focus of the investigation is on the formation of a variety of extended networks through hy­dro­gen bonding and, in some crystals, coordinate bonds when bridging coligands (L) are employed. Crystals of [Cu2(Hhba)4(di­ox­ane)2]·4(di­ox­ane) consist of the expected Cu dimer with the Hhba− anions forming hy­dro­gen bonds to 1,4-di­ox­ane mol­ecules which block network formation. In the case of crystals of com­position [Et4N][Cu2(Hhba)2(H0.5hba)2(CH3OH)(H2O)]·2(di­ox­ane), Li[Cu2(Hhba)2(H0.5hba)2(H2O)2]·3(di­ox­ane)·4H2O and [Cu2(Hhba)2(H0.5hba)2(H0.5DABCO)2]·3CH3OH (DABCO is 1,4-di­aza­bicyclo­[2.2.2]octa­ne), square-grid hy­dro­gen-bonded networks are generated in which the complex serves as one type of 4-con­necting node, whilst a second 4-con­necting node is a hy­dro­gen-bonding motif assembled from four phenol/phenolate groups. Another two-dimensional (2D) network based upon a related square-grid structure is formed in the case of [Et4N]2[Cu2(Hhba)2(hba)2(di­ox­ane)2][Cu2(Hhba)4(di­ox­ane)(H2O)]·CH3OH. In [Cu2(Hhba)4(H2O)2]·2(Et4NNO3), a square-grid structure is again apparent, but, in this case, a pair of nitrate anions, along with four phenolic groups and a pair of water mol­ecules, combine to form a second type of 4-con­necting node. When 1,8-bis­(di­methyl­amino)­naphthalene (bdn, `proton sponge') is used as a base, another square-grid network is generated, i.e. [Hbdn]2[Cu2(Hhba)2(hba)2(H2O)2]·3(di­ox­ane)·H2O, but with only the copper dimer complex serving as a 4-con­necting node. Complex three-dimensional networks are formed in [Cu2(Hhba)4(O-bipy)]·H2O and [Cu2(Hhba)4(O-bipy)2]·2(di­ox­ane), where the potentially bridging 4,4'-bi­pyridine N,N'-dioxide (O-bipy) ligand is employed. Rare cases of mixed car­box­yl­ate copper dimer complexes were obtained in the cases of [Cu2(Hhba)3(OAc)(di­ox­ane)]·3.5(di­ox­ane) and [Cu2(Hhba)2(OAc)2(DABCO)2]·10(di­ox­ane), with each structure possessing a 2D network structure. The final com­pound re­por­ted is a simple hy­dro­gen-bonded chain of com­position (H0.5DABCO)(H1.5hba), formed from the reaction of H2hba and DABCO.

The use of artificial intelligence to process diffraction images is challenged by the need to assemble large and precisely designed training data sets. To address this, a codebase called Resonet was developed for synthesizing diffraction data and training residual neural networks on these data. Here, two per-pattern capabilities of Resonet are demonstrated: (i) interpretation of crystal resolution and (ii) identification of overlapping lattices. Resonet was tested across a compilation of diffraction images from synchrotron experiments and X-ray free-electron laser experiments. Crucially, these models readily execute on graphics processing units and can thus significantly outperform conventional algorithms. While Resonet is currently utilized to provide real-time feedback for macromolecular crystallography users at the Stanford Synchrotron Radiation Lightsource, its simple Python-based interface makes it easy to embed in other processing frameworks. This work highlights the utility of physics-based simulation for training deep neural networks and lays the groundwork for the development of additional models to enhance diffraction collection and analysis.

Here, a machine-learning method based on a kinetically informed neural network (NN) is introduced. The proposed method is designed to analyze a time series of difference electron-density maps from a time-resolved X-ray crystallographic experiment. The method is named KINNTREX (kinetics-informed NN for time-resolved X-ray crystallography). To validate KINNTREX, multiple realistic scenarios were simulated with increasing levels of complexity. For the simulations, time-resolved X-ray data were generated that mimic data collected from the photocycle of the photoactive yellow protein. KINNTREX only requires the number of intermediates and approximate relaxation times (both obtained from a singular valued decomposition) and does not require an assumption of a candidate mechanism. It successfully predicts a consistent chemical kinetic mechanism, together with difference electron-density maps of the intermediates that appear during the reaction. These features make KINNTREX attractive for tackling a wide range of biomolecular questions. In addition, the versatility of KINNTREX can inspire more NN-based applications to time-resolved data from biological macromolecules obtained by other methods.

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

Mineral identification and quantification are key to the understanding and, hence, the capacity to predict material properties. The method of choice for mineral quantification is powder X-ray diffraction (XRD), generally using a Rietveld refinement approach. However, a successful Rietveld refinement requires preliminary identification of the phases that make up the sample. This is generally carried out manually, and this task becomes extremely long or virtually impossible in the case of very large datasets such as those from synchrotron X-ray diffraction computed tomography. To circumvent this issue, this article proposes a novel neural network (NN) method for automating phase identification and quantification. An XRD pattern calculation code was used to generate large datasets of synthetic data that are used to train the NN. This approach offers significant advantages, including the ability to construct databases with a substantial number of XRD patterns and the introduction of extensive variability into these patterns. To enhance the performance of the NN, a specifically designed loss function for proportion inference was employed during the training process, offering improved efficiency and stability compared with traditional functions. The NN, trained exclusively with synthetic data, proved its ability to identify and quantify mineral phases on synthetic and real XRD patterns. Trained NN errors were equal to 0.5% for phase quantification on the synthetic test set, and 6% on the experimental data, in a system containing four phases of contrasting crystal structures (calcite, gibbsite, dolomite and hematite). The proposed method is freely available on GitHub and allows for major advances since it can be applied to any dataset, regardless of the mineral phases present.

Reaction of copper(I)chloride with 2,3-di­methyl­pyrazine in ethanol leads to the formation of the title compound, poly[[μ-chlorido-μ-(2,3-di­methyl­pyrazine)-copper(I)] ethanol hemisolvate], {[CuCl(C6H8N2)]·0.5C2H5OH}n or CuCl(2,3-di­methyl­pyrazine) ethanol hemisolvate. Its asymmetric unit consists of two crystallographically independent copper cations, two chloride anions and two 2,3-di­methyl­pyrazine ligands as well as one ethanol solvate mol­ecule in general positions. The ethanol mol­ecule is disordered and was refined using a split model. The methyl H atoms of the 2,3-di­methyl­pyrazine ligands are also disordered and were refined in two orientations rotated by 60° relative to each other. In the crystal structure, each copper cation is tetra­hedrally coordinated by two N atoms of two bridging 2,3-di­methyl­pyrazine ligands and two μ-1,1-bridg­ing chloride anions. Each of the two copper cations are linked by pairs of bridging chloride anions into dinuclear units that are further linked into layers via bridging 2,3-di­methyl­pyrazine coligands. These layers are stacked in such a way that channels are formed in which the disordered solvent mol­ecules are located. The topology of this network is completely different from that observed in the two polymorphic modifications of CuCl(2,3-di­methyl­pyrazine) reported in the literature [Jess & Näther (2006). Inorg. Chem. 45, 7446–7454]. Powder X-ray diffraction measurements reveal that the title compound is unstable and transforms immediately into an unknown crystalline phase.

In cellulo crystallization is a rare event in nature. Recent advances that have made use of heterologous overexpression can promote the intracellular formation of protein crystals, but new tools are required to detect and characterize these targets in the complex cell environment. The present work makes use of Mask R-CNN, a convolutional neural network (CNN)-based instance segmentation method, for the identification of either single or multi-shaped crystals growing in living insect cells, using conventional bright field images. The algorithm can be rapidly adapted to recognize different targets, with the aim of extracting relevant information to support a semi-automated screening pipeline, in order to aid the development of the intracellular protein crystallization approach.

X-ray reflectometry (XRR) is a powerful tool for probing the structural characteristics of nanoscale films and layered structures, which is an important field of nanotechnology and is often used in semiconductor and optics manufacturing. This study introduces a novel approach for conducting quantitative high-resolution millisecond monochromatic XRR measurements. This is an order of magnitude faster than in previously published work. Quick XRR (qXRR) enables real time and in situ monitoring of nanoscale processes such as thin film formation during spin coating. A record qXRR acquisition time of 1.4 ms is demonstrated for a static gold thin film on a silicon sample. As a second example of this novel approach, dynamic in situ measurements are performed during PMMA spin coating onto silicon wafers and fast fitting of XRR curves using machine learning is demonstrated. This investigation primarily focuses on the evolution of film structure and surface morphology, resolving for the first time with qXRR the initial film thinning via mass transport and also shedding light on later thinning via solvent evaporation. This innovative millisecond qXRR technique is of significance for in situ studies of thin film deposition. It addresses the challenge of following intrinsically fast processes, such as thin film growth of high deposition rate or spin coating. Beyond thin film growth processes, millisecond XRR has implications for resolving fast structural changes such as photostriction or diffusion processes.

Due to the ambiguity related to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This ambiguity poses limitations on standard neural networks, constraining the range and number of considered parameters in previous machine learning solutions. To overcome this challenge, a novel training procedure has been designed which incorporates dynamic prior boundaries for each physical parameter as additional inputs to the neural network. In this manner, the neural network can be trained simultaneously on all well-posed subintervals of a larger parameter space in which the inverse problem is underdetermined. During inference, users can flexibly input their own prior knowledge about the physical system to constrain the neural network prediction to distinct target subintervals in the parameter space. The effectiveness of the method is demonstrated in various scenarios, including multilayer structures with a box model parameterization and a physics-inspired special parameterization of the scattering length density profile for a multilayer structure. In contrast to previous methods, this approach scales favourably when increasing the complexity of the inverse problem, working properly even for a five-layer multilayer model and a periodic multilayer model with up to 17 open parameters.

Recent developments in synchrotron radiation facilities have increased the amount of data generated during acquisitions considerably, requiring fast and efficient data processing techniques. Here, the application of dense neural networks (DNNs) to data treatment of X-ray diffraction computed tomography (XRD-CT) experiments is presented. Processing involves mapping the phases in a tomographic slice by predicting the phase fraction in each individual pixel. DNNs were trained on sets of calculated XRD patterns generated using a Python algorithm developed in-house. An initial Rietveld refinement of the tomographic slice sum pattern provides additional information (peak widths and integrated intensities for each phase) to improve the generation of simulated patterns and make them closer to real data. A grid search was used to optimize the network architecture and demonstrated that a single fully connected dense layer was sufficient to accurately determine phase proportions. This DNN was used on the XRD-CT acquisition of a mock-up and a historical sample of highly heterogeneous multi-layered decoration of a late medieval statue, called `applied brocade'. The phase maps predicted by the DNN were in good agreement with other methods, such as non-negative matrix factorization and serial Rietveld refinements performed with TOPAS, and outperformed them in terms of speed and efficiency. The method was evaluated by regenerating experimental patterns from predictions and using the R-weighted profile as the agreement factor. This assessment allowed us to confirm the accuracy of the results.

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