A machine learning-assisted study of the formation of oxygen vacancies in anatase titanium dioxide.

Defect engineering of semiconductor photocatalysts is critical in reducing the reaction barriers. The generation of surface oxygen vacancies allows substantial tuning of the electronic structure of anatase titanium dioxide (TiO2), but disclosing the vacancy formation at the atomic level remains complex or time-consuming. Herein, we combine density functional theory calculations with machine learning to identify the main factors affecting the formation of oxygen defects and accelerate the prediction of vacancy formation. The results show that the first two-layer oxygen atoms on the typical surfaces of TiO2, including (100), (110), and (211) facets, are more likely to be activated when the gas is more reduced, the pressure is higher, and the reduction temperature is increased. Through machine learning, we can conveniently predict the formation of oxygen defects with high accuracy. Furthermore, we present an equation with acceptable accuracy for quantitatively describing the formation of oxygen vacancies in different chemical environments. Our work provides a fast and efficient strategy for characterizing the surface structure with atomic defects.

Growth Modulation of High-Entropy Alloys for Electrocatalytic Methanol Oxidation Reaction.

High-entropy alloy (HEA) electrocatalysts have exhibited remarkable catalytic performance because of their synergistic interactions among multiple metals. However, the growth mechanism of HEAs remains elusive, primarily due to the constraints imposed by the current synthesis methodologies for HEAs. In this work, an innovative electrodeposition method was developed to fabricate Pt-based nanocomposites (Pt1Bi2Co1Cu1Ni1/CC), comprising HEA nanosheets and carbon cloths (CCs). The reaction system could be effectively monitored by taking samples out from the system during the reaction process, facilitating in-depth insight into the growth mechanism underlying the material formation. In particular, Pt1Bi2Co1Cu1Ni1/CC nanocomposites show superior methanol oxidation reaction (MOR) performance (mass activity up to 5.02 A mgPt-1). Upon structural analysis, the d-band center of Pt1Bi2Co1Cu1Ni1/CC is lower in comparison with that of Pt1Bi2/CC and Pt/CC, demonstrating the formation of a rich-electron structure. Both the uniformity of HEAs and the carbon-supported effect could provide additional active sites. These findings suggest that the strong electronic interaction within HEAs and additional active sites can effectively modulate the catalytic structure of Pt, which benefits the enhanced CO tolerance and MOR performance.

Precise Construction of the Triple-Phase Boundary and Its Antiphosphate Poisoning Effect in the Confined Region.

As a critical component for the oxygen reduction reaction (ORR), platinum (Pt) catalysts exhibit promising catalytic performance in High-temperature-proton exchange membrane fuel cells (HT-PEMFCs). Despite their success, HT-PEMFCs primarily utilize phosphoric acid-doped polybenzimidazole (PA-PBI) as the proton exchange membrane, and the phosphoric acid within the PBI matrix tends to leach onto the Pt-based layers, easily causing toxicity. Herein, we first propose UiO-66@Pt3Co1-T composites with precisely engineered interfacial structures. The UiO-66@Pt3Co1-T exhibits an octahedral porous framework with uniform structural dimensions and even distribution of surface nanoparticles, which demonstrate superior ORR performance compared to commercial Pt/C. The unique structure and morphology of the composites also exhibit a favorable half-wave potential in different concentrations of phosphoric acid electrolyte, regulated by the phosphoric acid adsorption site and intensity.This finding suggests that the incorporation of Co could effectively modulate the Pt d-band center, thereby enhancing the ORR performance. Furthermore, the selective adsorption of phosphoric acid by ZrO2 enables precise control over the phosphoric acid distribution. Notably, the retention of the octahedral framework post high-temperature treatment facilitates the establishment of dual transport pathways for gases and protons, leading to a stable and efficient triple-phase boundary.

Mimicking large spot-scanning radiation fields for proton FLASH preclinical studies with a robotic motion platform.

Previously, a synchrotron-based horizontal proton beamline (87.2 MeV) was successfully commissioned to deliver radiation doses in FLASH and conventional dose rate modes to small fields and volumes. In this study, we developed a strategy to increase the effective radiation field size using a custom robotic motion platform to automatically shift the positions of biological samples. The beam was first broadened with a thin tungsten scatterer and shaped by customized brass collimators for irradiating cell/organoid cultures in 96-well plates (a 7-mm-diameter circle) or for irradiating mice (1-cm2 square). Motion patterns of the robotic platform were written in G-code, with 9-mm spot spacing used for the 96-well plates and 10.6-mm spacing for the mice. The accuracy of target positioning was verified with a self-leveling laser system. The dose delivered in the experimental conditions was validated with EBT-XD film attached to the 96-well plate or the back of the mouse. Our film-measured dose profiles matched Monte Carlo calculations well (1D gamma pass rate >95%). The FLASH dose rates were 113.7 Gy/s for cell/organoid irradiation and 191.3 Gy/s for mouse irradiation. These promising results indicate that this robotic platform can be used to effectively increase the field size for preclinical experiments with proton FLASH.

Fit Entropy-Based Dynamic Communication Resource Slicing-Optimization Method in Smart Distribution Grids on the Medium-Low-Voltage Side.

With the rapid development of new energy and smart technology, the demand for inter-device communication in medium-low-voltage smart distribution grids has sharply increased, leading to a surge in the variety and quantity of communication services. To meet the needs of diverse and massive communication services, deploying service function chains to flexibly combine virtual resources has become crucial. This paper proposes an optimization method based on fit entropy and network utility to address the limited communication network resources in medium-low-voltage smart distribution grids. This was conducted by modeling the distribution grid as a three-domain model consisting of a service domain, a logical domain, and a physical domain and transforming it into a hierarchical bipartite hypergraph-matching problem, which is a complex combinatorial optimization problem. This paper introduces two matching optimization algorithms: "business domain-logic domain-physical domain integration" and "service domain-logic domain, logic domain-physical domain two-stage", which effectively address this problem based on fit entropy and utility. The simulation results demonstrate that these algorithms significantly improve service success rates and resource utilization, enhancing overall network utility.

Rational Linker Design for Enhanced Performance of MOF-Derived Catalysts in Electrochemical Urea Synthesis.

2D metal-organic frameworks (2D MOFs) offer promising electrocatalytic potential for urea synthesis, yet the underlying reaction mechanisms and structure-activity relationships remain unclear. Using Cu-BDC as a model, density functional theory (DFT) calculations to elucidate these aspects are conducted. The results reveal a novel coupling mechanism involving *NO─CO and *NO─*ONCO, emphasizing the impact of linker modifications on Cu spin states and charge distribution. Notably, Cu-BDC-NH2 and Cu─BDC─OH emerge as promising catalysts. Additionally, structure-activity relationships through descriptors like d-band center, IE ratio, and L(Cu─O), providing insights for rational catalyst design is established. These findings pave the way for optimized catalysts and sustainable urea production, opening avenues for future research and technological advancements.

Function of NLRP3 inflammasome activation in multiple myeloma.

OBJECTIVE: The drug resistance of multiple myeloma (MM) cells is one of the main causes of relapse, refractory and progression of MM. METHODS: First, Western blot analysis was used to detect the expression levels of NLRP3, ASC, pro-IL-1ß and cleaved IL-1ß, and RT-qPCR was used to detect the mRNA expression levels of them. The expression levels of IL-1ß and IL-18 in the supernatant were detected by ELISA, and the expression levels of these factors in the activated group and the control group were compared to verify the activation of BMMCs and KM3. RESULT: 1. The protein expression of NLRP3 and cleavd-IL-1ß in the BMMCs cells was significantly higher than that of the control group (P < 0.05). The mRNA expression levels of caspase-1 and IL-1ß were higher than those of the control group (P = 0.03, P = 0.02). 2. The protein expression levels of NLRP3 and cleaved-IL-1ß in the KM3 cells were significantly higher than those of the control group (P < 0.05). The expressions of caspase-1 mRNA(P = 0.016) and IL-1ß mRNA(P = 0.037) were significantly increased compared with the control group. 3. The early apoptosis results of BMMCs showed that the apoptosis rate of the LPS+ATP+Dex group was lower than that of the Dex group (P = 0.017). The early apoptosis rate of the LPS+ATP+Dex+Vel group was decreased compared with the Dex+Vel group (P = 0.045). 4. The early apoptosis rate of KM3 in the LPS+ATP+Dex group was lower than that in the Dex group (P = 0.03). CONCLUSION: 1. LPS+ATP can activate NLRP3 inflammasome in multiple myeloma cells. 2. Activation of NLRP3 inflammasome inhibits the early apoptosis of myeloma cells induced by dexamethasone and bortezomib.

Electron Deficiency is More Important than Conductivity in C-N Coupling for Electrocatalytic Urea Synthesis.

The electrocatalytic C-N coupling from CO2 and nitrate emerges as one of the solutions for waste upgrading and urea synthesis. In this work, we constructed electron-deficient Cu sites by the strong metal-polymer semiconductor interaction, to boost efficient and durable urea synthesis. In situ Raman spectroscopy identified the existence of electron-deficient Cu sites and was able to withstand electrochemical reduction conditions. Operando synchrotron-radiation Fourier transform infrared spectroscopy and theoretical calculations disclosed the vital role of electron-deficient Cu in adsorption and C-N coupling of oxygen-containing species. The electron-deficient Cu displayed a high urea yield rate of 255.0 mmol h-1 g-1 at -1.4 V versus the reversible hydrogen electrode and excellent electrochemical durability, superior than that of non-electron-deficient counterpart with conductive carbon material as the support. It can be concluded that the regulation of site electronic structure is more important than the optimization of catalyst conductive properties in the C-N coupling reactions.

A Study of Assisted Screening for Alzheimer's Disease Based on Handwriting and Gait Analysis.

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that is not easily detected in the early stage. Handwriting and walking have been shown to be potential indicators of cognitive decline and are often affected by AD. Objective: This study proposes an assisted screening framework for AD based on multimodal analysis of handwriting and gait and explores whether using a combination of multiple modalities can improve the accuracy of single modality classification. Methods: We recruited 90 participants (38 AD patients and 52 healthy controls). The handwriting data was collected under four handwriting tasks using dot-matrix digital pens, and the gait data was collected using an electronic trail. The two kinds of features were fused as inputs for several different machine learning models (Logistic Regression, SVM, XGBoost, Adaboost, LightGBM), and the model performance was compared. Results: The accuracy of each model ranged from 71.95% to 96.17%. Among them, the model constructed by LightGBM had the best performance, with an accuracy of 96.17%, sensitivity of 95.32%, specificity of 96.78%, PPV of 95.94%, NPV of 96.74%, and AUC of 0.991. However, the highest accuracy of a single modality was 93.53%, which was achieved by XGBoost in gait features. Conclusions: The research results show that the combination of handwriting features and gait features can achieve better classification results than a single modality. In addition, the assisted screening model proposed in this study can achieve effective classification of AD, which has development and application prospects.

Atomic-Dispersed Cu Catalysts for Electrochemical Nitrate Reduction: Coordination Engineering and Fundamental Insights.

The development of Cu-based atomic dispersed catalysts with tailored coordination environments represents a significant step forward in enhancing the electrocatalytic reduction of nitrate to ammonia. By precisely modulating the electronic structures of Cu active centers, the binding strength of the *NO3 intermediates is successfully tuned, thereby substantially improving the catalytic activity toward electrochemical nitrate reduction reaction (eNO3RR). This study reveals that the N4-coordinated Cu single-atom catalyst (Cu-SAC) exhibits superior performance due to its robust interaction with coordinating atoms. Notably, this optimized catalyst achieves a low limiting potential of -0.38 V, while the dual-atom system further reduces this value to -0.32 V, demonstrating exceptional activity. Detailed electronic structure analysis, including the examination of d-band centers, Bader charges, and projected density of states (PDOS), provides a comprehensive understanding of the origin of this high activity. Specifically, the high and concentrated density of states near the Fermi level plays a crucial role in facilitating the electrocatalytic nitrate reduction process. This work not only offers crucial insights into the underlying mechanisms of eNO3RR but also provides valuable guidelines for the rational design of highly efficient electrocatalysts for this important reaction.

iProps: A Comprehensive Software Tool for Protein Classification and Analysis With Automatic Machine Learning Capabilities and Model Interpretation Capabilities.

Protein classification is a crucial field in bioinformatics. The development of a comprehensive tool that can perform feature evaluation, visualization, automated machine learning, and model interpretation would significantly advance research in protein classification. However, there is a significant gap in the literature regarding tools that integrate all these essential functionalities. This paper presents iProps, a novel Python-based software package, meticulously crafted to fulfill these multifaceted requirements. iProps is distinguished by its proficiency in feature extraction, evaluation, automated machine learning, and interpretation of classification models. Firstly, iProps fully leverages evolutionary information and amino acid reduction information to propose or extend several numerical protein features that are independent of sequence length, including SC-PSSM, ORDip, TRC, CTDC-E, CKSAAGP-E, and so forth; at the same time, it also implements the calculation of 17 other numerical features within the software. iProps also provides feature combination operations for the aforementioned features to generate more hybrid features, and has added data balancing sampling processing as well as built-in classifier settings, among other functionalities. Thus, It can discern the most effective protein class recognition feature from a multitude of candidates, utilizing three automated machine learning algorithms to identify the most optimal classifiers and parameter settings. Furthermore, iProps generates a detailed explanatory report that includes 23 informative graphs derived from three interpretable models. To assess the performance of iProps, a series of numerical experiments were conducted using two well-established datasets. The results demonstrated that our software achieved superior recognition performance in every case. Beyond its contributions to bioinformatics, iProps broadens its applicability by offering robust data analysis tools that are beneficial across various disciplines, capitalizing on its automated machine learning and model interpretation capabilities. As an open-source platform, iProps is readily accessible and features an intuitive user interface, ensuring ease of use for individuals, even those without a background in programming.

Cuproptosis, the novel type of oxidation-induced cell death in thoracic cancers: can it enhance the success of immunotherapy?

Copper is an important metal micronutrient, required for the balanced growth and normal physiological functions of human organism. Copper-related toxicity and dysbalanced metabolism were associated with the disruption of intracellular respiration and the development of various diseases, including cancer. Notably, copper-induced cell death was defined as cuproptosis which was also observed in malignant cells, representing an attractive anti-cancer instrument. Excess of intracellular copper leads to the aggregation of lipoylation proteins and toxic stress, ultimately resulting in the activation of cell death. Differential expression of cuproptosis-related genes was detected in normal and malignant tissues. Cuproptosis-related genes were also linked to the regulation of oxidative stress, immune cell responses, and composition of tumor microenvironment. Activation of cuproptosis was associated with increased expression of redox-metabolism-regulating genes, such as ferredoxin 1 (FDX1), lipoic acid synthetase (LIAS), lipoyltransferase 1 (LIPT1), dihydrolipoamide dehydrogenase (DLD), drolipoamide S-acetyltransferase (DLAT), pyruvate dehydrogenase E1 subunit alpha 1 (PDHA1), and pyruvate dehydrogenase E1 subunit beta (PDHB)). Accordingly, copper-activated network was suggested as an attractive target in cancer therapy. Mechanisms of cuproptosis and regulation of cuproptosis-related genes in different cancers and tumor microenvironment are discussed in this study. The analysis of current findings indicates that therapeutic regulation of copper signaling, and activation of cuproptosis-related targets may provide an effective tool for the improvement of immunotherapy regimens.

Investigation of intra-fractionated range guided adaptive proton therapy (RGAPT): II. Range-shift compensated on-line treatment adaptation and verification.

We previously proposed range-guided adaptive proton therapy (RGAPT) that uses mid-range treatment beams as probing beams and intra-fractionated range measurements for online adaptation. In this work, we demonstrated experimental verification and reported the dosimetric accuracy for RGAPT. A STEEV phantom was used for the experiments, and a 3 × 3 × 3 cm3cube inside the phantom was assigned to be the treatment target. We simulated three online range shift scenarios: reference, overshoot, and undershoot, by placing upstream Lucite sheets, 4, 0, and 8 that corresponded to changes of 0, 6.8, and -6.8 mm, respectively, in water-equivalent path length. The reference treatment plan was to deliver single-field uniform target doses in pencil beam scanning mode and generated on the Eclipse treatment planning system. Different numbers of mid-range layers, including single, three, and five layers, were selected as probing beams to evaluate beam range (BR) measurement accuracy in positron emission tomography (PET). Online plans were modified to adapt to BR shifts and compensate for probing beam doses. In contrast, non-adaptive plans were also delivered and compared to adaptive plans by film measurements. The mid-range probing beams of three (5.55MU) and five layers (8.71MU) yielded accurate range shift measurements in 60 s of PET acquisition with uncertainty of 0.5 mm while the single-layer probing (1.65MU) was not sufficient for measurements. The adaptive plans achieved an average gamma (2%/2 mm) passing rate of 95%. In contrast, the non-adaptive plans only had an average passing rate of 69%. RGAPT planning and delivery are feasible and verified by the experiments. The probing beam delivery, range measurements, and adaptive planning and delivery added a small increase in treatment delivery workflow time but resulted in substantial dose improvement. The three-layer mid-range probing was most suitable considering the balance of high range measurement accuracy and the low number of probing beam layers.

Investigation of intra-fractionated range guided adaptive proton therapy: I. On-line PET imaging and range measurement.

Objective.Develop a prototype on-line positron emission tomography (PET) scanner and evaluate its capability of on-line imaging and intra-fractionated proton-induced radioactivity range measurement.Approach.Each detector consists of 32 × 32 array of 2 × 2 × 30 mm3Lutetium-Yttrium Oxyorthosilicate scintillators with single-scintillator-end readout through a 20 × 20 array of 3 × 3 mm2Silicon Photomultipliers. The PET can be configurated with a full-ring of 20 detectors for conventional PET imaging or a partial-ring of 18 detectors for on-line imaging and range measurement. All detector-level readout and processing electronics are attached to the backside of the system gantry and their output signals are transferred to a field-programable-gate-array based system electronics and data acquisition that can be placed 2 m away from the gantry. The PET imaging performance and radioactivity range measurement capability were evaluated by both the offline study that placed a radioactive source with known intensity and distribution within a phantom and the online study that irradiated a phantom with proton beams under different radiation and imaging conditions.Main results.The PET has 32 cm diameter and 6.5 cm axial length field-of-view (FOV), ∼2.3-5.0 mm spatial resolution within FOV, 3% sensitivity at the FOV center, 18%-30% energy resolution, and ∼9 ns coincidence time resolution. The offline study shows the PET can determine the shift of distal falloff edge position of a known radioactivity distribution with the accuracy of 0.3 ± 0.3 mm even without attenuation and scatter corrections, and online study shows the PET can measure the shift of proton-induced positron radioactive range with the accuracy of 0.6 ± 0.3 mm from the data acquired with a short-acquisition (60 s) and low-dose (5 MU) proton radiation to a human head phantom.Significance.This study demonstrated the capability of intra-fractionated PET imaging and radioactivity range measurement and will enable the investigation on the feasibility of intra-fractionated, range-shift compensated adaptive proton therapy.

A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features.

Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.

Ultra-Low-Potential Methanol Oxidation on Single-Ir-Atom Catalyst.

Methanol oxidation plays a central role to implement sustainable energy economy, which is restricted by the sluggish reaction kinetics due to the multi-electron transfer process accompanied by numerous sequential intermediate. In this study, an efficient cascade methanol oxidation reaction is catalyzed by single-Ir-atom catalyst at ultra-low potential (<0.1 V) with the promotion of the thermal and electrochemical integration in a high temperature polymer electrolyte membrane electrolyzer. At the elevated temperature, the electron deficient Ir site with higher methanol affinity could spontaneous catalyze the CH3OH dehydrogenation to CO under the voltage, then the generated CO and H2 was electrochemically oxidized to CO2 and proton. However, the methanol cannot thermally decompose with the voltage absence, which confirm the indispensable of the coupling of thermal and electrochemical integration for the methanol oxidation. By assembling the methanol oxidation reaction with hydrogen evolution reaction with single-Ir-atom catalysts in the anode chamber, a max hydrogen production rate reaches 18 mol gIr -1 h-1, which is much greater than that of Ir nanoparticles and commercial Pt/C. This study also demonstrated the electrochemical methanol oxidation activity of the single atom catalysts, which broadens the renewable energy devices and the catalyst design by an integration concept.

Electrocatalysis Boosts the Methanol Thermocatalytic Dehydrogenation for High-Purity H2 and CO Production.

Hydrogen production from methanol represents an energy-sustainable way to produce ethanol, but it normally results in heavy CO2 emissions. The selective conversion of methanol into H2 and valuable chemical feedstocks offers a promising strategy; however, it is limited by the harsh operating conditions and low conversion efficiency. Herein, we realize efficient high-purity H2 and CO production from methanol by coupling the thermocatalytic methanol dehydrogenation with electrocatalytic hydrogen oxidation on a bifunctional Ru/C catalyst. Electrocatalysis enables the acceleration of C-H cleavage and reduces the partial pressure of hydrogen at the anode, which drives the chemical equilibrium and significantly enhances methanol dehydrogenation. Furthermore, a bilayer Ru/C + Pd/C electrode is designed to mitigate CO poisoning and facilitate hydrogen oxidation. As a result, a high yield of H2 (558.54 mmol h-1 g-1) with high purity (99.9%) was achieved by integrating an applied cell voltage of 0.4 V at 200 °C, superior to the conventional thermal and electrocatalytic processes, and CO is the main product at the anode. This work presents a new avenue for efficient H2 production together with valuable chemical synthesis from methanol.

A Novel Polymer-Encapsulated Multi-Imaging Modality Fiducial Marker with Positive Signal Contrast for Image-Guided Radiation Therapy.

BACKGROUND: Current fiducial markers (FMs) in external-beam radiotherapy (EBRT) for prostate cancer (PCa) cannot be positively visualized on magnetic resonance imaging (MRI) and create dose perturbation and significant imaging artifacts on computed tomography (CT) and MRI. We report our initial experience with clinical imaging of a novel multimodality FM, NOVA. METHODS: We tested Gold Anchor [G-FM], BiomarC [carbon, C-FM], and NOVA FMs in phantoms imaged with kilovoltage (kV) X-rays, transrectal ultrasound (TRUS), CT, and MRI. Artifacts of the FMs on CT were quantified by the relative streak artifacts level (rSAL) metric. Proton dose perturbations (PDPs) were measured with Gafchromic EBT3 film, with FMs oriented either perpendicular to or parallel with the beam axis. We also tested the performance of NOVA-FMs in a patient. RESULTS: NOVA-FMs were positively visualized on all 4 imaging modalities tested. The rSAL on CT was 0.750 ± 0.335 for 2-mm reconstructed slices. In F-tests, PDP was associated with marker type and depth of measurement (p < 10-6); at 5-mm depth, PDP was significantly greater for the G-FM (12.9%, p = 10-6) and C-FM (6.0%, p = 0.011) than NOVA (4.5%). EBRT planning with MRI/CT image co-registration and daily alignments using NOVA-FMs in a patient was feasible and reproducible. CONCLUSIONS: NOVA-FMs were positively visible and produced less PDP than G-FMs or C-FMs. NOVA-FMs facilitated MRI/CT fusion and identification of regions of interest.

A Fault Diagnosis Method for 5G Cellular Networks Based on Knowledge and Data Fusion.

As 5G networks become more complex and heterogeneous, the difficulty of network operation and maintenance forces mobile operators to find new strategies to stay competitive. However, most existing network fault diagnosis methods rely on manual testing and time stacking, which suffer from long optimization cycles and high resource consumption. Therefore, we herein propose a knowledge- and data-fusion-based fault diagnosis algorithm for 5G cellular networks from the perspective of big data and artificial intelligence. The algorithm uses a generative adversarial network (GAN) to expand the data set collected from real network scenarios to balance the number of samples under different network fault categories. In the process of fault diagnosis, a naive Bayesian model (NBM) combined with domain expert knowledge is firstly used to pre-diagnose the expanded data set and generate a topological association graph between the data with solid engineering significance and interpretability. Then, as the pre-diagnostic prior knowledge, the topological association graph is fed into the graph convolutional neural network (GCN) model simultaneously with the training data set for model training. We use a data set collected by Minimization of Drive Tests under real network scenarios in Lu'an City, Anhui Province, in August 2019. The simulation results demonstrate that the algorithm outperforms other traditional models in fault detection and diagnosis tasks, achieving an accuracy of 90.56% and a macro F1 score of 88.41%.

A Universal Approach for Sustainable Urea Synthesis via Intermediate Assembly at the Electrode/Electrolyte Interface.

Electrocatalytic C-N coupling process is indeed a sustainable alternative for direct urea synthesis and co-upgrading of carbon dioxide and nitrate wastes. However, the main challenge lies in the unactivated C-N coupling process. Here, we proposed a strategy of intermediate assembly with alkali metal cations to activate C-N coupling at the electrode/electrolyte interface. Urea synthesis activity follows the trend of Li+