High Performance Research Articles

High rate capability performance of cobalt-free lithium-rich Li1.2Ni0.18Mn0.57Al0.05O2 cathode material synthesized via co-precipitation method

High rate capability performance of cobalt-free lithium-rich Li1.2Ni0.18Mn0.57Al0.05O2 cathode material synthesized via co-precipitation method

79 Using artificial intelligence and machine learning to automate injury prevention surveillance data

Abstract Background The Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) is a pan-Canadian injury and poisoning surveillance system that tracks patients presenting to Canadian Emergency Departments (EDs). Like many surveillance systems, data is input in a manual fashion with humans screening all charts. Natural language processing algorithms using transformer models can analyze text from triage notes of previous CHIRPP-positive patients to train models to predict future CHIRPP patients. We sought to automate these functions using machine learning and artificial intelligence. Objectives The objective of this initiative is to automate detection of injured patients using artificial intelligence. Once charts are identified, a further summarization model will automatically generate a narrative of the events surrounding the injury, as required for the surveillance system. Design/Methods ED notes were obtained from an electronic health record for all patients visiting the ED from October 2018 to December 2021 (n =203626). All charts were manually reviewed to determine if patients met the criteria for CHIRPP. CHIRPP-positive patients made up 24.8% (n = 50499) of the total presentations. Patient charts were randomly split into train (70%) and validation (30%) datasets. Pre-trained distilbert-base-uncased model from Huggingface was utilized. The best model according to AUROC was selected for further development. For note summarization, we fine-tuned a multi-task model (t5-small) from the Huggingface repository. We used human summaries from the same dataset (October 2018-December 2021) as training data (49,078 note, summary pairs). A 70/30 train/validation split was performed. Results Our model captured 90% of all positive cases while reducing the total number of charts for review by 77%, 75% and 63% for train, validation and test sets respectively. See AUC curve (figure 1). Based on 2022 volumes this is a reduction to 24,027 from 64,938 charts. For note summarization, when we compared the cosine similarities of human summaries to actual note text vs t5-small summaries to note text we saw remarkable similarity implying high model performance. Conclusion Our model has shown that high volumes of charts that were previously screened manually can utilize natural language processing to automate the task with minimal sacrifices in terms of data capture and accuracy, dramatically reducing cost while improving speed to reporting. Our summarization model also showed potential for further automating this process. This application to an injury prevention program has wide scale applicability and scalability.

Architecting V2O5 with a Triune Crystal Water-Amorphous-Crystalline Feature for Robust Zinc-Ion Batteries.

The notorious collapse of the electrode structure and strong electrostatic interactions in aqueous zinc-ion batteries (AZIBs) limit the achievement of a long cycle life. Herein, by designing an ordered/disordered hybrid structure, we have effectively preserved the integrity of the V2O5·1.6H2O (VOH) electrode. Moreover, our approach facilitates the release of stress concentration contributed to by the amorphous component, alleviating the strong electrostatic interaction merited by crystal water and promoting the diffusion kinetics of Zn2+ assisted by the crystalline component. Noteworthy, the crystal water serves as an interlayer pillar significantly enhancing the structural stability of the electrode. As a result, our VOH electrode exhibits high electrochemical performance. It delivered 227.8 mAh g-1 at a higher current density of 2 A g-1, and a high cycle life of 1000 cycles with 95% capacity retention was achieved at a current density of 1 A g-1.

Cross-national Psychometric Evaluation of the Jefferson Scale of Empathy-Medical Student Version.

This study aimed to examine the factorial structure of the Jefferson Scale of Empathy-Medical student version (JSE-S) and assess items' discriminatory ability at higher and lower empathy levels in medical student populations from different countries and languages. JSE-S datasets were retrieved from previous studies of 4113 first- and/or second-year medical students from Switzerland, Portugal, Italy, Brazil, France, Spain, New Zealand, UK, Ireland, Mexico, and Peru. Parallel principal component analyses and item response theory were applied. Excluding item 18, the Compassionate Care subscale emerged in five samples (Switzerland, Portugal, Italy, France and UK/Ireland), Perspective Taking in three (Switzerland, Portugal and UK/Ireland) and Walking in Patient's Shoes in five (Switzerland, Portugal, Spain, UK/Ireland and Mexico/Peru). All subscales had items providing overlapping information. The JSE-S subscales had reliable high test performance at low empathy levels, while when the JSE-S increased, the standard error also increased. There was no consistent support across countries for the JSE-S three-factorial structure. Items provided redundant information and discriminatory power decreased when aiming to differentiate students at high empathy levels. A JSE-S revision to improve cross-cultural comparability, results' generalizability and validity should focus on suppressing or reformulating items that cannot differentiate medical students' empathy.

Thin-film electrodes of dielectric elastomer-based actuators for an active vibration control system

Precision research and technological equipment, as a rule, is not able to provide its specification characteristics without a high-quality vibration protection system. Active vibration control of an object is provided with the help of an additional source of movement, an actuator. The most promising high accuracy actuators are based on smart materials, such as materials with shape memory, piezoelectric and magnetostrictive materials, electro- and magnetic active fluids and elastomers. Dielectric elastomer is one of the types of electroactive polymers. Actuators based on a dielectric elastomer show high performance in terms of accuracy and speed and operate due to the controllable deformation of the elastomer under the action of a high voltage electric field. The paper provides a comparison of actuators based on sheet and thin film control electrodes. The influence of the quality of the polymer surface and the type of electrodes on the travel range of the actuator and maximum amplitude of vibrations the system can suppress on the basis of a dielectric elastomer is estimated. The formation of the electrode by magnetron sputtering in vacuum makes it possible to create a thin-film layer of copper that covers the elastomer, despite the developed surface. The effect of ion treatment of an elastomer before coating on the quality of the formed electrode is considered. After the ion treatment, the surface of the elastomer acquires a more uniform regular structure. A thin-film electrode layer is formed according to the topology of the elastomer to an accomplished standard.

ASIP Performance Enhancement by Hazard Control through Scoreboard

The application-specific instruction set processor (ASIP) has been gradually accepted in AI, communication, media, game and industry control. The digital signal processor (DSP) is a typical ASIP, whose benefits include high performance in specific domains, low power consumption, high flexibility and low silicon consumption. One of the challenges for DSP design is to handle problems induced by datapath acceleration. The datapath acceleration (instruction fusion, black box instructions) induces control complexities. To most efficiently utilize hardware, control challenges can be summarized as RAW (Read-After-Write) handling, hardware hazard handling, and WAW (Write-After-Write) handling. Both an advanced compiler and hardware hazard handler can be used as solutions. In this paper, we introduced both solutions and exposed the benefits from the hardware solution. The benefits include utilizing low silicon to achieve higher performance and program memory reduction on chip. In summary, our solution only uses 0.91% extra silicon area yet achieves 32.75% performance improvement. So, the overall performance-to-cost ratio could be evaluated as 32%.

Multi-stage precipitation modeling for AA 7050 hole repairs in additive friction stir deposition

A multi-stage precipitation model is formulated to predict the microstructural evolution and explain the high performance of additive friction stir deposited aluminum alloy 7050 (AA 7050) for hole repair. The first stage is the heating process due to the high-temperature thermomechanical process of the stir. In this process, small η precipitates dissolve as they lose their stability with increasing temperature, and this causes the volume fraction of η precipitates to decrease and the concentration of Mg and Zn in the matrix to increase. The second stage is the cooling process at the end of the repair where material feeding ends and the tool is lifted away. Heterogeneous nucleation of η precipitates may occur and as the temperature cools below 250 °C, Guinier–Preston (GP) zones start to form. The final stage is the natural aging process, where the η′ precipitate starts to grow. The volume fraction and precipitate radius are predicted for each type of precipitate. Furthermore, the fine η′ precipitates and GP zones with a decent volume fraction improve the material strength and fatigue life.

TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks

This paper introduces Tensor Visibility Graph-enhanced Attention Networks (TVGeAN), a novel graph autoencoder model specifically designed for MTS learning tasks. The underlying approach of TVGeAN is to combine the power of complex networks in representing time series as graphs with the strengths of Graph Neural Networks (GNNs) in learning from graph data. TVGeAN consists of two new main components: TVG which extend the capabilities of visibility graph algorithms in representing MTSs by converting them into weighted temporal graphs where both the nodes and the edges are tensors. Each node in the TVG represents the MTS observations at a particular time, while the weights of the edges are defined based on the visibility angle algorithm. The second main component of the proposed model is GeAN, a novel graph attention mechanism developed to seamlessly integrate the temporal interactions represented in the nodes and edges of the graphs into the core learning process. GeAN achieves this by using the outer product to quantify the pairwise interactions of nodes and edges at a fine-grained level and a bilinear model to effectively distil the knowledge interwoven in these representations. From an architectural point of view, TVGeAN builds on the autoencoder approach complemented by sparse and variational learning units. The sparse learning unit is used to promote inductive learning in TVGeAN, and the variational learning unit is used to endow TVGeAN with generative capabilities. The performance of the TVGeAN model is extensively evaluated against four widely cited MTS benchmarks for both supervised and unsupervised learning tasks. The results of these evaluations show the high performance of TVGeAN for various MTS learning tasks. In particular, TVGeAN can achieve an average root mean square error of 6.8 for the C-MPASS dataset (i.e., regression learning tasks) and a precision close to one for the SMD, MSL, and SMAP datasets (i.e., anomaly detection learning tasks), which are better results than most published works.

Formulating Brown Algae Derived Phloroglucinol-Based Epoxy Resin for High Performance Applications

Formulating Brown Algae Derived Phloroglucinol-Based Epoxy Resin for High Performance Applications

Pt Nanoparticles on Multi-Walled Carbon Nanotubes with High CO Tolerance for Methanol Electrooxidation

Anode catalysts are important for direct methanol fuel cells (DMFCs) of energy conversion. Herein, we report a novel strategy by ethylene glycol-based deep eutectic solvents (EG-DESs) for the fabrication of a multi-walled carbon nanotubes (MWCNTs)-supported Pt nanoparticles catalyst (referred to as Pt/CNTs-EG-DES). The Pt/CNTs-EG-DES catalyst provides an increased electrochemically active surface area (ECSA) and shows remarkably improved electrocatalytic performance towards methanol oxidation reaction compared to Pt/CNTs-W (fabricated in water) and commercial Pt/C catalysts. The improved performance is attributed to the generation of more Pt–O bonds which change the electronic states of the Pt atoms and the special node structure that obtains more active sites for a high CO resistance. This study suggests an effective synthesis strategy for Pt-based electrocatalysts with high performance for DMFC applications.

Research on bridge safety early warning method based on strain energy theory and health monitoring data

Bridges are technology-intensive and heavily invested permanent infrastructure. After completion and opening to traffic, bridge structures are easy to be affected by factors such as traffic load and atmospheric environment. Therefore, it is necessary to do safety warning and evaluation of bridges, especially the abnormal behavior in the early stages of bridge operation. In this research paper, a large-span continuous rigid frame bridge installed with the health monitoring system (HMS), of which a large amount of health monitoring data are collected by the HMS, is used as an example, and then a bridge safety early warning method is proposed when the bridge is during early operating period. First of all, the research finding that the internal stress of the prototype bridge obeys normal distribution through statistical analysis is used; next, we deduce that the strain energy inside the prototype bridge is subject to the Non-central Chi-square distribution combined with the strain energy and statistical theories; in the end, the key probability density distribution function of strain energy and its parameters are derived by using the key stress distribution function of the high performance concrete C50 strength grade used in the prototype bridge. The method recommended in this paper is conducive to the formulation of bridge preventive maintenance strategies.

7-axis synchronization-integrated on-the-fly laser texturing system of freeform surface: Design and development

Abstract While laser surface texturing (LST) is a promising manufacturing technique for surface functionalization, simultaneously realizing high precision and high efficiency in the LST of complex curved surface is challenging, due to continuously varied geometries of laser-matter incidence. In the present work, we propose a novel manufacturing system of 7-axis on-the-fly LST for complex curved surface, based on the integrated synchronization of 5-axis linkage motion platform with 2-axis galvanometer. Specifically, the algorithm for decomposing spatial texture trajectory on curved surface into low-frequency and high-frequency parts is established, based on which the kinematic model of synchronized 7-axis system is developed to derive the motion of each axis in both 5-axis linkage motion platform and 2-axis galvanometer simultaneously. Subsequently, the synchronized 7-axis LST system is experimentally realized, including the setup of mechanical stages integrated with optical path, the configuration of numerical control unit, and the development of processing software. Finally, case study of 7-axis on-the-fly LST of freeform aluminum surface is performed, and the advantages in terms of processing efficiency and texturing accuracy over 5-axis linkage LST are demonstrated. The correlation of reduced following errors between mechanical stages with the promoted performance of curved surface texturing by the 7-axis on-the-fly LST is further analyzed. Current work provides a feasible solution for establishing the manufacturing system for high performance LST of complex curved surface.

Bioleaching of Industrial Metallic Steel Waste by Mixed Cultures of Thermoacidophilic Archaea

Different mixed cultures of extremely thermoacidophilic microorganisms were used for bioleaching of metalliferous industrial dust waste derived from the basic oxygen furnace (BOF) steelmaking process. Such mixed cultures can extract various metals from multi-metallic BOF-dust waste, improving the metal dissolution and bioleaching performance in frames of metal recycling processes to assist circular economies and waste management. The results of the investigation showed that mixed cultures of thermoacidophilic archaea of the order Sulfolobales (Acidianus spp., Sulfolobus spp., and Metallosphaera sedula) during their growth in laboratory glass bioreactors provided a superior bioleaching system to Acidianus manzaensis alone. Depending on the composition of mixed thermoacidophilic cultures, extraction of various metals from BOF-dust could be achieved. Among the three different types of mixed cultures tested, the mixed culture system of A. manzaensis, A. brierleyi, and S. acidocaldarius was most effective for extraction of major elements (Fe, Ca, Zn, Mn, and Al). The mixed culture of A. manzaensis, A. brierleyi, and M. sedula showed high performance for bioleaching of most of the minor elements (Cu, Ni, Pb, Co, Mo, and Sr). The efficient ability of mixed cultures to colonise the mineral matrix of the metal waste product was observed via scanning electron microscopy, while their metal extraction capacities were analysed by inductively coupled plasma mass spectrometry. These investigations will promote the further design of microbial consortia in order to break down the solid matrix and efficiently extract metals from metalliferous waste materials.

Sodium Alginate/MXene-Based Flexible Humidity Sensors with High-Humidity Durability and Application Potentials in Breath Monitoring and Non-Contact Human–Machine Interfaces

Flexible humidity sensors (FHSs) with fast response times and durability to high-humidity environments are highly desirable for practical applications. Herein, an FHS based on crosslinked sodium alginate (SA) and MXene was fabricated, which exhibited high sensitivity (impedance varied from 107 to 105 Ω between 10% and 90% RH), good selectivity, prompt response times (response/recover time of 4 s/11 s), high sensing linearity (R2 = 0.992) on a semi-logarithmic scale, relatively small hysteresis (~5% RH), good repeatability, and good resistance to highly humid environments (negligible changes in sensing properties after being placed in 98% RH over 24 h). It is proposed that the formation of the crosslinking structure of SA and the introduction of MXene with good conductivity and a high specific surface area contributed to the high performance of the composite FHS. Moreover, the FHS could promptly differentiate the respiration status, recognize speech, and measure fingertip movement, indicating potential in breath monitoring and non-contact human–machine interactions. This work provides guidance for developing advanced flexible sensors with a wide application scope in wearable electronics.

Hydrodynamic model-driven evolutionary algorithm-based operation optimization of an experimental drainage pumping station

ABSTRACT The effective operation of pumping stations plays a crucial role in urban flood management. However, challenges persist in optimizing pumping station operations, including inaccuracies in characterizing flood propagation and the high computational costs associated with optimization. This study introduces a novel optimization approach for pumping station operation that integrates a hydrodynamic model with evolutionary algorithms, leveraging data-driven technology. The method iteratively computes operation rules using the adaptive particle swarm optimization (APSO) algorithm to identify optimal solutions. The hydrodynamic model accurately simulates flood propagation and provides hydraulic parameters for the objective function and constraints of the APSO algorithm. With the predictive capability of the Kriging model, the optimization enhances efficiency by reducing the frequency of calls to the hydrodynamic model. A study case of a flood management digital twin experimental platform was then taken for the application. Compared to initial operation rules, the objective function value of the proposed method is reduced by 28.7, 32.5, and 25%, respectively, under varying magnitudes of unsteady flood inflows, demonstrating high performances in both flood mitigation and operation cost control. Moreover, the method only requires 70 calls to the hydrodynamic model to formulate the decision operation rule.

High performance method for COPD features extraction using complex network.

Objectives. The paper proposes a novel methodology for the classification of Chronic Obstructive Pulmonary Disease (COPD) utilizing respiratory sound attributes.Methods. The approach involves segmenting respiratory sounds into individual breaths and conducting extensive studies on this dataset. Spectral Transforms, various Wavelet Transforms are applied to capture distinct signal features. Complex Network is also employed to extract characteristic elements, generating novel representations of spectrogram data based on graph factors, including entropy, density, and position. The normalized and enriched data is then used to develop COPD classifiers using six machine learning algorithms, fine-tuning with appropriate training details and hyperparameter tuning.Results. Our results demonstrate robust performance, with ROC curves consistently exhibiting an Area Under the Curve (AUC) > 96% across different time-frequency transformations. Notably, the Random Forest algorithm achieves an AUC of 99.67%, outperforming other algorithms. Moreover, the Wavelet Daubechies 2 (Db2) consistently approaches 98% accuracy, particularly noteworthy in conjunction with the Naive Bayes algorithm.Conclusion. This study diagnosis patients through spectrogram images extracted from lung sounds. The application of Inverse Transforms, Complex Network, and Optimized Classification Algorithms yielded results beyond expectations. This methodology provides a promising approach for accurate COPD diagnosis, leveraging Machine Learning techniques applied to respiratory sound analysis.

Efficient Deep-Blue Fluorescent OLEDs Based on a D-π-A Emitter Profiting From Intramolecular Hydrogen Bonds and a Hybridized Excited State.

High performance deep-blue emitters with a Commission International de l'Eclairage (CIE) coordinate of CIEy≤0.08 are highly desired in ultrahigh-definition displays. Herein, we designed and synthesized an efficient D-π-A deep-blue emitter, 2-(6-([1,1' : 3',1''-terphenyl]-5'-yl)pyridin-3-yl)-1-phenyl-1H-phenanthro[9,10-d] imidazole (mPTPH), using the synergistic effect of intramolecular hydrogen bond (H-bond) and hybridized excited state. Single-crystal structure analysis confirmed that there exist intra- and intermolecular H-bond interactions which could inhibit the structure vibration and increase photoluminescence efficiency. The photophysical and theoretical results show that mPTPH exhibited hybridized local and charge-transfer (HLCT) feature with strong deep-blue emission. Ultimately, the non-doped device based on mPTPH exhibited high maximum luminance of 20610 cd m-2. The doped device achieved high maximum external quantum efficiency of 5.4 % and small efficiency roll-off with deep-blue emission peak of 413 nm and CIE coordinate of (0.16, 0.08).

The effect of mesoporous silica doped with silver nanoparticles on glass ionomer cements; physiochemical, mechanical and ion release analysis

BackgroundThe purpose of the study was to evaluate the effect of adding mesoporous silica with silver nanoparticles to conventional glass ionomer cements (GIC) on its, physical, chemical, mechanical properties and ion release analysis.MethodsSynthesized mesoporous silica with silver nanoparticles were added in 1, 3 and 5% by weight to the liquid component of GIC forming three experimental groups and compared with plain GIC as control group. Physical and chemical characterization were conducted using nano-computerized tomography (NanoCT) and Fourier transform infrared spectroscopy. Surface microhardness, water sorption and solubility were analyzed. Ion release was investigated using Inductive Coupled Plasma-Optical Emission Spectroscopy and High Performance Liquid Chromatography. Statistical analysis between different groups for the set parameters using parametric and non-parametric tests. The results were analysed using one way analysis of variance (p < 0.05).ResultsSynthesized mesoporous silica with silver nanoparticles were of 7.28 ± 5.0 nm in diameter with a spherical morphology. NanoCT revealed less porosities for 3 wt %. Microhardness showed a statistically significant difference for 5 wt% at day 1 and 21 ( p < 0.0001). Water sorption values decreased significantly on day 14 compared to day 7 for control, 1, 3 and 5 wt%. Control specimens showed highest concentration of fluoride release followed by 5, 3 and 1 wt%.ConclusionMesoporous silica with silver nanoparticles modified glass ionomer cements showed comparable microhardness to conventional GIC. Ion release was evident from the modified specimens. Silver remained within the GIC for atleast four weeks following incorporation.

Performance prediction applying different reduced turbulence models to the SMART tokamak

Abstract The SMall Aspect Ratio Tokamak (SMART) is currently being&amp;#xD;commissioned at the University of Seville and will be able to compare the&amp;#xD;performance of positive and negative triangularity plasmas at low aspect ratio.&amp;#xD;Predictive simulations have been performed for different machine scenarios and&amp;#xD;heating schemes using the TRANSP code. The objectives of these simulations&amp;#xD;are to predict the parameters expected in positive triangularity plasmas, to&amp;#xD;guide diagnostic development, and to validate transport models. Several reduced&amp;#xD;turbulence models have been used to predict electron and ion temperatures for the&amp;#xD;operational phase 2. All models provide similar results from approximately midradius&amp;#xD;to the separatrix but important discrepancies are found in the core region.&amp;#xD;These positive triangularity results are compared with experiments from a similar&amp;#xD;size machine like GLOBUS-M2. The multi-mode model (MMM) shows the best&amp;#xD;agreement. Simulations with different boundary conditions have been performed&amp;#xD;and no strong differences have been observed between them. The impact of neutral&amp;#xD;beam injection (NBI) on the predicted profiles has also been addressed. Rotation&amp;#xD;reduces turbulence levels so higher temperatures are achieved when included in&amp;#xD;the simulations. Studying the different contributions to the thermal diffusivities,&amp;#xD;it is observed that electron temperature gradient (ETG) turbulence dominates&amp;#xD;at the plasma core while mictro-tearing modes (MTM) dominate at the edge&amp;#xD;in the electron channel. In the ion channel, the neoclassical contribution is&amp;#xD;dominant at the core and at the very edge while the Weiland component, which&amp;#xD;includes ion temperature gradient mode (ITG), trapped electron mode (TEM),&amp;#xD;kinetic ballooning mode (KBM), peeling mode (PM) and collisionless and collision&amp;#xD;dominated magnetohydrodynamic (MHD) modes governs the mid-radius region.&amp;#xD;For phase 3, two plasmas with different electron densities have been studied. The&amp;#xD;case with lower density matches well a specific discharge of GLOBUS-M2. The&amp;#xD;higher density plasma shows high performance with βN ≈ 3.8.

Enhanced Cross Layer Refinement Network for robust lane detection across diverse lighting and road conditions

With the rapid development of autonomous driving technology, lane detection, a key component of intelligent vehicle systems, is crucial for ensuring road safety and efficient vehicle navigation. In this paper, a new lane detection method is proposed to address the problem of degraded performance of existing lane detection methods when dealing with complex road environments. The proposed method evolves from the original Cross Layer Refinement Network (CLRNet) by incorporating two of our carefully designed core components: the Global Feature Optimizer (GFO) and the Adaptive Lane Geometry Aggregator (ALGA). The GFO is a multi-scale attention mechanism that mimics the human visual focusing ability, effectively filtering out unimportant information and focusing on the image regions most relevant to the task. The ALGA is a shape feature-aware aggregation module that utilizes the shape prior of lanes to enhance the correlation of anchor points in an image, better fusing global and local information. By integrating both components into CLRNet, an enhanced version called Enhanced CLRNet (E-CLRNet) is presented, which exhibits higher performance stability in complex roadway scenarios. Experiments on the CULane dataset reveal that E-CLRNet demonstrates superior performance stability over the original CLRNet in complex scenarios, including curves, shadows, missing lines, and dazzling light conditions. In particular, in the curves, the F1 score of E-CLRNet is improved by almost 3% over the original CLRNet. This study not only improves the accuracy and performance stability of lane detection but also provides a new solution for the application of autonomous driving technology in complex environments, which promotes the development of intelligent vehicle systems.