Regulation of anion-Na+ coordination chemistry in electrolyte solvates for low-temperature sodium-ion batteries.

High-performance sodium storage at low temperature is urgent with the increasingly stringent demand for energy storage systems. However, the aggravated capacity loss is induced by the sluggish interfacial kinetics, which originates from the interfacial Na+ desolvation. Herein, all-fluorinated anions with ultrahigh electron donicity, trifluoroacetate (TFA-), are introduced into the diglyme (G2)-based electrolyte for the anion-reinforced solvates in a wide temperature range. The unique solvation structure with TFA- anions and decreased G2 molecules occupying the inner sheath accelerates desolvation of Na+ to exhibit decreased desolvation energy from 4.16 to 3.49 kJ mol-1 and 24.74 to 16.55 kJ mol-1 beyond and below -20 °C, respectively, compared with that in 1.0 M NaPF6-G2. These enable the cell of Na||Na3V2(PO4)3 to deliver 60.2% of its room-temperature capacity and high capacity retention of 99.2% after 100 cycles at -40 °C. This work highlights regulation of solvation chemistry for highly stable sodium-ion batteries at low temperature.

A biomimetic eye with a hemispherical perovskite nanowire array retina.

Human eyes possess exceptional image-sensing characteristics such as an extremely wide field of view, high resolution and sensitivity with low aberration1. Biomimetic eyes with such characteristics are highly desirable, especially in robotics and visual prostheses. However, the spherical shape and the retina of the biological eye pose an enormous fabrication challenge for biomimetic devices2,3. Here we present an electrochemical eye with a hemispherical retina made of a high-density array of nanowires mimicking the photoreceptors on a human retina. The device design has a high degree of structural similarity to a human eye with the potential to achieve high imaging resolution when individual nanowires are electrically addressed. Additionally, we demonstrate the image-sensing function of our biomimetic device by reconstructing the optical patterns projected onto the device. This work may lead to biomimetic photosensing devices that could find use in a wide spectrum of technological applications.

Probing FeSi, a d-electron topological Kondo insulator candidate, with magnetic field, pressure, and microwaves.

Recently, evidence for a conducting surface state (CSS) below 19 K was reported for the correlated d-electron small gap semiconductor FeSi. In the work reported herein, the CSS and the bulk phase of FeSi were probed via electrical resistivity ρ measurements as a function of temperature T, magnetic field B to 60 T, and pressure P to 7.6 GPa, and by means of a magnetic field-modulated microwave spectroscopy (MFMMS) technique. The properties of FeSi were also compared with those of the Kondo insulator SmB6 to address the question of whether FeSi is a d-electron analogue of an f-electron Kondo insulator and, in addition, a "topological Kondo insulator" (TKI). The overall behavior of the magnetoresistance of FeSi at temperatures above and below the onset temperature TS = 19 K of the CSS is similar to that of SmB6. The two energy gaps, inferred from the ρ(T) data in the semiconducting regime, increase with pressure up to about 7 GPa, followed by a drop which coincides with a sharp suppression of TS. Several studies of ρ(T) under pressure on SmB6 reveal behavior similar to that of FeSi in which the two energy gaps vanish at a critical pressure near the pressure at which TS vanishes, although the energy gaps in SmB6 initially decrease with pressure, whereas in FeSi they increase with pressure. The MFMMS measurements showed a sharp feature at TS ≈ 19 K for FeSi, which could be due to ferromagnetic ordering of the CSS. However, no such feature was observed at TS ≈ 4.5 K for SmB6.

Routes to high-performance layered oxide cathodes for sodium-ion batteries.

Sodium-ion batteries (SIBs) are experiencing a large-scale renaissance to supplement or replace expensive lithium-ion batteries (LIBs) and low energy density lead-acid batteries in electrical energy storage systems and other applications. In this case, layered oxide materials have become one of the most popular cathode candidates for SIBs because of their low cost and comparatively facile synthesis method. However, the intrinsic shortcomings of layered oxide cathodes, which severely limit their commercialization process, urgently need to be addressed. In this review, inherent challenges associated with layered oxide cathodes for SIBs, such as their irreversible multiphase transition, poor air stability, and low energy density, are systematically summarized and discussed, together with strategies to overcome these dilemmas through bulk phase modulation, surface/interface modification, functional structure manipulation, and cationic and anionic redox optimization. Emphasis is placed on investigating variations in the chemical composition and structural configuration of layered oxide cathodes and how they affect the electrochemical behavior of the cathodes to illustrate how these issues can be addressed. The summary of failure mechanisms and corresponding modification strategies of layered oxide cathodes presented herein provides a valuable reference for scientific and practical issues related to the development of SIBs.

Pre-Oxidation Strategy Transforming Waste Foam to Hard Carbon Anodes for Boosting Sodium Storage Performance.

Large reserves, high capacity, and low cost are the core competitiveness of disordered carbon materials as excellent anode materials for sodium-ion batteries (SIBs). And the existence and improper treatment of a large number of organic solid wastes will aggravate the burden on the environment, therefore, it is significant to transform wastes into carbon-based materials for sustainable energy utilization. Herein, a kind of hard carbon materials are reported with waste biomass-foam as the precursor, which can improve the sodium storage performance through pre-oxidation strategy. The introduction of oxygen-containing groups can promote structural cross-linking, and inhibit the melting and rearrangement of carbon structure during high-temperature carbonization that produces a disordered structure with a suitable degree of graphitization. Moreover, the micropore structure are also regulated during the high-temperature carbonization process, which is conducive to the storage of sodium ions in the low-voltage plateau region. The optimized sample as an electrode material exhibits excellent reversible specific capacity (308.0 mAh g-1) and initial Coulombic efficiency (ICE, 90.1%). In addition, a full cell with the waste foam-derived hard carbon anode and a Na3V2(PO4)3 cathode is constructed with high ICE and energy density. This work provides an effective strategy to conversion the waste to high-value hard carbon anode for sodium-ion batteries.

An Intrinsic Stable Layered Oxide Cathode for Practical Sodium-Ion Battery: Solid Solution Reaction, Near-Zero-Strain and Marvelous Water Stability.

Non-aqueous solvents, in particular N,N-dimethylaniline (NMP), are widely applied for electrode fabrication since most sodium layered oxide cathode materials are readily damaged by water molecules. However, the expensive price and poisonousness of NMP unquestionably increase the cost of preparation and post-processing. Therefore, developing an intrinsically stable cathode material that can implement the water-soluble binder to fabricate an electrode is urgent. Herein, a stable nanosheet-like Mn-based cathode material is synthesized as a prototype to verify its practical applicability in sodium-ion batteries (SIBs). The as-prepared material displays excellent electrochemical performance and remarkable water stability, and it still maintains a satisfactory performance of 79.6% capacity retention after 500 cycles even after water treatment. The in situ X-ray diffraction (XRD) demonstrates that the synthesized material shows an absolute solid-solution reaction mechanism and near-zero-strain. Moreover, the electrochemical performance of the electrode fabricated with a water-soluble binder shows excellent long-cycling stability (67.9% capacity retention after 500 cycles). This work may offer new insights into the rational design of marvelous water stability cathode materials for practical SIBs.

Research on the Construction of a Series of Transition Metal-Substituted Keggin-Type TMSPOMs@PCN-224 Composites through the Encapsulation Method and Their Electron Transfer Mechanism in CO2RR.

In order to take advantage of the distinct reversible multielectron transfer properties of polyoxometalates (POMs) and increase the electron density at the active sites during the electrochemical reduction of CO2 (CO2RR), a range of transition metal-doped polyoxometalates (TMSPOMs) was entrapped within the porphyrin-based framework of PCN-224 via an encapsulation method, known as TMSPOMs@PCN-224 (TMSPOMs = [XW11O39MII(H2O)]n-, [XW11O40VIV]n-, M = CoII, MnII; X = Si, n = 6; X = P, n = 5). The central elements (Si, P) and the incorporated transition metals (VIV, CoII, and MnII) both play a role in adjusting the electronic structure and electron transfer during the CO2RR process. Remarkably, the composite material with cobalt substitution displayed significantly improved performance. Through fine-tuning the POM loading, the electrocatalytic activity was optimized, leading to an impressive Faradaic efficiency for CO production (FECO) of 89.9% for SiW11Co@PCN-224, a significant improvement compared to the 12.1% FECO of PCN-224. Furthermore, the electrochemical stability of this catalyst was demonstrated over 20 h. Comparative analyses involving six composite materials indicated a relationship between the negative charge of the polyanions and their ability to facilitate effective electron transfer, ultimately enhancing the catalyst's performance. Meanwhile, these findings were supported by density functional theory (DFT) calculations.

Short-Term Exposure to Fine Particulate Matter and Ozone: Source Impacts and Attributable Mortalities.

Short-term exposure to particles with aerodynamic diameters less than 2.5 µm (PM2.5) and ozone (O3) are important risk factors for human health. Despite the awareness of reducing attributable health burden, region-specific and source-specific strategies remain less explored due to the gap between precursor emissions and health effects. In this study, we isolate the health burden of individual sector sources of PM2.5 and O3 precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), across the globe. Specifically, we estimate mortalities attributable to short-term exposure using machine-learning-based daily exposure estimates and quantify sectoral impacts using chemical transport model simulations. Globally, short-term exposure to PM2.5 and O3 result in 713.5 (95% Confidence Interval: 598.8-843.3) thousand and 496.3 (371.3-646.1) thousand mortalities in 2019, respectively, of which 12.5% are contributed by fuel-related NOx emissions from transportation, energy, and industry. Sectoral impacts from anthropogenic NOx and VOC emissions on health burden vary significantly among seasons and regions, requiring a target shift from transportation in winter to industry in summer for East Asia, for instance. Emission control and health management are additionally complicated by unregulated natural influences during climatic events. Fire-sourced NOx and VOC emissions, respectively, contribute to 8.5 (95% CI: 6.2-11.7) thousand and 4.8 (3.6-5.9) thousand PM2.5 and O3 mortalities, particularly for tropics with high vulnerability to climate change. Additionally, biogenic VOC emissions during heatwaves contribute to 1.8 (95% CI: 1.5-2.2) thousand O3-introduced mortalities, posing challenges in urban planning for high-income regions, where biogenic contributions to health burden during heatwaves are 13% of anthropogenic contributions annually. Our study provides important implications for temporally dynamic and sector-targeted emission control and health management strategies, which are of urgency under the projection of continuously increasing energy consumption and changing climate.

Satellite NO2 Retrieval Complicated by Aerosol Composition over Global Urban Agglomerations: Seasonal Variations and Long-Term Trends (2001-2018).

Tropospheric nitrogen dioxide (NO2) poses a serious threat to the environmental quality and public health. Satellite NO2 observations have been continuously used to monitor NO2 variations and improve model performances. However, the accuracy of satellite NO2 retrieval depends on the knowledge of aerosol optical properties, in particular for urban agglomerations accompanied by significant changes in aerosol characteristics. In this study, we investigate the impacts of aerosol composition on tropospheric NO2 retrieval for an 18 year global data set from Global Ozone Monitoring Experiment (GOME)-series satellite sensors. With a focus on cloud-free scenes dominated by the presence of aerosols, individual aerosol composition affects the uncertainties of tropospheric NO2 columns through impacts on the aerosol loading amount, relative vertical distribution of aerosol and NO2, aerosol absorption properties, and surface albedo determination. Among aerosol compositions, secondary inorganic aerosol mostly dominates the NO2 uncertainty by up to 43.5% in urban agglomerations, while organic aerosols contribute significantly to the NO2 uncertainty by -8.9 to 37.3% during biomass burning seasons. The possible contrary influences from different aerosol species highlight the importance and complexity of aerosol correction on tropospheric NO2 retrieval and indicate the need for a full picture of aerosol properties. This is of particular importance for interpreting seasonal variations or long-term trends of tropospheric NO2 columns as well as for mitigating ozone and fine particulate matter pollution.

Interfacial Engineering for Oriented Crystal Growth toward Dendrite-Free Zn Anode for Aqueous Zinc Metal Battery.

Zn deposition with a surface-preferred (002) crystal plane has attracted extensive attention due to its inhibited dendrite growth and side reactions. However, the nucleation and growth of the Zn(002) crystal plane are closely related to the interfacial properties. Herein, oriented growth of Zn(002) crystal plane is realized on Ag-modified surface that is directly visualized by in situ atomic force microscopy. A solid solution HCP-Zn (~1.10 at. % solubility of Ag, 30 °C) is formed on the Ag coated Zn foil (Zn@Ag) and possesses the same crystal structure as Zn to reduce its nucleation barrier caused by their lattice mismatch. It merits oriented Zn deposition and corrosion-resistant surface, and presents long cycling stability in symmetric cells and full cells coupled with V2O5 cathode. This work provides insights into interfacial regulation of Zn anodes for high-performance aqueous zinc metal batteries.

Insight into the Role of Fluoroethylene Carbonate on the Stability of Sb||Graphite Dual-Ion Batteries in Propylene Carbonate-Based Electrolyte.

Sodium dual-ion batteries (Na-DIBs) have attracted increasing attention due to their high operative voltages and low-cost raw materials. However, the practical applications of Na-DIBs are still hindered by the issues, such as low capacity and poor Coulombic efficiency, which is highly correlated with the compatibility between electrode and electrolyte but rarely investigated. Herein, fluoroethylene carbonate (FEC) is introduced into the electrolyte to regulate cation/anion solvation structure and the stability of cathode/anode-electrolyte interphase of Na-DIBs. The FEC modulates the environment of PF6 - solvation sheath and facilitates the interaction of PF6 - on graphite. In addition, the NaF-rich interphase caused by the preferential decomposition of FEC effectively inhibits side reactions and pulverization of anodes with the electrolyte. Consequently, Sb||graphite full cells in FEC-containing electrolyte achieve an improved capacity, cycling stability and Coulombic efficiency. This work elucidates the underlying mechanism of bifunctional FEC and provides an alternative strategy of building high-performance dual ion batteries.

Resolving the Origins of Superior Cycling Performance of Antimony Anode in Sodium-ion Batteries: A Comparison with Lithium-ion Batteries.

Alloying-type antimony (Sb) with high theoretical capacity is a promising anode candidate for both lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). Given the larger radius of Na+ (1.02 Å) than Li+ (0.76 Å), it was generally believed that the Sb anode would experience even worse capacity degradation in SIBs due to more substantial volumetric variations during cycling when compared to LIBs. However, the Sb anode in SIBs unexpectedly exhibited both better electrochemical and structural stability than in LIBs, and the mechanistic reasons that underlie this performance discrepancy remain undiscovered. Here, using substantial in situ transmission electron microscopy, X-ray diffraction, and Raman techniques complemented by theoretical simulations, we explicitly reveal that compared to the lithiation/delithiation process, sodiation/desodiation process of Sb anode displays a previously unexplored two-stage alloying/dealloying mechanism with polycrystalline and amorphous phases as the intermediates featuring improved resilience to mechanical damage, contributing to superior cycling stability in SIBs. Additionally, the better mechanical properties and weaker atomic interaction of Na-Sb alloys than Li-Sb alloys favor enabling mitigated mechanical stress, accounting for enhanced structural stability as unveiled by theoretical simulations. Our finding delineates the mechanistic origins of enhanced cycling stability of Sb anode in SIBs with potential implications for other large-volume-change electrode materials.

Deep learning-assisted identification and quantification of aneurysmal subarachnoid hemorrhage in non-contrast CT scans: Development and external validation of Hybrid 2D/3D UNet.

Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.

Novel chromosomes and genomes provide new insights into evolution and adaptation of the whole genome duplicated yeast-like fungus TN3-1 isolated from natural honey.

Aureobasidium melanogenum TN3-1 strain and A. melanogenum P16 strain were isolated from the natural honey and the mangrove ecosystem, respectively. The former can produce much higher pullulan from high concentration of glucose than the latter. In order to know what happened to their genomes, the PacBio sequencing and Hi-C technologies were used to create the first high-quality chromosome-level reference genome assembly of A. melanogenum TN3-1 (51.61 Mb) and A. melanogenum P16 (25.82 Mb) with the contig N50 of 2.19 Mb and 2.26 Mb, respectively. Based on the Hi-C results, a total of 93.33% contigs in the TN3-1 strain and 92.31% contigs in the P16 strain were anchored onto 24 and 12 haploid chromosomes, respectively. The genomes of the TN3-1 strain had two subgenomes A and B. Synteny analysis showed that the genomic contents of the two subgenomes were asymmetric with many structural variations. Intriguingly, the TN3-1 strain was revealed as a recent hybrid/fusion between the ancestor of A. melanogenum CBS105.22/CBS110374 and the ancestor of another unidentified strain of A. melanogenum similar to P16 strain. We estimated that the two ancient progenitors diverged around 18.38 Mya and merged around 10.66-9.98 Mya. It was found that in the TN3-1 strain, telomeres of each chromosome contained high level of long interspersed nuclear elements (LINEs), but had low level of the telomerase encoding gene. Meanwhile, there were high level of transposable elements (TEs) inserted in the chromosomes of the TN3-1 strain. In addition, the positively selected genes of the TN3-1 strain were mainly enriched in the metabolic processes related to harsh environmental adaptability. Most of the stress-related genes were found to be related to the adjacent LTRs, and the glucose derepression was caused by the mutation of the Glc7-2 in the Snf-Mig1 system. All of these could contribute to its genetic instability, genome evolution, high stress resistance, and high pullulan production from glucose.

Upadacitinib Therapy Reduces Ulcerative Colitis Symptoms as Early as Day 1 of Induction Treatment.

BACKGROUND & AIMS: We evaluated the efficacy of once-daily (QD) upadacitinib 45 mg, an oral, reversible Janus kinase inhibitor, on early symptomatic improvement for ulcerative colitis (UC). Post hoc analyses were performed on pooled data from 2 replicate, phase 3, multicenter induction trials, U-ACHIEVE Induction and U-ACCOMPLISH, to determine the earliest time point of efficacy onset. METHODS: Diary entry data through 14 days from the first dose of placebo or upadacitinib 45 mg QD were analyzed for daily improvement in UC symptoms (stool frequency, rectal bleeding, abdominal pain, and bowel urgency). Changes in inflammatory markers, high-sensitivity C-reactive protein (hs-CRP), and fecal calprotectin (FCP) were assessed at week 2 and quality of life (QoL) at weeks 2 and 8. Regression analysis determined the association between changes in UC symptoms and the likelihood of achieving clinical remission/response per Adapted Mayo score at week 8. RESULTS: Overall, 988 patients (n = 328 placebo, n = 660 upadacitinib) were analyzed. Patients treated with upadacitinib demonstrated significant improvements vs placebo in all UC symptoms between days 1 and 3 and maintained through day 14. A >50% reduction from baseline in hs-CRP and FCP levels was achieved by 75.7% and 48.2% of patients, respectively (P < .001 vs placebo). Increased rates of clinical remission/response per Partial Mayo score from week 2 (26.9%/59.4% upadacitinib 45 mg QD vs 4.3%/22.3% placebo, P < .001) and significant improvements in QoL at weeks 2 and 8 were observed. Early improvement in stool frequency and bowel urgency by day 3 and reductions in hs-CRP and FCP by week 2 were significantly associated with clinical remission/response at week 8. CONCLUSIONS: Upadacitinib 45 mg QD provided rapid relief of UC symptoms from day 1. CLINICALTRIALS: gov: U-ACHIEVE Induction (NCT02819635) and U-ACCOMPLISH (NCT03653026).

A Lightweight Fault-Detection Scheme for Resource-Constrained Solar Insecticidal Lamp IoTs.

The Solar Insecticidal Lamp Internet of Things (SIL-IoTs) is an emerging paradigm that extends Internet of Things (IoT) technology to agricultural-enabled electronic devices. Ensuring the dependability and safety of SIL-IoTs is crucial for pest monitoring, prediction, and prevention. However, SIL-IoTs can experience system performance degradation due to failures, which can be attributed to complex environmental changes and device deterioration in agricultural settings. This study proposes a sensor-level lightweight fault-detection scheme that takes into account realistic constraints such as computational resources and energy. By analyzing fault characteristics, we designed a distributed fault-detection method based on operation condition differences, interval number residuals, and feature residuals. Several experiments were conducted to validate the effectiveness of the proposed method. The results demonstrated that our method achieves an average F1-score of 95.59%. Furthermore, the proposed method only consumes an additional 0.27% of the total power, and utilizes 0.9% RAM and 3.1% Flash on the Arduino of the SIL-IoTs node. These findings indicated that the proposed method is lightweight and energy-efficient.

Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things.

Sensors have been used in various agricultural production scenarios due to significant advances in the Agricultural Internet of Things (Ag-IoT), leading to smart agriculture. Intelligent control or monitoring systems rely heavily on trustworthy sensor systems. Nonetheless, sensor failures are likely due to various factors, including key equipment malfunction or human error. A faulty sensor can produce corrupted measurements, resulting in incorrect decisions. Early detection of potential faults is crucial, and fault diagnosis techniques have been proposed. The purpose of sensor fault diagnosis is to detect faulty data in the sensor and recover or isolate the faulty sensors so that the sensor can finally provide correct data to the user. Current fault diagnosis technologies are based mainly on statistical models, artificial intelligence, deep learning, etc. The further development of fault diagnosis technology is also conducive to reducing the loss caused by sensor failures.

Three-Dimensional Nanopillar Arrays-Based Efficient and Flexible Perovskite Solar Cells with Enhanced Stability.

Perovskite nanopillars (PNPs) are propitious candidates for solar irradiation harvesting and are potential alternatives to thin films in flexible photovoltaics. To realize efficient daily energy output, photovoltaics must absorb sunlight over a broad range of incident angles and wavelengths congruent with the solar spectrum. Herein, we report highly periodic three-dimensional (3D) PNP-based flexible photovoltaics possessing a core-shell structure. The vertically aligned PNP arrays demonstrate up to 95.70% and 75.10% absorption at peak and under an incident angle of 60°. The efficient absorption and the orthogonal carrier collection facilitate an external quantum efficiency of 84.0%-89.18% for broadband wavelength. PNPs have been successfully implemented in flexible solar cells. The porous alumina membrane protects PNPs against water and oxygen intrusion and thereby imparts robustness to photovoltaic devices. Meanwhile, the excellent tolerance to mechanical stress/strain enables our unique PNP-based device to provide efficient solar-to-electricity conversion while undergoing mechanical bending.

Assessment of Radiology Artificial Intelligence Software: A Validation and Evaluation Framework.

Artificial intelligence (AI) software in radiology is becoming increasingly prevalent and performance is improving rapidly with new applications for given use cases being developed continuously, oftentimes with development and validation occurring in parallel. Several guidelines have provided reporting standards for publications of AI-based research in medicine and radiology. Yet, there is an unmet need for recommendations on the assessment of AI software before adoption and after commercialization. As the radiology AI ecosystem continues to grow and mature, a formalization of system assessment and evaluation is paramount to ensure patient safety, relevance and support to clinical workflows, and optimal allocation of limited AI development and validation resources before broader implementation into clinical practice. To fulfil these needs, we provide a glossary for AI software types, use cases and roles within the clinical workflow; list healthcare needs, key performance indicators and required information about software prior to assessment; and lay out examples of software performance metrics per software category. This conceptual framework is intended to streamline communication with the AI software industry and provide healthcare decision makers and radiologists with tools to assess the potential use of these software. The proposed software evaluation framework lays the foundation for a radiologist-led prospective validation network of radiology AI software. Learning Points: The rapid expansion of AI applications in radiology requires standardization of AI software specification, classification, and evaluation. The Canadian Association of Radiologists' AI Tech & Apps Working Group Proposes an AI Specification document format and supports the implementation of a clinical expert evaluation process for Radiology AI software.

Hybrid Threshold Denoising Framework Using Singular Value Decomposition for Side-Channel Analysis Preprocessing.

The traces used in side-channel analysis are essential to breaking the key of encryption and the signal quality greatly affects the correct rate of key guessing. Therefore, the preprocessing of side-channel traces plays an important role in side-channel analysis. The process of side-channel leakage signal acquisition is usually affected by internal circuit noise, external environmental noise, and other factors, so the collected signal is often mixed with strong noise. In order to extract the feature information of side-channel signals from very low signal-to-noise ratio traces, a hybrid threshold denoising framework using singular value decomposition is proposed for side-channel analysis preprocessing. This framework is based on singular value decomposition and introduces low-rank matrix approximation theory to improve the rank selection methods of singular value decomposition. This paper combines the hard threshold method of truncated singular value decomposition with the soft threshold method of singular value shrinkage damping and proposes a hybrid threshold denoising framework using singular value decomposition for the data preprocessing step of side-channel analysis as a general preprocessing method for non-profiled side-channel analysis. The data used in the experimental evaluation are from the raw traces of the public database of DPA contest V2 and AES_HD. The success rate curve of non-profiled side-channel analysis further confirms the effectiveness of the proposed framework. Moreover, the signal-to-noise ratio of traces is significantly improved after preprocessing, and the correlation with the correct key is also significantly enhanced. Experimental results on DPA v2 and AES_HD show that the proposed noise reduction framework can be effectively applied to the side-channel analysis preprocessing step, and can successfully improve the signal-to-noise ratio of the traces and the attack efficiency.