Good Agreement Research Articles

Design and Analysis of Surface-Mounted Permanent-Magnet Field-Modulation Machine for Achieving High Power Factor

Along with the establishment of general air-gap field modulation theory, a new type of field-modulation machine has attracted increasing attention. However, the low power factor always plagued its popularization. This paper is to focus on the simplest structure surface-mounted permanent-magnet field-modulation (SMPMFM) machine, aiming at exploring the implementation of high power-factor design. The key is that the power factor calculation formula is derived parametrically, hence the originally vague causal relationship between the structural parameters and power factor become clear, guided by which the initial structure design for high power factor achievement is determined, meanwhile the power factor adjustment evaluation and the electromagnetic performance comparison with the help of finite element analysis (FEA) are conducted to verify the validity of the proposed power factor calculation formula. Finally, a feasible high-power-factor SMPMFM prototype is fabricated and the experimental testing is performed, which has a good agreement with the formular calculation and FEA results. Hence, the validity of the proposed analysis for achieving high power-factor design of field-modulation machines is verified.

A method of determining the bremsstrahlung flux-weighted average photonuclear cross section

Based on a developed analytical model, a method is proposed for measuring the photonuclear cross section averaged over bremsstrahlung flux without application of additional target-monitor of photon flux. The method involves the use of a thin isotopic target, that completely overlaps the photon beam (a photonuclear converter), as well as an algorithm for processing the data on the yield of a reaction under study in such a target. The novel technique was validated on the reactions 100Mo(γ,n)99Mo and 58Ni(γ,n)57Ni in the range of photon end-point energy of 40.7–93.9 MeV. The photon flux-weighted average cross sections of the reactions measured experimentally are in good agreement with Monte Carlo simulations and TALYS predictions on their excitation functions.

Multioutput Wireless Charger for Drone Swarms With Reduced Switch Requirements and Independent Regulation Capability

This paper develops a cost-effective and efficient wireless charger with multiple outputs based on newly proposed H3 bridge inverter, which has the benefits of reduced switch requirements and independent regulation capability. The output power of each channel is regulated by asymmetric duty cycle control. With the help of auxiliary LC branch, soft switching is ensured in spite of load variations. The sequential logic of driver signals is analyzed in detail to explore the independent regulation characteristic. For arbitrary 2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> (or 2 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> +1) outputs, the total number of required switches is only 3 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> (or 3 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> +2) and the voltage stress of switches is the input voltage, which can reduce switch requirements. Simulation and experimental results are in good agreement with each other, proving the proposed wireless charger as a feasible solution for drone swarms.

Establishment and Validation of a Blood Test-based Nomogram to Diagnose Patients with AFP-negative HCC.

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer death worldwide. Alpha-protein (AFP) is the most widely used blood biomarker for HCC. However, elevated serum AFP is only observed in part of HCC. This study aimed to develop an efficient nomogram model to distinguish patients with alpha- protein-negative HCC and liver cirrhosis. A total of 1130 patients (508 HCC patients + 622 cirrhosis patients) were enrolled in the training cohort. A total of 244 HCC patients and 246 cirrhosis patients were enrolled in the validation cohort. A total of 41 parameters about blood tests were analyzed with logistic regression. The nomogram was based on independent factors and validated both internally and externally. Independent factors were eosinophils %, hemoglobin concentration distribution width, fibrinogen, platelet counts, total bile acid, and mitochondria aspartate aminotransferase. The calibration curve for the probability of HCC showed good agreement between prediction by nomogram and actual observation. The concordance index was 0.851. In the validation cohort, the nomogram distinguished HCC from liver cirrhosis with an area under the curve of receiver operating characteristic of 0.754. This proposed nomogram was an accurate and useful method to distinguish patients with AFP-negative HCC from liver cirrhosis.

Investigating the Mixture Quality in Multi-Injector Burner Systems, Part II: Model Application

Abstract Multi-injector burner systems offer a high spatial-temporal mixture homogeneity due to their small size and thus have a high NOx emission reduction potential at increasing flame temperatures. This potential is reduced due to the sensitivity of the mixing quality to inflow distortions caused by the flow path in the burner head. A deep understanding of the link between the inflow conditions and the mixture quality helps to optimize the flow field upstream of the injectors within the spatial and pressure loss constraints of gas turbine combustors to obtain minimal NOx emissions. This paper presents a new model approach for determining the mixture quality in multi-injector burners with output-based proper orthogonal decomposition (O-POD). The sensitivity to the inflow distortion is considered with the so-called observable vector, which describes the injector inflow conditions and is given as model input. The target quantity is the probability mass function (PMF) of the equivalence ratio at the injector outlet. This model allows a fast estimation of the mixing PMF for arbitrary inflow conditions, which is otherwise only accessible with complex time-resolved experimental or numerical approaches. The performance of the model was demonstrated with 21 reference datasets, for which a good agreement between the experimental results and the model output was obtained.

Numerical Investigation of a Hydrogen–Air Flame for NOx Prediction

Abstract Sustainable aviation fuels are a major candidate to reduce pollutant emissions in future aeronautical engines. Recently, the use of hydrogen as a fuel has gained a high interest partly because its combustion is free from carbon dioxide, a greenhouse gas, and produces few pollutants, mainly nitrogen oxides (NOx). Over the last decades, efforts on numerical methods for combustion simulation in aero-engines have largely been focused on kerosene-air combustion. However, the current transition may have a significant impact on the computational methodologies for combustor design. Hydrogen defines novel modeling issues and challenges the current state of art on numerical methodologies. The current study presents a numerical investigation of a hydrogen–air burner using large-eddy simulations (LES) with a focus on NOx prediction. The considered configuration is a two-staged combustor, similar to the well-known RQL (Rich-Quench-Lean) technology, supplied by a single coaxial injector characterized experimentally. Two combustion models are investigated: (i) tabulated chemistry based on premixed flamelets (ii) transported chemistry description by using a 21-species chemical scheme. Numerical results are compared with experimental data (NOx concentrations, temperature distributions, pressure losses). A focus on model predictions is carried out. Results show a good agreement to predict the main flow characteristics and the premixed flame position over different operating points and geometries for both frameworks. In contrast, NOx emissions are more sensitive: while the overall trend is well captured, the quantification is more scattered. Finally, an in-depth analysis is proposed to link NOx production with the nonpremixed flame size.

Nonlinear Analytical Solution in Axial Flux Permanent Magnet Machines Using Scalar Potential

Analytical model (AM) has significant value on early design stage for electric machines, in terms of both time and accuracy. AM can be developed based on vector or scalar potential, but all AMs for armature reaction are using the vector potential, and it is a difficult matter to model armature reaction by scalar potential. This is the gap that the work here proposed aims to fill. We, for the first time, report the load magnetic field prediction by using magnetic scalar potential. Also, the proposed nonlinear AM is developed for the surface mounted permanent magnet machine. The proposed model is development based on the two-dimensional harmonic subdomain approach by solving the Maxwell's equations. The magnetic sources, viz., the armature reaction is regarded as line current and solved at the connection boundaries. The comprehensive derivations and calculation algorithm are explained in detail. Moreover, the analytical model is developed for an axial flux PM machine, both finite element model and experiment are verified the correctness of proposed model, and good agreement is achieved. Moreover, a slightly modification can also be applied to the radial flux PM machines.

Indication of a fast ejecta fragment in the atomic cloud interacting with the southwestern limb of SN 1006

Context. Supernova remnants interacting with molecular and atomic clouds are interesting X-ray sources for studies of broadband nonthermal emission. X-ray line emission in these systems can be produced by different processes, such as low-energy cosmic rays (LECRs) interacting with the cloud and fast ejecta fragments moving in the cloud. Aims. This paper is aimed at studying the origin of the non-thermal X-ray emission of the southwestern limb of SN 1006 beyond the main shock to determine whether the emission is due to LECRs diffusing in the cloud or to ejecta knots moving into the cloud. Methods. We analyzed the X-ray emission of the southwestern limb of SN 1006, where the remnant interacts with an atomic cloud, using three different X-ray telescopes: NuSTAR, Chandra, and XMM-Newton. We also performed a combined spectro-imaging analysis of this region. Results. Our analysis of the nonthermal X-ray emission of the southwestern limb of SN 1006 interacting with an atomic cloud has led to the detection of an extended X-ray source in the atomic cloud, approximately 2 pc upstream of the shock front. The source is characterized by a hard continuum (described by a power law with photon index Γ ∼ 1.4) and by Ne, Si, and Fe emission lines. The observed flux suggests that the origin of the X-ray emission is not associated with LECRs interacting with the cloud. On the other hand, the spectral properties of the source, together with the detection of an IR counterpart visible with Spitzer-MIPS at 24 μm, are in good agreement with the general expectations for a fast ejecta fragment moving within the atomic cloud. Conclusions. We detected X-ray and IR emission from a possible ejecta fragment, with an approximate radius of 1 × 1017 cm and approximate mass of 10−3 M⊙ at about 2 pc out of the shell of SN 1006, in the interaction region between the southwestern limb of the remnant and the atomic cloud.

Effects of hammer configurations on pearl millet grinding system with a hammer mill: theory and experiment

In grinding processes using hammer mills, the configuration, number and speed of hammers are some of the main factors that can affect system performance. This paper aims to investigate the effects of hammer configurations in terms of specific energy consumption (SEC), grinding mass efficiency, and productivity. These effects were studied theoretically on the basis of classical grinding laws and experimentally with four different hammer configurations. From theoretical studies, a decreasing power model of SEC versus hammer configurations was developed, which was then validated with a determination coefficient of 0.99 in experiments using a 2 HP-DC hammer mill. The good agreement between theoretical and experimental results confirms that the specific energy consumption and the productivity are directly dependent on hammer configurations, but the effects are not significant for grinding mass efficiency.

Experimental modeling of slip velocity in an L-shape pulsed packed extraction column using response surface methodology

The “L-shaped pulsed packed extraction column,” a distinctive form of extraction column, is used in this study to examine the impacts of operational parameters and the physical features of the liquid systems on the slip velocity in a pilot plant. This assessment is done using response surface methodology (RSM). The operational factors of pulsation intensity, dispersed phase flow rate, and continuous phase flow rate are examined through the use of central composite design (CCD) on slip velocity. In the absence and presence of a mass transfer agent (acetone), two common liquid-liquid systems—butyl acetate and water and toluene and water—were used as the chemical systems. Interfacial tension, dispersion phase flow rate, and pulsation intensity are among the parameters that have been found to have a substantial impact on slip velocity. However, the slip velocity was either unaffected or was very slightly affected by the continuous phase flow rate and the mass transfer agent's presence. In addition, new practical models with R2 > 0.93 are presented to predict slip velocity by RSM. Finally, new comprehensive correlation based on dimensional analysis procedure, is put out to precisely forecast the slip velocity. For all of the investigated operating parameters and chemical systems, there was good agreement between predictions and experiments.

High Tidal Volume, High Positive End Expiratory Pressure and Apneic Breath Hold Strategies (Lung Navigation Ventilation Protocol) With Cone Beam Computed Tomography Bronchoscopic Biopsy of Peripheral Lung Lesions: Results in 100 Patients.

A dedicated anesthesia protocol for bronchoscopic lung biopsy-lung navigation ventilation protocol (LNVP)-specifically designed to mitigate atelectasis and reduce unnecessary respiratory motion, has been recently described. LNVP demonstrated significantly reduced dependent ground glass, sublobar/lobar atelectasis, and atelectasis obscuring target lesions compared with conventional ventilation. In this retrospective, single-center study, we examine the impact of LNVP on 100 consecutive patients during peripheral lung lesion biopsy. We report the incidence of atelectasis using cone beam computed tomography imaging, observed ventilatory findings, anesthesia medications, and outcomes, including diagnostic yield, radiation exposure, and complications. Atelectasis was observed in a minority of subjects: ground glass opacity atelectasis was seen in 30 patients by reader 1 (28%) and in 18 patients by reader 2 (17%), with good agreement between readers (κ = 0.78). Sublobar/lobar atelectasis was observed in 23 patients by reader 1 and 26 patients by reader 2, also demonstrating good agreement (κ = 0.67). Atelectasis obscured target lesions in very few cases: 0 patients (0%, reader 1) and 3 patients (3%, reader 2). Diagnostic yield was 85.9% based on the AQuIRE definition. Pathology demonstrated 57 of 106 lesions (54%) were malignant, 34 lesions (32%) were benign, and 15 lesions (14%) were nondiagnostic. Cone beam computed tomography images confirmed low rates of atelectasis, high tool-in-lesion confirmation rate, and high diagnostic yield. LNVP has a similar safety profile to conventional bronchoscopy. Most patients will require intravenous fluid and vasopressor support. Further study of LNVP and other ventilation protocols are necessary to understand the impact of ventilation protocols on bronchoscopic peripheral lung biopsy.

Online Monitoring of Iron Ore Pellet Size Distribution Using Lightweight Convolutional Neural Network

An improved U-Net based convolutional neural network (CNN) is proposed in this paper for online size monitoring of iron ore pellets. The proposed CNN model is a lightweight version of U-Net. In the proposed model each convolution block of the encoder subnet consists of a concatenated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\times 1$</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3\times 3$</tex-math> </inline-formula> filter kernel, a batch normalization layer, and a ReLU nonlinearity. The proposed CNN model efficiently detects the pellets even under variable illumination conditions and images with overlapping pellets. The proposed model is evaluated using images of iron ore pellets acquired at the discharge end of a pelletizer disc. The proposed CNN model performs satisfactorily using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 39% less learnable parameters, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 16% reduced computational cost than the original U-Net model. The proposed model outperforms the conventional U-Net, different variants of U-Nets, FCN, and SegNet in weighted IoU, BF Score, global accuracy, SSIM, and PSNR. Experimental validation also shows a good agreement between the obtained pellet size distribution and the manual sieving result. This makes the proposed algorithm suitable for online pellet size-distribution measuring instruments. This method provides a more accurate and fast size monitoring system for iron ore pellets, ideal for real-time industrial applications. The image dataset and source codes are available at the below given link. https://github.com/aryadeo/Pellet_Size_Distribution. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Due to the advent of digitalization in industries, several attempts are being made to use innovative industrial monitoring systems in steel manufacturing plants. Most of the pellet plants in steel industries use human supervision for pellet growth monitoring. In this paper, a camera-based pellet size distribution monitoring method is proposed. A novel deep learning-based algorithm is used for the detection of pellets from the captured image followed by computation of pellet size analysis. Due to low computational complexity and better accuracy, the proposed method is suitable for online pellet size distribution system development compared to other previously proposed methods. However, protection from the harsh industrial environment is required for the uninterrupted and continuous operation of the system. As a future scope, the obtained pellet size information can be used as one of the control variables in designing a control system for the automated operation of the pelletizer disc.

Experimental and Computational Investigation of Aerodynamic Interactions in Quadrotor Configurations

The influence of interactional effects on quadrotor performance in forward flight was evaluated taking into account square and diamond configurations, forward and backward tilt angles, and a range of hub spacings including overlapping blades. The analysis was based on the wind-tunnel measurements and simulations from four midfidelity computational methods and the high-fidelity tool. The outcome indicates that the efficiency of a diamond configuration improves by 5% in comparison with isolated rotors for nonoverlapping rotor spacings, while the interactions in square alignments are detrimental for all analyzed test cases with the optimum at 0.04D blade overlap. The trend is more pronounced for the backward rotor tilt with intensified interactions, for which the efficiency of the diamond configuration increases by 11% at 1.2D rotor spacing. The computational results showed good agreement with the measurement data for the forward rotor plane tilt; however, for the backward tilt angle, the spread between the calculated values, especially for torque, could be observed with the general trends maintained. The study shows negligible impact of the rotor phasing on the quadrotor efficiency.

Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients.

To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS). Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated. Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms' predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms. CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.

Mechanistic model of critical heat flux in rod bundles based on a high-precision subchannel code

A mechanistic model for predicting critical heat flux (CHF) in rod bundles is developed based on the high-precision subchannel code ATHAS. To account for the non-uniform distribution of quality in subchannels caused by the presence of mixing vane grids (MVGs) and guide tubes (GTs), further subdivision of conventional subchannels and the incorporation of MVG crossflow models are implemented in ATHAS to provide a more detailed local flow field for the CHF model. The CHF mechanism model is developed based on the concepts of bubble crowding and assumption of local phenomenon. An equivalent-tube concept is proposed to convert the turbulent intensity distribution on the tube surface to that on the rod bundle surface. The present model is implemented in ATHAS and evaluated against CHF experimental data of rod bundles under the conditions of interest for pressurized water reactors (PWRs). The predicted results show good agreement with the experimental CHF data, with an average error and root mean square of 0.99% and 4.69%, respectively, for a total of 601 data points. Furthermore, owing to the use of the subchannel subdivision method, the present model does not require any cold wall effect correction and can accurately predict the radial position of CHF.

Impact toughness and dynamic constitutive model of geopolymer concrete after water saturation

The dynamic compression test of geopolymer concrete (GC) before and after water saturation was carried out by the split Hopkinson pressure bar (SHPB). And the effects of water saturation and strain rate on impact toughness of GC were studied. Based on Weibull statistical damage distribution theory, the dynamic constitutive model of GC after water saturation was constructed. The results show that the dynamic peak strain and specific energy absorption of GC have strain rate strengthening effect before or after water saturation. The impact toughness of GC decreases after water saturation. The size distribution of GC fragments has fractal characteristics, and the fractal dimension of GC fragments after water saturation is smaller than that before water saturation. The dynamic constitutive model based on Weibull statistical damage distribution theory can accurately describe the impact mechanical behavior of GC after water saturation, and the model fitting curves are in good agreement with the experimental stress–strain curves.

Non-immersive virtual reality based treatment for children with unilateral cerebral palsy: Preliminary results.

Unilateral cerebral palsy (UCP) represents about 30-40% of overall cerebral palsy diagnoses. Upper limb impairment has a significant negative impact on activities of daily living (ADL), and recent studies have shown that the use of virtual reality (VR) can increase motivation and promote an improvement in ADL. This preliminary study was aimed at exploring the acceptability and usability of a VR rehabilitation treatment, using the VITAMIN Platform, for children with UCP. A secondary goal of the study was to compare the results of usual standardized clinical scales and questionnaires with kinematic results as well as with the quantitative measures acquired by the VITAMIN platform in each exercise of the rehabilitation sessions. Six children with UCP (aged 7-15) were recruited for a preliminary investigation in using a non-immersive VR system. The treatment was composed of 10 weekly sessions of 45 minutes. Each child played five types of exergames, using the impaired upper limb to hit virtual objects projected on a wide screen. Standardized clinical scales, kinematic analysis, and questionnaires were used to extensively assess upper limb function before and at the end of treatment. Five typically-developing children provided a reference for the instrumented kinematic assessment. At the end of the treatment, Melbourne Assessment 2 (MA2) scores increased for all the participants (mean increase in range of movement (ROM) + 19.1%, accuracy + 4.6%, dexterity + 13.1%, fluency + 10.3%). Shoulder flexion-extension ROM also improved (mean increase + 10.5°), and according to the kinematic analysis, shoulder movements became more similar to reference profiles. These results were confirmed by a general improvement in performing ADL, assessed by the ABILHAND-Kids questionnaire. Finally, a general agreement among the different measures and indexes emerged from the acquired data. The results show that VR treatment with the VITAMIN platform could be engaging and functional for rehabilitation of children with UCP. The good agreement among the qualitative and quantitative measures and indexes confirms the potential of such novel treatment. However, due to the limited sample size and small number of sessions, further and larger investigations are required to evaluate the effectiveness and to generalize the results.

Anisotropic elastoplasticity and fracture of SCFR-PEEK composites in complex biaxial loading: Experiments and modelling

This research aims to gain insight into the anisotropic deformation and fracture behaviours of short carbon fibre reinforced polyether-ether ketone composites (SCFR-PEEK) under complex loading using experimental and modelling approaches. Novel biaxial tensile experiments using optimised cruciform specimens are designed and performed to study the anisotropic elastoplasticity and fracture behaviours of SCFR-PEEK in various stress states. An advanced anisotropic constitutive model was developed, experimentally calibrated, numerically implemented, and successfully verified by mechanical responses of biaxial tension with various stress ratios. Furthermore, using a hybrid experimental-numerical approach, the biaxial failure stress at crack initiation sites captured by a high-speed camera in biaxial tension was obtained. Employing experimental failure stresses at various uniaxial and biaxial stress states, a maximum stress based failure criterion was developed, which is coupled with the anisotropic elastoplastic model, thus enabling the description the anisotropic elastoplastic and failure behaviours under complex loading. The prediction of the failure of SCFR-PEEK in complex biaxial stress states using the coupled model is in good agreement with biaxial tensile experimental results using circular-notched specimens. Therefore, the developed model is verified to be capable of describing both elastoplastic deformation and fracture failure behaviours of SCFR-PEEK composites in complex loading.

Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images

Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset. This reference standard also provides for the first time a benchmark for computer vision algorithms. In addition, in line with other papers, we also evaluate our algorithm at slide-level by measuring the agreement between the algorithm and six pathologists on TPS quantification. We find a moderately low interobserver agreement at cell-level level (mean reader-reader F1 score = 0.68) which our algorithm sits slightly under (mean reader-AI F1 score = 0.55), especially for cases from the clinical center not included in the training set. Despite this, we find good AI-pathologist agreement on quantifying TPS compared to the interobserver agreement (mean reader-reader Cohen’s kappa = 0.54, 95% CI 0.26–0.81, mean reader-AI kappa = 0.49, 95% CI 0.27—0.72). In conclusion, our deep learning algorithm demonstrates promise in detecting PD-L1 expression at a cellular level and exhibits favorable agreement with pathologists in quantifying the tumor proportion score (TPS). We publicly release our models for use via the Grand-Challenge platform.

A framework for risk assessment of groundwater contamination integrating hydrochemical, hydrogeological, and electrical resistivity tomography method.

Groundwater contamination have been widely concerned. To reliably conduct risk assessment, it is essential to accurately delineate the contaminant distribution and hydrogeological condition. Electrical resistivity tomography (ERT) has become a powerful tool because of its high sensitivity to hydrochemical parameters, as well as its advantages of non-invasiveness, spatial continuity, and cost-effectiveness. However, it is still difficult to integrate hydrochemical, hydrogeological, and ERT datasets for risk assessment. In this study, we develop a general framework for risk assessment by sequentially jointing hydrochemical, hydrogeological, and ERT surveys, while establishing petrophysical relationships among these data. This framework can be used in groundwater-contaminated site and help to delineate the distribution of contaminants. In this study, it was applied to a nitrogen-contaminated site where field ERT survey and borehole information provided valuable measurement data for validating the consistency of contamination and hydrogeological condition. Risk assessment was conducted based on the refined results by the establishment of relationship between conductivity and contaminants concentration with . The contamination source was identified and the transport direction was predicted with the good agreement of = 0.965 between simulated and observed groundwater head, which can help to propose measures for anti-seepage and monitoring. This study thus enhances the reliability of risk assessment and prediction through a thought-provoking innovation in the realm of groundwater environmental assessment.