Maximum Value Research Articles

High-temperature water vapor sensors based on rare-earth-doped barium cerate

In this novel study, BaCe0.9Y0.1O3–δ, BaCe0.9Eu0.1O3–δ, BaCe0.9Nd0.1O3–δ, and BaCe0.9Dy0.1O3–δ powders were synthesized by the self-combustion method and processed into thick porous films (60–70 % porosity) to investigate the water vapor sensing properties in the 400–700 °C temperature range. All samples showed a stable response value to water vapor in the whole temperature range, expressed as impedance ratio in dry and wet argon (ZdryAr/ZwetAr), and were able to detect 0.03 vol% of water vapor at 550 °C within the impedance range of 103 Ω at 100 Hz. The response values increased with the partial pressure of water and decreased with the temperature, whereas the maximal value of 3.41 reached the BaCe0.9Eu0.1O3–δ sample at 550 °C and p(H2O) = 4.28 kPa. The average response time was several seconds and only slightly changed with the material type and experimental conditions. The recovery time depended on temperature and the ZdryAr/ZwetAr ratio, whereas the increase in the gas flow rate from 100 cm3/min to 200 cm3/min significantly reduced the recovery time for the BaCe0.9Eu0.1O3–δ sample from 230 s to 55 s at 550 °C and p(H2O) = 2.14 kPa. All the samples exhibited stability and a high degree of reversibility after multiple exchanges of wet and dry atmospheres at different temperatures. Yet, their tendency to deteriorate in the presence of CO2 might challenge a potential application in aggressive environments.

Fatty alcohols as sustainable compatibilizer agents for the compound of natural rubber/silica: The curing, rubberiness and tensile strength behaviours

Fatty alcohols are derived from palm oil production and, stearyl and lauryl alcohols are examples of them. The stearyl and lauryl alcohols have the potential to be used as rubber additives for the compounding and vulcanizing processes. These materials are relatively new as rubber additives and, they were added into silica-filled natural rubber (NR) compound. The aim of using them is to increase the grade of silica dispersion inside the NR matrix. Initially, the NR was filled by (precipitate) silica at 30 parts per hundred of NR, whilst the fatty alcohols were added into the silica-filled NR with variable concentrations from 1 to 4 phr, separately. The compounds of NR/silica/stearyl alcohol and NR/silica/lauryl alcohol have been vulcanised at 150 °C by implementing a semi-efficient rubber composition. From the vulcanization behaviour, it was observed that both fatty alcohols have the role of compatibilizer and plasticising agents for the compound of NR/silica. Those fatty alcohols decreased the viscousness but increased the curing rate index (CRI). The more concentration of fatty alcohols was supplied, the less viscousness and the higher the CRI. Those fatty alcohols also shifted the crosslinking level and tensile strength. It also observed that the break elongation or rubberiness has been shifted up. Tensile strength has been shifted to a maximal value with a 2 phr of stearyl alcohol or 3 phr of lauryl alcohol addition.

Novel Tools for Single Comparative and Unified Evaluation of Qualitative and Quantitative Bioassays: SS/PV-ROC and SS-J/PV-PSI Index-ROC Curves with Integrated Concentration Distributions, and SS-J/PV-PSI Index Cut-Off Diagrams.

Background: This investigation is both a study of potential non-invasive diagnostic approaches for the bladder cancer biomarker UBC® Rapid test and a study including novel comparative methods for bioassay evaluation and comparison that uses bladder cancer as a useful example. The objective of the paper is not to investigate specific data. It is used only for demonstration, partially to compare ROC methodologies and also to show how both sensitivity/specificity and predictive values can be used in clinical diagnostics and decision making. This study includes ROC curves with integrated cut-off distribution curves for a comparison of sensitivity/specificity (SS) and positive/negative predictive values (PPV/NPV or PV), as well as SS-J index/PV-PSI index-ROC curves and SS-J/PV-PSI index cut-off diagrams (J = Youden, PSI = Predictive Summary Index) for the unified direct comparison of SS-J/PV results achieved via quantitative and/or qualitative bioassays and an identification of optimal separate or unified index cut-off points. Patients and Methods: According to the routine diagnostics, there were 91 patients with confirmed bladder cancer and 1152 patients with no evidence of bladder cancer, leading to a prevalence value of 0.073. This study performed a quantitative investigation of used-up test cassettes from the visual UBC® Rapid qualitative point-of-care assay, which had already been applied in routine diagnostics. Using a photometric reader, quantitative data could also be obtained from the test line of the used cassettes. Interrelations between SS and PV values were evaluated using cumulative distribution analysis (CAD), SS/PV-ROC curves, SS-J/PV-PSI index-ROC curves, and the SS-J/PV-PSI index cut-off diagram. The maximum unified SS-J/PV-PSI index value and its corresponding cut-off value were determined and calculated with the SS-J/PV-PSI index cut-off diagram. Results: The use of SS/PV-ROC curves with integrated cut-off concentration distribution curves provides improved diagnostic information compared to "traditional" ROC curves. The threshold distributions integrated as curves into SS/PV-ROC curves and SS-J/PV-PSI index-ROC curves run in opposite directions. In contrast to the SS-ROC curves, the PV-ROC and the novel PV-PSI index-ROC curves had neither an area under the curve (AUC) nor a range from 0% to 100%. The cut-off level of the qualitative assay was 7.5 µg/L, with a sensitivity of 65.9% and a specificity of 63.3%, and the PPV was 12.4% and the NPV was 95.9%, at a threshold value of 12.5 µg/L. Based on these set concentrations, the reader-based evaluation revealed a graphically estimated 5% increase in sensitivity and a 13% increase in specificity, as compared to the visual qualitative POC test. In the case of predictive values, there was a gain of 8% for PPV and 10% for NPV. The index values and cut-offs were as follows: visual SS-J index, 0.328 and 35 µg/L; visual PV-PSI index, 0.083 and 5.4 µg/L; maximal reader Youden index, 0.0558 and 250 µg/L; and maximal PV-PSI index, 0.459 and 250 µg/L, respectively. The maximum unified SS-J/PV-PSI index value was 0.32, and the cut-off was 43 µg/L. The reciprocal SS-J index correctly detected one out of three patients, while the reciprocal PV-PSI index gave one out of twelve patients a correct diagnosis. Conclusions: ROC curves including cut-off distribution curves supplement the information lost in "traditionally plotted" ROC curves. The novel sets of ROC and index-ROC curves and the new SS/PV index cut-off diagrams enable the simultaneous comparison of sensitivity/specificity and predictive value profiles of diagnostic tools and the identification of optimal cut-off values at maximal index values, even in a unifying SS/PV approach. Because the curves within an SS-J/PV-PSI index cut-off diagram are distributed over the complete cut-off range of a quantitative assay, this field is open for special clinical considerations, with the need to vary the mentioned clinical diagnostic parameters. Complete or partial areas over the x-axis (AOX) can be calculated for summarized quantitative or qualitative effectivity evaluations with respect to single and/or unified SS-J and PV-PSI indices and with respect to single, several, or several unified assays. The SS-J/PV-PSI index-AOX approach is a new tool providing additional joint clinical information, and the reciprocal SS-J indices can predict the number of patients with a correct diagnosis and the number of persons who need to be examined in order to correctly predict a diagnosis of the disease. These methods could be used in applications like medical or plant epidemiology, machine learning algorithms, and neural networks.

Diffusion prediction of competitive information with time-varying attractiveness in social networks

The ubiquity of social media has facilitated the simultaneous dissemination of large-scale information within online social networks. By assuming that information attractiveness is static, numerous studies have been devoted to the analysis of multiple information dissemination. However, real-world information attractiveness often exhibits variations throughout the dissemination process. This paper delves into the study of how time-varying information attractiveness influences the diffusion process, particularly in the context of competitive information dissemination regarding multiple products. First, we propose a Markov multi-information diffusion model, incorporating three critical parameters: the attractiveness degree, the information-boom time, and the information prosperity index, to address the dynamic nature of attractiveness. The basic reproduction number is derived, and the accuracy of the proposed model is verified. Furthermore, our numerical simulation results illustrate that both the maximal attractiveness value and the information prosperity duration significantly enhance information competitiveness, while delayed information boom time may undermine this competitiveness. In addition, it is indicated that improving the peak attractiveness is the key for time-varying information to achieve a better spreading effect. Moreover, we find that the growth of information attractiveness can even mitigate the impact of blocking the spread caused by information discarding behavior, highlighting intrinsic quality as the paramount determinant of information promotion outcomes.

Gradient Flow of the Sinai–Ruelle–Bowen Entropy

Motivated by an extension to Gallavotti–Cohen Chaotic Hypothesis, we study local and global existence of a gradient flow of the Sinai–Ruelle–Bowen entropy functional in the space of transitive Anosov maps. For the space of expanding maps on the unit circle, we equip it with a Hilbert manifold structure using a Sobolev norm in the tangent space of the manifold. Under the additional measure-preserving assumption and a slightly modified metric, we show that the gradient flow exists globally and every trajectory of the flow converges to a unique limiting map where the SRB entropy attains the maximal value. In a simple case, we obtain an explicit formula for the flow’s ordinary differential equation representation. This gradient flow has close connection to a nonlinear partial differential equation, a gradient-dependent diffusion equation.

Monitoring the Wear Trends in Wind Turbines by Tracking Fourier Vibration Spectra and Density Based Support Vector Machines

To make wind power more competitive, it is necessary to reduce turbine downtime and reduce costs associated with wind turbine operation and maintenance (O&M). Incorporating machine learning in the development of condition-based predictive maintenance methodologies for wind turbines can enhance their efficiency and reliability. This paper presents a monitoring method that utilizes Density Based Support Vector Machines (DBSVM) and the evolutionary Fourier spectra of vibrations. This method allows for the smart monitoring of the function evolution of the turbine. A complex optimal function (FO) for 5-degree order has been developed that will be the boundary function of the DBSVM to be timely determined from the Fourier spectrum through the magnitude–frequency and place of the failure occurring in the wind turbine drivetrains. The trend of the failure was constructed with the maximal values of the optimal frequency function for both yesthe cases of the upwind and downwind parts of the gearbox.

Robust Design Optimization of Electric Machines with Isogeometric Analysis

In electric machine design, efficient methods for the optimization of the geometry and associated parameters are essential. Nowadays, it is necessary to address the uncertainty caused by manufacturing or material tolerances. This work presents a robust optimization strategy to address uncertainty in the design of a three-phase, six-pole permanent magnet synchronous motor (PMSM). The geometry is constructed in a two-dimensional framework within MATLAB®, employing isogeometric analysis (IGA) to enable flexible shape optimization. The main contributions of this research are twofold. First, we integrate shape optimization with parameter optimization to enhance the performance of PMSM designs. Second, we use robust optimization, which creates a min–max problem, to ensure that the motor maintains its performance when facing uncertainties. To solve this bilevel problem, we work with the maximal value functions of the lower-level maximization problems and apply a version of Danskin’s theorem for the computation of generalized derivatives. Additionally, the adjoint method is employed to efficiently solve the lower-level problems with gradient-based optimization. The paper concludes by presenting numerical results showcasing the efficacy of the proposed robust optimization framework. The results indicate that the optimized PMSM designs not only perform competitively compared to their non-robust counterparts but also show resilience to operational and manufacturing uncertainties, making them attractive for industrial applications.

Transformation of internal solitary waves at the edge of ice cover

Abstract. Internal wave-driven mixing is an important factor in the balance of heat and salt fluxes in the polar regions of the ocean. Transformation of internal waves at the edge of the ice cover can enhance the mixing and melting of ice in the Arctic Ocean and Antarctica. In the polar oceans, internal solitary waves (ISWs) are generated by various sources, including tidal currents over bottom topography, the interaction of ice keels with tides, time-varying winds, vortices, and lee waves. In this study, a numerical investigation of the transformation of ISWs propagating from open water in the stratified sea under the edge of the ice cover is carried out to compare the depression ISW transformation and loss of energy on smooth ice surfaces, including those on the ice shelf and glacier outlets, with the processes beneath the ridged underside of the ice. They were carried out using a non-hydrostatic model that is based on the Reynolds-averaged Navier–Stokes equations in the Boussinesq approximation for a continuously stratified fluid. The Smagorinsky turbulence model extended for stratified fluid was used to describe the small-scale turbulent mixing explicitly. Two series of numerical experiments were carried out in an idealized 2D setup. The first series aimed to study the processes of the ISWs of depression transformation under an ice cover of constant submerged ice thickness. Energy loss was estimated based on a budget of depth-integrated pseudoenergy before and after the wave transformation. The transformation of ISWs of depressions is controlled by the blocking parameter β, which is the ratio of the minimum thickness of the upper layer under the ice cover to the incident wave amplitude. The energy loss was relatively small for large positive and large negative values of β. The maximal value of energy loss was about 38 %, and it was reached at β≈0 for ISWs. In the second series of experiments, a number of keels were located on the underside of the constant-thickness ice layer. The ISW transformation under ridged ice also depends on the blocking parameter β. For large keels (β<0), more than 40 % of energy is lost on the first keel, while for relatively small keels (β>0.3), the losses on the first keel are less than 6 %. Energy losses due to all keels depend on the distance between them, which is characterized by the parameter μ, i.e. the ratio of keel depth to the distance between keels. In turn, for a finite length of the ice layer, the distance between keels depends on the keel quantity.

On stability behaviors of 5D M-theory black objects

Using N = 2 supergravity formalism, we investigate certain behaviors of five-dimensional black objects from the compactification of M-theory on a Calabi–Yau three-fold. The manifold has been constructed as the intersection of two homogeneous polynomials of degrees (ω + 2, 1) and (2, 1) in a product of two weighted projective spaces given by WP4(ω,1,1,1,1)×P1 . First, we determine the allowed electric charge regions of the BPS and non BPS black holes obtained by wrapping M2-branes on appropriate two cycles in such a proposed Calabi–Yau three-fold. After that, we calculate the entropy of these solutions which takes a maximal value corresponding to ω = 1 defining the ordinary projective space P4 . For generic values of ω, we show that the non BPS states are unstable. Then, we conduct a similar study of five-dimensional black strings. Concerning the allowed magnetic charge regions of the BPS and non BPS black stringy solutions derived from M5-branes on dual divisors, we calculate the tension taking a minimal value for P4 . By determining the recombination factor, we show that the non-BPS black string states are stable in the allowed regions in the magnetic charge space.

Optimal hybrid classification model for event recommendation system

There is a growing need for recommender systems and other ML-based systems as an abundance of data is now available across all industries. Various industries are currently using recommender systems in slightly different ways. These programs utilize algorithms to propose appropriate products to consumers based on their prior choices and interactions. Moreover, Systems for recommending events to users suggest pertinent happenings that they might find interesting. As opposed to an object recommender that suggests books or movies; event-based recommender systems typically require distinct algorithms. A developed event recommendation method is introduced which includes two stages: feature extraction and recommendation. In stage, I, a Set of features like personal willingness, community willingness, informative content, edge weight, and node interest degree are extracted. Stage II of the event recommendation system performs a hybrid classification by combining LSTM and CNN. In the LSTM classifier, optimal tuning is done by Improvised Cat and Mouse optimization (ICMO) algorithm. The results of the ICMO technique at an 80% training percentage have the maximum sensitivity value of 95.19%, whereas those of the existing approaches SSA, DINGO, BOA, and CMBO have values of 93.89%, 93.35%, 92.36%, and 92.24%. Finally, the best result is then determined by evaluating the whole performance.

Electrochemically Driven Transformation of Olivine LiFePO4 to Olivine NaFePO4 in Cells with Sodium Anode and Carbonate Electrolyte

AbstractThe direct electrochemical transformation of commercial LiFePO4 to NaxFePO4 olivine in a Na||LiFePO4 electrochemical cell with sodium electrolyte has been studied. Electrolytes containing 1 M of NaClO4 in ethylene carbonate (EC), dimethyl carbonate (DMC) and fluoroethylene carbonate (FEC) have been probed, and in the solvents with the ratio of EC:DMC:FEC=0.4:0.5:0.1 by volume, maximal values of the specific capacity (113 mAh g−1) with good cycling stability and maximal discharge currents are achieved upon sodiation. To determine the composition of the cathode material and utilization factor of LiFePO4, the dependence of open circuit voltage on specific capacity has been investigated. The general formula of the sodiation product can be written as Na0.88FePO4. Its theoretic specific capacity may be estimated as 135 mAh g−1. The composition of the products obtained is supported by XRD analysis, which suggests that in discharged cathodes, Coulombic influence of LiFePO4 crystallites on neighboring NaxFePO4 crystallites may enlarge the unit cell of the former and reduce its size of the latter. Probably, shrinkage in the presence of LiFePO4 stabilizes the olivine structure of NaxFePO4 and prevents its transformation from electrochemically active olivine to inactive maricite.

Tight Upper Bound for the Maximal Expectation Value of the N$N$‐Partite Generalized Svetlichny Operator

AbstractGenuine multipartite non‐locality is not only of fundamental interest but also serves as an important resource for quantum information theory. The ‐partite scenario and provide an analytical upper bound on the maximal expectation value of the generalized Svetlichny inequality achieved by an arbitrary ‐qubit system is considered. Furthermore, the constraints on quantum states for which the upper bound is tight are also presented and illustrated by noisy generalized Greenberger‐Horne‐Zeilinger states. Especially, the new techniques proposed to derive the upper bound allow more insights into the structure of the generalized Svetlichny operator and enable us to systematically investigate the relevant properties. As an operational approach, the variation of the correlation matrix defined makes it more convenient to search for suitable unit vectors that satisfy the tightness conditions. Finally, the results give feasible experimental implementations in detecting the genuine multipartite non‐locality and can potentially be applied to other quantum information processing tasks.

Machine learning developed a fibroblast-related signature for predicting clinical outcome and drug sensitivity in ovarian cancer.

Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores. The Lasso + StepCox[forward] method-based predicting model having a highest average C index of 0.69 was referred as the optimal CRS and it had a stable and powerful performance in predicting clinical outcome of OC patients, with the 1-, 3-, and 5-year area under curves were 0.699, 0.708, and 0.767 in the Cancer Genome Atlas cohort. The C index of CRS was higher than that of tumor grade, clinical stage, and many developed signatures. Low CRS score demonstrated lower tumor immune dysfunction and exclusion score, lower immune escape score, higher PD1&CTLA4 immunophenoscore, higher tumor mutation burden score, higher response rate and better prognosis in OC, suggesting a better immunotherapy response. OC patients with low CRS score had a lower half maximal inhibitory concentration value of some drugs (Gemcitabine, Tamoxifen, and Nilotinib, etc) and lower score of some cancer-related hallmarks (Notch signaling, hypoxia, and glycolysis, etc). The current study developed an optimal CRS in OC, which acted as an indicator for the prognosis, stratifying risk and guiding treatment for OC patients.

Diagnostic Performance of 18F-FDG Positron Emission Tomography/Computed Tomography and Blood Test Parameters for Pulmonary Inflammatory Pseudotumor.

Pulmonary inflammatory pseudotumor (PIP) is an inflammatory proliferative tumor-like lesion that frequently exhibits hypermetabolism on 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography imaging (PET/CT) and is readily misdiagnosed as a malignant tumor. The purpose of this study was to identify PIP by combining PET/computed tomography metabolic and blood test characteristics with machine learning. We recruited 27 patients with PIP and 28 patients with lung cancer (LC). The PET metabolic and blood test parameters were collected, and the differences between the groups were evaluated. In addition, we combined the support vector machine (SVM) classifier with the indicators that differed between the groups to classify PIP and LC. For PET metabolic parameters, our findings showed that, as compared with the LC group, maximal standardized uptake value (P< 0.001, t = -4.780), Mean standardized uptake value SUVmean, P< 0.001, t = -4.946), and SD40% (P< 0.001, t = -4.893) were considerably reduced in the PIP group, whereas CV40% (P= 0.004, t = 3.012) was significantly greater. For blood test parameters, the total white blood cell count (P< 0.001, t= 6.457) and absolute neutrophil count (P< 0.001, t= 6.992) were substantially higher in the PIP group than in the LC group. Furthermore, the performance of SVM trained solely on PET metabolic parameters (mean area under the curve [AUC] = 0.84) was comparable to that of SVM trained solely on blood test parameters (mean AUC = 0.86). Surprisingly, utilizing the combined parameters increased SVM performance significantly (mean AUC = 0.98). PET metabolic and blood test parameters differed significantly between the PIP and LC groups, and the SVM paradigm using these significantly different features has the potential to be used to classify PIP and LC, which has important clinical implications.

Bottom spectrum of three-dimensional manifolds with scalar curvature lower bound

A classical result of Cheng states that the bottom spectrum of complete manifolds of fixed dimension and Ricci curvature lower bound achieves its maximal value on the corresponding hyperbolic space. The paper establishes an analogous result for three-dimensional complete manifolds with scalar curvature lower bound subject to some necessary topological assumptions. The rigidity issue is also addressed and a splitting theorem is obtained for such manifolds with the maximal bottom spectrum.

Hybrid Fabry–Perot interferometer: Polymer-induced microcavity for temperature and gas pressure measurement

Developing new Fabry–Perot interferometer (FPI) with high sensitivity has always been a focus in the field of optical fiber sensing. This work proposed and experimentally demonstrated a novel hybrid-structured FPI based on harmonic Vernier effect and traditional Vernier effect, utilizing a polydimethylsiloxane (PDMS) microcavity. The reference FPI is formed by the hollow core fiber, which is insensitive to temperature and gas pressure change. The sensing element is formed by two parts including a short single-mode fiber (SMF) and a PDMS microcavity. Due to the similar refractive index between the SMF and PDMS microcavity, there is very weak reflection at the interface. Only reflection at the interface between the PDMS microcavity and the air is observed. Additionally, five hybrid FPIs consisting of PDMS microcavity were fabricated and applied for temperature and gas pressure measurement, which exhibited high temperature and gas pressure sensitivities with maximal values of 5.49 nm/°C, and 22.97 nm/MPa, respectively.

Laboratory and field measurements of water relations, photosynthetic parameters, and hydration traits in macrolichens in a tropical lower montane rainforest in Thailand.

Ecophysiological studies of lichens in tropical Asia are rare, and additional studies can increase the understanding of lichen life in this region. The main aim of this study was to observe the relationships between water availability and photosynthetic parameters, as well as hydration trait parameters, in macrolichens during the rainy and dry seasons in a tropical forest. A total of 11 lichen species growing in a lower montane rainforest in Thailand were collected and studied. The results clearly showed that the specific thallus mass (STM), net photosynthetic rate (Pn), the potential quantum yield of primary photochemistry (Fv/Fm), chlorophyll content, and carotenoid content of almost all lichens were lower in the dry season than in the rainy season. Field measurements in the dry season revealed that only the foliose chlorolichen Parmotrema tinctorum was metabolically active and exhibited slight carbon assimilation. In the rainy season, all lichens started their photosynthesis in the early morning, reached maximal values, declined, and ceased when the thalli desiccated. The photosynthetically active period of the lichens was approximately 2-3h in the morning, and the activities of the cyanolichens ended approximately 30min after the chlorolichens. The hydration trait parameters, including the STM, maximal water content (WCmax), and water holding capacity (WHC), were greater in the cyanolichens. In addition, the maximal Pn (Pnmax) and optimal water content (WCopt) for Pn were also greater in the cyanolichens, but the maximal Fv/Fm (Fv/Fmmax) was lower. The cyanolichens compensated for their inability to use humid air to restore photosynthesis by having higher water content and storage, higher photosynthetic rates, and longer photosynthetically active periods. This study provides additional insights into lichen ecophysiology in tropical forests that can be useful for lichen conservation.

Advanced confidence methods in deep learning

The typical aim of classification tasks is to maximize the accuracy of the predicted label for a given input. This accuracy increases with the confidence, which is the maximal value of the output units, and when the accuracy equals confidence, calibration is achieved. Herein, several methods are proposed to enhance the accuracy of inputs with similar confidence, extending significantly beyond calibration. Using the first gap between the maximal and second maximal output values, the accuracy of the inputs with similar confidence is enhanced. The extension of the confidence or confidence gap to their minimal value among a set of augmented inputs further enhances the accuracy of inputs with similar confidence. Enhanced accuracies are demonstrated on EfficientNet-B0 trained on ImageNet and CIFAR-100, and VGG-16 trained on CIFAR-100. The results suggest improved applications for high-accuracy classification tasks that require manual operation for a given fraction of low-accuracy inputs.

Diagnostic value of 18F-fluorodeoxyglucose positron emission tomography/computed tomography imaging in pediatric opsoclonus myoclonus ataxia syndrome presenting with neuroblastoma.

Early precision diagnosis and effective treatment of opsoclonus myoclonus ataxia syndrome (OMAS) patients presenting with neuroblastoma can prevent serious neurological outcomes. To assess the diagnostic value of 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging in pediatric OMAS with neuroblastoma. A retrospective evaluation of 45 patients diagnosed with OMAS who underwent 18F-FDG PET/CT was performed. A univariate analysis was performed to compare clinical characteristics between OMAS with and without neuroblastoma. Univariate and multivariate logistic regression analyses were applied to identify independent risk factors for OMAS with neuroblastoma and to develop the clinical model. Finally, independent risk factors and PET/CT were fitted to build the combined model for the diagnosis of OMAS with neuroblastoma and presented as a nomogram. Receiver operating characteristic curve, decision curve, and calibration curveanalyses were conducted to evaluate the performance of the models. Among 45 patients, 27 were PET/CT-positive, 23/27 lesions were neuroblastoma, and four were false positives. One of the false positive patients was confirmed to be adrenal reactive hyperplasia by postoperative pathology, and the symptoms of OMAS disappeared in the remaining three cases during clinical follow-up. The average maximal standardized uptake value of PET/CT-positive lesions was 2.6. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of PET/CT were 100%, 81.8%, 85.2%, 100%, and 91.1%, respectively. Age at diagnosis, lactate dehydrogenase, and neuron-specific enolase showed statistically significant differences between OMAS with and without neuroblastoma. Lactate dehydrogenase was identified as the independent risk factor to develop the clinical model, and the clinical model demonstrated an area under the curve (AUC) of 0.82 for the diagnosis of OMAS with neuroblastoma, with an AUC as high as 0.91 when combined with PET/CT. The decision curve analysis and calibration curve demonstrated that the nomogram had good consistency and clinical usefulness. In patients with OMAS, 18F-FDG PET/CT has a high diagnostic accuracy in detecting tumors of the neuroblastoma, especially when combined with the independent risk factor serum lactate dehydrogenase.

Optimal thermometers with spin networks

The heat capacity C of a given probe is a fundamental quantity that determines, among other properties, the maximum precision in temperature estimation. In turn, C is limited by a quadratic scaling with the number of constituents of the probe, which provides a fundamental limit in quantum thermometry. Achieving this fundamental bound with realistic probes, i.e. experimentally amenable, remains an open problem. In this work, we tackle the problem of engineering optimal thermometers by using networks of spins. Restricting ourselves to two-body interactions, we derive general properties of the optimal configurations and exploit machine-learning techniques to find the optimal couplings. This leads to simple architectures, which we show analytically to approximate the theoretical maximal value of C and maintain the optimal scaling for short- and long-range interactions. Our models can be encoded in currently available quantum annealers, and find application in other tasks requiring Hamiltonian engineering, ranging from quantum heat engines to adiabatic Grover’s search.