Application of supervised self-organizing maps in breast cancer diagnosis by total synchronous fluorescence spectroscopy

Data from total synchronous fluorescence spectroscopy (TSFS) measurements of normal and malignant breast tissue samples are introduced in supervised self-organizing maps, a type of artificial neural network (ANN), to obtain diagnosis. Three spectral regions in both TSFS patterns and first-derivative TSFS patterns exhibited clear differences between normal and malignant tissue groups, and intensities measured from these regions served as inputs to neural networks. Histology findings are used as the gold standard to train self-organizing maps in a supervised way. Diagnostic accuracy of this procedure is evaluated with sample test groups for two cases, when the neural network uses TSFS data and when the neural network uses data from first-derivative TSFS. In the first case diagnostic sensitivity of 87.1% and specificity of 91.7% are found, while in the second case sensitivity of 100% and specificity of 94.4% are achieved.     

Effects of Al2O3 particle reinforcement on the lubricated sliding wear behavior of ZA-27 alloy composites

The present investigation deals with the effect of Al₂O₃ particle reinforcement on the lubricated sliding behavior of ZA-27 alloy. The composites with 3, 5, and 10 wt% of Al₂O₃ particles were produced by the compocasting procedure. Tribological properties of alloy and composites were studied, using block-on-disk tribometer at different specific loads and sliding speeds. The test results revealed that composite specimens exhibited significantly lower wear rate, but higher coefficient of friction than the matrix alloy specimens in all the combinations of applied loads and sliding speeds. The improved antiwear characteristics of the composites were influenced by positive effects of higher frictional heating on compatibility of the composite phases and suppressing micro-cracking tendency. Due to that, effects of reinforcing hard particles were manifested through the reduced wear rate of composites, especially in conditions of higher load, lower sliding speeds and higher Al₂O₃ particle content. In present wear tests, the significant forming of mechanically mixed layers was not noticed, what is confirmed by the SEM microphotographs.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

The Interplay of Lung Cancer,COVID-19, and Vaccines

Patients with cancer are more susceptible to a higher risk of coronavirus infection and its severe complications than the general population. In addition, these patients were not included in the pivotal clinical trials for COVID-19 vaccines. Therefore, considerable uncertainty remains regarding the management of cancer patients during the COVID-19 pandemic and the safety of COVID-19 vaccinations in cancer patients. In this review, we summarize the current knowledge generated from the beginning of the COVID-19 pandemic on the vulnerability of cancer patients to the coronavirus disease, as well as the effectiveness of COVID-19 vaccines in this population. We also discuss the available data on the effects of anticancer treatment with immune checkpoint inhibitors on the immune responses to SARS-CoV-2 in cancer patients. Special attention in this review will be given to patients with lung cancer, as such patients are at an increased risk for severe effects from COVID-19.     

Morphology evolution during cooling of quiescent immiscible polymer blends: matrix crystallization effect on the dispersed phase coalescence

The influence of the matrix crystallization on the coalescence of the dispersed phase particles, in quiescent immiscible polymer blends, is a topic that is scientifically addressed scarcely. The coarsening of the phase structure that is induced by the matrix crystallizing domains was studied using the well-established system comprising a polypropylene and an ethylene–propylene rubber (PP/EPR blends). This subject is of great importance as the effectiveness in the toughening of PP is directly determined by the EPR particle size. Cooling experiments were commenced for resolving the correlation among the imposed cooling conditions, the formed matrix crystalline morphology, and the coalescence of the dispersed phase particles. A confirmation of the profound effect of the PP crystallization on the coalescence of EPR particles was undoubtedly obtained. The contribution of the crystallization to the coalescence of the dispersed phase particles is largest at a finite rate of cooling. A thorough discussion regarding the observed effects, encompassing a potential rejection or an engulfing of the dispersed phase particles by the growing crystallites, was undertaken.     

Delay compensated control of the Stefan problem and robustness to delay mismatch

This paper presents a control design for the one‐phase Stefan problem under actuator delay via a backstepping method. The Stefan problem represents a liquid‐solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. The actuator delay is modeled by a first‐order hyperbolic partial differential equation (PDE), resulting in a cascaded transport‐diffusion PDE system defined on a time‐varying spatial domain described by an ordinary differential equation (ODE). Two nonlinear backstepping transformations are utilized for the control design. The setpoint restriction is given to guarantee a physical constraint on the proposed controller for the melting process. This constraint ensures the exponential convergence of the moving interface to a setpoint and the exponential stability of the temperature equilibrium profile and the delayed controller in the norm. Furthermore, robustness analysis with respect to the delay mismatch between the plant and the controller is studied, which provides analogous results to the exact compensation by restricting the control gain.     

Adaptive nonlinear output-feedback schemes with Marino-Tomeicontroller

Three new adaptive nonlinear output-feedback schemes are presented. The first scheme employs the tuning functions design. The other two employ a novel estimation-based design consisting of a strengthened controller-observer pair and observer-based and swapping-based identifiers. They remove restrictive growth and matching conditions present in the previous output-feedback nonlinear estimation-based designs and allow a systematic improvement of transient performance