Dealing with logical omniscience: Expressiveness and pragmatics

We examine four approaches for dealing with the logical omniscience problem and their potential applicability: the syntactic approach, awareness, algorithmic knowledge, and impossible possible worlds. Although in some settings these approaches are equi-expressive and can capture all epistemic states, in other settings of interest (especially with probability in the picture), we show that they are not equi-expressive. We then consider the pragmatics of dealing with logical omniscience—how to choose an approach and construct an appropriate model.     

An adaptive learning regulator for uncertain minimum phase systems with undermodeled unknown exosystems

The design of an adaptive learning regulator is addressed for uncertain minimum phase linear systems (with known bounds, known upper bound on system order, known relative degree, known high frequency gain sign) and for unknown exosystems (with unknown order, uncertain frequencies). On the basis of a known bound on system uncertainties and a known bound on the modeled exosystem frequencies, a new adaptive output error feedback control algorithm is proposed which guarantees exponential convergence of both the output and the control input errors into residual bounds which decrease as the exosystem modeling error decreases. Exponential convergence of both errors to zero is obtained when the regulator exactly models all exosystem excited frequencies, while asymptotic convergence of both errors to zero is achieved when the actual exosystem is overmodeled by the regulator. The new algorithm generalizes existing learning controllers since, in the case of periodic references and/or disturbances, the knowledge of the period is not required.     

Taxonomy-based relaxation of query answering in relational databases

Traditional information search in which queries are posed against a known and rigid schema over a structured database is shifting toward a Web scenario in which exposed schemas are vague or absent and data come from heterogeneous sources. In this framework, query answering cannot be precise and needs to be relaxed, with the goal of matching user requests with accessible data. In this paper, we propose a logical model and a class of abstract query languages as a foundation for querying relational data sets with vague schemas. Our approach relies on the availability of taxonomies, that is, simple classifications of terms arranged in a hierarchical structure. The model is a natural extension of the relational model in which data domains are organized in hierarchies, according to different levels of generalization between terms. We first propose a conservative extension of the relational algebra for this model in which special operators allow the specification of relaxed queries over vaguely structured information. We also study equivalence and rewriting properties of the algebra that can be used for query optimization. We then illustrate a logic-based query language that can provide a basis for expressing relaxed queries in a declarative way. We finally investigate the expressive power of the proposed query languages and the independence of the taxonomy in this context.     

Correlation between In Vitro Neutralization Assay and Serological Tests for Protective Antibodies Detection

Serological assays are useful in investigating the development of humoral immunity against SARS-CoV-2 in the context of epidemiological studies focusing on the spread of protective immunity. The plaque reduction neutralization test (PRNT) is the gold standard method to assess the titer of protective antibodies in serum samples. However, to provide a result, the PRNT requires several days, skilled operators, and biosafety level 3 laboratories. Therefore, alternative methods are being assessed to establish a relationship between their outcomes and PRNT results. In this work, four different immunoassays (Roche Elecsys^® Anti SARS-CoV-2 S, Snibe MAGLUMI^® SARS-CoV-2 S-RBD IgG, Snibe MAGLUMI^® 2019-nCoV IgG, and EUROIMMUN^® SARS-CoV-2 NeutraLISA assays, respectively) have been performed on individuals healed after SARS-CoV-2 infection. The correlation between each assay and the reference method has been explored through linear regression modeling, as well as through the calculation of Pearson’s and Spearman’s coefficients. Furthermore, the ability of serological tests to discriminate samples with high titers of neutralizing antibodies (>160) has been assessed by ROC curve analyses, Cohen’s Kappa coefficient, and positive predictive agreement. The EUROIMMUN^® NeutraLISA assay displayed the best correlation with PRNT results (Pearson and Spearman coefficients equal to 0.660 and 0.784, respectively), as well as the ROC curve with the highest accuracy, sensitivity, and specificity (0.857, 0.889, and 0.829, respectively).     

Microbiota Composition in Diverticular Disease: Implications for Therapy

Gut microbiota (GM) composition and its imbalance are crucial in the pathogenesis of several diseases, mainly those affecting the gastrointestinal tract. Colon diverticulosis and its clinical manifestations (diverticular disease, DD) are among the most common digestive disorders in developed countries. In recent literature, the role of GM imbalance in the onset of the different manifestations within the clinical spectrum of DD has been highlighted. This narrative review aims to summarize and critically analyze the current knowledge on GM dysbiosis in diverticulosis and DD by comparing the available data with those found in inflammatory bowel disease (IBD). The rationale for using probiotics to rebalance dysbiosis in DD is also discussed.     

Potential of the Stromal Matricellular Protein Periostin as a Biomarker to Improve Risk Assessment in Prostate Cancer

Prostate cancer (PCa) ranges from indolent to aggressive tumors that may rapidly progress and metastasize. The switch to aggressive PCa is fostered by reactive stroma infiltrating tumor foci. Therefore, reactive stroma-based biomarkers may potentially improve the early detection of aggressive PCa, ameliorating disease classification. Gene expression profiles of PCa reactive fibroblasts highlighted the up-regulation of genes related to stroma deposition, including periostin and sparc. Here, the potential of periostin as a stromal biomarker has been investigated on PCa prostatectomies by immunohistochemistry. Moreover, circulating levels of periostin and sparc have been assessed in a low-risk PCa patient cohort enrolled in active surveillance (AS) by ELISA. We found that periostin is mainly expressed in the peritumoral stroma of prostatectomies, and its stromal expression correlates with PCa grade and aggressive disease features, such as the cribriform growth. Moreover, stromal periostin staining is associated with a shorter biochemical recurrence-free survival of PCa patients. Interestingly, the integration of periostin and sparc circulating levels into a model based on standard clinico-pathological variables improves its performance in predicting disease reclassification of AS patients. In this study, we provide the first evidence that circulating molecular biomarkers of PCa stroma may refine risk assessment and predict the reclassification of AS patients.     

Energy-efficient operation of indirect adiabatic data center cooling systems via Newton-like phasor extremum seeking control

This paper considers the problem of optimizing the operation of Indirect Adiabatic Cooling (IAC) systems with application to data centers. Optimal operation is achieved when the required cooling demand is satisfied at the minimum energy cost. For this purpose, we design a supervisory control system, where the higher layer determines the optimal set-points for the local controllers by employing an Extremum Seeking Control (ESC) scheme. In particular, we consider a Newton-like phasor ESC, which augments the derivative estimator underlying the phasor approach to capture also the Hessian of the plant index and then it uses these estimates to steer the system along a Newton-like direction. The effectiveness of the considered approach is tested in simulation by exploiting a Matlab-based simulation environment including an IAC system and a computer room.     

Experimental investigation and numerical modelling of density-driven segregation in an annular shear cell

Granular materials segregate spontaneously due to differences in particle size, shape, density and flow behaviour. In this paper we experimentally investigate density-difference-driven segregation for a range of density ratios and a range of heavy particle concentrations. The experiments are conducted in an annular shear cell with rotating bumpy bottom that yields an exponential shear profile. The cell is initially filled with a layer of light particles and an upper layer of heavier grains and, on top, a load provides confinement. The segregation process is filmed through the transparent side-wall with a camera, and the evolution of particle concentration in space and time is evaluated by means of post-processing image analysis. We also propose a continuum-approach to model density-driven segregation. We use a segregation-diffusion transport equation, constitutive relations for effective viscosity and friction coefficient, and a segregation velocity analogous to the Stokes’ law. The model, which is validated by comparison with experimental findings, can successfully predict density-driven segregation at different density ratios and volumetric fraction.     

脂肪酶解聚壳聚糖及其衍生物的研究

壳聚糖及其衍生物能很容易被脂肪酶解聚。当壳聚糖乳酸盐溶液与脂肪酶在２５℃下作用１０ｍｉｎ后，其溶液粘度被降低到初始值的３５％，分子量从约７００ｋＤａ减少到１３ｋＤａ（还原糖滴定法测定）。脂肪酶浓度在４．５×１０－３～９×１０－１ｇ／Ｌ范围内，解聚初速度与酶浓度成对数线性关系。Ｎ－羧甲基壳聚糖比壳聚糖更容易被脂肪酶解聚。