Scan line void fabric anisotropy tensors of granular media

Soil fabric anisotropy tensors are related to the statistical distribution of orientation of different microstructural vector-like entities, most common being the contact normal vectors between particles, which are extremely difficult to determine for real granular materials. On the other hand, void fabric based tensors can be determined by image based quantification methods of voids (graphical approaches), which are well defined and easy to apply to both physical and numerical experiments. A promising void fabric characterization approach is based on the scan line method. Existing scan line based definitions of void fabric anisotropy tensors are shown analytically to inherit a shortcoming, since numerous small void segments in a sample have an inordinate contribution towards unwarranted isotropy. Discrete Element Method (DEM) of analysis subsequently confirms this analytical proof. The fact that such scan line void fabric tensor definitions yield acceptable results when used in conjunction with physical image-based measurements, is shown to be attributed to the natural “cut off” of smaller void segments that occurs during such measurements. This is the motivation to propose using the existing definition of void fabric tensors, with exclusion of void segments less than a “cut off” value associated with an internal length of the granular assembly. In addition, an entirely new void fabric tensor was introduced using the squared length, instead of the length of a void segment, as the weighting factor for the definition of the scan line void fabric tensor. It was found by means of DEM analysis that both alternative definitions are void of the aforementioned shortcoming and compatible with existing image quantification methods of void fabric anisotropy.     

A diversity-driven structure learning algorithm for building hierarchical neuro-fuzzy classifiers

In this article, an efficient structure learning algorithm is proposed for the development of self-organizing neuro-fuzzy multilayered classifiers (SONeFMUC). These classifiers are hierarchical structures comprising small-scale fuzzy-neuron classifiers (FNCs), interconnected along multiple layers. At each layer, parent FNCs are combined to construct a descendant FNC at the next layer with enhanced classification qualities. The SONeFMUC structure is progressively expanded by generating new layers based on the principles of the Group Method of Data Handling (GMDH) algorithm, which is appropriately adapted to handle classification tasks. Traditional GMDH proceeds blindly to the construction of all possible parent FNC pairs from the previous layer to obtain the individuals in the next layer without paying due attention to the diversity of the FNC combinations. However, previous experimentation shows that a large number of descendant FNCs exhibit similar or slightly better classification capabilities than their parent FNCs. This causes an increase of the computational cost required for structure learning, without a direct impact on the accuracy of the obtained models. In this paper, a modified version of GMDH is devised for effective identification of the SONeFMUC structure. We incorporate the Proportion of Specific Agreement (Ps) as a means to evaluate the diversity of the FNC pairs. In the devised method, only complementary FNCs are combined, i.e., FNCs which commit errors at different pattern subspaces, to construct a descendant FNC at the next layer. Accordingly, a computational reduction is achieved while high classification accuracy is maintained. The efficiency of the proposed structure learning is tested on a diverse set of benchmark datasets using land cover classification from multispectral images as a real-world application.     

Thymic Epithelial Neoplasms: Focusing on the Epigenetic Alterations

Thymic Epithelial Neoplasms (TENs) represent the most common tumors of the thymus gland. Epigenetic alterations are generally involved in initiation and progression of various cancer entities. However, little is known about the role of epigenetic modifications in TENs. In order to identify relevant studies, a literature review was conducted using the MEDLINE and LIVIVO databases. The search terms thymoma, thymic carcinoma, thymic epithelial neoplasm, epigenetics, DNA methylation, HDAC and miRNA were employed and we were able to identify forty studies focused on TENs and published between 1997 and 2021. Aberrant epigenetic alterations seem to be involved in the tumorigenesis of thymomas and thymic carcinomas, with numerous studies reporting on non-coding RNA clusters and altered gene methylation as possible biomarkers in different types of TENs. Interestingly, Histone Deacetylase Inhibitors have shown potent antitumor effects in clinical trials, thus possibly representing effective epigenetic therapeutic agents in TENs. Additional studies in larger patient cohorts are, nevertheless, needed to verify the clinical utility and safety of novel epigenetic agents in the treatment of patients with TENs.     

Identification of the geographical origin of pumpkin seed oil by the use of rare earth elements and discriminant analysis

The aim of this work was to develop an analytical method to assure the geographical origin of Styrian pumpkin seed oil, a high priced local product which is protected by the European Union. The development of such a method is not only of interest for scientists but also of importance for producer that want to guarantee the purity and quality of their products as well as the consumer wanting to know the origin of the food products.Since the rare earth elements (REE) concentrations are extremely low in pumpkin seed oil a fast and sensitive analytical method with ICP-MS had to be developed and validated. In the current project, pumpkin seeds from different regions in Austria were sampled. The REE patterns in the extracted oil of these seeds were determined and a preliminary classification with discriminant analysis was successfully done on a statistical basis.The study demonstrates that pumpkin seed oils from different geographical origin show variable rare earth element patterns making it possible to trace the origin of these oils.     

Energy polarization and popular representation: Evidence from the Russian Duma

In this article we introduce the term “energy polarization” to explain the politics of energy market reform in the Russian Duma. Our model tests the impact of regional energy production, party cohesion and ideology, and electoral mandate on the energy policy decisions of the Duma deputies (oil, gas, and electricity bills and resolution proposals) between 1994 and 2003. We find a strong divide between Single-Member District (SMD) and Proportional Representation (PR) deputies High statistical significance of gas production is demonstrated throughout the three Duma terms and shows Gazprom's key position in the post-Soviet Russian economy. Oil production is variably significant in the two first Dumas, when the main legislative debates on oil privatization occur. There is no constant left–right continuum, which is consistent with the deputies' proclaimed party ideology. The pro- and anti-reform poles observed in our Poole-based single dimensional scale are not necessarily connected with liberal and state-oriented regulatory policies, respectively. Party switching is a solid indicator of Russia's polarized legislative dynamics when it comes to energy sector reform.     

Fluorouracil and leucovorin with or without interferon alfa-2b in advanced colorectal cancer: analysis of a prospective randomized phase III trial. Hellenic Cooperative Oncology Group

PURPOSE: To investigate if double modulation of fluorouracil (5-FU) with leucovorin (folinic acid [FA]) and interferon alfa-2b (IFN 2b) improves responses and survival in comparison to single modulation of 5-FU with FA. PATIENTS AND METHODS: One hundred six patients with histologically confirmed advanced colorectal cancer, measurable disease, and without previous chemotherapy were prospectively randomized into two groups. Patients in group A received 5-FU 450 mg/m2 as an intravenous bolus in the midinfusion of FA weekly. FA was given at a dose of 200 mg/m2 in 500 mL 0.9% normal saline solution in 2-hour infusion. Patients in group B received exactly the same regimen plus IFN 2b 5 million units subcutaneously three times weekly. RESULTS: All patients were well balanced in both groups regarding age, sex, performance status, number, and site of metastasis. One hundred two patients were assessable. All patients have died. There was no difference in response between the two groups (7.8% v 9.8%). Median survival was 10.1 months in group A, and 7.2 months in group B (P = .00189). Median time to progression was 8.4 and 5.2 months, respectively (P = .00196). Overall, better performance status and older age had a positive impact on survival. Toxicity was the most important and catastrophic aspect of this study. Patients who received IFN 2b had significantly worse anemia, neutropenia, diarrhea, anorexia, weight loss, flu-like syndrome, and psychological reactions. CONCLUSION: Based on this final analysis, the addition of IFN 2b to the combination of 5-FU and FA enhances toxicity and contributes to decreased survival.     

Pipelined recurrent fuzzy neural networks for nonlinear adaptive speech prediction.

A class of pipelined recurrent fuzzy neural networks (PRFNNs) is proposed in this paper for nonlinear adaptive speech prediction. The PRFNNs are modular structures comprising a number of modules that are interconnected in a chained form. Each module is implemented by a small-scale recurrent fuzzy neural network (RFNN) with internal dynamics. Due to module nesting, the PRFNNs offer a number of desirable attributes, including decomposition of the modeling task, enhanced temporal processing capabilities, and multistage dynamic fuzzy inference. Tuning of the PRFNN adaptable parameters is accomplished by a series of gradient descent methods with different weighting of the modules and the decoupled extended Kalman filter (DEKF) algorithm, based on weight grouping. Extensive experimentation is carried out to evaluate the performance of the PRFNNs on the speech prediction platform. Comparative analysis shows that the PRFNNs outperform the single-RFNN models in terms of the prediction gains that are obtained and computational efficiency. Furthermore, PRFNNs provide considerably better performance compared to pipelined recurrent neural networks, for models with similar model complexity.     

On a generalized approach to manufacturing energy efficiency

Energy efficiency has become a very significant factor, requiring its inclusion in the manufacturing decision-making attributes. This paper proposes a generalized approach to manufacturing energy efficiency. The basic element of the approach is the division of energy efficiency definition and study into four manufacturing levels, namely process, machine, production line, and factory. Process-level definitions are provided for the majority of manufacturing processes. A machine-level study indicates and solves difficulties, generated by the workpiece geometry, and points out the interaction with the process level through factors, such as the process time. Moreover, machine tool peripherals are responsible for a significant portion of the consumed energy, and classification based on the dependence of their consumption on process variables is required. Studies made on the production line and factory levels show that energy efficiency, at these levels, is heavily dependent on production planning and scheduling and can be improved through the appropriate utilization of machines, with the inclusion of shutdown and eco-modes. Finally, a case study is presented, showing that many of the difficulties towards the optimization of energy efficiency can be dealt with successfully, using the proposed generalized approach.     

On the selection and design of the proper roof pond variant for passive cooling purposes

The present paper aims to fully summarize the current scientific and technological experience focusing on the comparative characteristics of roof pond variants. The design guidelines and pond characteristics provide the opportunity to make the proper decision of a roof pond variant for cooling purposes. The following systems are under detailed investigation: covered/uncovered pond with/without sprays, skytherm, energy roof, coolroof, walkable pond, wet gunny bags, cool-pool shaded and ventilated pond. A brief background of the motivation behind the creation of the above variants is provided. The advantages and disadvantages of ponds as well as the design considerations and state of the art are discussed. Additionally, in the present study a detailed comparison is performed in terms of effectiveness and cooling demand reduction. A complete set of criteria affecting the choice of the proper roof pond are also analyzed. Finally, a decision support flowchart, is provided based on the various criteria and parameters.