Narrow Results

Quantitative three dimensional analysis is possible, in principle, by PIXE tomography. But, the inherent problems in quantitation, restrictions on the sample geometry and preparation, and specimen damage due to high fluences make this method unsuitable for many biological samples. The specimen under investigation, a murine artery, was around a millimeter in diameter and the induced atherosclerotic lesion was spread few hundreds of micrometers across the length of the artery. Since no tomographic experiments were possible, we chose to do the 3D quantitative analysis by means of PIXE Stacking. Herein, thin serial sections of the specimen are prepared and measured by conventional ion beam techniques. The resultant two dimensional quantitative element maps are stacked and aligned to reconstruct a quantitative volume of the specimen. Although the reconstructed dimension has poorer spatial resolution as compared with the measured dimensions, new information can still be gained from it. The three dimensional element distribution of the atherosclerotic lesion shows calcification on the outer surface of the artery, which otherwise would not have been easily visible in the two dimensional analysis.

We report on theoretical investigation of interactions of different mechanisms and their influence onto DNA cooperativity of helix–coil transition (melting). Using the modified version of microscopic Potts-like one-dimensional model we showed that increased stacking results in decreased correlation length. The decrease in cooperativity is explained as a result of combined hydrogen bonding and stacking.

Multi-label classification problem, where each instance can be associated with multiple labels, has received considerable attention from machine learning community. To address the inherent challenges of multi-label classification including data imbalance, label dependence, and high dimensionality, ensemble approaches have been developed, gaining popularity across various real-world applications. This paper proposes a novel ensemble method called ConfBoost that addresses these challenges and enhances the generalization ability of learning systems. ConfBoost which is a meta-model based on a weighted stacking paradigm using local confidence, combines heterogeneous and complementary ensembles of multi-label classifiers. The proposed approach achieves two main objectives: Firstly, by focusing on label weights based on their confidence scores, the model can generate more relevant predictions and enhance the accuracy at the base-level by mitigating the impact of irrelevant labels during the stacking process. Moreover, assigning higher weights to certain labels exhibits better discrimination and adaptability to capture complex label relationships. Second, applying adjusted thresholds enables the model to generate predictions adapted to the specific characteristics of each label, effectively addressing imbalanced label distributions. Extensive experiments on publicly available datasets demonstrate that ConfBoost outperforms conventional combination methods and consistently surpasses related state-of-the-art methods. These findings highlight the effectiveness and potential of ConfBoost as an advanced ensemble method for multi-label classification tasks.

Since the last two decades, the trend of device miniaturization has increased to get better performance with a smaller area of the logic functions. In deep submicron regime, the demand of fabrication of nanoscale Complementary metal oxide semiconductor (CMOS) VLSI circuits has increased due to evaluation of modern successful portable systems. Leakage power dissipation and reliability issues are major concerns in deep submicron regime for VLSI chip designers. Power supply voltage has been scaled down to maintain the performance yield in future deep submicron regime. The threshold voltage is the critical parameter to trade-off the performance yield and leakage power dissipation in nanoscaled devices. Low threshold voltage improves the device characteristics with large leakage power in nanoscaled devices. Several leakage reduction techniques at different levels are used to mitigate the leakage power dissipation. Lower leakage power increases the reliability by reducing the cooling cost of the portable systems. In this article, we are presenting the explanatory general review of the commonly used leakage reduction techniques at circuit level. We have analyzed the NAND3 gate using HSPICE EDA tool for leakage power dissipation at different technology nodes in active as well as standby modes. Process, voltage and temperature effects are checked for reliability purpose. Our comparative results and discussion of different leakage reduction techniques are very useful to illustrate the effective technique in active and standby modes.

By scaling the semiconductor industry to nano-scale era, design and prototyping cost of cell-based Application-Specific Integrated Circuits (ASICs) becomes more expensive and it makes Field Programmable Gate Arrays (FPGAs) more popular among designers. However, there is a gap between FPGAs and ASICs in terms of timing, dynamic power consumption and logic density. Three-dimensional integration, particularly in the full monolithic process, has been considered as a promising solution to reduce the performance gap of ASICs and FPGAs. In this paper, two new architectures for the monolithically integrated 3D-FPGAs are introduced. In order to exploit the great potentials of the suggested architectures, a new three-dimensional FPGA placement algorithm is proposed thereafter. The proposed placement algorithm, named JABE, is the first of its kind that enables designers to take advantages of the large number of vertical interconnections in the monolithically stacked 3D-FPGAs. Our experiments show a 24% timing improvement for the new architectures and CAD algorithms compared with the conventional TSV-based 3D-FPGAs and design flows. In addition, improvements in terms of the total wirelength and area footprint are reported for the proposed placement algorithms and new architectures.

One of the paramount issues in the field of VLSI design is the rapid increase in power consumption. Therefore, it is necessary to develop power-efficient circuits. Here, three new simple architectures are presented for a Dynamic Double Edge Triggered Flip-flop named as Transistor Count Reduction Flip-flop, S-TCRFF (Series Stacking in TCRFF) and FST in TCRFF (Forced Stacking of Transistor in TCRFF). The first one features a dynamic design comprising of transmission gate in which total transistor count has greatly reduced without affecting the logic, thereby attaining better power and speed performance. For the reduction of static power, two types of stacking called series and forced transistor stacking are applied. The circuits are simulated using Cadence Virtuoso in 45nm CMOS technology with a power supply of 1V at 500MHz when input switching activity is 25%. The simulated results indicated that the new designs (TCRFF, S-TCRFF and FST in TCRFF) excelled in different circuit performance indices like Power-Delay-Product (PDP), Energy-Delay-Product (EDP), average and leakage power with less layout area compared with the performance of nine recently proposed FF designs. The improvement in PDPdq value was up to 89.2% (TCRFF), 89.9% (S-TCRFF) and 90.3% (FST in TCRFF) with conventional transmission gate FF (TGFF).

Nowadays, there is no doubt that machine learning techniques can be successfully applied to data mining tasks. Currently, the combination of several classifiers is one of the most active fields within inductive machine learning. Examples of such techniques are boosting, bagging and stacking. From these three techniques, stacking is perhaps the less used one. One of the main reasons for this relates to the difficulty to define and parameterize its components: selecting which combination of base classifiers to use, and which classifier to use as the meta-classifier. One could use for that purpose simple search methods (e.g. hill climbing), or more complex ones (e.g. genetic algorithms). But before search is attempted, it is important to know the properties of the search space itself. In this paper we study exhaustively the space of Stacking systems that can be built by using four base learning systems: C4.5, IB1, Naive Bayes, and PART. We have also used the Multiple Linear Response (MLR) as meta-classifier. The properties of this state-space obtained in this paper will be useful for designing new Stacking-based algorithms and tools.

It is commonly understood that machine learning algorithms discover and extract knowledge based on data at hand. However, a huge amount of knowledge is available which is in machine-readable format and ready for inclusion in machine learning algorithms and models. In this paper, we propose a framework that integrates domain knowledge in form of ontologies/hierarchies into logistic regression using stacked generalization. Namely, relations from ontology/hierarchy are used in stacking manner in order to obtain higher, more abstract concepts. Obtained concepts are further used for prediction. The problem we solved is unplanned 30-days hospital readmission, which is considered as one of the major problems in healthcare. Proposed framework yields better results compared to Ridge, Lasso, and Tree Lasso Logistic Regression. Results suggest that the proposed framework improves AUC by up to 9.5% on pediatric datasets and up to 4% on morbidly obese patients’ datasets and also improves AUPRC by up to 5.7% on pediatric datasets and up to 2.6% on morbidly obese patients’ datasets on average. This indicates that the inclusion of domain knowledge improves the predictive performance of Logistic Regression.

A system of stacked rigid blocks can be found in many applications of non-structural components: a statue resting on the top of a table is such an example. Previous studies usually assume that the friction at contact interface is so large that only rocking motion can be activated. However, this assumption may not be realistic when assessing the seismic response of unanchored non-structural components. Motivated from this constraint, this paper contributes to the state-of-the-art research of the classical rocking problem by presenting a numerical model within which sliding, rocking and sliding-rocking response of stacked rigid blocks can be computed by the time-history analysis. The exact fully nonlinear equations of motion, transition criteria for different response modes and treatment to handle the impact are presented in detail. The accuracy of the developed model is validated. A case study is also provided to investigate the overall failure probability of the stacked rigid blocks with realistic friction coefficient. In this particular case study, it is also shown that increasing the friction coefficient makes the stacked rigid blocks more susceptible to failure.

The influence of water soluble cationic meso-tetra(4N-hydroxyethyl)pyridyl porphyrin (H₂THOEtPyP4) and it's metal complexes with Ni(II), Cu(II), Co(II) and Zn(II) on hydrodynamic and spectral behavior of DNA solutions has been studied by viscometry and UV-vis absorption methods. It was shown that the presence of planar porphyrins, such as H₂THOEtPyP4, NiTHOEtPyP4 and CuTHOEtPyP4 leads to an increase in viscosity at relatively small concentrations, and then decreases to stable values. Such behavior corresponds to intercalation of these porphyrins in DNA structure, which results in a decrease of helical twist and lengthening of the DNA molecule. In the case of porphyrins with axial ligands, such as ZnTHOEtPyP4 and CoTHOEtPyP4, the relative viscosity decreases, which is explained by self-stacking of these porphyrins on DNA surface. Calculation and interpretation of binding parameters (K_b and n) demonstrated good agreement of viscometric and spectrophotometric measurements.

The influence of porphyrin molecules configuration on their ability to affect on DNA structure were investigated for water-soluble cationic meso-tetra-(3N-hydroxyethylpyridyl) porphyrin (H₂THOEtPyP3) and its metal complexes with Cu, Ni, Co, Zn using viscometry and UV-vis absorption spectroscopy. A comparative analysis was performed with the results of previously conducted similar studies of meso-tetra-(4N-hydroxyethylpyridyl) porphyrin (H₂THOEtPyP4). Viscosity measurements show that the change in position of peripheral radicals on pyridylic ring has absolutely no effect on the laws of interaction of investigated porphyrins with DNA in case of outside binders such as CoTHOEPyP3 and ZnTHOEPyP3. In the presence of planar porphyrins, such as CuTHOEPyP3 and NiTHOEPyP3 the laws of structural changes of DNA are the same as in the case of H₂THOEtPyP3, with some differences. Comparison of different locations of peripheral radicals on pyridylic rings leads to the conclusion that H₂THOEtPyP3 and its metal complexes bind to DNA preferably more than H₂THOEtPyP4 and its metal complexes. This fact may be explained only suggesting that porphyrins with hydroxyethyl groups at 3N-position are favorably located relative to the DNA helix axis than at 4N-position.

We prepared a free-base 10,20-diphenyl-5,15-diazaporphyrin and its Ni(II), Cu(II), Pd(II), and Pt(II) complexes. These molecules uniformly form an infinite one-dimensional $\pi$-stacking array in the solid state. The four-fold intermolecular hydrogen bonding interactions between the meso-nitrogen atom of the diazaporphyrin core and the ortho-hydrogen atom of the phenyl substituent dominate the crystal packing arrangement. The present study revealed that the meso-nitrogen atom of diazaporphyrins would be used as an inherent hydrogen bonding acceptor to construct supramolecular assemblies.

Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection.