Multi-objective rule mining using a chaotic particle swarm optimization algorithm

In this paper, classification rule mining which is one of the most studied tasks in data mining community has been modeled as a multi-objective optimization problem with predictive accuracy and comprehensibility objectives. A multi-objective chaotic particle swarm optimization (PSO) method has been introduced as a search strategy to mine classification rules within datasets. The used extension to PSO uses similarity measure for neighborhood and far-neighborhood search to store the global best particles found in multi-objective manner. For the bi-objective problem of rule mining of high accuracy/comprehensibility, the multi-objective approach is intended to allow the PSO algorithm to return an approximation to the upper accuracy/comprehensibility border, containing solutions that are spread across the border. The experimental results show the efficiency of the algorithm.     

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.     

Highly Functional TNTs with Superb Photocatalytic,Optical, and Electronic Performance Achieving Record PV Efficiency of 10.1% for 1D‐Based DSSCs

Different nanostructures of TiO₂ play an important role in the photocatalytic and photoelectronic applications. TiO₂ nanotubes (TNTs) have received increasing attention for these applications due to their unique physicochemical properties. Focusing on highly functional TNTs (HF‐TNTs) for photocatalytic and photoelectronic applications, this study describes the facile hydrothermal synthesis of HF‐TNTs by using commercial and cheaper materials for cost‐effective manufacturing. To prove the functionality and applicability, these TNTs are used as scattering structure in dye‐sensitized solar cells (DSSCs). Photocatalytic, optical, Brunauer‐Emmett‐Teller (BET), electrochemical impedance spectrum, incident‐photon‐to‐current efficiency, and intensity‐modulated photocurrent spectroscopy/intensity‐modulated photovoltage spectroscopy characterizations are proving the functionality of HF‐TNTs for DSSCs. HF‐TNTs show 50% higher photocatalytic degradation rate and also 68% higher dye loading ability than conventional TNTs (C‐TNTs). The DSSCs having HF‐TNT and its composite‐based multifunctional overlayer show effective light absorption, outstanding light scattering, lower interfacial resistance, longer electron lifetime, rapid electron transfer, and improved diffusion length, and consequently, J _SC, quantum efficiency, and record photoconversion efficiency of 10.1% using commercial N‐719 dye is achieved, for 1D‐based DSSCs. These new and highly functional TNTs will be a concrete fundamental background toward the development of more functional applications in fuel cells, dye‐sensitized solar cells, Li‐ion batteries, photocatalysis process, ion‐exchange/adsorption process, and photoelectrochemical devices.     

Nanomembrane‐Based,Thermal‐Transport Biosensor for Living Cells

Knowledge of materials' thermal‐transport properties, conductivity and diffusivity, is crucial for several applications within areas of biology, material science and engineering. Specifically, a microsized, flexible, biologically integrated thermal transport sensor is beneficial to a plethora of applications, ranging across plants physiological ecology and thermal imaging and treatment of cancerous cells, to thermal dissipation in flexible semiconductors and thermoelectrics. Living cells pose extra challenges, due to their small volumes and irregular curvilinear shapes. Here a novel approach of simultaneously measuring thermal conductivity and diffusivity of different materials and its applicability to single cells is demonstrated. This technique is based on increasing phonon‐boundary‐scattering rate in nanomembranes, having extremely low flexural rigidities, to induce a considerable spectral dependence of the bandgap‐emission over excitation‐laser intensity. It is demonstrated that once in contact with organic or inorganic materials, the nanomembranes' emission spectrally shift based on the material's thermal diffusivity and conductivity. This NM‐based technique is further applied to differentiate between different types and subtypes of cancer cells, based on their thermal‐transport properties. It is anticipated that this novel technique to enable an efficient single‐cell thermal targeting, allow better modeling of cellular thermal distribution and enable novel diagnostic techniques based on variations of single‐cell thermal‐transport properties.     

Evaluation of alumina reinforced oil fly ash composites prepared by spark plasma sintering

The thermomechanical behavior of micro/nano-alumina (Al₂O₃) ceramics reinforced with 1-5 wt.% of acid-treated oil fly ash (OFA) was investigated. Composites were sintered using spark plasma sintering (SPS) technique at a temperature of 1400°C by applying a constant uniaxial pressure of 50 MPa. It was evaluated that the fracture toughness of micro- and nanosized composites improved in contrast with the monolithic alumina. Highest fracture toughness value of 4.85 MPam^1/2 was measured for the nanosized composite reinforced with 5 wt.% OFA. The thermal conductivity of the composites (nano-/microsized) decreased with the increase in temperature. However, the addition of OFA (1-5 wt.%) in nanosized alumina enhanced the thermal conductivity at an evaluated temperature. Furthermore, a minimum thermal expansion value of 6.17 ppm*K⁻¹ was measured for nanosized Al₂O₃/5 wt.% OFA composite. Microstructural characterization of Al₂O₃-OFA composites was done by x-ray diffraction and Raman spectroscopy. Oil fly ash particles were seen to be well dispersed within the alumina matrix. Moreover, the comparative analysis of the nano-/microsized Al₂O₃/OFA composites shows that the mechanical and thermal properties were improved in nanosized alumina composites.     

Ternary adsorbent photocatalyst hybrid (APH) nanomaterials for improved abstraction of tetracycline from water

ABSTRACT

Current health situations have instigated increased frequency of taking antibiotics for cure of infections but this amplified use is posing threats to environment. This research is focused to remove Tetracycline (TC), an antibiotic from water, using an advanced nanohybrid that compact the properties of adsorbent and photocatalyst. Compared to conventional methods for remediation of TC, large surface area (452 m²g^?1) adsorbent photocatalyst hybrid (APH) g-C₃N₄/α-MoO₃/ZIF-67 (CMZ) have found much effective as 97% degradation of TC is observed in 110 min with 0.1 g of APH. Increasing g-C₃N₄ in the hybrids has improved percent degradation of TC molecules. CMZ-3 is found as a potential candidate for water treatment.     

A fuzzy goal programming model for the simple U-line balancing problem with multiple objectives

A fuzzy goal programming model for the simple U-line balancing (SULB) problem with multiple objectives is presented. In real life applications of the SULB problem with multiple objectives, it is difficult for the decision-maker(s) to determine the goal value of each objective precisely as the goal values are imprecise, vague, or uncertain. Therefore a fuzzy goal programming model is developed for this purpose. The proposed model is the first fuzzy multi-objective decision-making approach to the SULB problem with multiple objectives which aims at simultaneously optimizing several conflicting goals. The proposed model is illustrated using an example. A computational study is conducted by solving a large number of test problems to investigate the relationship between the fuzzy goals and to compare them with the goal programming model proposed by Gökçen and A?pak (Gökçen, H. and A?pak, K., European Journal of Operational Research, 171, 577–585, 2006). The results of the computational study show that the proposed model is more realistic than the existing models for the SULB problem with multiple objectives and also provides increased flexibility for the decision-maker(s) to determine different alternatives.