Ti3AlC2 coated D-shaped fiber saturable absorber for Q-switched pulse generation

In this letter, a titanium aluminum carbide (Ti3AlC2) coated D-shaped fiber is proposed and demonstrated as a new saturable absorber (SA) for Q-switched laser pulse generation. In preparing the SA, the Ti3AlC2 powder is dispersed in liquid polyvinyl alcohol (PVA) before the solution is dropped and left to dry onto a polished surface of D-shape fiber. The SA is added to an erbium-doped fiber laser (EDFL) cavity to modulate the cavity loss for Q-switching. The Q-switched laser is obtained at 1 561 nm. The pulse width of the pulses can be varied between 7.4 µs and 5.1 µs with a corresponding repetition rate range from 41.26 kHz to 54.35 kHz, when the pump power is increased from 42.2 mW to 71.5 mW. At 71.5 mW pump, the pulse energy is obtained at 70.3 nJ. The signal-to-noise ratio (SNR) of the fundamental frequency is recorded at 67 dB, which indicates the stability of the laser.     

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.     

A collaborative behavior-based approach for handling ambiguity, uncertainty, and vagueness in robot natural language interfaces

Service robots are used by ordinary people in their houses and offices. For such users, a desirable way to communicate with robots is through natural language interfaces. So far, techniques for developing robot natural language interfaces are far from mature. A major challenge is handling ambiguity, uncertainty, and vagueness in parsing, object resolution, and vague natural language words. In this research, we develop a new approach called the collaborative behavior-based approach, in which behaviors of robots and behaviors of human users, as well as the changes of object states caused by the behaviors, are taken into consideration integratedly in processing natural language user instructions. In this paper, we analyze the special features of a human–robot interface that may affect language understanding, describe our approach that is designed based on the features, and present the implementation and some experimental results.     

Angstrom-range optical path-length measurement with a high-speed scanning heterodyne optical interferometer

A highly accurate method of optical path-length measurement is introduced by use of a scanning heterodyne optical interferometer with no moving parts. The instrument has demonstrated the potential to measure optical path length at angstrom resolution over continuous thickness in the micrometer range. This optical path length can be used to calculate the thickness of any material if the refractive index is known or to measure the refractive index of the material if the thickness is known. The instrument uses a single acousto-optic device in an in-line ultra-stable reflective geometry to implement rapid scanning in the microsecond domain for thickness measurements of the test medium.     

Structure and composition changes in the cell wall in relation to texture of shiitake mushrooms (Lentinula edodes) stored in modified atmosphere packaging

BACKGROUND: Firmness in shiitake mushroom (Lentinula edodes) is an important textural attribute affecting consumer attitudes toward freshness and quality. In this study, the effects of modified atmosphere packaging (MAP) treatments on structure and composition changes in cell walls in relation to the texture of mushrooms were investigated. RESULTS: Shiitake mushrooms were packaged in low density polyethylene bags with no holes (M₀₎, two microholes (M₁), four macroholes (M₂), stored at 4 °C for 16 days with non‐wrapped mushrooms as control. Control mushrooms showed the highest firmness value due to significant increase of cellulose and chitin. All three MAP treatments reduced losses of protein and polysaccharides; the M₂ treatment can best preserve the original texture while mushrooms in M₀ became soft and deteriorated, possibly due to higher CO₂ accumulation, lower cellulose and chitin content. Transmission electron microscopy performed on caps at harvest and after 16 days indicated that disintegration of plasmalemma had been alleviated by M₂ treatment, leading to better preservation of the cell wall. CONCLUSION: Our results suggest that differences in firmness of shiitake mushrooms during storage may be due to differences in cellulose and chitin concentrations. M₂ treatment may be a useful way of maintaining shiitake mushrooms texture during storage at 4 °C. Copyright © 2010 Society of Chemical Industry     

Solvent-Aided Crystallization for Biodiesel Purification

The application of solvent-aided crystallization (SAC) is based on the addition of a solvent, here 1-butanol, to crude biodiesel to catalyze the purification process by separating biodiesel from contaminants via crystallization process. Response surface methodology was applied to optimize the process parameters of SAC, represented by biodiesel purity. The purified biodiesel was analyzed by means of gas chromatography-mass spectrometry for the composition of the present fatty acid methyl ester (FAME). Under the predicted optimum process conditions within the experimental ranges for the highest biodiesel purity, the predicted biodiesel purity was 99.375 %.     

Intelligent Transmission Control for Efficient Operations in SDN

Although the Software-Defined Network (SDN) is a well-controlled and efficient network but the complexity of open flow switches in SDN causes multiple issues. Many solutions have been proposed so far for the prevention of errors and mistakes in it but yet, there is still no smooth transmission of pockets from source to destination specifically when irregular movements follow the destination host in SDN, the errors include packet loss, data compromise etc. The accuracy of packets received at their desired destination is possible if networks for pockets and hosts are monitored instead of analysis of network snapshot statistically for the state, as these approaches with open flow switches, discover bugs after their occurrence. This article proposes a design to achieve the said objective by defining the Intelligent Transmission Control Layer (ITCL) layer. It monitors all the connections of end hosts at their specific locations and performs necessary settlements when the connection state changes for one or multiple hosts. The layer informs the controller regarding any state change at one period and controller collects information of end nodes reported via ITCL. Then, updates flow tables accordingly to accommodate a location-change scenario with a route-change policy. ICTL is organized on prototype-based implementation using the popular POX platform. In this paper, it has been discovered that ITCL produces efficient performance in the trafficking of packets and controlling different states of SDN for errors and packet loss.     

A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.

     

Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review

Trabecular bone holds the utmost importance due to its significance regarding early bone loss. Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture. The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging (MRI) technique. These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis. The things that were considered before the selection of the articles for the systematic review were language, research field, and electronic sources. Only those articles written in the English language were selected as it is the most prominent language used in scientific, engineering, computer science, and biomedical researches. This literature review was conducted on the articles published between 2006 and 2020. A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening. The findings from those researches were compiled at the end of the result section. This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone.