Heat and mass transfer from a single horizontal tube of an evaporative tubular heat exchanger

In this investigation, water side heat transfer coefficients without air flow from a single horizontal tubes are determined. Mass transfer coefficients are determined with water and air flow from the same tube. The total energy dissipated by inside hot fluid when only water is falling is compared with that when both the air and water flow past the tube. The water side heat transfer coefficient and mass transfer coefficient are given by empirical relations h_w = 6.0(Re_p)^0.18(Re_w)^0.87 and K = 3.5(Re_p)^0.18(Re_a)^0.28 (Re_w)^0.54, respectively. The ratio of energies dissipated with water and air flow and with only water flow increases with Re_w and Re_a and its maximum value is 1.72 in the range of variables used.     

Audit and its impact in the management of early prostatic cancer

An analysis was made of a prospective database of 614 men with newly diagnosed carcinoma of the prostate who presented between January 1978 and December 1990; 3-monthly updates were available on their clinical, haematological and biochemical parameters and 6-monthly updates on chest X-rays, bone scans and skeletal X-rays. It was found that 107 men (mean age 73.5 years) had early disease at presentation. Their management was based on regular surveillance and the treatment deferred until disease progression or development of symptoms. The audit of outcome measured various clinical events. Four patients (3.7%) developed local failure, 11 (10.3%) developed bone metastases, 3 (2.8%) died of cancer with a median survival of 6.3 years, and 34 (31.8%) died of intercurrent disease with a median survival of 2.6 years. The observed survival for 12 years of the whole group was similar to the expected survival for an age-matched population in Scotland. The standardised mortality ratio was 81 (95% confidence limits 57-112).     

Subwavelength Nanopatch Cavities for Semiconductor Plasmon Lasers

We propose and analyze a family of nanoscale cavities for electrically pumped surface-emitting semiconductor lasers that use surface plasmons to provide optical mode confinement in cavities which have dimensions in the 100-300-nm range. The proposed laser cavities are in many ways nanoscale optical versions of micropatch antennas that are commonly used at microwave/RF frequencies. Surface plasmons are not only used for mode confinement but also for output beam shaping to realize single-lobe far-field radiation patterns with narrow beam waists from subwavelength size cavities. We identify the cavity modes with the largest quality factors and modal gain, and show that in the near-IR wavelength range (1.0-1.6 mum) cavity losses (including surface plasmon losses) can be compensated by the strong mode confinement in the gain region provided by the surface plasmons themselves and the required material threshold gain values can be smaller than 700 cm^-1.     

Preparation and characterization of biodegradable waterabsorbent PAN/SS nanocomposite

Polyacrylonitrile (PAN)/sodium silicate (SS) nanocomposite was prepared via nonconventional emulsion method using an in situ developed transition metal complex Cu(II)/glycine taking ammonium persulfate (APS) as initiator, with a novel motive of converting hydrophobic homopolymer PAN into hydrophilic nano material via nanotechnology by the inclusion of SS to the homopolymer. UV–visible spectral analysis was carried out which revealed various interactions between the in situ developed complex with other reaction components. The formation of the PAN/SS nanocomposite was confirmed by infrared spectra (IR). Furthermore, as evidenced by transmission electron microscopy (TEM), the composite so obtained was found to have nano scale structure. X‐ray diffraction (XRD) was carried out suggesting that the silicate layers were exfoliated during the polymerization process. An increase in the thermal stability for the developed nanocomposite was recorded by thermogravimetric analysis (TGA). Surprisingly, it was also found that the PAN/SS nanocomposite showed considerable amount of waterabsorbency and was biodegradable as tested by activated sludge and cultured media and further confirmed by scanning electron microscopy (SEM). POLYM. COMPOS., 2008. © 2008 Society of Plastics Engineers     

Preparation and analysis of poly(l-lactic acid) composites with oligo(d-lactic acid)-grafted cellulose

α-Cellulose extracted from jute fiber was grafted with oligo( d -lactic acid) (ODLA) via a graft polycondensation reaction in the presence of para-toluene sulfonic acid and potassium persulfate in toluene at 130 °C for 9 h under 380 mmHg. ODLA was synthesized by the ring-opening polymerization of d -lactides in the presence of stannous octoate (0.03 wt % lactide) and d -lactic acid at 140 °C for 10 h. Composites of poly( l -lactic acid) (PLLA) with the ODLA-grafted α-cellulose were prepared by the solution-mixing and film-casting methods. The grafting of ODLA onto α-cellulose was confirmed by Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). The analysis of the composites was performed with FTIR spectroscopy, SEM, wide-angle X-ray diffraction, and thermogravimetric analysis. The distribution of the grafted α-cellulose in the composites was uniform and showed better compatibility with PLLA through intermolecular hydrogen bonding. Only homocrystalline structures of PLLA were present in the composites, and the thermal stability increased with increasing percentage of grafted α-cellulose. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47424.     

An Expert Approach for Data Flow Prediction: Case Study of Wireless Sensor Networks

The data flow is an important parameter used in the optimization problem of Wireless Sensor Networks. This paper presents an expert approach for improved data flow prediction based on data discretization and artificial intelligence. The proposed approach has been implemented on various machine learning methods (a total of 17 methods). This data flow prediction is based on the dataset generated from the simulations with NS-2.35 for multiple Wireless Sensor Networks (5- to -50 nodes). The performance comparison of different machine learning models with continuous data and discretized data is also presented. The proposed approach considerably reduces the execution time of the machine learning models for training purposes and also enhances the accuracy of prediction. The result analysis shows that the proposed approach is better compared to various machine learning methods. Also, the proposed approach is able to handle both continuous and discrete data. The datasets used in this work are available as a supplement at NDS and DDS link.