An analytical method and a two-dimensional finite element model for treating the problem of laser heating and melting has been applied to aluminum 2519T87and stainless steel 304. The time needed to melt and vaporize and the effects of laser power density on the melt depth for two metals are also obtained. In addition, the depth profile and time evolution of the temperature before melting and after melting are given, in which a discontinuity in the temperature gradient is obviously observed due to the latent heat of fusion and the increment in thermal conductivity in solid phase. The analytical results that induced by  laser irradiation is in good agreement with numerical results.

The development of better tools for diagnosis and more accurate prognosis of cancer includes the search for biomarkers; molecules whose presence, absence or change in quantity or structure is associated with a particular tumour or prognosis/therapeutic outcome. While biomarkers need not be functionally relevant, if cell survival, then they could also provide new targets for therapeutic drugs. In recent years attention has been applied to a group of proteins known as cancer testis antigens (CT antigens) [1]. These proteins are products of genes whose expression was normally confined to the testis, yet they are expressed in tumour cells. CT genes are bound to serve a wide array of roles in the testes, which have many highly differentiated cel

In this paper, series of new complexes of Manganese(II), Cobalt(II), Nickel (II) Cupper(II) Zinc(II), Cadmium(II) and Mercury (II) are prepared from the new ligand [2-(3-benzoylthioureido)-3-(-4- hydroxyphenyl) propanoic acid (BHP) derived from tyrosine and benzoylisothiocyanate .Chemical structures are obtained from their 1 H, 13CNMR spectra (for BHP), elemental microanalyses, molar conductance, FTIR, UV–Vis, magnetic susceptibility in addition to TGA/DTG and DSC analysis, the suggested geometry for all complexes was tetrahedral. The biological activity of BHP and its complexes has been extensively studied against two bacterial species Staphylococcus aurous (G+) and Escherichia coli (G-) by agar-well diffusion technique, where Mn(II), Co

اثناء تفاعل الديزنة تكونت صبغة أزو جديدة عن طريق تفاعل 3-امينوفينول مع 2,4,6-ثلاثي هيدروكسي اسيتوفينون . ثم تم تفاعل هذا الليكاند مع بعض ايونات العناصر الكروم والحديد الروديوم والروثينيوم بتكفؤهم الثلاثي والكوبلت الثنائي والموليبدينوم سداسي التكافؤ مكونة معقدات فلزية مختلفة بأشكال هندسية متعددة. تم ملاحظة تناسق مجموعة الازو مع ايونات العناصر من خلال ملاحظة ظهور حزم امتصاص الفلز مع النتروجين والاوكسجين ب

This work involves synthesis of some new heterocyclic compounds including 1, 3-diazetine. The new Schiff bases [VI] ad derived from 3-((5-hydrazinyl-4-phenyl-4H-1, 2, 4-triazol-3-yl) methyl)-1H-indole [V] which was synthesized by refluxing 5-((1H-indol-3-yl) methyl)-4-phenyl-4H-1, 2, 4-triazole-3-thiol [IV] with hydrazine hydrate in absolute ethanol and this amino compound [V] condensation with different aromatic aldehydes in absolute ethanol to yielded a new Schiff bases [VI] ad. N-acyl compounds [VII] ad were synthesized by addition reaction of acetyl chloride to imine group of Schiff bases in dry benzene. The new diazetine derivatives [VIII] ad synthesized by the reaction of N-acyl compounds [VII] ad with sodium azide in dimethylformamid

TNF-α-induced osteoclastogenesis is central to post-menopausal and inflammatory bone loss, however, the effect of phytoestrogens on TNF-α-induced bone resorption has not been studied. The phytoestrogens genistein, daidzein, and coumestrol directly suppressed TNF-α-induced osteoclastogenesis and bone resorption. TRAP positive osteoclast formation and resorption area were significantly reduced by genistein (10(-7) M), daidzein (10(-5) M), and coumestrol (10(-7) M), which was prevented by the estrogen antagonist ICI 182,780. TRAP expression in mature TNF-α-induced osteoclasts was also significantly reduced by these phytoestrogen concentrations. In addition, in the presence of ICI 182,780 genistein and coumestrol (10(-5) -10(-6) M) augmente

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback