Background: Restoration of the gingival margin of Class II cavities with composite resin continues to be problematic, especially where no enamel exists for bonding to the gingival margin. The aim of study is to evaluate the marginal leakage at enamel and cementum margin of class II MOD cavities using amalgam restoration and modern composite restorations Filtek™ P90, Filtek™ Z250 XT (Nano Hybrid Universal Restorative) and SDR bulk fill with different restoratives techniques. Materials and method: Eighty sound maxillary first premolar teeth were collected and divided into two main groups, enamel group and cementum group (40 teeth) for each group. The enamel group was prepared with standardized Class II MOD cavity with gingival margin (1 mm above C.E.J) on both box sides. While the cementum group with the gingival margin (1 mm below C.E.J) on both sides. The enamel and cementum groups were then subdivided into eight subgroups for each (five teeth) with 10 boxes for each group. Subgroups within the main group named according to materials and techniques that were used with it as following: Amalgam subgroup (Permite, SDI), SDR subgroup (DENTSPLY) with bulk technique, Filtek™ P90 subgroup (3M ESPE) with three incremental techniques (Oblique, Horizontal and Centripetal technique), and Filtek™ Z250XT subgroup (3M ESPE) with three incremental techniques (Oblique, Horizontal and Centripetal technique).After specimens were stored in distilled water at 37°C for 7 days. All specimens were subjected to thermocycling at (5° to 55 °C). Microleakage was evaluated by stereomicroscope (20 X). Data were analyzed statistically by Kruskal-Wallis test and Mann-Whitney U-test. Result: All experimental groups showed leakage at cementum more than enamel groups. SDR bulk fill subgroup showed the highest marginal leakage among all experimental groups followed by Filtek™ Z250 XT subgroup with horizontal technique at both enamel and cementum groups. Silorane and Filtek™ Z250 XT subgroups with oblique technique showed the least marginal leakage followed by centripetal technique at both enamel and cementum groups. Amalgam restoration subgroup shows lesser leakage than SDR bulk fills subgroup significantly at both enamel and cementum groups. While it show higher leakage than Silorane subgroup with oblique technique significantly at enamel margin only. Conclusion: The limiting factors for marginal leakage are technique and material dependent.
In solar-thermal adsorption/desorption processes, it is not always possible to preserve equal operating times for the adsorption/desorption modes due to the fluctuating supply nature of the source which largely affects the system’s operating conditions. This paper seeks to examine the impact of adopting unequal adsorption/desorption times on the entire cooling performance of solar adsorption systems. A cooling system with silica gel–water as adsorbent-adsorbate pair has been built and tested under the climatic condition of Iraq. A mathematical model has been established to predict the system performance, and the results are successfully validated via the experimental findings. The results show that, the system can be operational
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FG Mohammed, HM Al-Dabbas, Science International, 2018 - Cited by 2
The data presented in this paper are related to the research article entitled “Novel dichloro(bis{2-[1-(4-methylphenyl)-1H-1,2,3-triazol-4-yl-κN3 ]pyridine-κN})metal(II) coordination compounds of seven transition metals (Mn, Fe, Co, Ni, Cu, Zn and Cd)” (Conradie et al., 2018) [1]. This paper presents characterization and structural data of the 2-(1-(4-methyl-phenyl)-1H-1,2,3-triazol-1-yl)pyridine ligand (L2 ) (Tawfiq et al., 2014) [2] as well as seven dichloro(bis{2- [1-(4-methylphenyl)-1H-1,2,3-triazol-4-yl-κN3 ]pyridine-κN})metal (II) coordination compounds, [M(L2 )2Cl2], all containing the same ligand but coordinated to different metal ions. The data illustrate the shift in IR, UV/VIS, and NMR (for diamagnetic complexes) peaks wh
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The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when
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