In this work, novel copolymers of poly(adipic anhydride-co-mannitol) were synthesized by melting condensation polymerization of poly(adipic anhydride) with five percentages of mannitol sugar, 1 to 5 Wt.%. These copolymers were purified and then, characterized by FT-IR, which was proved that the cross-linking reaction was caused by nucleophilic attack of mannitol hydroxyl group to acidic anhydride groups of poly(adipic anhydride) backbone and new ester groups were formed and appeared. Also, modified organic-soluble chitosan, N-maleoyl-chitosan, were synthesized by grafting reaction of chitosan with maleic anhydride in DMF as solvent, and it was also purified and characterized by FT-IR. Biodegradation in vitro of the IPNs of poly(adipic anhydride-co-mannitol)-N-maleoyl chitosan networks were evaluated by hydrolytic degradation studies at three different media (PBS, SIF and SGF) for 18 weeks with 92% as maximum degradation and it was found that minimum weight loss of IPNs was noticeably shown in SIF. In addition, hydrolytic degradation percent was decreased with increasing mannitol proportions.
The polymeric complexes were obtained from the reaction of polymeric Schiff base.N-crotonyl-2-hydroxyphenylazomethine (HL), with divalent metals Pt (II), Cr (II). The modes of bonding and overall geometry of the complexes were determine through spectroscopic methods and compared with that reported from analogous monomeric ligand. This study revealed square planer geometry around the metal center for [Pt(L)Cl] and distorted octahedral geometry for Cr complex [Cr(L)Cl(H2O)2].
Vapor-liquid equilibrium data are presented for the binary systems n-hexane - 1-propanol, benzene - 1-propanol and n-hexane – benzene at 760 mm of mercury pressure. In addition ternary data are presented at selected compositions with respect to the 1-propanol in the 1-propanol, benzene, n-hexane system at 760 mmHg. The results indicate the relative volatility of n-hexane relative to benzene increases appreciably with addition of 1-propanol.
The discourse surrounding lingual sovereignty within the African postcolonial context is profoundly intertwined with the fabric of cultural identity and self-determination. Language serves not merely as a conduit for communication but as a repository for a people's collective consciousness, encapsulating their traditions, thoughts, and perspectives. In the realm of postcolonial literature, this dialogue often grapples with the paradox of expressing indigenous narratives through the linguistic tools of former colonizers. Chinua Achebe's seminal work, "Things Fall Apart," exemplifies this conundrum, artfully weaving the orature and culture of Umuofia within the English language. Achebe's choice to write in English—a language imposed upon hi
... Show MoreBackground: Unlike normal EEG patterns, the epileptiform abnormal pattern is characterized by different mor phologies such as the high-frequency oscillations (HFOs) of ripples on spikes, spikes and waves, continuous and sporadic spikes, and ploy2 spikes. Several studies have reported that HFOs can be novel biomarkers in human epilepsy study. S) Method: To regenerate and investigate these patterns, we have proposed three large scale brain network models (BNM by linking the neural mass model (NMM) of Stefanescu-Jirsa 2D (S-J 2D) with our own structural con nectivity derived from the realistic biological data, so called, large-scale connectivity connectome. These models include multiple network connectivity of brain regions at different
... Show MoreCybersecurity involves protecting computer networks, systems, and data from unauthorized access and disruptions using advanced technologies. The purpose of this research is to establish a novel cyber security framework for strengthening cloud data protection. In this paper, we propose a novel Dung Beetle optimization-redefined Intelligent Random Forest (DB-IRF) for accurate detection of intrusions in a cloud environment. We obtained a dataset that includes cloud system logs and network traffic data, including normal and malicious activities, to train our proposed model. We utilized z-score normalization to pre-process the gathered raw data. Our suggested model enhances classification accuracy by integrating DB optimization with the
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