Polymeric microsphere devices occupy a wide range in the field of controlled drug delivery. Subcutaneous injectable preparations of Poly(Lactide-co-Glycolide) (PLGA) microsphere of Daptomycine were prepared by solvent extraction/evaporation technique using different copolymers ratio and molecular weights. Four formulations were prepared (F1-F4) and characterized in term of particle size, surface morphology, bulk density and porosity in addition to the drug content. The effects of the above parameters on the in-vitro release study were evaluated. These formulas were evaluated also for their in-vivo release profile using rat (as an animal model) and

Fine aggregates used for concrete works in Sulaymaniyah city frequently fail to meet the standard requirements for gradation and fineness modulus in cement concrete. This paper aims to critically evaluate gradation, fineness modulus, and clay contents of various natural sands produced and used for concrete work in the region.  Sixteen field sand samples were collected from various sites in Darbandikhan (5 samples), Qalat Dizah (5 samples), Koysinjaq (5 samples), and Piramagroon (1 sample) confirming to ASTM D75. The field samples were parted into test specimens based on ASTM C702. Then, sieve analysis was carried out on the oven-dry test specimens in compliance with ASTM C136. The test results of fine aggregates wer

Groundwater quality deterioration due to anthropogenic natural activities and its immense utilization in various sectors is considered a great concern. The aim of this study is to determine the groundwater quality parameters at various sources in and around Dhaka city and compare them with Bangladesh drinking water standards. In this study, six groundwater quality parameters (pH, DO, COD, TS, TDS, and arsenic) and ten groundwater samples are analyzed to determine the water quality. The collected samples have maximum and minimum pH values of 6.9 and 6.4, respectively. Maximum and minimum DO values are 0.3 and 0.1 mg/L, respectively. The arsenic concentration is 0 mg/L for all collected groundwater samples. The maximum and minimum COD

Lasmiditan (LAS) was formulated as a nanoemulsion based in situ gel (NEIG)with the aim of improving its oral bioavailability via application intranasally. The solubility of LAS in oils, emulsifiers, and co-emulsifiers was determined to identify nanoemulsion (NE)components. Phase diagrams were constructed to identify the area of nanoemulsification. LAS NE was formulated using the spontaneous nanoemulsification method. Four NEs (F19, F24, F31, and F34) containing 7-15 % oleic acid (OA) as an oily phase, 40-55% labrasol (LR), and transcutol (TC) as emulsifier mixture at (1:1), (2:1), (3:1), and (1:2) ratio with 30-53 % (w/w) aqueous phase, having suitable optical transparency of 95–98%, globule size of 104-140 nm and polydisper

    Multivariate Non-Parametric control charts were used to monitoring the data that generated by using the simulation, whether they are within control limits or not. Since that non-parametric methods do not require any assumptions about the distribution of the data.  This research aims to apply the multivariate non-parametric quality control methods, which are Multivariate Wilcoxon Signed-Rank ( ) , kernel principal component analysis (KPCA) and k-nearest neighbor ( −

To perform a secure evaluation of Indoor Design data, the research introduces a Cyber-Neutrosophic Model, which utilizes AES-256 encryption, Role-Based Access Control, and real-time anomaly detection. It measures the percentage of unpredictability, insecurity, and variance present within model features. Also, it provides reliable data security. Similar features have been identified between the final results of the study, corresponding to the Cyber-Neutrosophic Model analysis, and the cybersecurity layer helped mitigate attacks. It is worth noting that Anomaly Detection successfully achieved response times of less than 2.5 seconds, demonstrating that the model can maintain its integrity while providing privacy. Using neutrosophic sim

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.