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In recent years, polymer-based embolisation beads have been used to deliver drugs for the treatment of cancer directly to the site of action. Known to be biocompatible implants, these beads have become an ideal drug delivery vehicle for parenteral administration yet have not been considered for the more commonly used drug delivery routes such as oral and intramuscular drug delivery. This work describes the application of a type of polymer beads, formerly used for embolisation, as a formulation option for oral and intramuscular delivery of a model drug, namely imipramine. Following successful incorporation within the beads, dissolution analysis confirmed the potential to provide a modified drug release profile. Thermogravimetric analysis (TGA) permitted determination of the total water content within the beads (96.8%) and differential scanning calorimetry (DSC) indicated that not all of the water within the beads was able to freeze, apportioned as 15.8% non-freezing, 25.1% loosely bound and the remaining 55.9% unbound. In the presence of drug, the size of the beads decreased with a reduced water content (95.4%) comprised of 16.7% non-freezing, 20.5% freezing bound and the remaining 58.2% % unbound.In conclusion, the results presented in this study confirm the ability of TGA and DSC to separate the differing types of water within the beads and furthermore, the potential of such beads for a far wider variety of formulation options than those previously adopted.

Stroke disease is a serious cause of death globally. Early predictions of the disease will save a lot of lives but most of the clinical datasets are imbalanced in nature including the stroke dataset, making the predictive algorithms biased towards the majority class. The objective of this research is to compare different data resampling algorithms on the stroke dataset to improve the prediction performances of the machine learning models. This paper considered five (5) resampling algorithms namely; Random over Sampling (ROS), Synthetic Minority oversampling Technique (SMOTE), Adaptive Synthetic (ADASYN), hybrid techniques like SMOTE with Edited Nearest Neighbor (SMOTE-ENN), and SMOTE with Tomek Links (SMOTE-TOMEK) and trained on six (6) machine learning classifiers namely; Logistic Regression (LR), Decision Tree (DT), K-nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), and XGBoost (XGB). The hybrid technique SMOTE-ENN influences the machine learning classifiers the best followed by the SMOTE technique while the combination of SMOTE and XGB perform better with an accuracy of 97.99% and G-mean score of 0.99, and auc_roc score of 0.99. Resampling algorithms balance the dataset and enhanced the predictive power of machine learning algorithms. Therefore, we recommend resampling stroke dataset in predicting stroke disease than modeling on the imbalanced dataset.

Access to clean water by all is one of the UN Sustainable Development Goals. To achieve this, there is need to prepare sustainable materials for efficient water purification. This research aims to immobilize the Polyoxomatelate-Ionic Liquid (POM-IL), {[CH3(CH2)6]4N}7[α-PW11O39] on nanoporous carbon (NC) prepared from coconut shell to form an advanced material (POM-IL@NC) for effective removal of multiple contaminants from polluted water. The POM-IL@NC material was characterized by Fourier Transform-Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD) Analysis and Brunauer-Emmett-Teller (BET) method. The material was then tested respectively for the removal of copper and lead using Atomic Absorption Spectroscopy (AAS) and methylene blue using Ultraviolet-Visible (UV-Vis) Spectroscopy from simulated polluted waters via adsorption. The material was further subjected to antimicrobial analysis. The results showed that the POM-IL@NC material performed better than the pristine NC in the removal of copper, lead and methylene blue from the contaminated water. The POM-IL@NC also exhibited significant improvement in antimicrobial activity than NC against Escherichia coli and Staphylococcus aureus. This shows that the new material could effectively be applied for multiple contaminants treatment of polluted water.

The number of Ontologies and the Linked Open Data (LOD) on the Web is constantly increasing. This type of complex and heterogeneous semi-structured data raises new opportunities and challenges for the data mining (DM) community. Semantic Web Association Rule Mining (SWARM) algorithm have the limitations of generating many non-interesting, duplicate and equivalents rules and low algorithm performance. This research demonstrates a procedure for improving the performance of SWARM in text mining by using RDF data. The result revealed significant reduction in the number of generated rules this is significant because it helps to address the problem of discovering duplicate/equivalent Association Rules from Semantic Web Data. Keywords: Semantic Web, Association Rules, Data Mining, Linked Data. Title: An Improved Semantic Items Generation Algorithm for Mining High Number of Significant Association Rules from Semantic Web Data Author: Isya Isyaku, Muhammad Sirajo Aliyu, Abubakar Aminu Muazu International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Vol. 11, Issue 2, April 2023 - June 2023 Page No: 77-86 Research Publish Journals Website: www.researchpublish.com Published Date: 15-May-2023 DOI: https://doi.org/ 10.5281/zenodo.7936390 Paper Download Link (Source) https://www.researchpublish.com/papers/an-improved-semantic-items-generation-algorithm-for-mining-high-number-of-significant-association-rules-from-semantic-web-data, International Journal of Computer Science and Information Technology Research, ISSN 2348-1196 (print), ISSN 2348-120X (online), Research Publish Journals, Website: www.researchpublish.com

In this article, inspired by the projection technique of Solodov and Svaiter, we exploit the simple structure, low memory requirement, and good convergence properties of the mixed conjugate gradient method of Stanimirović et al. [New hybrid conjugate gradient and broyden-fletcher-goldfarbshanno conjugate gradient methods, J. Optim. Theory Appl. 178 (2018), no. 3, 860–884] for unconstrained optimization problems to solve convex constrained monotone nonlinear equations. The proposed method does not require Jacobian information. Under monotonicity and Lipschitz continuity assumptions, the global convergence properties of the proposed method are established. Computational experiments indicate that the proposed method is computationally efficient. Furthermore, the proposed method is applied to solve the ℓ 1 {\ell }_{1} -norm regularized problems to decode sparse signals and images in compressive sensing.

Variational inclusion is an important general problem consisting of many useful problems like variational inequality, minimization problem and nonlinear monotone equations. In this article, a new scheme for solving variational inclusion problem is proposed and the scheme uses inertial and relaxation techniques. Moreover, the scheme is self adaptive, that is, the stepsize does not depend on the factorial constants of the underlying operator, instead it can be computed using a simple updating rule. Weak convergence analysis of the iterates generated by the new scheme is presented under mild conditions. In addition, schemes for solving variational inequality problem and split feasibility problem are derived from the proposed scheme and applied in solving Nash-Cournot equilibrium problem and image restoration. Experiments to illustrate the implementation and potential applicability of the proposed schemes in comparison with some existing schemes in the literature are presented.

The study examined the effect of feeding albino rats with 25, 50, and 75% Palmyrah Palm (Borassus aethiopum) shoots with respect to their body weight, liver and kidney function indices. Standard methods of biochemical analyses were employed using albino rats as the experimental animals. No mortality was observed in the LD50 test throughout the period of 48 h. Rats fed with 25 and 50% B. aethiopum shoots showed a gradual increase in the body weight throughout the period of treatment, but those fed with 75% of the shoots experienced a significant (p>0.05) decrease in body weight at the 3rd and 4th week of treatment compared to the control group. The results also showed changes in the serum total protein, albumin, globulin, glucose and bilirubin but were not significantly different (p>0.05) compared to the control group. The serum enzymes activities that is aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP) were significantly (p>0.05) elevated compared to the control group which suggested toxicity induced by some of the phytocompounds present in the feed. Serum creatinine, urea, uric acid, and electrolytes (Na+, K+) of rats fed with 75% B. aethiopum shoots showed significant (p>0.05) changes compared to the control group. The results showed that B. aethiopum shoots have a relatively low or no toxicity. Key words: Borassus aethiopum, shoots, albino rats, liver function, kidney function.

In this paper. a matrix-free method for solving large-scale system of nonlinear equations is presented. The method is derived via quasi-Newton approach, where the approximation to the Broyden's update is sufficiently done by constructing diagonal matrix using acceleration parameter. A fascinating feature of the method is that it is a matrix-free, so is suitable for solving large-scale problems. Furthermore, the convergence analysis and preliminary numerical results that is reported using a benchmark test problems, shows that the method is promising.

The aim of this paper is to study some properties of both the coincidence point set and the set of common fixed points of two or more digitally continuous mappings. Moreover, we study how rigidity may affect the sets of coincidence and homotopy coincidence points. Additionally, we investigate if some well-established results in the Nielsen classical topology regarding the sets of coincidence for many maps remain valid in the digital topological setting.

A new strong convergence iterative method for solving a split variational inclusion problem involving a bounded linear operator and two maximally monotone mappings is proposed in this article. The study considers an iterative scheme comprised of inertial extrapolation step together with the Mann-type step. A strong convergence theorem of the iterates generated by the proposed iterative scheme is given under suitable conditions. In addition, methods for solving variational inequality problems and split convex feasibility problems are derived from the proposed method. Applications of solving Nash-equilibrium problems and image restoration problems are solved using the derived methods to demonstrate the implementation of the proposed methods. Numerical comparisons with some existing iterative methods are also presented.

Ficus carica fruits are widely consumed in most part of rural areas in Northern part of Nigeria. This study was carried out to evaluate the ascorbic acid contents, tocopherols content, total polyphenols (as garlic acid equivalents), total flavonoids (as quercetin equivalents) and antioxidants capacity of Ficus carica fruits on a dry weight basis (DW). The contents of Ascorbic acid were determined colourimetrically using 2,6-dichloroindophenol; total polyphenolic compounds by Folin-Ciocalteu reagent, vitamin E was determined spectrophotometrically using standard α-tocopherols and antioxidant scavenging activity by DPPH. The value recorded was 37.00 ± 1.59 mg/100 g, 0.7 ± 0.1 mg/100 g, 384 ± 3.11 mgGAE/100 g, 21.63 ± 1.89 mgQE/100 g, 66.82 ± 7.80% and 560.25 ± 2.89%, respectively for ascorbic acid, tocopherols, total polyphenols, total flavonoids, 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging activity and antioxidant capacity respectively. The results obtained showed that the fruits if properly utilized can serve as a supplement of ascorbic acid, tocopherols and some polyphenols which important antioxidants with a wide range of biological benefits.

The limited oil reserves, pollution concerns, global warming has led to an increased financial support for sustainable and environmental friendly sources of energy. In the last decades there is an increasing interest in the development of the bioethanol production from lignocellulosic residues, which do not compete directly with food. However, the low efficient conversion of cellulosic biomass to biofuels hinders its success hence the need to constantly explore new, cheap and readily available raw materials. In this work, the capability of typed strains ofZ. mobilis, S.cerevisiaeas well as the co culture ofZ. mobilisandS.cerevisiae(1:1) to produce ethanol by saccharification and fermentation process from tomato stalk was investigated and compared. The results shows the optimal ethanol yield of 4.00, 6.70 and 10.97% (v/v) was obtained fromS. cerevisiae, Z. mobilisand the co culture ofS. cerevisiae and Z. mobilisafter 6 days of fermentation. The combination ofS. cerevisiaeandZ.mobilisyielded the highest ethanol when compared with the yields obtained when the organisms were used separately since the combination of the bacteria and yeast undergoes EMP and ED pathways which converts sugar into alcohol. The identity of the produced ethanol was confirmed by its density and IR spectrum. The results of this study suggest that agro waste should no longer be discarded but converted to useful products like bioethanol since it contains fermentable sugars.

In this paper, we introduce the concept of a partial $$b_v(s)$$ -metric space as a generalization of a partial metric space and a $$b_v(s)$$ -metric space. Some topological properties of the space and some fixed point results are established. To validate our findings, some illustrative examples are given.

Sensor nodes in wireless sensor networks are deployed to observe the surroundings for some phenomenon of interest. The fundamental issue in observing such environments is the area coverage which reflects how well the region is monitored. The nonuniform sensor nodes distribution in a certain region caused by random deployment might lead to coverage holes/gaps in the network. One of the solutions to improve area coverage after initial deployment is by sensor nodes mobility. However, the main challenge in this approach is how to increase area coverage with the least energy consumption. This research work aims to improve area coverage with minimal energy consumption and faster convergence rate. The Edge Based Centroid (EBC) algorithm is presented to improve the area coverage with faster convergence rate in a distributed network. The simulation based performance evaluations of the proposed algorithms are carried out in terms of area coverage, convergence rate, and energy efficiency. Compared to the existing works, EBC improved area coverage with faster convergence. It is concluded that the proposed algorithm has improved area coverage with faster convergence and minimal energy consumption.

Sensors are becoming more popular in many applications with the mission of sending and receiving information from the monitoring environment. However coverage and prolonging the network lifetime are challenging issues in disaster monitoring application. In this paper Sensors and RFID's are integrated together not only to reduce the cost of deployment but also to reduce the energy consumption and increase the coverage of the monitoring region, four different scenarios are been used and a flooding algorithm was developed to ensure an efficient message broadcast all over the network in order to reduce energy consumption in the network and to have full coverage of the monitoring region.

In this paper, the authors use the idea of $\beta _{F_{L}}$ -admissible mappings to prove some L-fuzzy fixed point theorems for a generalized contractive L-fuzzy mappings. Some examples and applications to L-fuzzy fixed points for L-fuzzy mappings in partially ordered metric spaces are also given, to support main findings.