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Fulltext Nigerian pharmacists' self-perceived competence and confidence to plan and conduct pharmacy practice research

Abubakar U, Sulaiman SA, Usman MN, Umar MD

Pharm Pract (Granada), 2018 03 27;16(1):1152.
PMID: 29619141 DOI: 10.18549/PharmPract.2018.01.1152

Background: Recent studies have revealed that pharmacists have interest in conducting research. However, lack of confidence is a major barrier.
Objective: This study evaluated pharmacists' self-perceived competence and confidence to plan and conduct health-related research.
Method: This cross sectional study was conducted during the 89th Annual National Conference of the Pharmaceutical Society of Nigeria in November 2016. An adapted questionnaire was validated and administered to 200 pharmacist delegates during the conference.
Result: Overall, 127 questionnaires were included in the analysis. At least 80% of the pharmacists had previous health-related research experience. Pharmacist's competence and confidence scores were lowest for research skills such as: using software for statistical analysis, choosing and applying appropriate inferential statistical test and method, and outlining detailed statistical plan to be used in data analysis. Highest competence and confidence scores were observed for conception of research idea, literature search and critical appraisal of literature. Pharmacists with previous research experience had higher competence and confidence scores than those with no previous research experience (p<0.05). The only predictor of moderate-to-extreme self-competence and confidence was having at least one journal article publication during the last 5 years.
Conclusion: Nigerian pharmacists indicated interest to participate in health-related research. However, self-competence and confidence to plan and conduct research were low. This was particularly so for skills related to statistical analysis. Training programs and building of Pharmacy Practice Research Network are recommended to enhance pharmacist's research capacity.
Fulltext Distributed Denial of Services (DDoS) attack detection in SDN using Optimizer-equipped CNN-MLP

Mehmood S, Amin R, Mustafa J, Hussain M, Alsubaei FS, Zakaria MD

PLoS One, 2025;20(1):e0312425.
PMID: 39869573 DOI: 10.1371/journal.pone.0312425

Software-Defined Networks (SDN) provides more control and network operation over a network infrastructure as an emerging and revolutionary paradigm in networking. Operating the many network applications and preserving the network services and functions, the SDN controller is regarded as the operating system of the SDN-based network architecture. The SDN has several security problems because of its intricate design, even with all its amazing features. Denial-of-service (DoS) attacks continuously impact users and Internet service providers (ISPs). Because of its centralized design, distributed denial of service (DDoS) attacks on SDN are frequent and may have a widespread effect on the network, particularly at the control layer. We propose to implement both MLP (Multilayer Perceptron) and CNN (Convolutional Neural Networks) based on conventional methods to detect the Denial of Services (DDoS) attack. These models have got a complex optimizer installed on them to decrease the false positive or DDoS case detection efficiency. We use the SHAP feature selection technique to improve the detection procedure. By assisting in the identification of which features are most essential to spot the incidents, the approach aids in the process of enhancing precision and flammability. Fine-tuning the hyperparameters with the help of Bayesian optimization to obtain the best model performance is another important thing that we do in our model. Two datasets, InSDN and CICDDoS-2019, are utilized to assess the effectiveness of the proposed method, 99.95% for the true positive (TP) of the CICDDoS-2019 dataset and 99.98% for the InSDN dataset, the results show that the model is highly accurate.
Fulltext Statistical Design of Experimental and Bootstrap Neural Network Modelling Approach for Thermoseparating Aqueous Two-Phase Extraction of Polyhydroxyalkanoates

Leong YK, Chang CK, Arumugasamy SK, Lan JC, Loh HS, Muhammad D, et al.

Polymers (Basel), 2018 Jan 30;10(2).
PMID: 30966168 DOI: 10.3390/polym10020132

At present, polyhydroxyalkanoates (PHAs) have been considered as a promising alternative to conventional plastics due to their diverse variability in structure and rapid biodegradation. To ensure cost competitiveness in the market, thermoseparating aqueous two-phase extraction (ATPE) with the advantages of being mild and environmental-friendly was suggested as the primary isolation and purification tool for PHAs. Utilizing two-level full factorial design, this work studied the influence and interaction between four independent variables on the partitioning behavior of PHAs. Based on the experimental results, feed forward neural network (FFNN) was used to develop an empirical model of PHAs based on the ATPE thermoseparating input-output parameter. In this case, bootstrap resampling technique was used to generate more data. At the conditions of 15 wt % phosphate salt, 18 wt % ethylene oxide⁻propylene oxide (EOPO), and pH 10 without the addition of NaCl, the purification and recovery of PHAs achieved a highest yield of 93.9%. Overall, the statistical analysis demonstrated that the phosphate concentration and thermoseparating polymer concentration were the most significant parameters due to their individual influence and synergistic interaction between them on all the response variables. The final results of the FFNN model showed the ability of the model to seamlessly generalize the relationship between the input⁻output of the process.
Fulltext Nonlinear Control of Fouling in Polyethylene Reactors

Rohman FS, Othman MR, Muhammad D, Azmi A, Idris I, Ilyas RA, et al.

ACS Omega, 2022 Nov 08;7(44):39648-39661.
PMID: 36385840 DOI: 10.1021/acsomega.2c03078

Fouling formation in reactor vessels poses a serious threat to the safe operation of the industrial low-density polyethylene (LDPE) polymerization. Fouling also degrades the polymer quality and causes productivity loss to some extent. In this work, neural Wiener model predictive control (NWMPC) is introduced to address the fouling concern. In addition, a soft sensor model is used to activate the fouling-defouling (F-D) mechanism when the fouling surpasses the thickness limit to prevent vessel overheating. NWMPC is proven to be fast, stable, and robust under various control scenarios. The use of a soft sensor model in conjunction with NWMPC enables the online monitoring and controlling of the F-D processes. When comparison is made with a state space (SSMPC) utilizing only the linear block, NWMPC is found to be able to control the LDPE grade with quicker grade transition and lower resource consumption.