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"Mohammad, Hassan"
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Exploring Computer Science Students’ Perception of ChatGPT in Higher Education: A Descriptive and Correlation Study
ChatGPT is an emerging tool that can be employed in many activities including in learning/teaching in universities. Like many other tools, it has its benefits and its drawbacks. If used properly, it can improve learning, and if used irresponsibly, it can have a negative impact on learning. The aim of this research is to study how ChatGPT can be used in academia to improve teaching/learning activities. In this paper, we study students’ opinions about how the tool can be used positively in learning activities. A survey is conducted among 430 students of an MSc degree in computer science at the University of Hertfordshire, UK, and their opinions about the tool are studied. The survey tries to capture different aspects in which the tool can be employed in academia and the ways in which it can harm or help students in learning activities. The findings suggest that many students are familiar with the tool but do not regularly use it for academic purposes. Moreover, students are skeptical of its positive impacts on learning and think that universities should provide more vivid guidelines and better education on how and where the tool can be used for learning activities. The students’ feedback responses are analyzed and discussed and the authors’ opinions regarding the subject are presented. This study shows that ChatGPT can be helpful in learning/teaching activities, but better guidelines should be provided for the students in using the tool.
Journal Article
Functional Outcome Following Proximal Tibial Osteosarcoma Resection and Reconstruction by Modular Endoprosthesis
Purpose
The proximal tibia is a common location for osteosarcoma. Modular endoprosthesis is a popular reconstructive option, yet it has been associated with many complications. Our study aimed to evaluate the functional outcome and complications of proximal tibial osteosarcoma patients treated by limb salvage using modular endoprosthesis.
Methods
A retrospective study of a prospective database was performed during the period between January 2000 and July 2017. Fifty-five patients with proximal tibial osteosarcoma underwent resection and modular endoprosthetic reconstruction. The functional outcome was evaluated using the Musculoskeletal tumor society scoring system and knee range of motion. Postoperative complications were classified according to Henderson classification; Type 1 (soft tissue failure), Type 2 (aseptic loosening), Type 3 (structural failure), Type 4 (infection) and Type 5 (local tumor progression).
Results
The mean follow-up period was 71.69 ± 49.76 months. The mean musculoskeletal tumor society score was 26.5 ± 2.22; the mean range of motion was 72.63 ± 25.07, and the mean extension lag was 15.09 ± 15.38. Type 1, type 2, type 3, type 4, and type 5 complications occurred in 7.3%, 14.5%, 21.8%, 23.6%, and 5.5%, respectively. Chest metastasis developed in 10 patients (18.2%). The estimated 5-year and 10-year survival rates for the treated patients were 83.6% and 79.9%.
Conclusions
Proximal tibial osteosarcoma reconstruction with a modular endoprosthesis is a reliable treatment option for retaining limb function. Most complications are manageable.
Journal Article
Metal nanoparticles as a potential technique for the diagnosis and treatment of gastrointestinal cancer: a comprehensive review
Gastrointestinal (GI) cancer is a major health problem worldwide, and current diagnostic and therapeutic approaches are often inadequate. Various metallic nanoparticles (MNPs) have been widely studied for several biomedical applications, including cancer. They may potentially overcome the challenges associated with conventional chemotherapy and significantly impact the overall survival of GI cancer patients. Functionalized MNPs with targeted ligands provide more efficient localization of tumor energy deposition, better solubility and stability, and specific targeting properties. In addition to enhanced therapeutic efficacy, MNPs are also a diagnostic tool for molecular imaging of malignant lesions, enabling non-invasive imaging or detection of tumor-specific or tumor-associated antigens. MNP-based therapeutic systems enable simultaneous stability and solubility of encapsulated drugs and regulate the delivery of therapeutic agents directly to tumor cells, which improves therapeutic efficacy and minimizes drug toxicity and leakage into normal cells. However, metal nanoparticles have been shown to have a cytotoxic effect on cells in vitro. This can be a concern when using metal nanoparticles for cancer treatment, as they may also kill healthy cells in addition to cancer cells. In this review, we provide an overview of the current state of the field, including preparation methods of MNPs, clinical applications, and advances in their use in targeted GI cancer therapy, as well as the advantages and limitations of using metal nanoparticles for the diagnosis and treatment of gastrointestinal cancer such as potential toxicity. We also discuss potential future directions and areas for further research, including the development of novel MNP-based approaches and the optimization of existing approaches.
Journal Article
MoO3/WO3/rGO as electrode material for supercapacitor and catalyst for methanol and ethanol electrooxidation
The potential of metal oxides in electrochemical energy storage encouraged our research team to synthesize molybdenum oxide/tungsten oxide nanocomposites (MoO
3
/WO
3
) and their hybrid with reduced graphene oxide (rGO), in the form of MoO
3
/WO
3
/rGO as a substrate with relatively good electrical conductivity and suitable electrochemical active surface. In this context, we presented the electrochemical behavior of these nanocomposites as an electrode for supercapacitors and as a catalyst in the oxidation process of methanol/ethanol. Our engineered samples were characterized by X-ray diffraction pattern and scanning electron microscopy. As a result, MoO
3
/WO
3
and MoO
3
/WO
3
/rGO indicated specific capacitances of 452 and 583 F/g and stability of 88.9% and 92.6% after 2000 consecutive GCD cycles, respectively. Also, MoO
3
/WO
3
and MoO
3
/WO
3
/rGO nanocatalysts showed oxidation current densities of 117 and 170 mA/cm
2
at scan rate of 50 mV/s, and stability of 71 and 89%, respectively in chronoamperometry analysis, in the MOR process. Interestingly, in the ethanol oxidation process, corresponding oxidation current densities of 42 and 106 mA/cm
2
and stability values of 70 and 82% were achieved. MoO
3
/WO
3
and MoO
3
/WO
3
/rGO can be attractive options paving the way for prospective alcohol-based fuel cells.
Journal Article
Prediction of diabetes disease using an ensemble of machine learning multi-classifier models
Background and objective
Diabetes is a life-threatening chronic disease with a growing global prevalence, necessitating early diagnosis and treatment to prevent severe complications. Machine learning has emerged as a promising approach for diabetes diagnosis, but challenges such as limited labeled data, frequent missing values, and dataset imbalance hinder the development of accurate prediction models. Therefore, a novel framework is required to address these challenges and improve performance.
Methods
In this study, we propose an innovative pipeline-based multi-classification framework to predict diabetes in three classes: diabetic, non-diabetic, and prediabetes, using the imbalanced Iraqi Patient Dataset of Diabetes. Our framework incorporates various pre-processing techniques, including duplicate sample removal, attribute conversion, missing value imputation, data normalization and standardization, feature selection, and k-fold cross-validation. Furthermore, we implement multiple machine learning models, such as k-NN, SVM, DT, RF, AdaBoost, and GNB, and introduce a weighted ensemble approach based on the Area Under the Receiver Operating Characteristic Curve (AUC) to address dataset imbalance. Performance optimization is achieved through grid search and Bayesian optimization for hyper-parameter tuning.
Results
Our proposed model outperforms other machine learning models, including k-NN, SVM, DT, RF, AdaBoost, and GNB, in predicting diabetes. The model achieves high average accuracy, precision, recall, F1-score, and AUC values of 0.9887, 0.9861, 0.9792, 0.9851, and 0.999, respectively.
Conclusion
Our pipeline-based multi-classification framework demonstrates promising results in accurately predicting diabetes using an imbalanced dataset of Iraqi diabetic patients. The proposed framework addresses the challenges associated with limited labeled data, missing values, and dataset imbalance, leading to improved prediction performance. This study highlights the potential of machine learning techniques in diabetes diagnosis and management, and the proposed framework can serve as a valuable tool for accurate prediction and improved patient care. Further research can build upon our work to refine and optimize the framework and explore its applicability in diverse datasets and populations.
Journal Article
Chaos synchronization using adaptive quantum neural networks and its application in secure communication and cryptography
This paper proposes an adaptive controller for chaos synchronization using quantum neural networks (QNN). The main purpose is to design a communication system for transmission of information securely. In many applications of chaotic systems, the exact model of system is not available and may involve uncertainties such as external disturbance and parametric uncertainties originating from environmental conditions. To estimate the uncertainties in the receiver and improve the accuracy of synchronization and recovering the message signal for secure communication applications, a QNN is used. The parameters of the proposed system should be estimated by applying the adaptive rules obtained by Lyapunov theorem. Taylor series expansion has been utilized to obtain a linear relation between the output of quantum neural network and its adaptive parameters. Simulation results show that the synchronization procedure for state variables of the master and slave systems is performed well with negligible synchronization error. Also, its application is investigated in secure communication and cryptography.
Journal Article
Rapidly, sensitive quantitative assessment of thiopental via forced stability indicating validated RP-HPLC method and its in-use stability activities
Thiopental sodium (Tho) is an intravenous anesthetic. The current study aimed to find a rapid RP-HPLC method of Tho analysis with high linearity, repeatability, sensitivity, selectivity, and inexpensive. In our developed method, there is no need to use special chemical reagents, a high percentage of organic solvent, a high flow rate, or a further guard column. The chromatographic system consists of an ODS column (150 mm × 4.6 mm × 5 μm). The mobile phase was prepared by mixing KH
2
PO
4
solution: methanol (40:60) with a flow rate of 1.2 mL/min at a detection wavelength of 230 nm, at room temperature using an injection volume of 10 μL. The method manifested a satisfied linearity regression R
2
(0.9997) with a good repeatability precision range (0.16–0.47%) with LOD and LOQ; 14.4 μg/mL and 43.6 μg/mL respectively. Additionally, the method proved its efficiency via system suitability achievement in robustness and ruggedness, according to the validation guidelines. The shorter analysis time makes the method very valuable in quality control to quantify the commercial Tho in pharmaceutical preparations. This improved HPLC method has been successfully applied for Tho analysis for Thiopental UP Pharma 500 mg vials and Thiopental Eipico 1.0 g vials in our routine finished and stability studies testing laboratories. Additionally, the detection limit of Tho has been tested in our quality control lab to detect the smallest amount of traces that may be present after the cleaning process of the production machines for cephalosporins preparations. The method has shown positive results for Tho in low-level raw materials and pharmaceutical formulations.
Journal Article