Comparative Performance Evaluation Research Articles

Comparative Performance Evaluation of Multimodal Large Language Models, Radiologist, and Anatomist in Visual Neuroanatomy Questions

This study examined the performance of four different multimodal Large Language Models (LLMs)—GPT4-V, GPT-4o, LLaVA, and Gemini 1.5 Flash—on multiple-choice visual neuroanatomy questions, comparing them to a radiologist and an anatomist. The study employed a cross-sectional design and evaluated responses to 100 visual questions sourced from the Radiopaedia website. The accuracy of the responses was analyzed using the McNemar test. According to the results, the radiologist demonstrated the highest performance with an accuracy rate of 90%, while the anatomist achieved an accuracy rate of 67%. Among the multimodal LLMs, GPT-4o performed the best, with an accuracy rate of 45%, followed by Gemini 1.5 Flash at 35%, ChatGPT4-V at 22%, and LLaVA at 15%. The radiologist significantly outperformed both the anatomist and all multimodal LLMs (p

Development of nitrogen and phosphorus dual-doped reduced graphene oxide from waste plastic for supercapacitor applications: Comparative electrochemical performance in different electrolytes

The persistent non-biodegradable nature of plastic highlights the urgent need for effective waste management and resource conservation, underscoring the crucial importance of recycling and upcycling within a cradle-to-cradle framework. This research introduces an eco-friendly and straightforward upcycling process for plastic waste, which produces significant quantities of reduced graphene oxide through a carefully designed 2-stage pyrolysis method. To enhance the electrochemical properties of the reduced graphene oxide, they were doped with heteroatoms (i.e. nitrogen and phosphorus) via a hydrothermal route. Also, as the nature of the electrolyte plays a significant role in electrochemical analysis, a comparative evaluation of the supercapacitive performance of the heteroatom-doped reduced graphene oxide was conducted across various aqueous electrolytes, including 1 M H2SO4, 6 M KOH, and 2 M KCl, as well as hydrogel polymer electrolytes such as 1 M H2SO4/1 M PVA, 2 M KCl/1 M PVA, and 6 M KOH/1 M PVA. Our results demonstrate that synthesized material from waste plastic exhibits excellent performance, particularly when combined with a 1 M H2SO4 electrolyte, achieving the highest specific capacitance of 407.6 F/g. In conclusion, this study presents a cost-effective and sustainable approach to promoting a circular economy by repurposing waste plastic for energy storage applications.

RAM-MEN: Robust authentication mechanism for IoT-enabled edge networks

The rapid expansion of Mobile Edge Computing (MEC) and the Internet of Things (IoT) has revolutionized technology by enabling real-time data processing at the network edge, which is essential for applications such as autonomous vehicles and smart cities. With the advent of 6G networks, which promise ultra-fast speeds, vast connectivity, and low-latency communication, MEC-IoT systems are becoming more powerful but also face significant security challenges. Existing authentication mechanisms (AMs) are often vulnerable to attacks like impersonation and insider threats. This paper introduces a novel lightweight AM, called RAM-MEN that employs cryptography and physically unclonable functions (PUFs) to secure IoT-enabled MEC environments in the 6G era. It protects against insider threats and fake MEC access points while ensuring efficiency and scalability. Additionally, the proposed RAM-MEN establishes a secure communication channel (session key) between IoT devices and the MEC server, enabling secure offloading of computationally intensive tasks. The security of the session is rigorously evaluated using formal methods, including Scyther and the random or real model, alongside informal approaches. Comparative performance evaluations show that the proposed RAM-MEN reduces communication costs by 21.54% to 45.53% and computational costs by 17.09% to 83.72%, while providing enhanced security features.

Transformers in Cybersecurity: Advancing Threat Detection and Response through Machine Learning Architectures

The increasing sophistication of cyber threats has outpaced the capabilities of traditional detection and response systems, necessitating the adoption of advanced machine learning architectures. This study investigates the application of Transformer-based models in cybersecurity, focusing on their ability to enhance threat detection and response. Leveraging publicly available datasets, including CICIDS 2017 and UNSW-NB15, the research employs a systematic methodology encompassing data preprocessing, model optimization, and comparative performance evaluation. The Transformer model, tailored for cybersecurity, integrates self-attention mechanisms and positional encoding to capture complex dependencies in network traffic data. The experimental results reveal that the proposed model achieves an accuracy of 97.8%, outperforming conventional methods such as Random Forest (92.3%) and deep learning approaches like CNN (94.1%) and LSTM (95.6%). Additionally, the Transformer demonstrates high detection rates across diverse attack types, with rates exceeding 98% for Denial of Service and Brute Force attacks. Attention heatmaps provide valuable insights into feature importance, enhancing the interpretability of the model’s decisions. Scalability tests confirm the model’s ability to handle large datasets efficiently, positioning it as a robust solution for dynamic cybersecurity environments. This research contributes to the field by demonstrating the feasibility and advantages of employing Transformer architectures for complex threat detection tasks. The findings have significant implications for developing scalable, interpretable, and adaptive cybersecurity systems. Future studies should explore lightweight Transformer variants and evaluate the model in operational environments to address practical deployment challenges.

Comparative analysis and statistical performance evaluation of fractional PID controllers in fuel cell choppers optimized with metaheuristic algorithms

Comparative analysis and statistical performance evaluation of fractional PID controllers in fuel cell choppers optimized with metaheuristic algorithms

Automated Pathologic TN Classification Prediction and Rationale Generation From Lung Cancer Surgical Pathology Reports Using a Large Language Model Fine-Tuned With Chain-of-Thought: Algorithm Development and Validation Study.

Traditional rule-based natural language processing approaches in electronic health record systems are effective but are often time-consuming and prone to errors when handling unstructured data. This is primarily due to the substantial manual effort required to parse and extract information from diverse types of documentation. Recent advancements in large language model (LLM) technology have made it possible to automatically interpret medical context and support pathologic staging. However, existing LLMs encounter challenges in rapidly adapting to specialized guideline updates. In this study, we fine-tuned an LLM specifically for lung cancer pathologic staging, enabling it to incorporate the latest guidelines for pathologic TN classification. This study aims to evaluate the performance of fine-tuned generative language models in automatically inferring pathologic TN classifications and extracting their rationale from lung cancer surgical pathology reports. By addressing the inefficiencies and extensive parsing efforts associated with rule-based methods, this approach seeks to enable rapid and accurate reclassification aligned with the latest cancer staging guidelines. We conducted a comparative performance evaluation of 6 open-source LLMs for automated TN classification and rationale generation, using 3216 deidentified lung cancer surgical pathology reports based on the American Joint Committee on Cancer (AJCC) Cancer Staging Manual8th edition, collected from a tertiary hospital. The dataset was preprocessed by segmenting each report according to lesion location and morphological diagnosis. Performance was assessed using exact match ratio (EMR) and semantic match ratio (SMR) as evaluation metrics, which measure classification accuracy and the contextual alignment of the generated rationales, respectively. Among the 6 models, the Orca2_13b model achieved the highest performance with an EMR of 0.934 and an SMR of 0.864. The Orca2_7b model also demonstrated strong performance, recording an EMR of 0.914 and an SMR of 0.854. In contrast, the Llama2_7b model achieved an EMR of 0.864 and an SMR of 0.771, while the Llama2_13b model showed an EMR of 0.762 and an SMR of 0.690. The Mistral_7b and Llama3_8b models, on the other hand, showed lower performance, with EMRs of 0.572 and 0.489, and SMRs of 0.377 and 0.456, respectively. Overall, the Orca2 models consistently outperformed the others in both TN stage classification and rationale generation. The generative language model approach presented in this study has the potential to enhance and automate TN classification in complex cancer staging, supporting both clinical practice and oncology data curation. With additional fine-tuning based on cancer-specific guidelines, this approach can be effectively adapted to other cancer types.

Blockchain-Assisted Secure and Lightweight Authentication Scheme for Multi-Server Internet of Drones Environments

Unmanned aerial vehicles (UAVs) have seen widespread adoption across diverse sectors, including agriculture, logistics, surveillance, and disaster management, due to their capabilities for real-time data acquisition and autonomous operations. The integration of UAVs with Internet of Things (IoT) systems further amplifies their functionality, enabling sophisticated applications such as smart city management and environmental monitoring. In this context, blockchain technology plays a pivotal role by providing a decentralized, tamper-resistant ledger that facilitates secure data exchange between UAVs and connected devices. Its transparent and immutable characteristics mitigate the risk of a single point of failure, thereby enhancing data integrity and bolstering trust within UAV–IoT communication networks. However, the interconnected nature of these systems introduces significant security challenges, including unauthorized access, data breaches, and a variety of network-based attacks. These issues are further compounded by the limited computational capabilities of IoT devices and the inherent vulnerabilities of wireless communication channels. Recently, a lightweight mutual authentication scheme using blockchain was presented; however, our analysis identified several critical security flaws in these existing protocols, such as drone impersonation and session key disclosure. To address these vulnerabilities, we propose a secure and lightweight authentication scheme for multi-server UAV–IoT environments. The proposed protocol effectively mitigates emerging security threats while maintaining low computational and communication overhead. We validate the security of our scheme using formal methods, including the Real-Or-Random (RoR) model and BAN logic. Comparative performance evaluations demonstrate that our protocol enhances security while also achieving efficiency, making it well-suited for resource-constrained IoT applications.

Elastance-based nonlinear dynamical controller synthesis for the in-vitro cardiovascular circulatory system: A backstepping approach

This paper investigates the application of a nonlinear system-dependent and system-independent dynamical backstepping (DBST) controller on a cardiovascular circulatory simulator (CCS) with non-triangular affine dynamics for the first time in literature. That is, this study introduces several novel contributions to the field, including the enhancement of cardiac output and physiological fidelity while addressing the limitations of existing control methods. Additionally, we present a comparative performance evaluation of feedback linearization (FBL) and the developed DBST controller with respect to integral squared error (ISE), integral absolute error (IAE), and integral time absolute error (ITAE), offering new insights into each control technique’s error minimization capabilities and overall system performance effectiveness. Furthermore, we pioneer the use of Monte-Carlo (MC) simulations as a reliable in-vitro alternative to clinical trials for controller performance assessment under intra- and inter-patient parameter variations. This innovative approach overcomes the limitations of in-vivo evaluations, enabling the analysis of crucial factors such as signal energy and reference tracking error value. Our results, supported by both MC simulations, demonstrate the complete superiority of the DBST controller over FBL in terms of error reduction and adaptability due to patient-to-patient variability, thus validating its potential for practical applications in cardiovascular circulatory systems.

A comparative numerical evaluation of solar air heater performance having W-contoured, taper-contoured and reverse taper-contoured turbulators

The use of different turbulators in solar air heaters can significantly impact their thermal and hydraulic performance. This study compares solar air heaters equipped with W-contoured, taper-contoured, and reverse taper-contoured turbulators. It examines heat transfer coefficients, pressure drops, velocity contours, turbulent kinetic energy contours, and thermal perfor-mance factors for these systems under varying operating conditions. The air Reynolds number ranges from 4000 to 18 000, while design parameters such as relative roughness height and relative pitch ratio remain constant for accurate comparison. The simulations were conducted with a uniform heat flux of 1200 W/m2. The W-shaped contour roughness achieved the greatest heat transfer coefficient, surpassing both the tapered and reverse tapered configurations. In terms of friction factor, the tapered contour on the absorber plate led, followed by the reverse tapered and W-shaped contours. Overall, the W-shaped contour delivered the best performance. At lower Reynolds numbers, the reverse tapered contour outperformed the tapered contour, whereas at higher Reynolds numbers, the tapered contour showed superior performance compared to the reverse tapered contour.

A Comparative Performance Evaluation of Mainstream Multiphase Models for Aerated Flow on Stepped Spillways

A systematic comparative evaluation of mainstream multiphase models for specific hydraulic structures is essential to investigate aerated flow characteristics and promote the models’ application. However, such evaluations remain scarce. In this paper, four multiphase models, namely the VOF, Mixture, and Eulerian models in Ansys Fluent and the aerated flow model in FLOW-3D (AFM-F3D), were comparatively introduced and tested for the aerated flow over stepped spillways. Simulation results, including water surface profiles, inception points of aeration, air concentrations, velocities, and turbulent kinetic energies from four models, are compared with each other and with available experimental data. It is discovered that both the VOF and Mixture models fail to reproduce the self-aeration and subsequent downward transport phenomenon. In contrast, AFM-F3D and the Eulerian models predict reliable aerated water surface profiles. AFM-F3D and the Eulerian model demonstrate superior performance in velocity and air concentration calculations, respectively. Based on overall performance, the Eulerian model is recommended for simulating aerated stepped spillway flows. These insights provide valuable guidance for selecting appropriate multiphase models based on specific engineering requirements.

Viral concentration method biases in the detection of viral profiles in wastewater.

Viral detection methodologies used for wastewater-based epidemiology (WBE) studies have a broad range of efficacies. The complex matrix and low viral particle load in wastewater emphasize the importance of the concentration method. This study focused on comparing three commonly used virus concentration methods: polyethylene glycol precipitation (PEG), immuno-magnetic nanoparticles (IMNP), and electronegative membrane filtration (EMF). Influent and effluent wastewater samples were processed by the methods and analyzed by DNA/RNA quantification and sequencing for the detection of human viruses. SARS-COV-2, Astrovirus, and Hepatitis C virus were detected by all the methods in both sample types. PEG precipitation resulted in the detection of 20 types of viruses in influent and 16 types in effluent samples. The corresponding number of virus types detected was 21 and 11 for IMNP, and 16 and 8 for EMF. Certain viruses were unique to only one concentration method. For example, PEG detected three types of viruses in influent and six types in effluent compared to IMNP, which detected seven types in influent and one type in effluent samples. However, the EMF method appeared to be the least effective, detecting three types in influent and none in effluent samples. Rotavirus was detected in influent sample using IMNP method, whereas EMF and PEG methods failed to yield a similar outcome. Consequently, the potential false negative results pose a risk to the credibility of WBE applications. Therefore, implementation of a proper concentration technique is critical to minimize method biases and ensure accurate viral profiling in WBE studies.IMPORTANCEIn recent years, significant research efforts have been focused on the development of viral detection methodology for wastewater-based epidemiology studies, showing a range of variability in detection efficacies. A proper methodology is essential for an appropriate evaluation of disease prevalence and community health in such studies and necessitates designing a concentration method based on the target pathogenic virus. There remains a need for comparative performance evaluations of methods in the context of detection efficiencies. This study highlights the significant impact of sample matrix, viral structure, and nucleic acid composition on the efficacy of viral concentration methods. Assessing WBE techniques to ensure accurate detection and understanding of viral presence within wastewater samples is critical for revealing viral profiles in municipality wastewater samples.

COMPARATIVE PERFORMANCE EVALUATION BETWEEN AJM AND HOT-AJM DURING MACHINING OF ZIRCONIA CERAMIC USING SILICON CARBIDE ABRASIVES

Abrasive jet machining (AJM) process is commonly used for cutting and drilling of brittle materials in which the phenomenon of material removal can be considered as mechanical erosion by the impingement of high-velocity abrasives. The focus of this research is to compare the performance and economic benefits of recently developed hot abrasive jet machining (hot-AJM) with traditional AJM techniques when machining zirconia ceramic using silicon carbide (SiC) grain particles. The modified AJM employed abrasive air jet (combination of hot abrasive and compressed air) to strike on the work surface for material erosion. Briefly, the cutting performance is investigated by comparing the technological characteristics like material removal rate, machining cost, and taper angle of the developed hole. Abrasive grain size, nozzle pressure, and stand-off distance are considered as the variable factors for machining trials in accordance with the design of experiments. The usage of hot abrasives in AJM improved the material erosion owing to the occurrence of plastic deformation followed by deep chipping on the machined surfaces. From the results, it was observed that the hot-AJM outperformed the normal AJM with regard to improved material removal, reduced dimensional deviation of hole and minimal machining cost. According to the findings of the cost assessment, the machining of zirconia ceramic using hot-AJM was more cost-effective than using normal AJM since it resulted 25% reduction in the total cost of production. The overall machining cost expenditure per unit in hot-AJM was lower (INR 123.12) than expenditure in traditional AJM (INR 153.47). Machining with hot-modified AJM, compared to normal AJM, offers a more techno-economically viable solution for enhancing machinability.

Comparative Analysis of Diagnostic Performance: Differential Diagnosis Lists by LLaMA3 Versus LLaMA2 for Case Reports.

Generative artificial intelligence (AI), particularly in the form of large language models, has rapidly developed. The LLaMA series are popular and recently updated from LLaMA2 to LLaMA3. However, the impacts of the update on diagnostic performance have not been well documented. We conducted a comparative evaluation of the diagnostic performance in differential diagnosis lists generated by LLaMA3 and LLaMA2 for case reports. We analyzed case reports published in the American Journal of Case Reports from 2022 to 2023. After excluding nondiagnostic and pediatric cases, we input the remaining cases into LLaMA3 and LLaMA2 using the same prompt and the same adjustable parameters. Diagnostic performance was defined by whether the differential diagnosis lists included the final diagnosis. Multiple physicians independently evaluated whether the final diagnosis was included in the top 10 differentials generated by LLaMA3 and LLaMA2. In our comparative evaluation of the diagnostic performance between LLaMA3 and LLaMA2, we analyzed differential diagnosis lists for 392 case reports. The final diagnosis was included in the top 10 differentials generated by LLaMA3 in 79.6% (312/392) of the cases, compared to 49.7% (195/392) for LLaMA2, indicating a statistically significant improvement (P<.001). Additionally, LLaMA3 showed higher performance in including the final diagnosis in the top 5 differentials, observed in 63% (247/392) of cases, compared to LLaMA2's 38% (149/392, P<.001). Furthermore, the top diagnosis was accurately identified by LLaMA3 in 33.9% (133/392) of cases, significantly higher than the 22.7% (89/392) achieved by LLaMA2 (P<.001). The analysis across various medical specialties revealed variations in diagnostic performance with LLaMA3 consistently outperforming LLaMA2. The results reveal that the LLaMA3 model significantly outperforms LLaMA2 per diagnostic performance, with a higher percentage of case reports having the final diagnosis listed within the top 10, top 5, and as the top diagnosis. Overall diagnostic performance improved almost 1.5 times from LLaMA2 to LLaMA3. These findings support the rapid development and continuous refinement of generative AI systems to enhance diagnostic processes in medicine. However, these findings should be carefully interpreted for clinical application, as generative AI, including the LLaMA series, has not been approved for medical applications such as AI-enhanced diagnostics.

Performance Assessment of the Small and Medium-Scale Coconut Weeder Cum Fertilizer Applicator Developed by Rajarata University of Sri Lanka

The enhancement of coconut yield significantly hinges on proper weeding and chemical fertilizer application. In Sri Lanka, several mechanical methods have been introduced for weeding and fertilizer application of coconut, but the traditional manual approach persists most popular, despite being time, labor cost intensive. Moreover, the introduced technologies were suited for large-scale coconut cultivation and could not cope with medium or small-scale. Furthermore, most of them are designed for a single application; weeding or fertilizing. Thus, the Faculty of Agriculture, Rajarata University of Sri Lanka (RUSL), developed a coconut weeder cum fertilizer applicator for medium and small-scale coconut cultivations. However, it has not been properly evaluated and recommended for farmers. Consequently, this study sought to evaluate the performance of the above machine by conducting a comparative performance evaluation with the conventional method. A healthy, well-maintained, proper-spacing coconut plantation was selected from the faculty research unit, and each weeding and fertilizer application method was replicated ten times. In terms of performance parameters: effective field capacity, field efficiency, and weeding efficiency in mechanical and manual methods were 0.172 ha h-1, 61.86%, 66.62%, and 0.048 ha h-1, 55.34%, 60.35%, respectively. Statistical analysis indicates the mechanical method had higher field capacity and efficiency than the manual method, while weeding efficiency was not significantly different (p≤0.05). In addition, the machine had a fertilizer spreading uniformity of around 99.98% when operating at an optimum speed of 1.45 km h-1. Fertilizer application rate and performance index as a weeder were 1.7 kg/min and 1145.86, respectively. Furthermore, the fuel consumption rate of the machine was 0.733 L h-1 and its break-even point was recorded as 2.85 ha yr-1. Consequently, the coconut weeder cum fertilizer applicator proves suitable for medium and small-scale coconut cultivations in Sri Lanka.

Comparative Evaluation of Hematological Parameters and Instrument Performance in Single and Double Plateletpheresis Procedures Using Haemonetics MCS+ and Trima Accel Systems

ObjectivesPlateletpheresis (PP) has become increasingly prevalent due to its cost-effectiveness and fewer immunological and infectious complications for recipients. This study compares hematological indices of platelet donors and instrument-related parameters in high-yield PP donors using Haemonetics MCS+ and Trima Accel. MethodsEligible and healthy PP donors meeting the platelet donation criteria were randomly selected.19 single-dose platelet (SDP), and 26 double-dose platelet (DDP) donors underwent PP using the Haemonetics MCS+, while 21 SDP and 21 DDP donors were processed using the Trima Accel system. Complete Blood Count (CBC) and hematological indices of donors between groups with both devices were measured with the cell counter. Platelet yield, collection efficiency (CE), and collection rate (CR) were evaluated for both devices. Results were reported using R-4.3.2 software and a p-value<0.05 was considered statistically significant ResultsThe Trima Accel processed significantly more blood volume and had shorter procedure times than MCS+. Platelet yield in the SDP group with Trima Accel was significantly higher than the Haemonetics MCS+. The Trima Accel demonstrated a significantly higher CR and CE than the MCS+ in both SDP and DDP groups. Post-PP lymphocyte counts significantly decreased with the Trima compared to the MCS+ in the SDP group. However, post-PP hematocrit (HCT), mean corpuscular volume (MCV), and mean platelet volume (MPV) in the DDP group with the MCS+ were significantly lower than Trima. ConclusionDouble-dose plateletpheresis (DDP) offers advantages in cost-effectiveness and platelet production, and although it reduces some hematological indices, these remain within normal limits. The Trima Accel may offer superior efficiency and processing times compared to the MCS+. However, careful monitoring of DDP donors following AABB standards remains essential.

Comparative Evaluation of Axial Performance of Reinforced Concrete and Fiber-reinforced Polymer Concrete Columns

Comparative Evaluation of Axial Performance of Reinforced Concrete and Fiber-reinforced Polymer Concrete Columns

Efficient and Compressed Deep Learning Model for Brain Tumour Classification With Explainable AI for Smart Healthcare and Information Communication Systems

ABSTRACTThe detection of brain tumours presents a significant challenge in the medical domain, where prompt and precise diagnosis is crucial as patient outcomes depend on it. Conventional deep neural networks perform well in carrying out various imaging tasks within the healthcare sector; however, their effectiveness often falls short of expectations in practical applications due to the substantial computational resources required and issues with reliability. In this research, an optimised and effective deep learning model founded on the DenseNet‐169 architecture is introduced for the classification of magnetic resonance imaging brain tumours, which is particularly advantageous for smart healthcare systems and information and communication technology (ICT) settings with limited computational capabilities. The model compression methodologies, including pruning and quantization, have been employed to significantly diminish the dimensions and intricacy of the model while achieving a classification accuracy of 97.07%. Furthermore, this endeavour necessitates the enhancement of the model's interpretability through the utilisation of explainable artificial intelligence methodologies such as Gradient‐weighted Class Activation Mapping (Grad‐CAM) and SHapley Additive exPlanations (SHAP), which will aid clinicians in highlighting crucial areas of the images and validating feature importance concerning the decisions rendered by the model. A comparative performance evaluation is conducted against DenseNet‐169, ResNet‐50 and various other models to delineate the superior efficacy of our model, rendering it exceptionally adept for knowledge‐driven, real‐time brain tumour diagnosis within smart healthcare and ICT systems where resources are constrained.

Flooded area detection and mapping from Sentinel-1 imagery. Complementary approaches and comparative performance evaluation

ABSTRACT The current study assesses the performance of several machine learning (ML) and deep learning (DL) models for detecting and mapping floods using Sentinel-1 SAR imagery. Three distinct approaches were used: pixel classification, object-based image analysis and object instance segmentation. The ML models are Random Forest, and Support Vector Machine and the DL models are U-NET, DeepLabV3 and Mask RCNN. Five different case studies were selected to test the models’ scalability. These areas are in Romania (Prut River, at the border between Romania, the Republic of Moldova and Ukraine, Timiș River, and Râul Negru River), the United States of America (Missouri River) and Australia (Broughton Creek). For each flood, five Sentinel-1 images were used, four collected before the flood and one collected after the flood. The intensity images were stacked and scaled in the range of the intensity thresholds associated with water and non-water so that all the case studies have the same margins for intensity. Samples of water, vegetation, agricultural fields, and bare soil were collected only from the Prut River case study and used in the training process. Out of all models, the U-Net model returned the highest accuracy with a value for Intersect over Union of 0.763 for a tile size of 128x128 pixels.

Comprehensive evaluation of solar floating photovoltaic prospective in Saudi Arabia: Comparative experimental investigation and thermal performance analysis

Recently, floating photovoltaic systems have been regarded as a promising technology for producing clean energy by utilizing the surface of water bodies, such as lakes, rivers and oceans. This study introduces a comparative experimental study and energy performance evaluation of a 1.0 kW offshore floating photovoltaic (FPV) system and a nearby traditional ground-based PV system (GPV) installed in the eastern province of Saudi Arabia. The FPV system was deployed in the Arabian Gulf, 25 m off the coast, at an average depth of 1 to 1.5 m depending on tide, wave, and current intensity. The FPV system employs a strong novel eco-friendly platform structure made of recycled buoyant materials such as engineered plastic drums and wood. This system is anchored with tension cables and concrete blocks that can withstand the changing sea water conditions. The GPV system, on the other hand, was installed 150 m inland from the shore. Real-time monthly data monitoring and assessment indicated that FPV systems outperformed GPV systems in terms of lower PV panel surface temperatures, higher power output, and panel efficiency. Based on daily average back surface temperatures, FPV system temperature was decreased by 7.5 % to 21.34 % when compared to GPV. Moreover, the FPV system efficiency was also increased by 12.2 % when compared to the GPV system. This study aims to assist in promoting the applicability of solar floating photovoltaic systems to synergistically fulfill the requirements of sustainable electricity production for the arid costal community in Saudi Arabia and similar areas.

Comparative Analysis of NURBS and Finite Element Method in Computational Fluid Dynamics Applications: Case Study on NACA 2412 Airfoil Aerodynamics

In this research, an attempt was made to employ the Non-Uniform Rational B-Splines (NURBS) method for a challenging computational fluid dynamics (CFD) problem of aerodynamics around NACA 2412 airfoils. The comparison was carried out thoroughly by using the same boundary conditions and geometry, comparing NURBS to standard FEM implementations. Our study was interested in demonstrating the foreseeable functionalities of NURBS for solving complex CFD problems and conducting a comparative effectiveness performance evaluation between them with traditional FEM methodologies.