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Recent Entries
J130115100926
Lung cancer is a major problem because it kills more people every year than any other type of cancer. If we can find the abnormal areas early, people are much more likely to recover. Doctors usually use a type of scan called a CT scan to look for these abnormal areas. The problem is that reviewing all the scan images is very tedious, and even experienced doctors can miss something. We wanted to see whether computers could help with this task. We tried using three kinds of computer programs, VGG16, ResNet50 and DenseNet121, to look at the pictures from the scan. We used a collection of pictures called the LIDC-IDRI collection to teach the computers what to look for. DenseNet121 performed best at identifying the regions. We think this is because it is good at using the information it has to make decisions. We also used a tool called Grad-CAM to see what the computers were actually looking at. It showed that the better programs focused on the abnormal areas, not just the normal tissue. Our results show that using computers to help with this task is promising, and it could help doctors make better decisions. We also know there is still a lot of work to do to make it perfect.
J129915100926
The purpose of this study is to advance knowledge for managers, policymakers, and researchers regarding the important key factors of successful Digital Transformation (DT) adoption, which can shed light on research gaps and help form a research agenda. A systematic literature review was conducted to analyse 26 peer-reviewed journal articles published between 2020 and 2025 from the Scopus, Emerald, Insight, Google Scholar, and ProQuest databases, following the PRISMA guidelines. Four DT adoption models, such as Diffusion of Innovation (DOI), Technology Acceptance Model (TAM), Task-Technology Fit (TTF), and Theory of Planned Behaviour (TPB), have been analysed to understand why people embrace DT either positively or negatively. According to the literature, the success factors for adopting DT have been identified. In addition, it is found that using only one DT adoption model does not ensure success. It is advisable to use an integrated multi-model or multiple frameworks for the theoretical adoption of DT. Using multiple frameworks makes DT adoption easier to understand. The Input-Process Output (IPO) schema allows consolidating the gaps in the present state and setting out the research agenda. The IPO schema can be considered a helpful tool for identifying research gaps and setting the research agenda, as well as for planning, decision-making, and policymaking.
I129715090826
This study presents a theoretical analysis of a parallel vertical-junction silicon solar cell under external electrical bias. The focus is on the influence of a biasing electric field on the solar cell’s performance under constant multispectral illumination at steady state. The analytical solution of the continuity equation, incorporating the quasi-neutral base approximation and boundary conditions that account for the recombination velocity at each junction, has enabled the derivation of an expression for the density of photogenerated minority charge carriers in the base. Consequently, expressions for photocurrent, photovoltage, electrical power, and capacitance have been derived and analysed as functions of the electric field and the minority-charge-carrier recombination velocity at the junction. Analysis of the results shows that applying an external electric field enhances the movement of minority charge carriers toward the junctions, thereby reducing recombination losses and promoting photocurrent generation by improving carrier collection. This movement of a large number of minority charge carriers toward the junctions under the influence of the electric field increases their density near the junctions. Consequently, the short-circuit photocurrent, open-circuit photovoltage, junction capacitance near open circuit, and maximum electrical power delivered by the solar cell all increase. The study also reveals a decrease in the diffusion potential as the electric field increases. Finally, the capacitance-photovoltage characteristic enables determination of the dark capacitance and the diffusion potential of the solar cell. The results show a relatively small increase in open-circuit voltage (Voc), from 0.58613 V at E = 0 V/cm to 0.58619 V at E = 6 V/cm. Short-circuit current density (Jsc) increases from 0.1032 A/cm² at E = 0 to 0.4168 A/cm² at E = 6 V/cm, representing a significant change. The diffusion potential (VD) decreases from 0.73 V to 0.42 V. The maximum power (Pmax) increases from 0.0507 W to 0.2348 W. Applying an external electric field to the parallel vertical-junction silicon solar cell is therefore an effective way to improve its electrical performance, which may prove useful for optimisation purposes. This work will be extended to include an experimental study of these phenomena and an analysis of the simultaneous influence of the electric field and temperature on the solar cell’s electrical parameters.
I128615090826
The increasing usage of distributed renewable energy resources has rapidly increased the transition to decentralised smart grids. This is where secure and efficient peer-to-peer (P2P) energy trading is crucial. However, conventional blockchain-based energy trading schemes suffer from high computational overhead, communication latency, and limited scalability, making them unsuitable for resource-constrained Internet of Things (IoT) networks. This research proposes a lightweight blockchain framework that incorporates Hyperledger Fabric with Practical Byzantine Fault Tolerance (PBFT) consensus, ZigbeePro communication, and Long Short-Term Memory (LSTM)-based energy demand forecasting to facilitate secure and intelligent decentralised energy trading. The framework was evaluated using MATLAB/Simulink simulation, NS-3, Hyperledger Fabric, and a Raspberry Pi/ESP32 prototype. The results of the experiment show that the proposed framework achieved an average latency of 48.9 ms, throughput of 185 transactions per second, packet delivery ratio of 97.8%, and support for up to 250 IoT nodes while maintaining low energy overhead. The LSTM forecasting model attained an R² of 0.964 with a MAPE of 4.7%, delivering accurate demand prediction for intelligent energy allocation. Compared with centralised and Proof-of-Work blockchain models, the proposed framework enhanced communication efficiency, scalability, and security integrating while reducing computational cost. These results demonstrated that lightweight blockchain, low-power communication, and Artificial Intelligence-based forecasting provides a practical and scalable solution for decentralised energy trading for the next-generation smart grids.
