Precise classification and detection of apple diseases are essential for efficient crop management and maximizing yield. This paper presents a fine-tuned EfficientNet-B0 convolutional neural network (CNN) for the automated classification of apple leaf diseases. The model builds upon a pre-trained EfficientNet-B0 base, enhanced through architectural modifications such as the integration of a global max pooling (GMP) layer, dropout, regularization, and full-model fine-tuning. To address class imbalance and improve generalization, the study adopts a holistic training strategy that integrates data augmentation, stratified data splitting, and class weighting, alongside transfer learning. The model is evaluated on the PlantVillage (PV) dataset and a curated Apple PV (APV) dataset and compared against EfficientNet-B0, EfficientNet-B3, Inception-v3, ResNet50, and VGG16 models. The fine-tuned model demonstrates outstanding test accuracies of 99.69% and 99.78% for classifying plant diseases using the APV and PV datasets, respectively. The fine-tuned model outperforms EfficientNet-B0, EfficientNet-B3, and VGG16 on both datasets and shows superior performance compared to Inception-v3 and ResNet-50 on the PV dataset. Both EfficientNet-B0 and the fine-tuned model demonstrate the lowest memory consumption and floating-point operations per second (FLOPs). Also, as compared to the EfficientNet-B0 model, the fine-tuned model achieves an 11% increase in accuracy on the APV dataset and a 49.5% accuracy improvement on the PV dataset, with approximately a 7-8% increase in both memory usage and FLOPs. The fine-tuned model thus emerges as an effective solution for plant leaf disease classification, delivering outstanding accuracy with optimized memory consumption and FLOPs, making it suitable for resource-constrained environments. This study demonstrates that fine-tuned CNN approaches, when combined with transfer learning, advanced data pre-processing, and architectural optimizations, can significantly enhance the accuracy of diseased leaf classification in crops with efficient implementation in limited-resource settings.

Due to the proliferation of extended travel range electric vehicles (EVs), these will travel through different networks that might be served by different utility companies. Therefore, we propose an architecture capable of offering a charging service to roaming vehicles. Furthermore, although the energy internet supports both the flow of energy and information, it does not support seamless EV roaming service, because it is based on a centralized architecture. The blockchain technology that is based on a decentralized system has the potential to support a secure billing platform for charging the EVs roaming through different electrical jurisdictions. Furthermore, the integration of artificial intelligence (AI) ensures that the participating players get a fair portion of the revenue. Thus, the objective of this paper is to develop an AI and blockchain integrated billing architecture that would offer a charging service to the “roaming” EVs and present a fair and unified billing solution.

The absence of cardiovascular disease (CVD) diagnostic and management solutions cause significant morbidity among populations in rural areas and the coronavirus disease of 2019 (COVID-19) emergency. To tackle this problem, in this paper, the development of an Internet of things (IoT) assisted ambulatory electrocardiogram (ECG) monitoring system is presented. The system’s wearable single-channel data acquisition device supports 25 h of continuous operation. A right leg drive (RLD) circuit supported analog frontend (AFE) with a high common mode rejection ratio (CMRR) of 121 dB and a digitally implemented notch filter is used to suppress power-line frequency interference. The wearable device continuously sends the collected ECG data via Bluetooth to the user’s smartphone. An application on the user’s smartphone renders real-time ECG trace and heart rate and detects abnormal heart rhythms. This data are then shared in real-time with the user’s doctor via a real-time cloud database. An application on the doctor’s smartphone allows real-time visualization of this data and detection of arrhythmias. Simulations and experimental results demonstrate that reliable ECG signals can be captured with low latency and the heart rate computation is comparable to a commercial application. Low cost, scalability, low latency, real-time ECG monitoring, and improved performance of the system make the system highly suitable for the real-time remote identification and management of CVDs in users of rural areas and in the COVID-19 pandemic.

A large potential exists in the Southeast Asia region for deployment of high-efficiency, low-emission (HELE) electricity generation technologies. A cost–benefit analysis of HELE technologies compared to the less efficient subcritical electricity generation plants is thus carried out to find a persuasive scenario supporting quicker transition from subcritical stations towards HELE technologies in the region. A levelized cost of electricity (LCOE) analysis is carried out for both technologies under four potential policy scenarios. Scenario 1 does not take into consideration any carbon pricing or costs associated with the desulphurization (deSOx) and denitrification (deNOx) facilities. Scenario 2 (Scenario 3) incorporates carbon pricing (costs associated with the deSOx and deNOx facilities), and Scenario 4 includes both carbon pricing and costs associated with the deSOx and deNOx facilities. Under each scenario, a sensitivity analysis is performed to evaluate the uncertainty affecting the future coal prices. This study demonstrates that HELE technologies are competitive against the subcritical plants under all four scenarios and both the technologies derive benefit from lifetime extensions and low coal prices. It is revealed that future deployments of HELE technologies can be best expedited by factoring in carbon pricing in LCOE costs of coal-fired power plants under Scenario 2.

The efforts towards the liberalization of electricity markets have sped up recently in some countries within the Association of Southeast Asian Nations (ASEAN) region. This step of opening up the electricity markets is aimed at establishing competitive and efficient electricity markets that not only reduce electricity prices, but also support a sustainable future by reducing carbon dioxide (${\mathrm{CO}}_2$) emissions from electricity generation and promoting the wider adoption of renewable energy (RE)-based electricity generation. This paper assesses the effects of the electricity market liberalization process in Singapore and the Philippines on these expected outcomes during the period 2015–2020. The regression analysis results suggest that in the specified period, the liberalization of the electricity market in Singapore has delivered both household and industry electricity price reductions and improvement in the RE share. However, there is no significant effect of the electricity market liberalization process on the electricity generated ${\mathrm{CO}}_2$ emissions. For the same period, the results imply that with the electricity market liberalization process in the Philippines, the electricity prices for household consumers and electricity-generated ${\mathrm{CO}}_2$ emissions have increased. Additionally, the liberalization process has no significant impact on both the RE share and industry electricity prices in the Philippines. To overcome the obstacles and strike a balance between the expected outcomes, policy recommendations are given for ASEAN economies following the pathway of liberalized electricity markets.