Due to the projected increase in food production by 70% in 2050, crops should be additionally protected from diseases and pests to ensure a sufficient food supply. Transfer deep learning approaches provide a more efficient solution than traditional methods, which are labor-intensive and struggle to effectively monitor large areas, leading to delayed disease detection. This study proposed a versatile module based on the Inception module, Mish activation function, and Batch normalization (IncMB) as a part of deep neural networks. A convolutional neural network (CNN) with transfer learning was used as the base for evaluated approaches for tomato disease detection: (1) CNNs, (2) CNNs with a support vector machine (SVM), and (3) CNNs with the proposed IncMB module. In the experiment, the public dataset PlantVillage was used, containing images of six different tomato leaf diseases. The best results were achieved by the pre-trained InceptionV3 network, which contains an IncMB module with an accuracy of 97.78%. In three out of four cases, the highest accuracy was achieved by networks containing the proposed IncMB module in comparison to evaluated CNNs. The proposed IncMB module represented an improvement in the early detection of plant diseases, providing a basis for timely leaf disease detection.

Atypical and subtle facial expression patterns in individuals with autism spectrum disorder (ASD) pose a significant challenge for automated emotion recognition. This study evaluates and compares the performance of convolutional neural networks (CNNs) and transformer-based deep learning models for facial emotion recognition in this population. Using a labeled dataset of emotional facial images, we assessed eight models across four emotion categories: natural, anger, fear, and joy. Our results demonstrate that transformer models consistently outperformed CNNs in both overall and emotion-specific metrics. Notably, the Swin Transformer achieved the highest performance, with an accuracy of 0.8000 and an F1-score of 0.7889, significantly surpassing all CNN counterparts. While CNNs failed to detect the fear class, transformer models showed a measurable capability in identifying complex emotions such as anger and fear, suggesting an enhanced ability to capture subtle facial cues. Analysis of the confusion matrix further confirmed the transformers’ superior classification balance and generalization. Despite these promising results, the study has limitations, including class imbalance and its reliance solely on facial imagery. Future work should explore multimodal emotion recognition, model interpretability, and personalization for real-world applications. Research also demonstrates the potential of transformer architectures in advancing inclusive, emotion-aware AI systems tailored for autistic individuals.

Over the last several years, cloud computing has imposed as a major paradigm in software development by providing computer resources over the Internet. Among various cloud service models, Backend as a Service (BaaS) stands out as a model that targets the specific needs of web and mobile developers. By providing the backend for applications, it facilitates and expedites the software development process. In order to prevent major problems with the use of third-party BaaS providers, this paper advocates building your own BaaS platform, as well as several works ahead of it. However, the development of a BaaS platform carries various challenges regarding architecture and design. This paper strives to define the core service offerings of a BaaS platform and propose a method for providing an architectural design of a BaaS platform based on a microservice architecture. Microservice architecture is the preferred architectural style for cloud solutions since it promotes loose coupling, ease of scaling and integration with third-party services, which are fundamental stipulations of BaaS platforms. The methodology adopted in designing a microservice-based BaaS platform was formed in accordance with an Application Programming Interface (API)-first approach, which strives to design a suitable, representative API of the platform. To the best of authors’ knowledge, this paper proposes the lowest-level design of a BaaS platform so far, describing the entity relations, integration patterns, and communication styles. Ultimately, the proposed design was implemented and tested for its functional requirements. In that regard, specific test cases that mirror the actual workflow of the BaaS platform were constructed.

Pneumonia remains a significant cause of morbidity and mortality among pediatric patients worldwide. Accurate and timely diagnosis is crucial for effective treatment and improved patient outcomes. Traditionally, pneumonia diagnosis has relied on a combination of clinical evaluation and radiologists’ interpretation of chest X-rays. However, this process is time-consuming and prone to inconsistencies in diagnosis. The integration of advanced technologies such as Convolutional Neural Networks (CNNs) into medical diagnostics offers a potential to enhance the accuracy and efficiency. In this study, we conduct a comprehensive evaluation of various activation functions within CNNs for pediatric pneumonia classification using a dataset of 5856 chest X-ray images. The novel Mish activation function was compared with Swish and ReLU, demonstrating superior performance in terms of accuracy, precision, recall, and F1-score in all cases. Notably, InceptionResNetV2 combined with Mish activation function achieved the highest overall performance with an accuracy of 97.61%. Although the dataset used may not fully represent the diversity of real-world clinical cases, this research provides valuable insights into the influence of activation functions on CNN performance in medical image analysis, laying a foundation for future automated pneumonia diagnostic systems.

This study aimed to test the effect of a 7-day high-salt (HS) diet on autonomic nervous system (ANS) activity in young healthy individuals and modulation of ANS on microvascular endothelial function impairment. 47 young healthy individuals took 7-day low-salt (LS) diet (3.5 g salt/day) followed by 7-day high-salt (HS) diet (~14.7 g salt/day). ANS activity was assessed by 24-h urine catecholamine excretion and 5-min heart rate variability (HRV). Skin post-occlusive reactive hyperemia (PORH) and acetylcholine-induced dilation (AChID) were assessed by laser Doppler flowmetry (LDF). Separately, mental stress test (MST) at LS and HS condition was conducted, followed by immediate measurement of plasma metanephrines’ level, 5-min HRV and LDF microvascular reactivity. Noradrenaline, metanephrine and normetanephrine level, low-frequency (LF) HRV and PORH and AChID significantly decreased following HS compared to LS. MST at HS condition tended to increase HRV LF/HF ratio. Spectral analysis of PORH signal, and AChID measurement showed that MST did not significantly affect impaired endothelium-dependent vasodilation due to HS loading. In this case, 7-day HS diet suppressed sympathetic nervous system (SNS) activity, and attenuated microvascular reactivity in salt-resistant normotensive individuals. Suppression of SNS during HS loading represents a physiological response, rather than direct pathophysiological mechanism by which HS diet affects microvascular endothelial function in young healthy individuals.