New technologies solve complex problems in agricultural production, enabling both food security and the optimization of the use of natural resources. In this paper, we will cover hydroponic agriculture, advanced monitoring, and data analysis. Plants interact with light, humidity, and temperature environmental factors. Light is one of the most important environmental factors for photosynthesis and growth. Various wavelengths have contrasting impacts on plant development and physiological traits. Artificial manipulation of light conditions may allow for an improvement in crop production and quality. Thus, the present study deals with the influence of diverse light wavelengths on the growth of strawberry plants cultivated in a hydroponic system. The growing processes of strawberry plants (Fragaria × ananassa) have been taken into consideration due to their importance as food and the sensitivity of their cultivation. It tests the influence of specific wavelengths on plant growth and development. State-of-the-art technologies include Arduino-based temperature and light sensors to monitor the cultivation conditions in real-time, while Convolutional Neural Networks trace the growth patterns and pests of the crops by images taken. This work models and predicts behaviors of plants under different light conditions using Machine Learning techniques, thus optimizing cultivar development with a view of maximum yield production. The results obtained show that red light promotes growth through enhanced flower and fruit development. Blue light is favored by robust leaf and stem growth since it is most effective in photosynthesis. Green lights, which help inner light penetration inside the leaf canopy, have less of an effect on photosynthesis. Yellow light also has some advantages in general growth but is inefficient compared with blue and red light. Result using CNN architecture, accuracy 89%. This work contributes to precision agriculture, sensor technology, and sustainable farming practices.

Regulatory peptides are widely used as immunomodulators, neuroprotectors, and neurometabolicstimulators. This work is devoted to the isolation of peptide complexes from the epidermal secretion of ascaleless fish species and the study of these complexes properties. It has been suggested that suchpeptides can suppress inflammation and promote the regeneration of human integumentary tissue.The skin secretion of the African catfish (Clarias gariepinus) was taken as a source of low-molecularpeptides. Peptide isolation was carried out according to the following scheme: acetic acid extraction inthe presence of a biogenic salt (MgCl2, CaCl2, ZnCl2, MgSO4, CaSO4); ultrasonic cavitation treatment;removal of major proteins by filtration; precipitation of low-molecular weight peptides using acetone.To qualitatively check the resulting product, IR spectroscopy and chromatomass spectrometry wereused. Using these methods, it was possible to prove that the isolated product is a complex containingtens of thousands of low molecular weight peptides. To confirm the antioxidant properties of theproduct, the autoxidation of adrenaline in an alkaline medium was used. Antioxidant properties aredecisive in wound healing preparations of peptide origin. It was found that the resulting peptidepreparation is capable of inhibiting the oxidation reaction of a 1% solution of adrenaline in a buffersolution, which clearly indicates its antioxidant properties. It has been shown that the use of CaCl2makes it possible to isolate the peptide fraction with the highest antioxidant activity.

In the era of big data, the sheer volume of information available for machine learning applications has grown exponentially. However, this increase in data often leads to a decrease in the quality of datasets due to issues such as noise, redundancy, and irrelevance, which can adversely affect the performance of predictive models. To address these challenges, dimensionality reduction techniques are employed, with feature selection being a prominent method. The objective of this research is to enhance machine learning prediction performance by developing a novel hybrid feature selection algorithm. This algorithm synergizes the strengths of cat swarm optimization (CSO) with those of the crow search algorithm (CSA), aiming to refine feature selection processes. The proposed hybrid algorithm was meticulously implemented and applied using the K-Nearest Neighbors (KNN) model on a diverse array of datasets. To rigorously evaluate its efficacy, the algorithm was tested on 12 carefully selected datasets, encompassing various domains and complexities. The performance was measured based on the accuracy of the machine learning predictions. Remarkably, the proposed hybrid algorithm achieved an average accuracy rate of 87%, which represents a significant improvement over previous approaches that had an average accuracy of 83%. These results underscore the potential of the proposed hybrid feature selection algorithm in enhancing the predictive capabilities of machine learning models by effectively reducing dimensionality and eliminating irrelevant features. The findings suggest that integrating CSO and CSA can lead to more robust feature selection mechanisms, thereby improving the overall quality and reliability of machine learning predictions.

Amidst the rapid proliferation of mHealth applications, questions persist regarding their efficacy, usability, and integration into oral health practice. This review aims to inform practitioners and stakeholders in the field of oral health on the potential of digital technologies to transform healthcare delivery and promote healthy behaviours. Key terms included "dental", "dentistry", "oral health", "dental treatment", and "tooth care". The Google Play Store search identified 130 applications, out of which 17 met our study objectives. Most applications (n=13) focused on providing dental appointments, oral health education, and promotion. Mobile Application Rating Scale (MARS) quality rating revealed that only 50% of these applications were high quality. Engagement and information were the least-scored subscales. The review highlights the many benefits that these digital tools provide, including online appointments, teleconsultations, and oral health educational resource access. Nevertheless, despite their potential, the current state of dental health applications leaves substantial room for development, especially in the areas of user involvement and information quality. The lack of reliable and accurate information in many applications may be harmful to users' health. To fully realize the potential of these digital technologies and enhance oral health outcomes on a larger scale, coordinated actions involving stakeholders from the technology and dentistry sectors are imperative.

The increasing number of online communities has led to significant growth in digital data in multiple languages on the Internet. Consequently, language processing and information retrieval have become important fields in the era of the Internet. Stemming, a crucial preprocessing tool in natural language processing and information retrieval, has been extensively explored for high-resource languages like English, German, and French. However, more extensive studies regarding stemming in the context of the Hausa language, an international language that is widely spoken in West Africa and one of the fastest-growing languages globally, are required.

This paper presents a rule-based model for stemming Hausa words. The proposed model relies on a set of rules derived from the analysis of Hausa word morphology and the rules for extracting stem forms. The rules consider the syntactic constraints, e.g., affixation rules, and performs a morphological analysis of the properties of the Hausa language, such as word formation and distribution.

The proposed model’s performance is evaluated against existing models using standard evaluation metrics. The evaluation method employed Sirstat’s approach, and a language expert assessed the system’s results. The model is evaluated using manual annotation of a set of 5,077 total words used in the algorithm, including 2,630 unique words and 3,766 correctly stemmed Hausa words. The model achieves an overall accuracy of 98.8%, demonstrating its suitability for use in applications such as natural language processing and information retrieval.

Introduction

Falls among older adults are a significant health concern as they can result in severe injuries or even death. This research aims to tackle this issue by creating a smart bracelet that utilizes Internet of Things (IoT) technology to detect falls and monitor the health parameters of aged individuals.

Methods

This project uses a quantitative research approach involving precise data analysis of sensor data from an accelerometer and a heart rate monitor. The bracelet incorporates an ESP32 microcontroller, a heart rate sensor, and an accelerometer. The device communicates via Wi-Fi using the MQTT protocol, sending data to a server for real-time monitoring and alerts. The bracelet was designed using 3D printing technology and assembled with lightweight, impact-resistant materials.

Results

The testing process involved both simulated and real-world scenarios to evaluate the bracelet's functionality. The accelerometer effectively detected falls by monitoring sudden changes in movement, while the heart rate sensor provided continuous health data. Most importantly, alerts were successfully transmitted to designated contacts via the IFTTT platform when a fall or abnormal health readings were detected, providing reassurance about the bracelet's effectiveness in emergencies. Data collected included heart rate variability and acceleration amplitude, confirming the device's accuracy in real-world conditions.

Conclusions

The smart bracelet successfully fulfilled the project's objectives by demonstrating reliable fall detection and health monitoring capabilities. While the device's physical design can be further refined, its functionality proved effective in preliminary tests. Future improvements will enhance sensor accuracy and user adaptability to ensure broader adoption among the senior population. The integration of IoT technology in healthcare devices shows promise in providing continuous and remote monitoring. With its potential to reduce the risks associated with falls among older adults, the smart bracelet shows potential in its impact on elderly care.

The acquisition of texts for the purpose of composing corpora in specific domains from sources on the social web is a process that requires analyzing the structures of websites where the texts are published. This involves searching for specific fields to guide the access of responsible agents, known as scrapers. With these texts in hand, performing more refined analyses focused on tasks such as named entity recognition, text summarization, sentiment mining, and associated classifications (e.g., opinion polarities) becomes possible. This article aims to demonstrate the process of acquiring news texts in the domain of public safety in Brazil to build corpora in the Portuguese language. Since Portuguese still lacks dedicated corpora on this topic, scraping agents were developed for three initial news sources in the Northeast region, specifically in the states of Alagoas, Pernambuco, and Rio Grande do Norte. Based on these scraping agents, the corpora
were stored in a cloud-based schema for use in an ongoing research project to analyze texts related to public safety to support decision-making processes. The constituted corpus enabled the execution of multiple preliminary analyses, including the identification of crime patterns, sentiment analysis in public security reports, and the mapping of risk areas. These analyses provided valuable information that can support the formulation of public policies and the development of more effective security strategies.

We aimed to develop a system that can analyze faces and predict how attractive humans will find them. This is a complex task because beauty perception is subjective and influenced by cultural background. Facial beauty prediction (FBP) is a significant visual recognition problem for the assessment of facial attractiveness, which is consistent with human perception. A deep learning method has recently demonstrated an amazing ability for feature representation and analysis; in particular, Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) are powerful tools for image analysis. The CNNs learn to identify features associated with attractiveness and use them to predict beauty scores for new faces. This paper proposes a new fusion ViTs–CNN network which incorporates the strengths of combining ViTs with CNNs to lead to improved performance and efficiency in predicting beauty scores. This approach takes pre-trained models from Mobilenetv3, DenseNet121 and InceptionV3, combines them with ViTs, and fine-tunes them to predict facial beauty. This approach can provide insights into how the models are leveraging the strengths of both architectures. Testing this method on the SCUT-FBP5500, the ViTs–CNN network achieved a Pearson coefficient of 0.9480. This indicates that the fusion ViTs–CNN network's facial beauty predictions are closer to human evaluation compared to traditional methods for assessing facial attractiveness.

In recent years, Artificial Neural Networks (ANNs) have stood out among machine learning algorithms, being successful in a huge range of applications, especially in recognizing image, audio and video patterns, as well as in natural language analysis. The use of activation functions plays a crucial role in the operation of these algorithms, directly influencing the representation capacity and training effectiveness of ANNs. The logistic (or sigmoid) function is often used as a standard activation function in many neural network models due to its favorable properties of non-linearity and smooth derivatives. However, the existing literature lacks in-depth investigations into the potential of the Skew-Logistic (SL) function as a viable alternative, especially in scenarios where asymmetry in the data is a common reality. This work aims to investigate the SL function as an activation function in ANNs, exploring its ability to deal with asymmetric data. To achieve this, the SL function was implemented computationally in different neural network architectures. The models were trained on various databases selected for the experiments, and their performance was evaluated using standard metrics such as accuracy, precision, recall and F1-score. This procedure was carried out in each experiment with the SL and sigmoid activation functions in order to compare them. The results indicate that SL can bring improvements to the models in some asymmetric data sets, in which a significant increase in performance metrics was observed compared to the traditional logistic function. It was also noted that in binary classification tasks, SL can improve accuracy or sensitivity, depending on the sign of the asymmetry factor selected, predicting fewer false positives or fewer false negatives. It is concluded that the SL function offers a viable and promising alternative to conventional activation functions, providing better adaptation to asymmetric datasets.

Super-resolution is essential for improving images in computer vision and image processing. When using super-resolution for images of natural landscapes, some challenges are encountered, related to factors such as degradation present in the real world with different levels of illumination and the presence of small details and noise that make the process of applying and reconstructing super-resolution more difficult. With the advances in Deep Learning techniques, Real-ESRGAN has stood out in the transformation of images from low to high resolution, and can be used to elucidate the challenges encountered in the super-resolution of natural landscapes. In this sense, this research applies Real-ESRGAN to images of natural landscapes with realistic degradation, with the aim of generating super-resolution images of real scenarios. Using the DIV2K, Landscape Pictures and Landscape Classification datasets, four training sessions were carried out, varying the iterations and adjusting hyperparameters. The inclusion of datasets focused on landscapes, in addition to DIV2K, and enriched the database, optimizing the model. A quantitative analysis was carried out, using MSE, PSNR, SSIM and NIQE to evaluate performance. The best experimental results achieved high-quality images with an MSE of 0.029, an NIQE of 2.5566, a PSNR of 22.43 and an SSIM of 0.525, preserving original details and structures. A qualitative analysis was also carried out to assess the visual characteristics of the images, confirming that the results generated achieved an improvement in visual quality. The results indicate that the Real-ESRGAN methodology, based on landscape-oriented datasets, is effective in improving image quality in a consistent and robust manner.