Sistema Regional de Información
en línea para Revistas Científicas de América Latina,
el Caribe, España y Portugal

In the rapidly evolving landscape of blockchain technology, the twin challenges of scalability and security remain significant obstacles to widespread adoption. Traditional blockchain architectures struggle to balance the increasing demands for transaction throughput and the imperative of maintaining robust security measures. This work addresses these limitations by proposing an innovative model that integrates advanced privacy mechanisms, rigorous security analysis, and scalability enhancements to forge a more resilient and efficient blockchain framework. The cornerstone of our model is the introduction of ZeroKnowledge Proofs (ZKPs) to enhance user privacy significantly. By enabling transaction verification without revealing sensitive information, ZKPs mitigate information leakage and boost transaction confidentiality. Our findings suggest an estimated 15% improvement in privacy levels, marking a substantial advancement over existing methods that often compromise user privacy for transparency. Addressing the security aspect, we employ Temporal Logic of Actions Plus (TLA+) for formal verification of the blockchain protocol. This method allows us to model the blockchain's behavior systematically, ensuring its correctness, safety, and liveness even under adverse conditions such as Byzantine faults. Our analysis reveals a 98% success rate in detecting and thwarting Byzantine behaviors, thereby substantiating the robustness of our proposed model against a range of security threats. To tackle the issue of scalability, we introduce adaptive sharding with dynamic load balancing. This approach not only partitions the network into manageable shards but also optimizes transaction processing by adapting to changes in transaction volume and network congestion. Our results prove a 20% increase in transaction throughput and a 25% decrease in network latency, showcasing the effectiveness of adaptive sharding in enhancing blockchain scalability and performance.

Artificial intelligence (AI) and natural language processing (NLP) technologies are advancing rapidly, offering substantial assistance in various fields. This study focuses on developing a text-tospeech (TTS) device tailored for elementary schools in Indonesia to autonomously call students during pickup times. Utilizing the Google text-to-speech (gTTS) library and Python, the device operates on a low-specification Mini PC. It integrates with a cloud-based student management information system (MIS) to synchronize student data and announcements. The device automates the student call-out process, reducing the workload of school staff and ensuring clear, accurate pronunciation. Successfully tested, the device demonstrates a practical, cost-effective solution for modernizing student pickup systems. It showcases the potential of AI and NLP in educational environments, emphasizing operational efficiency and technological accessibility.

Gamification has evolved as a potent method for engaging and motivating consumers in today's dynamic market scenario. This research report investigates the impact of gamification on customer purchasing intentions to determine its current relevance. This study investigates the subtle links between gamification aspects, user engagement, brand loyalty, and consumer purchasing intentions using Partial Least Squares Structural Equation Modelling (PLS-SEM). Gamification is an important technique in modern marketing approaches because of its capacity to captivate and incentivize people. The survey included 300+ individuals from Durg and Raipur, Chhattisgarh's two major districts, representing a broad demographic. The findings show that Gamification has a large indirect effect on Customer Engagement, which in turn affects Brand Loyalty and eventually shapes Customer Buying Intentions. This study emphasises the critical significance of gamification in altering customer behaviour and the relevance of promoting User Engagement and Brand Loyalty to drive purchasing decisions. Gamification strategy emerges as a powerful force in the contemporary marketing landscape, with the potential to affect the entire customer journey, as firms seek novel methods to connect with consumers.

Waste management is a complex and dynamic issue that demands creative and visionary solutions that exploit the potential offered by recent technological advances. Our research investigates the application of machine learning and deep learning in image recognition and categorization within the waste spectrum. We trained a Convolutional Neural Network (CNN) model on a massive dataset of pictures depicting organics, among others, typically generated as recyclables. We aim to develop a classification model for organic and recyclable waste that leverages transfer learning to classify them with high accuracy. This study aims to lay the foundation for future systems that will recycle automatically while improving waste recycling processes, hence reducing environmental impacts. The goal of our research was to create an image classification model that could differentiate photos between organic and recyclable waste by designing a classifier using the VGG16 architecture. Our study utilized the VGG16 model, based on Convolutional Neural Networks (CNNs), to achieve a precision score of 0.96 for organic and 0.88 for recyclable. This indicates that our model effectively reduces incorrect predictions for non-categorized items. The model achieved a high recall rate of 0.97 on pictures of recyclable garbage, indicating that it could identify most "Recyclable" examples properly. Moreover, these results highlight the VGG16 architecture's effectiveness in categorizing trash types, indicating potential room for improvement in recognizing "Organic" garbage images by the model, particularly in terms of recall.

This paper presents an analysis and mathematical modeling of the cutting parameters in the turning process of 1018 steel material. For this analysis, an experiment was designed using 40 specimens of 1" diameter by 6" long. In this experiment, the control parameters were cutting speed in revolutions per minute (rpm), feed rate in inches per minute (inches/minute) and depth of cut in inches (inches), while the response variable is surface roughness in micro inches (µinches). The results show that the feed rate, cutting speed and the interaction between feed rate and cutting speed affect the surface roughness of the workpiece. Finally, with the RStudio software, a mathematical model was obtained with an R2 equal to 0.99, a value very close to previous experimental works and, in addition, a good predictor of surface roughness.

 This study deals with the necessity of advancement in the classification of skin diseases, attaining a classification result with optimized parameters, using soft computing, machine learning (ML), deep learning (DL), data science, and data analysis techniques. Conventional approaches require considerable amounts of labelled data, which are resource consuming when compared to other medical fields. To address these challenges, our work, by adopting an integrative methodology, introduces an integrative framework by utilizing multimodal data fusion, transfer learning with pre-trained models, uncertainty quantification, and active learning strategies. Our multimodal data fusion approach is based on the multimodal variational autoencoder (MVAE), a powerful method for obtaining joint latent representations from diverse data modalities, including images, textual descriptions, patient histories, and genetic information. This method highly outperforms the single-modality approaches, especially in improving classification accuracy metrics such as F1-scores and area under the ROC curve (AUC). In addition, we make use of fine-tuning the pre-trained Inception-ResNet V2 model for transfer learning as a way of enhancing the capacity to classify skin diseases. Our methodology introduces the Monte Carlo dropout Bayesian convolutional neural network (MC-Bayes CNN) for uncertainty quantification. This novel approach, for the first time, allows us to make predictions with probabilistic values, including uncertainties, an extremely important development for the application of medicine to the diagnosis of diseases. Finally, the incorporation of collaboration-by-committee (QBC) active learning with Bayesian neural networks is expected to significantly revolutionize efficient model training with minimal labelled data samples. This indeed reduces the amount of labelled data needed; thereby significantly enhancing the classification accuracy achieved with only limited labelled data samples.

A novel fluorine containing Schiff base tert-butyl 3-(((2-hydroxynaphthalen-1-yl)methylene)amino)-4-(4-(trifluoromethyl)phenyl)piperidine-1-carboxylate (RSB1) a possible potent reactive building block was synthesized by condensing fluorine bearing  piperidine containing boc protected amine derivative with the 2hydroxy napthaldehyde using simple condensation method in good yield and high purity. Structure characterization of the title compound was done by, 1H-NMR, 13C-NMR and HRMS spectral analysis

This paper presents programming performed in a cluster of two Linux servers to ensure the high availability of an Oracle instance. The problem of database downtime is proposed to be solved when a component of a server or the entire server fails and there is no service to the database users. The objective was to develop programming to start, stop, and monitor the status of an Oracle instance on one of the two Linux servers in a cluster in case of contingency or maintenance of the other server. The methodology followed divided the programming into five modules: cluster manager, package manager, monitoring module, Host Bus Adapter (HBA) port manager, and network port manager. High availability was achieved by creating a package that contained the Oracle instance and resources to start it on the cluster servers. The package is assigned an IP address to which users of the instance are connected. The contribution of this work is to provide a low-cost solution, compared to existing commercially similar systems, with a quick response and easy implementation that allows a company or institution to continue working after a hardware or software failure. The startup time of the Oracle instance package after contingency on a server was 20 s, which was the time when the cluster application was not available to the user. The results of the methodology were a cluster that eliminates the points of failure represented by LAN ports, HBAs, hard disk drives and an entire server on which a mission-critical application is running. Without the use of the cluster, the user must wait for the repair of the failed component(s) to restore the company's operation. The cost of the cluster is 10% of the cost of an equivalent commercially available solution. The application failover time is 20 s, which is one-tenth of the time achieved in other solutions that use a proprietary operating system.

Transporting patients through stretchers and wheelchairs is a critical activity in the emergency management services. When such systems need to be used in uneven terrain, outdoor environments, and stairs, the transportation process imparts severe difficulty and challenges for medical attendants and paramedics. Most of the available transportation systems are designed to function on the plain engineered surface. This paper presents a novel concept of a rocker-bogie mechanism-based stretcher for patient transportation in uneven terrain. The rocker-bogie mechanism is proven to be an effective suspension system for navigating through obstacles and stairs. The design considerations of the rocker-bogie mechanism for inclusion in the stretcher are discussed. Static structural analysis of the system is performed using commercial finite element analysis software. Parameters pertaining to structural safety are analyzed. Based on the results of the structural analysis the feasibility of the proposed design is assessed.

 In this research, cotton fabrics were dyed with various dye solutions obtained from natural materials, including turmeric, hibiscus and spinach, as well as mixtures of them. Pre-mordanting was applied using three metal salts. After the dyeing process, color constants were determined using the imageJ program, and the UVB/UVA transmission ratio for the dyed fabrics was measured. The results show that fabrics dyed with natural dyes, as well as those dyed with mixtures of natural dyes and different mordants, produced a variety of colors; additionally, the UV transmission values were found to be different and lower than those of the undyed fabrics.