The underwater acoustic sensor network is a large network consisting of many operating sensor nodes that surround a transmitting node. The communication process faces substantial disturbances caused by the everchanging nature of the underwater acoustic channel, which is characterized by fluctuating properties in both time and location. Therefore, the underwater acoustic communication system has difficulties in reducing interference and improving communication efficiency and quality by using adaptive modulation. This work presents a model that aims to tackle these difficulties by suggesting an optimum route selection and safe data transmissionapproach in UASN using sophisticated technology. The suggested approach for transferring safe data in UASN via optimum route selection consists of two main stages. Nodes are first chosen based on restrictions such as energy, distance, and connection quality, which are quantified in terms of throughput. Moreover, the process of forecasting energy is made easier by using sophisticated machine learning methods like transformer models. The ideal route is generated using a hybrid optimization technique called enhanced swarm optimization, which combines ideas from particle swarm optimization and genetic algorithms. Afterward, data is safely transported via the most efficient route by using fully homomorphic encryption. Finally, the ESO+ transformer model that was created is tested against established benchmark models, showcasing its strong and reliable performance. The proposed model demonstrates remarkable performance with an accuracy of 95.12%, precision of 94.83%, specificity of 93.65%, sensitivity of 95.28%, false positive rate of 4.72%, F1 score of 94.95%, Matthews correlation coefficient of 94.85%, false negative rate of 4.72%, negative predictive value of 95.15%, and false discovery rate of 5.15% when trained on a learning percentage of 70%.

This paper details the development of PID controller tuning, based on the implementation of advanced optimization techniques in MATLAB to find the optimal gains for control actions. The methodology used to create the solutions was that of genetic algorithms, an artificial intelligence technique developed in the 1970s and inspired by Darwin's natural selection, within the field of evolutionary computing. Its implementation is based on selection, crossover, and mutation processes, which allow the solutions to iteratively converge towards increasingly optimal results. Two different genetic algorithms were programmed and designed. The first focused exclusively on a single objective, which was the settling time; while the second was based on a multi-objective technique that additionally considered the maximum overshoot, rise time, and delay time. Different fitness functions were developed to create these neural models; subsequently, the gain results obtained from these genetic methods were compared with those proposed by analytical and experimental methods, both in the field of simulation and in physical implementation. The analysis of the responses validated theefficiency and effectiveness of the proposed algorithms for controller tuning, showing better performance with the gains obtained through genetic algorithms.

In the realm of optical fiber communication systems, maximizing transmission efficiency stands as a paramount objective. This study embarks on an innovative approach, merging wavelengthdivision multiplexing (WDM) with dispersion compensation fiber (DCF), to address the persistent challenges of signal degradation due to dispersion. Drawing from comprehensive simulations and meticulous analysis, our research reveals the transformative potential of this integrated solution. By seamlessly integrating WDM and DCF, we achieve remarkable enhancements in transmission performance, characterized by superior signal fidelity, unprecedented transmission distances, and unparalleled data rates. This study not only underscores the technological advancements propelling optical communication systems into a new era of efficiency but also heralds the dawn of a paradigm shift in high-speed, long-distance communication networks.

Direct methods for optimal tuning of the parameters of a PID-type controller are developed using the theory of LQR when the order of the plant model equation is known. This is achieved through two fundamental results. The first one defines a link between the weighting matrix of the LQR performance index with the desired temporal specifications of the closed-loop controlled response, expressed through a desired overshoot and settling time, as well as the tracking of a constant reference input with zero steady-state error. The second result establishes the connection between the state feedback solution of the LQR problem and the parameters of the PID-type controller. The design method is validated through simulation studies on a heat flow experiment, a coupled tank system, and a radar antenna.

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.

Implementing I4.0 technologies to promote a safe work environment is essential to safeguard worker safety and health (OSH). An analysis based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the adapted Technology Organization and Environment (TOE) model led the association between applications based on the Industrial Internet of Things (IIoT) and workers’ attitude toward these ap- plications in large Ecuadorian companies. Service & Commerce business companies gathering most workers, predominantly between 18 and 43 years old (63.86%), shows a positive and significant correlation between operation and supervision responsibilities, educational level, and the use of portable devices, especially in OSH (78.31%). A positive correlation is depicted between perceived ease of use (EU), perceived usefulness (PU), and risk perception (RP). The use of portable devices for work activities, combined with the detection of issues in the workflow, is positively correlated with sharing data to identify risks, bridging collective and individual OSH. Monitoring activities, data collection, and training concentrate the key technological applications in large companies in Ecuador.

This research paper shows the design and implementation of a 5.8 GHz microstrip patch antenna (MPA). Experimental investigations have concluded that employing a 0.8 mm high Rogers Rt-duroid substrate along with an inset feeding technique produces optimal outcomes to fulfill the demands of 5G applications. The designed antenna demonstrates 8.09 dBi directivity, a 7.38 dB gain, and -20 dB return loss at its resonant frequency. To further verify the performance of antenna, it was fabricated and evaluated using the CST Microwave Studio program, a 3D simulation tool specifically designed for antenna design and parameter calculation

Lightning is the primary cause of outages for many high voltage transmission lines. One approach to reduce the frequency of these outages is the improvement of the grounding system using chemical ground enhancers to better disperse transient or fault currents into the soil. In this paper, the objective is to use graphene nano particles to improve the performance of commercial ground enhancers by exploration of 15 different commercial graphene nano particles used to reformulate 19 commercial ground enhancers and one natural ground enhancer. Incorporation of nano graphene particles is by high shear mixing method. The results show reduction in two of the commercial ground enhancers’ resistivity values by a factor of 60. Moreover, lightning impulse tests show that the use of graphene reduce deterioration of the ground enhancer, which can occur after high current events, thereby prolonging its efficacy. Due to the superior electrical conductivity and chemical stability of the graphene, the reformulated ground enhancers improve short-term performance in managing lightning current impulses, and provide a solution, which is likely to perform better over long periods. However, the compatibility of the constituent parts of the formulation is critical to long-term performance.

Hate speech on social media, especially against women and immigrants, is a major issue. Twitter, which promotes public discourse and diverse viewpoints, explicitly rejects violence, discrimination, and assaults based on race, nationality, ethnicity, social status, sexual orientation, age, disability, or severe illness. Hate speech harms individuals and communities, but the volume of internet content makes routine detection impractical. This challenge highlights the need to address and develop effective hate speech detection and categorization systems for women and immigrants. This research describes the deployment of two advanced machine learning paradigms, the Random Forest and Support Vector Machine (SVM), using text pre-processing, post-processing, and advanced text embedding techniques like TF-IDF, CBOW, and GloVE. Detailed categorization of a Twitter dataset into hate speech and subclassification into aggressive and targeted dimensions is the main goal. Based on the complex interaction of text embeddings and classification typology, model efficacy is carefully assessed. When combined with TF-IDF embeddings, the Random Forest classifier excels at hate speech categorization. Concurrently, merging GloVE embeddings with the SVM algorithm exhibits outstanding precision in discriminating between aggressive, non-aggressive, targeted, and non-targeted categories. Also, CBOW embeddings work well for broader hate speech classification. Thus, this work improves social media hate speech identification by providing theoretical and practical insights

The Gulf Intracoastal Waterway (GIWW) is one of the most highly used corridors in the U.S. inland waterway commerce network, necessitating accurate travel time estimation for operational planning (departure times and achieve on-time arrivals). This paper addresses the significant need for assessing GIWW travel times and proposes a two-phase approach utilizing Automatic Identification System (AIS) data and other data sets. In the initial phase, forecasting models and event evaluation methods are applied to predict travel times based on specific events, and in the second phase, the impact of different variables on system performance is investigated. The results indicate that sample count (completed trips through a link) does not significantly influence travel time across any link. The statistical analysis highlights two critical conditions affecting travel time: dredging and shoaling. Furthermore, the analysis presented in this paper estimates the expected magnitude of these events and their probability of occurrence. By applying the proposed methodology to estimate travel times of the GIWW, this paper contributes to the enhancing travel time estimation tools, offering valuable information for decision-makers, operators, and users navigating this crucial waterway.