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Background: For the enhancement of agricultural productivity, while ensuring sustainability, this study delves into the under-explored domain of monitoring legume crop health and growth. Traditional methods of crop assessment encounter limitations, prompting a push for innovation by integrating advanced remote sensing technologies and artificial intelligence (AI). The purpose is to revolutionize crop assessment techniques and overcome existing constraints. Methods: The data was collected using a combination of satellite imagery and ground-based sensors, resulting in a rich repository of multispectral and spatial information. By using the capabilities of AI, a robust model was developed to interpret the gathered data, offering a detailed insight into the health and growth dynamics of legume crops. The AI algorithms not only identify anomalies but also forecast future states, facilitating timely interventions and informed decision-making in agriculture. Result: The findings of this study signify a significant change in precision agriculture, where the synergy of remote sensing and AI optimizes resource allocation, minimizes environmental impact and maximizes crop yields. The research unlocks the potential to transform legume farming practices, promoting sustainability and ushering in an era of data-driven cultivation. The implications extend beyond the legume crop sector, influencing the broader agricultural landscape with the promise of more efficient and sustainable practices. [ABSTRACT FROM AUTHOR]

Background: Soybean cultivation faces challenges from pest infestations, necessitating advanced and proactive pest management strategies. Traditional methods often lag in early detection, resulting in substantial crop losses. This study addresses this gap by employing machine learning, specifically sequential convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to transform early pest detection in soybean fields. The approach leverages a comprehensive dataset comprising high-resolution crop images and environmental variables, offering insights into soybean ecosystems. Methods: Using CNNs for picture processing and RNNs for collecting temporal relationships in environmental factors, a complex deep learning model is created. The dataset contains 1050 images of soybeans including pests such as Anticarsia, Coccinellidae and without pests (healthy). The model is trained for 20 epochs. The model has been carefully validated for accuracy, sensitivity and efficacy in early insect identification. The diversity of the dataset ensures that the model may be adjusted to a range of soybean growing situations. Result: The outcomes demonstrate the model's unparalleled effectiveness, routinely outperforming conventional techniques with an accuracy rate of 95%. Its exceptional sensitivity reduces financial and environmental expenses, highlighting its versatility in a range of soybean growing environments. In light of the difficult global agricultural landscape, this study offers a novel strategy for proactive and sustainable pest management, which is essential to guaranteeing strong soybean crop yields. [ABSTRACT FROM AUTHOR]

In pursuit of enhancing the Wireless Sensor Networks (WSNs) energy efficiency and operational lifespan, this paper delves into the domain of energy-efficient routing protocols. InWSNs, the limited energy resources of Sensor Nodes (SNs) are a big challenge for ensuring their efficient and reliable operation.WSN data gathering involves the utilization of a mobile sink (MS) to mitigate the energy consumption problem through periodic network traversal. The mobile sink (MS) strategy minimizes energy consumption and latency by visiting the fewest nodes or predetermined locations called rendezvous points (RPs) instead of all cluster heads (CHs). CHs subsequently transmit packets to neighboring RPs. The unique determination of this study is the shortest path to reach RPs. As the mobile sink (MS) concept has emerged as a promising solution to the energy consumption problem in WSNs, caused by multi-hop data collection with static sinks. In this study, we proposed two novel hybrid algorithms, namely" Reduced k-means based on Artificial Neural Network "(RkM-ANN) and "Delay Bound Reduced kmeans with ANN" (DBRkM-ANN) for designing a fast, efficient, and most proficient MS path depending upon rendezvous points (RPs). The first algorithm optimizes the MS's latency, while the second considers the designing of delay-bound paths, also defined as the number of paths with delay over bound for the MS. Both methods use a weight function and k-means clustering to choose RPs in a way that maximizes efficiency and guarantees network-wide coverage. In addition, a method of using MS scheduling for efficient data collection is provided. Extensive simulations and comparisons to several existing algorithms have shown the effectiveness of the suggested methodologies over a wide range of performance indicators. [ABSTRACT FROM AUTHOR]

The increased adoption of Internet of Medical Things (IoMT) technologies has resulted in the widespread use of Body Area Networks (BANs) in medical and non-medical domains. However, the performance of IEEE 802.15.4-based BANs is impacted by challenges related to heterogeneous data traffic requirements among nodes, including contention during finite backoff periods, association delays, and traffic channel access through clear channel assessment (CCA) algorithms. These challenges lead to increased packet collisions, queuing delays, retransmissions, and the neglect of critical traffic, thereby hindering performance indicators such as throughput, packet delivery ratio, packet drop rate, and packet delay. Therefore, we propose Dynamic Next Backoff Period and Clear Channel Assessment (DNBP-CCA) schemes to address these issues. The DNBP-CCA schemes leverage a combination of the Dynamic Next Backoff Period (DNBP) scheme and the Dynamic Next Clear Channel Assessment (DNCCA) scheme. The DNBP scheme employs a fuzzy Takagi, Sugeno, and Kang (TSK) model's inference system to quantitatively analyze backoff exponent, channel clearance, collision ratio, and data rate as input parameters. On the other hand, the DNCCA scheme dynamically adapts the CCA process based on requested data transmission to the coordinator, considering input parameters such as buffer status ratio and acknowledgement ratio. As a result, simulations demonstrate that our proposed schemes are better than some existing representative approaches and enhance data transmission, reduce node collisions, improve average throughput, and packet delivery ratio, and decrease average packet drop rate and packet delay. [ABSTRACT FROM AUTHOR]

This study delves into the realm of optical soliton solutions within the intricate framework of highly dispersive couplers integrated with optical metamaterials featuring a parabolic law nonlinear refractive index. Employing the extended auxiliary equation method, the investigation systematically unveils various soliton solutions, including dark solitons, bright solitons, singular solitons, and a distinctive amalgamation of bright and singular solitons. The results illuminate the diverse dynamics of solitons in these complex systems, offering crucial insights into the interplay among dispersion, nonlinearity, and metamaterial characteristics. Beyond enhancing the theoretical understanding of soliton behavior in optical metamaterial couplers, the findings hold practical significance by potentially influencing the design and optimization of optical communication devices and systems. [ABSTRACT FROM AUTHOR]

The ball and balancer system is a popular research platform for studying underactuated mechanical systems and developing control algorithms. It is a well-known two-dimensional balancing problem that has been addressed by a variety of controllers. This research work proposes two controllers that are proportional integral derivative-second derivative-proportional integrator ( PIDD 2 -PI) controller and tilt integral derivative with filter (TID-F) controller in a multivariate, electromechanical, and nonlinear under-actuated ball and balancer system. Integral Time Absolute Error (ITAE) is an objective function used for designing controllers because of its ability to be more sensitive to overshooting as well as reduced settling time and steady-state error. As part of the analysis, four metaheuristic optimization algorithms are compared in the optimization of proposed control strategies for cascaded control of the ball and balancer system. The algorithms are the Grey Wolf optimization algorithm (GWO), Cuckoo Search algorithm (CSA), Gradient Base Optimization (GBO), and Whale Optimization Algorithm (WOA). The effectiveness of proposed controllers PIDD 2 -PI and TID-F is investigated to be better in terms of transient time response than proportional integral derivative (PID), proportional integral-derivative (PI-D), proportional integral-proportional derivative (PI-PD) and proportional integral derivative-second derivative-proportional derivative ( PIDD 2 -PD). Moreover, these two proposed controllers have also been compared with recently published work. During the analysis, it is shown that the proposed control strategies exhibit significantly greater robustness and dynamic responsiveness compared to other structural controllers. The proposed controller WOA- PIDD 2 -PI reduced the 73.38% settling time and 88.16% rise time compared to classical PID. The other proposed controller GWO-TID-F reduced 58.06% the settling time and 26.96% rise time compared to classical PID. These results show that proposed controllers are particularly distinguished in terms of rise time, settling time, maximum overshoot, and set-point tracking. [ABSTRACT FROM AUTHOR]