Search Results (1,202)

In this paper, we propose an enhanced clustering protocol that integrates an improved K-means with a Mobility-Aware Cluster Head-Election Scored (IK-MACHES) algorithm, designed for extending the lifetime and operational efficiency of Wireless Sensor Network (WSN) with mobility. Variety approaches applying Low Energy Adaptive Clustering Hierarchy (LEACH) often struggle to manage optimal energy distribution due to their static clustering and limited cluster head (CH) selection criteria, primarily focusing on the proximity of residual energy or distance. Thus, this paper proposes an algorithm that takes into account both the residual energy of sensor nodes and the distance between the cluster’s central point to the base station (BS), which ultimately enhances the network’s lifetime. Additionally, our approach incorporates mobility considerations, enhancing the adaptability of the mobility environments, such as autonomous vehicular networks. Our proposed method first constructs the cluster’s configuration and then elects the CH applying an improved K-means clustering algorithm—one of the machine learning methods—integrated with a proposed IK-MACHES mechanism. Three CH scoring strategies in the proposed IK-MACHES protocol evaluate the residual energy of the nodes, their distance to the BS and the cluster central point, and relative node’s mobility. The simulation results demonstrate that the proposed approach improves performance in terms of the first node dead (FND) and 80% alive nodes metrics with mobility, compared to other LEACH protocols such as classical LEACH, LEACH-B, Improved-LEACH, LEACH with K-means, Particle Swarm Optimization (PSO), and LEACH-GK protocol, thereby enhancing network lifetime through optimal CH selection. Full article

Background: Multispectral autofluorescence lifetime imaging systems have recently been developed to quickly and non-invasively assess tissue properties for applications in oral cancer diagnosis. As a non-traditional imaging modality, the autofluorescence signal collected from the system cannot be directly visually assessed by a clinician and a model is needed to generate a diagnosis for each image. However, training a deep learning model from scratch on small multispectral autofluorescence datasets can fail due to inter-patient variability, poor initialization, and overfitting. Methods: We propose a contrastive-based pre-training approach that teaches the network to perform patient normalization without requiring a direct comparison to a reference sample. We then use the contrastive pre-trained encoder as a favorable initialization for classification. To train the classifiers, we efficiently use available data and reduce overfitting through a multitask framework with margin delineation and cancer diagnosis tasks. We evaluate the model over 67 patients using 10-fold cross-validation and evaluate significance using paired, one-tailed t-tests. Results: The proposed approach achieves a sensitivity of 82.08% and specificity of 75.92% on the cancer diagnosis task with a sensitivity of 91.83% and specificity of 79.31% for margin delineation as an auxiliary task. In comparison to existing approaches, our method significantly outperforms a support vector machine (SVM) implemented with either sequential feature selection (SFS) (p = 0.0261) or L1 loss (p = 0.0452) when considering the average of sensitivity and specificity. Specifically, the proposed approach increases performance by 2.75% compared to the L1 model and 4.87% compared to the SFS model. In addition, there is a significant increase in specificity of 8.34% compared to the baseline autoencoder model (p = 0.0070). Conclusions: Our method effectively trains deep learning models for small data applications when existing, large pre-trained models are not suitable for fine-tuning. While we designed the network for a specific imaging modality, we report the development process so that the insights gained can be applied to address similar challenges in other non-traditional imaging modalities. A key contribution of this paper is a neural network framework for multi-spectral fluorescence lifetime-based tissue discrimination that performs patient normalization without requiring a reference (healthy) sample from each patient at test time. Full article

In heterogeneous wireless sensor networks (HWSNs), optimizing energy efficiency presents significant challenges due to variations in node energy levels and the complexity of the network environment. This paper introduces an energy efficiency optimization algorithm for HWSNs based on the Deep Q-Network (HDQN). The algorithm aims to address these challenges by selecting the optimal information transmission path. The HDQN leverages energy differences between nodes and real-time environmental data to enhance network efficiency. Its reward function takes into account node distance, remaining energy, and relay node count to balance node participation and minimize overall energy consumption. The Deep Q-Network (DQN) uses the mean squared error for precise reward estimation, and an improved packet header structure supports effective routing decisions. Simulation results show that the HDQN significantly outperforms existing algorithms—EEHCHR, 2L-HMGEAR, NCOGA, DEEC, and SEP—in terms of energy efficiency, network lifetime, and robustness, demonstrating its potential to advance the performance of HWSNs. The research results of the paper provide a theoretical basis for future energy efficiency research in wireless communication and contribute to the study of the new generation of wireless networks. Full article

To facilitate energy-efficient information dissemination from multiple sensors to the sink within Wireless Sensor Networks (WSNs), in-network data fusion is imperative. This paper presents a new WSN topology that incorporates the Mobility-Efficient Data Fusion (MEDF) algorithm, which integrates a data-compression protocol with an adaptive-clustering mechanism. The primary goals of this topology are, first, to determine a dynamic sequence of cluster heads (CHs) for each data transmission round, aiming to prolong network lifetime by implementing an adaptive-clustering mechanism resilient to network dynamics, where CH selection relies on residual energy and minimal communication distance; second, to enhance packet delivery ratio (PDR) through the application of a data-compression technique; and third, to mitigate the hot-spot issue, wherein sensor nodes nearest to the base station endure higher relay burdens, consequently influencing network longevity. To address this issue, mobility models provide a straightforward solution; specifically, a Random Positioning of Grid Mobility (RPGM) model is employed to alleviate the hot-spot problem. The simulation results show that the network topology incorporating the proposed MEDF algorithm effectively enhances network longevity, optimizes average energy consumption, and improves PDR. Compared to the Energy-Efficient Multiple Data Fusion (EEMDF) algorithm, the proposed algorithm demonstrates enhancements in PDR and energy efficiency, with gains of 5.2% and 7.7%, respectively. Additionally, it has the potential to extend network lifetime by 13.9%. However, the MEDF algorithm increases delay by 0.01% compared to EEMDF. The proposed algorithm is also evaluated against other algorithms, such as the tracking-anchor-based clustering method (TACM) and Energy-Efficient Dynamic Clustering (EEDC), the obtained results emphasize the MEDF algorithm’s ability to conserve energy more effectively than the other algorithms. Full article

In Wireless Sensor Networks (WSNs), an efficient clustering technique is critical in optimizing the energy level of networked sensors and prolonging the network lifetime. While the traditional bee colony optimization technique has been widely used as a clustering technique in WSN, it mostly suffers from energy efficiency and network performance. This study proposes a Bee Colony Optimization that synergistically combines K-mean algorithms (referred to as K-BCO) for efficient clustering in heterogeneous sensor networks. This is to develop a robust and efficient clustering algorithm that addresses the challenges of energy consumption and network performance in WSNs. The K-BCO algorithm outperformed comparative clustering algorithms such as H-LEACH, DBCP, and ABC-ACO in average error rate (AER), average data delivery rate (ADDR), and average energy consumption (AEC) for transmitting data packets from sensors to cluster heads. The K-BCO outperformed other algorithms in terms of ADDR at 95.00% against H-LEACH (75.86%), DBCP (72.07%) and ABC-ACO (90.08%). The findings indicate that the K-BCO not only optimizes energy consumption but also guarantees more stable and robust solutions, thereby extending the network lifetime of WSNs. Thus, K-BCO is recommended to practitioners in wireless sensor networks as it paves the way for more efficient and sustainable wireless communication. Full article

Wireless body area networks (WBANs), which continually gather and transmit patient health data in real time, are essential for improving healthcare administration. Patient outcomes can be improved by sending these data to medical professionals for prompt review and treatment. For the effective deployment of WBANs, communication solutions are necessary to maximize critical performance parameters, such as low power consumption, minimal delay, and acceptable data rates, while guaranteeing dependable transmission. Two prominent technologies in this field are LoRaWAN, which is renowned for its long-range capabilities and energy efficiency, and IEEE 802.15.6, which was created especially for short-range medical applications with high data throughput. This study provides a comparative evaluation of these two technologies to determine their suitability for diverse WBAN healthcare scenarios. By using the NS3, a simulation was performed to calculate six key performance metrics: throughput, arrival rate, delay, energy consumption, packet delivery ratio (PDR), and network lifetime. The study analyzed each technology’s performance under varying node counts. At a density of 50 nodes, IEEE 802.15.6 demonstrated superior throughput, with 45 kbps, compared to LoRaWAN, and a higher PDR of 30%. Additionally, IEEE 802.15.6 showed a higher arrival rate, of 0.33%, than LoRaWAN. On the other hand, LoRaWAN showed notable strengths in energy consumption, with only 42 J, compared to IEEE 802.15.6, and significantly lower delay, with a delay of 7 s. Additionally, LoRaWAN offered an extended network lifetime, of 18 h, compared to IEEE 802.15.6. Full article

Mobile ad hoc networks (MANETs) are autonomous systems composed of multiple mobile nodes that communicate wirelessly without relying on any pre-established infrastructure. These networks operate in highly dynamic environments, which can compromise their ability to guarantee consistent link lifetimes, security, reliability, and overall stability. Factors such as mobility, energy availability, and security critically influence network performance. Consequently, the selection of paths and relay nodes that ensure stability, security, and extended network lifetimes is fundamental in designing routing protocols for MANETs. This selection is pivotal in maintaining robust network operations and optimizing communication efficiency. This paper introduces a sophisticated algorithm for selecting multipoint relays (MPRs) in MANETs, addressing the challenges posed by node mobility, energy constraints, and security vulnerabilities. By employing a multicriteria-weighted technique that assesses the mobility, energy levels, and trustworthiness of mobile nodes, the proposed approach enhances network stability, reachability, and longevity. The enhanced algorithm is integrated into the Optimized Link State Routing Protocol (OLSR) and validated through NS3 simulations, using the Random Waypoint and ManhattanGrid mobility models. The results indicate superior performance of the enhanced algorithm over traditional OLSR, particularly in terms of packet delivery, delay reduction, and throughput in dynamic network conditions. This study not only advances the design of routing protocols for MANETs but also significantly contributes to the development of robust communication frameworks within the realm of smart mobile communications. Full article

In order to address the problems of low spectrum efficiency in current communication systems and extend the lifetime of energy-constrained relay devices, this paper proposes a novel dual-hop free-space optical (FSO) system that integrates cognitive radio (CR) and energy harvesting (EH). In this system, the source node communicates with two users at the terminal via FSO and terahertz (THz) hard-switching links, as well as a multi-antenna relay for non-orthogonal multiple access (NOMA). There is another link whose relay acts as both the power beacon (PB) in the EH system and the primary network (PN) in the CR system, achieving the double function of auxiliary transmission. In addition, based on the three possible practical working scenarios of the system, three different transmit powers of the relay are distinguished, thus enabling three different working modes of the system. Closed-form expressions are derived for the interruption outage probability per user for these three operating scenarios, considering the Gamma–Gamma distribution for the FSO link, the $\alpha -\mu$ distribution for the THz link, and the Rayleigh fading distribution for the radio frequency (RF) link. Finally, the numerical results show that this novel system can be adapted to various real-world scenarios and possesses unique advantages. Full article

Ce-based catalysts have been widely used in the removal of nitrogen oxides from industrial flue gas because of their good catalytic performance and environmental friendliness. However, the mechanism of alkali metal poisoning in Ce-based catalysts remains to be further studied. This work involves the preparation of the K/Na-poisoned CeWTi catalyst via the impregnation method for assessing its performance in NO removal. Experiments show that both K and Na exhibit detrimental effects on the CeWTi catalyst, and the loading of alkali metal reduces the specific surface area and pore volume of the catalyst. Furthermore, the presence of alkaline metals results in a notable decline in the CeWTi acid concentration, particularly in Lewis acid sites. Concurrently, the levels of Ce³⁺, oxygen vacancies, and reducing agents on the catalyst surface decrease, leading to diminished reduction capability and eventual catalyst deactivation. The application of a BP neural network for catalyst activity prediction yielded an average relative error of approximately 0.73%, indicating enhanced accuracy in prediction outcomes. This work explored the cause of alkali metal poisoning of the CeWTi catalyst and provided an effective prediction method for the lifetime of CeWTi catalyst, which provided theoretical guidance for the engineering application of Ce-based catalysts. Full article

The emerging wireless energy transfer technology enables sensor nodes to maintain perpetual operation. However, maximizing the network performance while preserving short charging delay is a great challenge. In this work, a Wireless Mobile Charger (MC) and a directional charger (DC) were deployed to transmit wireless energy to the sensor node to improve the network’s throughput. To the best of our knowledge, this is the first work to optimize the data sensing rate and charging delay by the joint scheduling of an MC and a DC. We proved we could transmit maximum energy to each sensor node to obtain our optimization objective. In our proposed work, a DC selected a total horizon of 360° and then selected the horizon of each specific ${90}^{\circ }$ area based on its antenna orientation. The DC’s orientation was scheduled for each time slot. Furthermore, multiple MCs were used to transmit energy for sensor nodes that could not be covered by the DC. We divided the rechargeable wireless sensor network into several zones via a Voronoi diagram. We deployed a static DC and one MC charging location in each zone to provide wireless charging service jointly. We obtained the optimal charging locations of the MCs in each zone by solving Mix Integral Programming for energy transmission. The optimization objective of our proposed research was to sense maximum data from each sensor node with the help of maximum energy. The lifetime of each sensor network could increase, and the end delay could be maximized, with joint energy transmission. Extensive simulation results demonstrated that our RWSNs were designed to significantly improve network lifetime over the baseline method. Full article

Pressurized high-temperature water attracts attention as a promising medium for chemical synthesis, biomass processing or destruction of hazardous waste. Adjustment to the desired solvent properties requires knowledge on the behavior of populations of hydrogen-bonded molecules. In this work, the interconnection between the hydrogen bond (HB) dynamics and the structural rearrangements of HB networks have been studied by molecular dynamics simulation using the modified central force flexible potential and the HB definition controlling pair interaction energy, HB length and HB angle. Time autocorrelation functions for molecular pairs bonded continuously and intermittently and the corresponding mean lifetimes have been calculated for conditions ranging from ambient to supercritical. A significant reduction in the continuous and intermittent lifetimes has been found between (293 K, 0.1 MPa) and (373 K, 25 MPa) and attributed to the decreasing size of patches embedded in the continuous HB network. The loss of global HB connectivity at ca. (573 K, 10 MPa) and the investigated supercritical conditions do not noticeably affect the HB dynamics. Over the whole temperature range studied, the reciprocal intermittent lifetime follows the transition state theory dependence on temperature with the activation energy of 10.4 kJ/mol. Calculations of the lifetime of molecules that do not form hydrogen bonds indicate that at supercritical temperatures, the role of reactions involving an unbound H₂O molecule as a reactant can be enhanced by lowering system density. Full article

The present study introduces a comprehensive, open-access atlas of mesoscale eddies in the global ocean, as identified and tracked by the TOEddies algorithm implemented on a global scale. Unlike existing atlases, TOEddies detects eddies directly from absolute dynamic topography (ADT) without spatial filtering, preserving the natural spatial variability and enabling precise, high-resolution tracking of eddy dynamics. This dataset provides daily information on eddy characteristics, such as size, intensity, and polarity, over a 30-year period (1993–2023), capturing complex eddy interactions, including splitting and merging events that often produce networks of interconnected eddies. This unique approach challenges the traditional single-trajectory perspective, offering a nuanced view of eddy life cycles as dynamically linked trajectories. In addition to traditional metrics, TOEddies identifies both the eddy core (characterized by maximum azimuthal velocity) and the outer boundary, offering a detailed representation of eddy structure and enabling precise comparisons with in situ data. To demonstrate its value, we present a statistical overview of eddy characteristics and spatial distributions, including generation, disappearance, and merging/splitting events, alongside a comparative analysis with existing global eddy datasets. Among the multi-year observations, TOEddies captures coherent, long-lived eddies with lifetimes exceeding 1.5 years, while highlighting significant differences in the dynamic properties and spatial patterns across datasets. Furthermore, this study integrates TOEddies with 23 years of colocalized Argo profile data (2000–2023), allowing for a novel examination of eddy-induced subsurface variability and the role of mesoscale eddies in the transport of global ocean heat and biogeochemical properties. This atlas aims to be a valuable resource for the oceanographic community, providing an open dataset that can support diverse applications in ocean dynamics, climate research, and marine resource management. Full article

Understanding the concept of dynamic aperture provides essential insights into nonlinear beam dynamics, beam losses, and the beam lifetime in circular particle accelerators. This comprehension is crucial for the functioning of modern hadron synchrotrons like the CERN Large Hadron Collider and the planning of future ones such as the Future Circular Collider. The dynamic aperture defines the extent of the region in phase space where the trajectories of charged particles are bounded over numerous revolutions, the actual number being defined by the physical application. Traditional methods for calculating the dynamic aperture depend on computationally demanding numerical simulations, which require tracking over multiple turns of numerous initial conditions appropriately distributed in phase space. Prior research has shown the efficiency of a multilayer perceptron network in forecasting the dynamic aperture of the CERN Large Hadron Collider ring, achieving a remarkable speed-up of up to 200-fold compared to standard numerical tracking tools. Building on recent advancements, we conducted a comparative study of various deep learning networks based on BERT, DenseNet, ResNet and VGG architectures. The results demonstrate substantial enhancements in the prediction of the dynamic aperture, marking a significant advancement in the development of more precise and efficient surrogate models of beam dynamics. Full article

Background/Objectives: Acute appendicitis (AA) is one of the most common causes of emergency department visits due to acute abdominal pain, with a lifetime risk of 7–8%. Managing AA presents significant challenges, particularly among vulnerable patient groups, due to its association with substantial morbidity and mortality. Methods: The EUPEMEN (European PErioperative MEdical Networking) project aims to optimize perioperative care for AA by developing multidisciplinary guidelines that integrate theoretical knowledge and clinical expertise from five European countries. This study presents the key elements of the EUPEMEN protocol, which focuses on reducing surgical stress, optimizing perioperative care, and enhancing postoperative recovery. Results: Through this standardized approach, the protocol aims to lower postoperative morbidity and mortality, shorten hospital stays, and improve overall patient outcomes. The recommendations are tailored to address the variability in clinical practice across Europe and are designed to be widely implementable in diverse healthcare settings. Conclusions: The conclusions drawn from this study highlight the potential for the EUPEMEN protocol to significantly improve perioperative care standards for AA, demonstrating its value as a practical, adaptable tool for clinicians. Full article

Leak detection in oil and gas pipeline networks is a climacteric and frequent issue in the oil and gas field. Many establishments have long depended on stationary hardware or traditional assessments to monitor and detect abnormalities. Rapid technological progress; innovation in engineering; and advanced technologies providing cost-effective, rapidly executed, and easy to implement solutions lead to building an efficient oil pipeline leak detection and real-time monitoring system. In this area, wireless sensor networks (WSNs) are increasingly required to enhance the reliability of checkups and improve the accuracy of real-time oil pipeline monitoring systems with limited hardware resources. The real-time transient model (RTTM) is a leak detection method integrated with LoRaWAN technology, which is proposed in this study to implement a wireless oil pipeline network for long distances. This study will focus on enhancing the LoRa network parameters, e.g., node power consumption, average packet loss, and delay, by applying several machine learning techniques in order to optimize the durability of individual nodes’ lifetimes and enhance total system performance. The proposed system is implemented in an OMNeT++ network simulator with several frameworks, such as Flora and Inet, to cover the LoRa network, which is used as the system’s network infrastructure. In order to implement artificial intelligence over the FLoRa network, the LoRa network was integrated with several programming tools and libraries, such as Python script and the TensorFlow libraries. Several machine learning algorithms have been applied, such as the random forest (RF) algorithm and the deep extreme learning machine (DELM) technique, to develop the proposed model and improve the LoRa network’s performance. They improved the LoRa network’s output performance, e.g., its power consumption, packet loss, and packet delay, with different enhancement ratios. Finally, a hybrid deep extreme learning machine model was built and selected as the proposed model due to its ability to improve the LoRa network’s performance, with perfect prediction accuracy, a mean square error of 0.75, and an exceptional enhancement ratio of 39% for LoRa node power consumption. Full article