Search Results (1,712)

We propose a DNA nucleobase sequencing device composed of zigzag graphene nanoribbon electrodes connected with a porphyrin molecule via carbon chains (GEPM). The connecting geometry between the nanoribbons with an even width number and the carbon chains is laterally symmetric to filter out electrons of specific modes. Various properties of the GEPM and of the GEPM + nucleobase systems, such as interaction energies, charge density differences, spin-differential electronic densities, and electric currents, are investigated using the density functional theory (DFT) combined with the non-equilibrium Green’s function (NEGF) method. The results show that the GEPM device holds promise for DNA sequencing with the measurement of the electric signals through it. The four nucleobases—adenine (A), cytosine (C), guanine (G), and thymine (T)—can be efficiently distinguished based on the conductance and current sensitivity when they are located on the porphyrin molecule of the GEPM device. The symmetry of the connecting geometry between the carbon chains and the nanoribbons selects Bloch states with specific symmetry to pass through the device and results in broad transmission valleys or gaps. In addition, the edge magnetism of graphene nanoribbons can further manipulate the transmission and then the sequencing effects. The device exhibits extremely high conductance sensitivity in the parallel magnetic configuration. This study explores the possible advantage of this technology compared with conventional nanopore sequencing devices and potentially expands the variety of available sequencing structures. Full article

With the rapid expansion of edge-5G-IoT ecosystems, the need for intelligent and adaptive resource management strategies has become a critical challenge. In these environments, Virtualized Network Functions (VNFs) deployed at the network edge must handle highly dynamic workloads, making fixed resource allocation inefficient. While over-provisioning can lead to unnecessary resource waste, an especially critical issue in edge environments with limited resources, under-provisioning can degrade performance and service quality. This paper presents an AI-based predictive auto-scaling framework designed to optimize resource allocation for VNFs in edge/5G-enabled IoT environments. The proposed approach evaluates and integrates different ML-based regression models to characterize VNF resource consumption, along with various forecasting methods to anticipate future workload fluctuations, enabling both vertical and horizontal auto-scaling. Extensive experiments with real-world traffic data demonstrate the effectiveness of our approach, showing significant improvements in resource efficiency compared to fixed allocation strategies. Full article

The disposal of orchard garbage (including pruning branches, fallen leaves, and non-biodegradable materials such as pesticide containers and plastic film) poses major difficulties for horticultural production and soil sustainability. Unlike general agricultural garbage, orchard garbage often contains both biodegradable organic matter and hazardous pollutants, which complicates efficient recycling. Traditional manual sorting methods are labour-intensive and inefficient in large-scale operations. To this end, we propose a lightweight YOLOv7-based detection model tailored for the orchard environment. By replacing the CSPDarknet53 backbone with MobileNetV3 and GhostNet, an average accuracy (mAP) of 84.4% is achieved, while the computational load of the original model is only 16%. Meanwhile, a supervised comparative learning strategy further strengthens feature discrimination between horticulturally relevant categories and can distinguish compost pruning residues from toxic materials. Experiments on a dataset containing 16 orchard-specific garbage types (e.g., pineapple shells, plastic mulch, and fertiliser bags) show that the model has high classification accuracy, especially for materials commonly found in tropical orchards. The lightweight nature of the algorithm allows for real-time deployment on edge devices such as drones or robotic platforms, and future integration with robotic arms for automated collection and sorting. By converting garbage into a compostable resource and separating contaminants, the technology is aligned with the country’s garbage segregation initiatives and global sustainability goals, providing a scalable pathway to reconcile ecological preservation and horticultural efficiency. Full article

The efficient migration of virtual machines (VMs) is critical for optimizing resource management, ensuring service continuity, and enhancing resiliency in cloud and edge computing environments, particularly as 6G networks demand higher reliability and lower latency. This study addresses the challenges of dynamically balancing server loads while minimizing downtime and migration costs under stochastic task arrivals and variable processing times. We propose a queuing theory-based model employing continuous-time Markov chains (CTMCs) to capture the interplay between VM migration decisions, server resource constraints, and task processing dynamics. The model incorporates two migration policies—one minimizing projected post-migration server utilization and another prioritizing current utilization—to evaluate their impact on system performance. The numerical results show that the blocking probability for the first VM for Policy 1 is 2.1% times lower than for Policy 2 and the same metric for the second VM is 4.7%. The average server’s resource utilization increased up to 11.96%. The framework’s adaptability to diverse server–VM configurations and stochastic demands demonstrates its applicability to real-world cloud systems. These results highlight predictive resource allocation’s role in dynamic environments. Furthermore, the study lays the groundwork for extending this framework to multi-access edge computing (MEC) environments, which are integral to 6G networks. Full article

Aboveground biomass (AGB) serves as a crucial indicator of the effectiveness of the Grain for Green Program (GGP), and its accurate estimation is essential for evaluating forest health and carbon sink capacity. However, due to the dominance of sparse forests in GGP stands, research in this area remains significantly limited. In this study, we developed the optimal tree height-diameter at breast height (DBH) growth models for major tree species and constructed a high-quality AGB sample dataset by integrating airborne LiDAR data and tree species information. Then, the AGB of the GGP stands was estimated using the Sentinel-2 data and the gradient boosting decision tree (GBDT) algorithm. The results showed that the AGB sample dataset constructed using the proposed approach exhibited strong consistency with field-measured data (R² = 0.89). The GBDT-based AGB estimation model shows high accuracy, with an R² of 0.96 and a root mean square error (RMSE) of 560 g/m². Key variables such as tasseled cap greenness (TCG), red-edge normalized difference vegetation index (RENDVI), and visible-band difference vegetation index (VDVI) were identified as highly important. This highlights that vegetation indices and tasseled cap transformation index information are key factors in estimating AGB. The AGB of major tree species in the new round of the GGP stands in Inner Mongolia ranged from 120 to 9253 g/m², with mean values of 978 g/m² for poplar, 622 g/m² for Mongolian Scots pine, and 313 g/m² for Chinese red pine species. This study offers a practical method for AGB estimation in GGP stands, contributing significantly to sustainable forest management and ecological conservation efforts. Full article

In the advancement of wireless communication, multiple-input, multiple-output (MIMO) detection has emerged as a promising technique to meet the high throughput requirements of 6G networks. Traditionally, MIMO detection relies on conventional algorithms, such as zero forcing and minimum mean square error, to mitigate interference and enhance the desired signal. Mathematically, these algorithms operate as linear transformations or functions of received signals. To further enhance MIMO detection performance, researchers have explored the use of nonlinear transformations and functions by leveraging deep learning structures and models. In this paper, we propose a novel model that integrates the Viterbi algorithm with a graph neural network (GNN) to improve signal detection in MIMO systems. Our approach begins by detecting the received signal using the VA, whose output serves as the initial input for the GNN model. Within the GNN framework, the initial signal and the received signal are represented as nodes, while the MIMO channel structure defines the edges. Through an iterative message-passing mechanism, the GNN progressively refines the initial signal, enhancing its accuracy to better approximate the originally transmitted signal. Experimental results demonstrate that the proposed model outperforms conventional and existing approaches, leading to superior detection performance. Full article

In 5G and beyond 5G networks, function placement is a crucial strategy for enhancing the flexibility and efficiency of the Radio Access Network (RAN). However, demonstrating optimal function splitting and placement to meet diverse user demands remains a significant challenge. The function placement problem is known to be NP-hard, and previous studies have attempted to address it using Deep Reinforcement Learning (DRL) approaches. Nevertheless, many existing methods fail to capture the network state in RANs with specific topologies, leading to suboptimal decision-making and resource allocation. In this paper, we propose a method referred to as GDRL, which is a deep reinforcement learning approach that utilizes graph neural networks to address the functional placement problem. To ensure policy stability, we design a policy gradient algorithm called Graph Proximal Policy Optimization (GPPO), which integrates GNNs into both the actor and critic networks. By incorporating both node and edge features, the GDRL enhances feature extraction from the RAN’s nodes and links, providing richer observational data for decision-making and evaluation. This, in turn, enables more accurate and effective decision outcomes. In addition, we formulate the problem as a mixed-integer nonlinear programming model aimed at minimizing the number of active computational nodes while maximizing the centralization level of the virtualized RAN (vRAN). We evaluate the GDRL across different RAN scenarios with varying node configurations. The results demonstrate that our approach achieves superior network centralization and outperforms several existing methods in overall performance. Full article

Fog computing introduces a new dimension to the network edge by pooling diverse resources (e.g., processing power, memory, and bandwidth). However, allocating resources from heterogeneous fog nodes often faces limited capacity. To overcome these limitations, integrating fog nodes with cloud resources is crucial, ensuring that Service Providers (SPs) have adequate resources to deliver their services efficiently. In this paper, we propose a game-theoretic model to describe the competition among non-cooperative SPs as they bid for resources from both fog and cloud environments, managed by an Infrastructure Provider (InP), to offer paid services to their end-users. In our game model, each SP bids for the resources it requires, determining its willingness to pay based on its specific service demands and quality requirements. Resource allocation prioritizes the fog environment, which offers local access with lower latency but limited capacity. When fog resources are insufficient, the remaining demand is fulfilled by cloud resources, which provide virtually unlimited capacity. However, this approach has a weakness in that some SPs may struggle to fully utilize the resources allocated in the Nash equilibrium-balanced cloud solution. Specifically, under a nondiscriminatory pricing scheme, the Nash equilibrium may enable certain SPs to acquire more resources, granting them a significant advantage in utilizing fog resources. This leads to unfairness among SPs competing for fog resources. To address this issue, we propose a price differentiation mechanism among SPs to ensure a fair allocation of resources at the Nash equilibrium in the fog environment. We establish the existence and uniqueness of the Nash equilibrium and analyze its key properties. The effectiveness of the proposed model is validated through simulations using Amazon EC2 instances, where we investigate the impact of various parameters on market equilibrium. The results show that SPs may experience profit reductions as they invest to attract end-users and enhance their quality of service QoS. Furthermore, unequal access to resources can lead to an imbalance in competition, negatively affecting the fairness of resource distribution. The results demonstrate that the proposed model is coherent and that it offers valuable information on the allocation of resources, pricing strategies, and QoS management in cloud- and fog-based environments. Full article

This paper introduces a novel framework enabling natural language question answering on Piping and Instrumentation Diagrams (P&IDs), addressing a critical gap between engineering design documentation and intuitive information retrieval. Our approach transforms static P&IDs into queryable knowledge bases through a three-stage pipeline. First, we recognize entities in a P&ID image and organize their relationships to form a base entity graph. Second, this entity graph is converted into a Labeled Property Graph (LPG), enriched with semantic attributes for nodes and edges. Third, a Large Language Model (LLM)-based information retrieval system translates a user query into a graph query language (Cypher) and retrieves the answer by executing it on LPG. For our experiments, we augmented a publicly available P&ID image dataset with our novel PIDQA dataset, which comprises 64,000 question–answer pairs spanning four categories: (I) simple counting, (II) spatial counting, (III) spatial connections, and (IV) value-based questions. Our experiments (using gpt-3.5-turbo) demonstrate that grounding the LLM with dynamic few-shot sampling robustly elevates accuracy by 10.6–43.5% over schema contextualization alone, even under high lexical diversity conditions (e.g., paraphrasing, ambiguity). By reducing barriers in retrieving P&ID data, this work advances human–AI collaboration for industrial workflows in design validation and safety audits. Full article

In this paper, package integration of glass–based passive components for 5G new radio (NR) millimeter–wave (mm wave) bands and an analysis of their system performance are presented. Passive components such as diplexers and couplers covering 5G NR mm wave bands n257, n258 and n260 are modeled, designed, fabricated and characterized individually along with their integrated versions. Non–contiguous diplexers are designed using three different types of filters, hairpin, interdigital and edge–coupled, and combined with a broadband coupler to emulate a power detection and control circuitry block in an RF transmitter chain. A panel–compatible semi–additive patterning (SAP) process is utilized to form high–precision redistribution layers (RDLs) on laminated glass substrate, onto which fine features with tight tolerance are added to fabricate these structures. The diplexers exhibit low insertion loss, low VSWR and high isolation, and have a small footprint. A system performance analysis using a co–simulation technique is presented for the first time to quantify the distortion in amplitude and phase produced by the fabricated passive component block in terms of error vector magnitude (EVM). Moreover, the scalability of this approach to compare similar passive components based on their specifications and signatures using a system–level performance metric such as EVM is discussed. Full article

This article provides a survey of polyhedral embeddings of triangular regular maps of genus g, $2⩽g⩽14$, and of neighborly spatial polyhedra. An old conjecture of Grünbaum from 1967, although disproved in 2000, lies behind this investigation. We discuss all duals of these polyhedra as well, whereby we accept, e.g., the Szilassi torus with its non-convex faces to be a dual of the Möbius torus. A numerical optimization approach by the second author for finding such embeddings was first applied to finding (unsuccessfully) a dual polyhedron of one of the 59 closed oriented surfaces with the complete graph of 12 vertices as their edge graph. The same method has been successfully applied for finding polyhedral embeddings of triangular regular maps of genus g, $2⩽g⩽14$. The effectiveness of the new method has led to ten additional new polyhedral embeddings of triangular regular maps and their duals. There do exist symmetrical polyhedral embeddings of all triangular regular maps with genus g, $2⩽g⩽14$, except in a single undecided case of genus 13. Among these results, there are three new Leonardo polyhedra, each with 156 vertices, 546 edges, and 364 triangular faces, based on the Hurwitz triplet of genus 14 with Conder notation R14.1, R14.2, and R14.3. Full article

The arrival of 5G technology is transforming the creation of smart cities by delivering unmatched speed, extremely low latency, and broad device connectivity. These developments allow for the effortless integration of IoT devices, live monitoring systems, and cutting-edge urban applications. This paper examines the impact of 5G in tackling significant urban challenges, including network overload, energy efficacy, and data security, while highlighting its revolutionary potential in improving smart city frameworks. An important emphasis is the fusion of 5G with real-time digital twins, which link physical and digital realms to enhance urban systems, refine resource management, and strengthen public safety. Even with its potential, the rollout of 5G encounters challenges such as expensive infrastructure, significant energy requirements, and limited signal reach. This research explores the present trends, current applications, and new challenges related to 5G in smart cities, providing insights into its constraints and approaches to address them. It summarizes the necessity of cooperation among stakeholders to realize 5G’s complete capabilities and to create scalable, secure, and sustainable solutions for smart cities. Full article

Heavy metal ion (HMI) contamination poses significant threats to public health and environmental safety, necessitating advanced detection technologies that are rapid, sensitive, and field-deployable. While conventional methods like atomic absorption spectroscopy (AAS) and inductively coupled plasma mass spectrometry (ICP-MS) remain prevalent, their limitations—including high costs, complex workflows, and lack of portability—underscore the urgent need for innovative alternatives. This review consolidates advancements in the last five years in microfluidic technologies for HMI detection, emphasizing their transformative potential through miniaturization, integration, and automation. We critically evaluate the synergy of microfluidics with cutting-edge materials (e.g., graphene and quantum dots) and detection mechanisms (electrochemical, optical, and colorimetric), enabling ultra-trace detection at parts-per-billion (ppb) levels. We highlight novel device architectures, such as polydimethylsiloxane (PDMS)-based labs-on-chip (LOCs), paper-based microfluidics, 3D-printed systems, and digital microfluidics (DMF), which offer unparalleled portability, cost-effectiveness, and multiplexing capabilities. Additionally, we address persistent challenges (e.g., selectivity and scalability) and propose future directions, including AI integration and sustainable fabrication. By bridging gaps between laboratory research and practical deployment, this review provides a roadmap for next-generation microfluidic solutions, positioning them as indispensable tools for global HMI monitoring. Full article

The rapid expansion of 5G networks and edge computing has amplified security challenges in Internet of Things (IoT) environments, including unauthorized access, data tampering, and DDoS attacks. This paper introduces EdgeChainGuard, a hybrid blockchain-based authentication framework designed to secure 5G-enabled IoT systems through decentralized identity management, smart contract-based access control, and AI-driven anomaly detection. By combining permissioned and permissionless blockchain layers with Layer-2 scaling solutions and adaptive consensus mechanisms, the framework enhances both security and scalability while maintaining computational efficiency. Using synthetic datasets that simulate real-world adversarial behaviour, our evaluation shows an average authentication latency of 172.50 s and a 50% reduction in gas fees compared to traditional Ethereum-based implementations. The results demonstrate that EdgeChainGuard effectively enforces tamper-resistant authentication, reduces unauthorized access, and adapts to dynamic network conditions. Future research will focus on integrating zero-knowledge proofs (ZKPs) for privacy preservation, federated learning for decentralized AI retraining, and lightweight anomaly detection models to enable secure, low-latency authentication in resource-constrained IoT deployments. Full article

Super-Resolution Land Surface Temperature (LST_SR) maps are essential for urban heat island (UHI) analysis and temperature monitoring. While much of the literature focuses on improving the resolution of low-resolution LST (e.g., MODIS-derived LST) using high-resolution space-borne data (e.g., Landsat-derived LST), Unmanned Aerial Vehicles (UAVs)/drone thermal imagery are rarely used for this purpose. Additionally, many deep learning (DL)-based super-resolution approaches, such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), require significant computational resources. To address these challenges, this study presents a novel approach to generate LST_SR maps by integrating Low-Resolution Landsat-8 LST (LST_LR) with High-Resolution PlanetScope images (I_HR) and UAV-derived thermal imagery (T_HR) using the Kolmogorov–Arnold Network (KAN) model. The KAN efficiently integrates the strengths of splines and Multi-Layer Perceptrons (MLPs), providing a more effective solution for generating LST_SR. The multi-step process involves acquiring and co-registering T_HR via the DJI Mavic 3 thermal (T) drone, I_HR from Planet (3 m resolution), and LST_LR from Landsat-8, with T_HR serving as reference data while I_HR and LST_LR are used as input features for the KAN model. The model was trained at two sites in Germany (Oberfischbach and Mittelfischbach) and tested at Königshain, achieving reasonable performance (RMSE: 4.06 °C, MAE: 3.09 °C, SSIM: 0.83, PSNR: 22.22, MAPE: 9.32%), and outperforming LightGBM, XGBoost, ResDensNet, and ResDensNet-Attention. These results demonstrate the KAN’s superior ability to extract fine-scale temperature patterns (e.g., edges and boundaries) from I_HR, significantly improving LST_LR. This advancement can enhance UHI analysis, local climate monitoring, and LST modeling, providing a scalable solution for urban heat mitigation and broader environmental applications. To improve scalability and generalizability, KAN models benefit from training on a more diverse set of UAV thermal imagery, covering different seasons, land use types, and regions. Despite this, the proposed approach is effective in areas with limited UAV data availability. Full article