Tailoring Semantic Communication at Network Edge: A Novel Approach Using Dynamic Knowledge Distillation

Semantic Communication (SemCom) systems, empowered by deep learning (DL), represent a paradigm shift in data transmission. These systems prioritize the significance of content over sheer data volume. However, existing SemCom designs face challenges when applied to diverse computational capabilities and network conditions, particularly in time-sensitive applications. A key challenge is the assumption that diverse devices can uniformly benefit from a standard, large DL model in SemCom systems. This assumption becomes increasingly impractical, especially in high-speed, high-reliability applications such as industrial automation or critical healthcare. Therefore, this paper introduces a novel SemCom framework tailored for heterogeneous, resource-constrained edge devices and computation-intensive servers. Our approach employs dynamic knowledge distillation (KD) to customize semantic models for each device, balancing computational and communication constraints while ensuring Quality of Service (QoS). We formulate an optimization problem and develop an adaptive algorithm that iteratively refines semantic knowledge on edge devices, resulting in better models tailored to their resource profiles. This algorithm strategically adjusts the granularity of distilled knowledge, enabling devices to maintain high semantic accuracy for precise inference tasks, even under unstable network conditions. Extensive simulations demonstrate that our approach significantly reduces model complexity for edge devices, leading to better semantic extraction and achieving the desired QoS.Comment: Accepted for the International Conference on Communications (ICC) 202

Oxo/dioxo-vanadium(V) complexes with Schiff base ligands derived from 4-amino-5-mercapto-3-phenyl-1,2,4-triazole

Vanadium complexes containing Schiff base ligands are of great importance and have numerous applications. A series of Schiff base ligands derived from 4-amino-5-mercapto-3-phenyl-1,2,4-triazole and different aldehydes were synthesized and combined with ammonium metavanadate in 2:1 and 1:1 molar ration to yield oxo- and dioxo-vanadium(V) complexes NH4[VO(La-f)2] and NH4[VO2(Lg-h)2], respectively. The structure of the synthesized compounds was confirmed by elemental analysis, UV, IR, NMR, MS and TGA. Complexes with bidentate and tridentate ligands were expected to possess a distorted square-pyramidal structure. The ligands and their complexes have been examined for antimicrobial activity against six types of bacteria and one kind of fungus that widely distributed in Albaha region, Kingdom of Saudi Arabia. The results indicate that some of the complexes were active against C. albicans fungus when used as powder, and no sound activity were shown against any type of tested bacteria

Inhibitory Effects of Chalcone on the Replication of Poliovirus in Vitro

The compound chalcon originally is extracted form some plant and herbs, the studies of the antiviral activity of this compound were done in two cell line cultures the L2OB and RD, the compound relatively non toxic to both cell lines of the concentration of 32?g/ml or less ,the compound have significantly anti poliovirus activity in both L2OB cell line and RD cell line, we find that the concentration of 0.03 ?g/ml or more inhibit the 100TCDID50 of the poliovirus .The therapeutic index(TI)used in this study to evaluate the drug activity ,( TI is the ratio of dose of drug which is just toxic to the cells to the does which is just inhibit the viral multiplication, if this index more than one the margin of safety of drug is according great ) .In this study the TI of chalcone against poliovirus is 266,therefore this compound if used in man have little or no side effect

The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey

As smart grids (SG) increasingly rely on advanced technologies like sensors and communication systems for efficient energy generation, distribution, and consumption, they become enticing targets for sophisticated cyberattacks. These evolving threats demand robust security measures to maintain the stability and resilience of modern energy systems. While extensive research has been conducted, a comprehensive exploration of proactive cyber defense strategies utilizing Deep Learning (DL) in {SG} remains scarce in the literature. This survey bridges this gap, studying the latest DL techniques for proactive cyber defense. The survey begins with an overview of related works and our distinct contributions, followed by an examination of SG infrastructure. Next, we classify various cyber defense techniques into reactive and proactive categories. A significant focus is placed on DL-enabled proactive defenses, where we provide a comprehensive taxonomy of DL approaches, highlighting their roles and relevance in the proactive security of SG. Subsequently, we analyze the most significant DL-based methods currently in use. Further, we explore Moving Target Defense, a proactive defense strategy, and its interactions with DL methodologies. We then provide an overview of benchmark datasets used in this domain to substantiate the discourse.{ This is followed by a critical discussion on their practical implications and broader impact on cybersecurity in Smart Grids.} The survey finally lists the challenges associated with deploying DL-based security systems within SG, followed by an outlook on future developments in this key field.Comment: To appear in the IEEE internet of Things journa

Empowering HWNs with Efficient Data Labeling: A Clustered Federated Semi-Supervised Learning Approach

Clustered Federated Multitask Learning (CFL) has gained considerable attention as an effective strategy for overcoming statistical challenges, particularly when dealing with non independent and identically distributed (non IID) data across multiple users. However, much of the existing research on CFL operates under the unrealistic premise that devices have access to accurate ground truth labels. This assumption becomes especially problematic in hierarchical wireless networks (HWNs), where edge networks contain a large amount of unlabeled data, resulting in slower convergence rates and increased processing times, particularly when dealing with two layers of model aggregation. To address these issues, we introduce a novel framework, Clustered Federated Semi-Supervised Learning (CFSL), designed for more realistic HWN scenarios. Our approach leverages a best-performing specialized model algorithm, wherein each device is assigned a specialized model that is highly adept at generating accurate pseudo-labels for unlabeled data, even when the data stems from diverse environments. We validate the efficacy of CFSL through extensive experiments, comparing it with existing methods highlighted in recent literature. Our numerical results demonstrate that CFSL significantly improves upon key metrics such as testing accuracy, labeling accuracy, and labeling latency under varying proportions of labeled and unlabeled data while also accommodating the non-IID nature of the data and the unique characteristics of wireless edge networks.Comment: Accepted for IEEE Wireless Communications and Networking Conference (WCNC) 202

Non- Newtonian Drag Reducing Flow Characteristics in Porous Media

In this paper, the properties affecting the flow behavior of a non-Newtonian drag reduction fluid through porous media were studied. The experimental work was carried out for the flow of pure water and dilute polyethylene oxide solution with concentrations C = 50, 100, 150, 200, and 250 ppm in a circular pipe with a 2.54 cm inside diameter filled with porous media (uniform size of plastic spheres with 5.5 mm in diameter). The experiments are utilized to show the effect of the variation of polyethylene oxide concentration on the pressure drop and friction factor by changing the flow rate and polyethylene oxide concentrations. To validate the experimental results, a comparison of pressure drop and friction factor for pure water is done with the Ergun model, which gives good agreement. The experiments show that the friction factor and the pressure drop are reduced by the increase in POE concentration. The drag reduction ratio increases with the increase in POE concentration and its effectiveness is higher at low velocities (between 0.012 and 0.068 m/s) than at higher velocities (from 0.068 to 0.157 m/s). A modified relationship was deduced as an extension of Ergun to describe the effect of POE concentrations as a drag reducing additive in the non-Newtonian fluid flow through a porous medium

Fair Selection of Edge Nodes to Participate in Clustered Federated Multitask Learning

Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distributed manner. While a similarity metric can provide client groups with specialized models according to their data distribution, this process can be time-consuming because the server needs to capture all data distribution first from all clients to perform the correct clustering. Due to resource and time constraints at the network edge, only a fraction of devices {is} selected every round, necessitating the need for an efficient scheduling technique to address these issues. Thus, this paper introduces a two-phased client selection and scheduling approach to improve the convergence speed while capturing all data distributions. This approach ensures correct clustering and fairness between clients by leveraging bandwidth reuse for participants spent a longer time training their models and exploiting the heterogeneity in the devices to schedule the participants according to their delay. The server then performs the clustering depending on predetermined thresholds and stopping criteria. When a specified cluster approximates a stopping point, the server employs a greedy selection for that cluster by picking the devices with lower delay and better resources. The convergence analysis is provided, showing the relationship between the proposed scheduling approach and the convergence rate of the specialized models to obtain convergence bounds under non-i.i.d. data distribution. We carry out extensive simulations, and the results demonstrate that the proposed algorithms reduce training time and improve the convergence speed while equipping every user with a customized model tailored to its data distribution.Comment: To appear in IEEE Transactions on Network and Service Management, Special issue on Federated Learning for the Management of Networked System