Named Data Networking (NDN), a data-centric enabled-cache architecture, as one of the candidates for the future Internet, has the potential to overcome many of the current Internet difficulties (e.g., security, mobility, multicasting). Influenced by using cache in intermediate equipment, NDN has gained attention as a prominent method of Internet content sharing. Managing the NDN caches and reducing the cache redundancy are the important goals in this paper. Our main contribution in this research is toward caching optimization in comparison with betweenness probabilistic in-network caching strategy. Therefore, with respect to combined impacts of long-term centrality-based metric and Linear Weighted Moving Average (LWMA) of short-term parameters such as user incoming pending requests and unique outgoing hit requests on caching management, a flexible probability caching strategy is proposed. Moreover, a simple Randomized-SVD approach is applied to combine averaged short-term and long-term metrics. The output of this data-fusion algorithm is used to allocate a proper probability to the caching strategy. Evaluation results display an increase in the hit ratios of NDN routers' content-stores for the proposed method. In addition, the producer's hit ratio and the Interest-Data Round Trip Time, compared to the betweenness scheme, is decreased.

With the proliferation of smart mobile devices, there is an ever-increasing demand for multimedia content. To avoid congestion in backhaul links, mobile edge caching is a promising solution that can reduce delivery delays and improve users' quality of experience. In this regard, the requested content can be downloaded from a nearby small cell access point (also called helper) instead of a base station with a lower delay. In this paper, we address the problem of finding the optimal cache data placement to minimize the total delivery delay. We suppose the users are flexible in the sense that they request a set of multiple files from the library with a unique feature and are satisfied if any file within the requested set is received. Moreover, in the system model, the interference and the mobility of users are considered. More precisely, the effect of interference from other helpers is incorporated in calculating the delivery delay, and a random waypoint model is exploited to address the mobility of users within the network. Because of the complexity of the problem, finding the optimal solution is NP-hard. We prove that the problem is in the form of maximizing a monotone submodular function subject to matroid constraints. We exploit this property to provide an efficient approximate solution (i.e., a greedy algorithm) that is guaranteed to perform within a constant of  as well as the optimal solution. Simulation results validate the efficiency of our proposed algorithm.

Recommender systems based on content-based and collaborative filtering techniques face significant challenges, including the cold-start problem and privacy concerns due to their reliance on user profiles and product metadata. This study presents an optimized pairwise association rules (PAR) algorithm that addresses these limitations by operating independently of personal user data while maintaining recommendation accuracy. The proposed solution incorporates three key enhancements: (1) a privacy-preserving design using only transactional co-occurrence patterns, (2) a caching mechanism for modular training models that reduces recommendation latency by up to 102%, and (3) asynchronous execution for efficient resource management. Evaluations on a dataset of 20,000 food items demonstrate the algorithm's effectiveness, showing 18.7% higher nDCG scores than conventional methods while maintaining sub-second response times even with large-scale catalogs. The PAR algorithm proves particularly robust in sparse-data scenarios and cold-start conditions, offering a practical alternative to traditional approaches.

The popularity of video-on-demand (VoD) streaming has grown dramatically over the World Wide Web. Most users in VoD P2P networks have to wait a long time in order to access their requesting videos. Therefore, reducing waiting time to access videos is the main challenge for VoD P2P networks. In this paper, we propose a novel algorithm for caching video based on peers' priority and video's popularity distribution. The proposed mechanism has been evaluated on two different kinds of topology, Erdos-Renyi Model and Barabasi-Albert Model. It's necessary to mention that scale-free topologies are much more similar to P2P networks like Internet; so it’s closer to reality much more. However, decreasing waiting time is more tangible in them too. The results demonstrate that how our caching mechanism can reduce delay, improve bandwidth consumption, and decrease transport costs. Finally we came to the conclusion that increasing networks' size and videos' chunks has led to decrease much more delay by using proposed algorithm.

There is a growing acceptance that general purpose parallel computing requires the use of a scalable shared memory environment. The Cray T3D, IBM SP2 and Intel Paragon message passing machines support a scalable interconnect for up to 100´s or 1000´s of processors, with linear increases in bisection bandwidth as the number of processors grow. Supporting a shared address space on these machines results in a two-level memory hierarchy, in which data are either local or shared across the machine. The next few years will see a trend towards cache coherent multiprocessors, using the techniques employed by machines such as the KSR (cach-only memory) and the DASH (distributed directories). This will simplify the programming model by processoring a single level memory hierarchy. This paper describes a highly scalable caching technique, which is targeted at a weakly coherent form of shared memory, supported by the WPRAM computational model. (A processor wishing to read newly written shared data must explicitly synchronize in some way with the writer of that data). The example provides supports coherency for barrier synchronisation operation, but can be extended to other forms. A case study using the simplex method for linear programming is given. Results are based on a simulation of a scalable distributed memory machine.

The field of vaccinology provides excellent promises to control different infectious and non-infectious diseases. Genetic immunization as a new tool in this area by using naked DNA has been shown to induce humoral as well as cellular immune responses with high efficiency. This demonstrates the enormous potential of this strategy for vaccination purposes. DNA vaccines have been widely used to develop vaccines against various pathogens as well as cancer, autoimmune diseases and allergy. However, despite their successful application in many pre-clinical disease models, their potency in human clinical trials has been insufficient to provide protective immunity. Several strategies have been applied to increase the potency of DNA vaccine. Among these strategies, the linkage of antigens to Heat Shock Proteins (HSPs) and the utilization of different delivery systems have been demonstrated as efficient approaches for increasing the potency of DNA vaccines. The uptake of DNA plasmids by cells upon injection is inefficient. Two basic delivery approaches including physical delivery to achieve higher levels of antigen production and formulation with microparticles to target Antigen-Presenting Cells (APCs) are effective in animal models. Alternatively, different regimens called prime-boost vaccination are also effective. In this regimen, naked DNA is utilized to prime the immune system and either recombinant viral vector or purified recombinant protein with proper adjuvant is used for boosting. In this review, we discuss recent advances in upgrading the efficiency of DNA vaccination in animal models. 

With the increased scope of fires and the widespread destruction of the environment and densely populated urban areas in recent years, researchers have investigated the adoption of rapid and effective firefighting solutions, particularly those based on wireless sensor networks (WSNs). In fact, by evaluating various statistical data and developing a new model of sensors, equipment, and intelligent technologies in a fire sensor network, an effective step toward controlling frequent fires on a wide scale and reducing environmental damage can be taken. In the proposed model, the mobile sensors or firefighting robots based on a fuzzy Q-learning (FQL) algorithm and using two learning strategies in the sensor network, namely partial and perfect, could be used to  surround fire in firefighting operations. We also formulate a sensor mode selection strategy as an optimization problem to maximize the lifetime of the energy harvesting-enabled WSN. Furthermore, we determine optimal upper and lower bounds for the number of active sensors in the fire detection system, guaranteeing that the target detection and false alarm probabilities are achieved. Computer simulations show that using such a solution in the optimal selection of moving sensors and determining the moving trajectory in rapid firefighting is effective.

Multi-label classification aims at assigning more than one label to each instance. Many real-world multi-label classification tasks are high dimensional, leading to reduced performance of traditional classifiers. Feature selection is a common approach to tackle this issue by choosing prominent features. Multi-label feature selection is an NP-hard approach, and so far, some swarm intelligence-based strategies and have been proposed to find a near optimal solution within a reasonable time. In this paper, a hybrid intelligence algorithm based on the binary algorithm of particle swarm optimization and a novel local search strategy has been proposed to select a set of prominent features. To this aim, features are divided into two categories based on the extension rate and the relationship between the output and the local search strategy to increase the convergence speed. The first group features have more similarity to class and less similarity to other features, and the second is redundant and less relevant features. Accordingly, a local operator is added to the particle swarm optimization algorithm to reduce redundant features and keep relevant ones among each solution. The aim of this operator leads to enhance the convergence speed of the proposed algorithm compared to other algorithms presented in this field. Evaluation of the proposed solution and the proposed statistical test shows that the proposed approach improves different classification criteria of multi-label classification and outperforms other methods in most cases. Also in cases where achieving higher accuracy is more important than time, it is more appropriate to use this method.