WiMA: Towards a Multi-Criterion Association in Software Defined Wi-Fi Networks

Despite the planned installation and operations of the traditional IEEE 802.11 networks, they still experience degraded performance due to the number of inefficiencies. One of the main reasons is the received signal strength indicator (RSSI) association problem, in which the user remains connected to the access point (AP) unless the RSSI becomes too weak. In this paper, we propose a multi-criterion association (WiMA) scheme based on software defined networking (SDN) in Wi-Fi networks. An association solution based on multi-criterion such as AP load, RSSI, and channel occupancy is proposed to satisfy the quality of service (QoS). SDN having an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance. To implement WiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator. The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30% and enhances the throughput by 20%–50%, hence maintaining user fairness and accommodating more wireless devices and traffic load in the network, when compared to traditional client-driven (CD) approach and state of the art Wi-Balance approach.     

Quantum Fuzzy Regression Model for Uncertain Environment

In the era of big data, traditional regression models cannot deal with uncertain big data efficiently and accurately. In order to make up for this deficiency, this paper proposes a quantum fuzzy regression model, which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation. In this paper, data envelopment analysis (DEA) is used to calculate the degree of importance of each data point. Meanwhile, Harrow, Hassidim and Lloyd (HHL) algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional data matrix calculation. The application of the quantum fuzzy regression model to small-scale financial data proves that its accuracy is greatly improved compared with the quantum regression model. Moreover, due to the introduction of quantum computing, the speed of dealing with high-dimensional data matrix has an exponential improvement compared with the fuzzy regression model. The quantum fuzzy regression model proposed in this paper combines the advantages of fuzzy theory and quantum computing which can efficiently calculate high-dimensional data matrix and complete parameter estimation using quantum computing while retaining the uncertainty in big data. Thus, it is a new model for efficient and accurate big data processing in uncertain environments.     

XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous detection model that dynamically trains small subsets to these issues. First, this research introduces a deep neural network (DNN)-based GANomaly for semi-supervised learning. Second, this paper presents the proposed adaptive algorithm for the DNN-based GANomaly, which is validated with four subsets of the adaptive dataset. Finally, this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations, reconstruction error visualization, and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage, semi-supervised learning, and adaptive learning. Compared to other single-class classification techniques, the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13% and 8% of F1 scores and 4.17% and 11.51% for accuracy, respectively. Furthermore, experiments of the proposed adaptive learning reveal mostly improved results over the initial values. An analysis and monitoring system based on the combination of the three explainable methodologies is also described. Thus, the proposed method has the potential advantages to be applied in practical industry, and future research will explore handling unbalanced real-time datasets in various scenarios.     

Interdiffusion in the carbides of the Nb-C system

Interdiffusion coefficients in Nb₂C and NbC_1−x were measured using bulk diffusion couples in the temperature range from 1400 °C to 1700 °C. Marker experiments were used to show that carbon is the only component undergoing significant diffusion in both carbides. Carbon concentrations were measured by difference using electron probe microanalysis, and interdiffusion coefficients were taken from Boltzmann-Matano analyses of the resulting concentration profiles. This analysis clearly showed that, in NbC_1−x, interdiffusion coefficient varies with carbon concentration, and is expressed by

Thermodynamic properties of alumina-saturated iron-carbon-bismuth alloys

Using direct and indirect equilibration methods, the solubilities of iron in molten bismuth and those of bismuth in molten iron-carbon alloys have been measured over the temperature range 1473-1873 K. The Henrian behaviour of bismuth in molten ferrous alloys allows the calculation of its activity coefficient, which in turn facilitates modelling as a function of temperature and carbon content, using the unified interaction parameter formalism developed by Pelton and Bale. The modelling results generate the expression: . A model has also been developed expressing bismuth content in molten iron-carbon alloys as a function of temperature and composition: .     

Tennessee's failed managed care program for mental health and substance abuse services

In July 1996, Tennessee initiated a managed mental health and substance abuse program called TennCare Partners. This publicly funded "carve-out" experiment started chaotically and soon deteriorated into a crisis. Many patients did not receive care or lost continuity of care, and the traditional "safety net" mental health system nearly disintegrated. This qualitative case study sought to ascertain the impact of the TennCare Partners program. It points out that the program's difficulties stemmed directly from a flawed design that spread funds previously earmarked for severely mentally ill patients across the entire Medicaid population. States contemplating similar reforms should strive to protect vulnerable patients by risk-adjusting capitation payments and by focusing resources on care for severely mentally ill persons. States should also minimize program complexity and ensure the accountability of managed care networks for their patients' behavioral health care needs.     

Purification and properties of beta-N-Acetylhexosaminidase from cabbage

beta-N-Acetylhexosaminidase was purified from the extract of cabbage by sequential steps of ammonium sulfate fractionation, chromatofocusing, DEAE-Sepharose CL-6B ion exchange chromatography and Sephacryl S-200 HR gel filtration. By these steps, the purity of the enzyme increased by 256 fold with a recovery of 8%. The purified enzyme was homogeneous as examined by native PAGE. It showed an optimal pH of 4, an optimal temperature of 60 degrees C and a Km of 0.94 mM for hydrolysis of pNp-beta-GlcNAc. The molecular mass of the enzyme determined from filtration through Sephacryl S-200 was 150 kDa. Three subunits with molecular mass of 64, 57 and 51 kDa were observed as determined by SDS-PAGE. NBS (0.025 mM), DEPC (3 mM) and WRK (30 mM) significantly inhibited the activity of the enzyme. The enzyme also showed activity toward pNp-beta-GalNAc, N,N'-diacetylchitobiose, N,N',N"-triacetylchitotriose and N,N',N",N"'-tetraacetyl chitotetraose but showed no activity toward pNp-alpha-GlcNAc, chitin and ethylene glycol chitin.     

0.618法最优确定盘状摆动凸轮机构的最小基圆半径

在满足机构许用压力角条件下,采用０．６１８法最优确定盘状摆动凸轮机械的最小基圆半径。     

正确把握“三个环节” 做好经常性思想工作

“及时发现,确实弄清,正确解决”是做好经常性思想工作的３个基本环 节。“及时发现”是做好经常性思想工作的前提,要做到及时发现,一要把握规律,搞好 预测;二要留心观察,掌握细微;三是个别交谈,讲究艺术;四要摸底测试,搞好普查; 五是会议收集,全面分析。“确实弄清”是做好经常性思想工作的关键,一要搞好调查 研究,弄清思想问题的真正原因;二要进行科学分析,弄清思想问题的本质。“正确解 决”是做好经常性思想工作的目的,一要动之以情;二要晓之以理;三要喻之以义;四 要导之以行。     

基于模糊神经网络及证据推理的数据融合系统

在研究战术弹道导弹电子对抗条件下作战仿真运用问题时,我们成功地将模糊神经网络与证据推理相结合,弥补了传统多传感器数据融合系统未考虑外界环境因素影响的缺陷,提高了数据融合系统的抗干扰能力,最后简单介绍了仿真系统软件的实现流程。这是作战仿真方法研究及实现的新途径。