A pure peer-to-peer approach for kNN query processing in mobile ad hoc networks

Together with advanced positioning and mobile technologies, P2P query processing has attracted a growing interest number of location-aware applications such as answering kNN queries in mobile ad hoc networks. It not only overcomes drawbacks of centralized systems, for example single point of failure and bottleneck issues, but more importantly harnesses power of peers’ collaboration. In this research, we propose a pure mobile P2P query processing scheme which primarily focuses on the search and validation algorithm for kNN queries. The proposed scheme is designed for pure mobile P2P environments with the absence of the base station support. Compared with centralized and hybrid systems, our system can reduce energy consumption more than six times by making use of data sharing from peers in a reasonable mean latency of processing time for networks with high density of moving objects as can be seen in the simulation results.     

基于B/S结构的在线报障系统的设计与实现

随着办公自动化的推广、计算机应用及网络应用的深入,计算机故障及网络故障越来越多,故障管理工作变得越来越繁重和复杂,如何有效合理地解决这些计算机应用过中的故障和网络故障等问题是维护部门的一项重要工作.针对茂名职业技术学院计算机应用特点和技术现状,结合学院计算机故障应急处理方案,提出了建立计算机故障在线报障系统的解决方案.主要利用UML设计出基于B/S结构的故障处理系统的各模块类图、用例图、活动图等,在Linux系统的基础上,用PHP作为开发工具、MYSQL作为后台数据库,实现了在线报障系统各模块的功能.在线报障应用系统的主要功能是用户报障、管理员查询故障、管理员接单、提出解决办法、故障处理结果汇报等功能,系统的应用过程符合茂名职业技术学院的相关管理办法,具有一定的实际应用价值,克服了传统人工报障效率低、维护时间长、成本过高的弊端,从而降低了维护成本、提高了设备及网络利用率、规范了故障处理过程,较大地提高了学院计算机及网络故障处理的质量,使得管理人员的技术水平和故障的处理效率得到很大的提高,并为实训中心及设备管理部门等提供了有效的决策支持.     

Grain size effects on cyclic fatigue and crack-growth resistance behaviour of partially stabilized zirconia

Cyclic fatigue crack growth and crack-resistance behaviour was studied in partially stabilized zirconia (PSZ) with three different cubic-phase grain sizes following sub-eutectoid heat treatments. Raman spectroscopy was used to determine the extent of phase transformation around the cracks for both cyclic and monotonic loading conditions. All tests were on long, through thickness cracks using compact-tension specimens. Predictions of crack-tip shielding were made following determination of toughening parameters using crackresistance data. It was found that the dominant factors affecting cyclic fatigue-crack growth were the level of crack-tip shielding, as a result of phase transformation, and the intrinsic toughness of the material. Grain size did not appear to significantly affect fatigue crack-growth behaviour.     

基于SURF-HOG与显著性特征的红外可见光图像配准融合北大核心CSCD

针对现有红外与可见光图像配准不精确,边缘及细节纹理缺失,融合时间较长,不能突出重点目标等不足,提出一种基于SURF-HOG描述符与红外显著性特征的红外与可见光图像融合方法。首先,在红外与可见光图像配准阶段,在SURF(Speed-Up Robust Features,SURF)框架内构建基于HOG(Histogram of Oriented Gradient,HOG)的特征点描述符,并通过NNDR(Nearest Neighbor Distance Ratio,NNDR)进行红外与可见光图像的特征点匹配;其次,在显著特征提取阶段,先通过四叉树算法对源红外图像分解,然后通过贝塞尔插值法重建红外图像背景,接着分别对红外图像中的背景及目标进行自适应抑制以提取目标红外显著性特征;最后,结合已配准的可见光图像与重建后的红外图像以获取最终融合结果。实验结果表明,所提方法对不同场景下的红外与可见光图像具有较高的配准精度,不同场景下的融合结果不但主观视觉上具有显著的目标特征,同时背景纹理和边缘细节清晰,整体对比度适宜,运行时间最短,并且在客观评价指标上也取得了较好的效果。     

中国电力中长期市场分时段交易价格形成机制及模型

电力中长期市场分时段交易是中国电力市场改革的重要举措之一。针对当前电力中长期市场分时段交易价格形成机制不合理、不完善的问题,提出了中国电力中长期市场分时段交易价格形成机制及其模型。首先,分析影响中国电力中长期分时段交易价格形成的因素,提出电力中长期交易的4种典型场景;然后,综合考虑生产成本和用户效用,探索性地提出适用于中长期分时段交易的系统平均成本定价、用户失负荷价值定价、系统边际成本定价和发电企业失负荷价值定价4种成本定价机制,构建了基于成本定价的分时段交易定价模型;最后,以某省实际数据为例进行测算,验证了模型的有效性。     

A Boronate Ester Driven Rechargeable Antibacterial Membrane for Fast Molecular Sieving

Membrane decorated with biocides is an effective way to suppress biofilm growth. However, their immediate biocidal effect usually suffers from a significant decline due to the irreversible consumption of the biocides. Here, a smart nanofiltration membrane is reported with rechargeable antibacterial capability that is fabricated by a facile interfacial polymerization via 3-aminophenylboronic acid and trimesoyl chloride on a polysulfone substrate. Biocides bearing diol groups can be grafted onto the membrane surface under neutral/alkaline condition and then released from the surface under acidic environment, due to the pH-responsive feature of boronate ester complexes. The resultant membrane exhibits integrated properties of fast bacterial inactivating efficiency, rechargeable antibacterial capability, and impressive stability. In addition, the achieved membrane shows remarkable separation efficiency to dye/monovalent salt system. The successful fabrication of the membrane with rechargeable anti-bacterial property provides new insights into the development of pH-responsive and sustainable antibacterial membranes.     

Comprehensively Strengthened Metal-Oxygen Bonds for Reversible Anionic Redox Reaction

Introducing anionic redox in layered oxides is an effective approach to breaking the capacity limit of conventional cationic redox. However, the anionic redox reaction generally suffers from excessive oxidation of lattice oxygen to O₂ and O₂ release, resulting in local structural deterioration and rapid capacity/voltage decay. Here, a Na_0.71Li_0.22Al_0.05Mn_0.73O₂ (NLAM) cathode material is developed by introducing Al³⁺ into the transition metal (TM) sites. Thanks to the strong Al–O bonding strength and small Al³⁺ radius, the TMO₂ skeleton and the holistic TM–O bonds in NLAM are comprehensively strengthened, which inhibits the excessive lattice oxygen oxidation. The obtained NLAM exhibits a high reversible capacity of 194.4 mAh g^-1 at 20 mA g^-1 and decent cyclability with 98.6% capacity retention over 200 cycles at 200 mA g⁻¹. In situ characterizations reveal that the NLAM experiences phase transitions with an intermediate OP4 phase during the charge–discharge. Theoretical calculations further confirm that the Al substitution strategy is beneficial for improving the overlap between Mn 3d and O 2p orbitals. This finding sheds light on the design of layered oxide cathodes with highly reversible anionic redox for sodium storage.     

Unveiling the Synergy of Interfacial Contact and Defects in α-Fe2O3 for Enhanced Photo-Electrochemical Water Splitting

Photo-electrochemical (PEC) water splitting is a promising method for converting solar energy into clean energy, but the mechanism of improving PEC efficiency through the interfacial contact and defect strategy remains highly controversial. Herein, reduced graphene oxide (rGO) and oxygen vacancies are introduced into α-Fe₂O₃ nanorod (NR) arrays using a simple spin-coating method and acid treatment. The resultant oxygen vacancy–α-Fe₂O₃/rGO-integrated system exhibits a higher photocurrent, four times than the pristine α-Fe₂O₃. It is well evidenced that the electronic interface interaction between α-Fe₂O₃ and rGO is boosted with the oxygen vacancies, facilitating electron transfer from α-Fe₂O₃ to rGO. Moreover, the oxygen vacancies not only create interband states in α-Fe₂O₃ that can trap photogenerated holes and thus facilitate charge separation but significantly also strengthen the adsorption of oxidative intermediates and reduce the energy barrier of rate-determining step during oxygen evolution reaction (OER). This study demonstrates an rGO–oxygen vacancy synergistic interfacial contact and defect modification approach to design semiconducting photocatalysts for high-efficiency solar energy capture and conversion. The generated principle is expected to be extendable to another material system.     

Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering for Noisy Data

Clustering is a crucial method for deciphering data structure and producing new information. Due to its significance in revealing fundamental connections between the human brain and events, it is essential to utilize clustering for cognitive research. Dealing with noisy data caused by inaccurate synthesis from several sources or misleading data production processes is one of the most intriguing clustering difficulties. Noisy data can lead to incorrect object recognition and inference. This research aims to innovate a novel clustering approach, named Picture-Neutrosophic Trusted Safe Semi-Supervised Fuzzy Clustering (PNTS3FCM), to solve the clustering problem with noisy data using neutral and refusal degrees in the definition of Picture Fuzzy Set (PFS) and Neutrosophic Set (NS). Our contribution is to propose a new optimization model with four essential components: clustering, outlier removal, safe semi-supervised fuzzy clustering and partitioning with labeled and unlabeled data. The effectiveness and flexibility of the proposed technique are estimated and compared with the state-of-art methods, standard Picture fuzzy clustering (FC-PFS) and Confidence-weighted safe semi-supervised clustering (CS3FCM) on benchmark UCI datasets. The experimental results show that our method is better at least 10/15 datasets than the compared methods in terms of clustering quality and computational time.     

Applying Wide & Deep Learning Model for Android Malware Classification

Android malware has exploded in popularity in recent years, due to the platform’s dominance of the mobile market. With the advancement of deep learning technology, numerous deep learning-based works have been proposed for the classification of Android malware. Deep learning technology is designed to handle a large amount of raw and continuous data, such as image content data. However, it is incompatible with discrete features, i.e., features gathered from multiple sources. Furthermore, if the feature set is already well-extracted and sparsely distributed, this technology is less effective than traditional machine learning. On the other hand, a wide learning model can expand the feature set to enhance the classification accuracy. To maximize the benefits of both methods, this study proposes combining the components of deep learning based on multi-branch CNNs (Convolutional Network Neural) with wide learning method. The feature set is evaluated and dynamically partitioned according to its meaning and generalizability to subsets when used as input to the model’s wide or deep component. The proposed model, partition, and feature set quality are all evaluated using the K-fold cross validation method on a composite dataset with three types of features: API, permission, and raw image. The accuracy with Wide and Deep CNN (WDCNN) model is 98.64%, improved by 1.38% compared to RNN (Recurrent Neural Network) model.