Person?×?Situation interactionism in self-encoding (Iam…when…): Implications for affect regulation and social information processing.

Although Person?×?Situation (P?×?S) interactionism is central in current social-cognitive conceptions of personality organization, its implications for the encoding of the self remain unexplored. Two studies examined the causal role of P?×?S interactionism in self-encoding on affect regulation and discriminative social perception. Following failure (Studies 1 and 2) and success (Study 2) ideation, participants were prompted to encode the self either in P?×?S interactionist terms (I am…when…) or in traitlike unconditional terms (I am…). Interactionist (compared with unconditional) self-encoding led to less affective extremity, suggesting that such encoding may prevent individuals from generalizing specific success and failure experiences to the self as a whole. Study 2 also found that interactionist self-encoding attenuated the endorsement of global stereotypes, suggesting that such encoding may enhance fine-grained social perception as well. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

A cross-layer fault tolerance management module for wireless sensor networks

It is a well-established fact that wireless sensor networks (WSNs) are very power constraint networks, but besides this, they are inherently more fault-prone than any other type of wireless network and their protocol design is very application specific. Major reasons for the faults are the unpredictable wireless communication channel, battery depletion, as well as fragility and mobility of the nodes. Furthermore, as traditional protocol design methods have proved inadequate, the cross-layer design (CLD) approach, which allows for interactions between different layers, providing more flexible and energy-efficient functionality, has emerged as a viable solution for WSNs. In this study we define a fault tolerance management module suitable to the requirements, limitations, and specifics of WSNs, encompassing methods for fault detection, fault prevention, fault management, and recovery. The suggested solution is in line with the CLD approach, which is an important factor in increasing the network performance. Through simulations the functionality of the network is evaluated, based on packet loss, delay, and energy consumption, and is compared with a similar solution not including fault management. The results achieved support the idea that the introduction of a unified approach to fault management improves the network performance as a whole.     

Comparison of acid and enzymatic hydrolysis of tobacco stalk xylan for preparation of xylooligosaccharides

Tobacco stalk (TS), a major agricultural waste in the Black Sea region of Turkey, was used for the production of xylooligosaccharides (XOs). It contains about 22 g/100 g xylan whose composition was determined as 93.5 g/100 g xylose, 6.54 g/100 g glucose and 11.2 g/100 g uronic acid after complete acid hydrolysis. XO production was performed by enzymatic and acid hydrolysis of xylan which was obtained by alkali extraction from tobacco stalk. In enzyme hydrolysis, xylan was hydrolyzed using a xylanase preparation and the effects of pH, temperature, hydrolysis period, substrate and enzyme concentrations on the xylooligosaccharide yield and degree of polymerization were investigated. For enzymatic hydrolysis under optimum conditions XO yield with respect to tobacco stalk xylan (TSX) was 8.2 g/100 g after 8 h and 11.4 g/100 g after 24 h reaction period. In the acid hydrolysis, sulfuric acid was used and the hydrolyzate contained different amount of oligosaccharides and monosaccharides. For acid hydrolysis under optimum conditions, XO yield with respect to TSX was 13.0 g/100 g. Enzymatically obtained oligosaccharides were purified via ultrafiltration by using 10 and 3 kDa membranes. After a two-step membrane processing, the permeate containing mostly oligosaccharides was obtained.     

A comparative analysis of the effects of computer and paper-based personalization on student achievement

The aim of this study is to investigate whether personalization of mathematical word problems affects student achievement; and if it does, to find out whether the outcomes vary depending on computer and class environment in the teaching of mathematics. The sample of the research, conducted in the 2006–2007 fall semester, consists of 90 seventh grade in four sub-groups. During the application in the classroom, paper-based personalized and paper-based non-personalized problems were given. As to the computer environment, web-based personalized and web-based non-personalized problems were presented. After the application, an achievement test was performed as an achievement post-test parallel to the pre-test. The results show no significant difference between the scores of the groups who used personalized materials and the groups who used non-personalized materials. The results also indicate that no significant difference exists between those who studied in the computer-based environment and those in the paper-based environment.     

Surface and bulk investigation of ZSM5 and Al-MCM-41 using synchrotron XPS, XANES, and hexane cracking

We present a comparative study of ZSM5 and Al-MCM-41 catalysts using spectroscopic and chemical techniques. The analysis of conventional and synchrotron XPS spectra of these catalysts reveals the presence of a topmost surface-related Si peak in addition to the bulk peak. XANES results suggest structural modification upon heating Al-MCM-41 at 500 °C. Depth-resolved XPS data show Al depletion from the surface of Al-MCM-41 in contrast to surface enrichment of Al in ZSM5. These surface modifications could be one of the reasons for the weak acidity of Al-MCM-41 in chemical reactions such as hexane cracking at different temperatures.     

计及机械损耗与饱和影响的感应电动机仿真

本文分析感应电动机的四种动态模型，即１．理想电机模型；２．计及励磁电抗Ｘｍ饱和影响的电机模型；３．计及机械损耗的电机模型；４．同时计及Ｘｍ饱和与机械损耗的电机模型。借助于时变的非线性方程组，通过一定简化、假设，得出电机的动态特性的仿真结果。对仿真结果与实验结果的比较，证实了动态分析是正确的。     

Identification of Anomaly Scenes in Videos Using Graph Neural Networks

Generally, conventional methods for anomaly detection rely on clustering, proximity, or classification. With the massive growth in surveillance videos, outliers or anomalies find ingenious ways to obscure themselves in the network and make conventional techniques inefficient. This research explores the structure of Graph neural networks (GNNs) that generalize deep learning frameworks to graph-structured data. Every node in the graph structure is labeled and anomalies, represented by unlabeled nodes, are predicted by performing random walks on the node-based graph structures. Due to their strong learning abilities, GNNs gained popularity in various domains such as natural language processing, social network analytics and healthcare. Anomaly detection is a challenging task in computer vision but the proposed algorithm using GNNs efficiently performs the identification of anomalies. The Graph-based deep learning networks are designed to predict unknown objects and outliers. In our case, they detect unusual objects in the form of malicious nodes. The edges between nodes represent a relationship of nodes among each other. In case of anomaly, such as the bike rider in Pedestrians data, the rider node has a negative value for the edge and it is identified as an anomaly. The encoding and decoding layers are crucial for determining how statistical measurements affect anomaly identification and for correcting the graph path to the best possible outcome. Results show that the proposed framework is a step ahead of the traditional approaches in detecting unusual activities, which shows a huge potential in automatically monitoring surveillance videos. Performing autonomous monitoring of CCTV, crime control and damage or destruction by a group of people or crowd can be identified and alarms may be triggered in unusual activities in streets or public places. The suggested GNN model improves accuracy by 4% for the Pedestrian 2 dataset and 12% for the Pedestrian 1 dataset compared to a few state-of-the-art techniques.     

Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines

Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers. In this paper, we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance. Research have mostly focused the problem of human detection in thin crowd, overall behavior of the crowd and actions of individuals in video sequences. Vision based Human behavior modeling is a complex task as it involves human detection, tracking, classifying normal and abnormal behavior. The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e., fill hole inside objects algorithm. Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm. The classification task is achieved using binary and multi class support vector machines. The proposed technique is validated through accuracy, precision, recall and F-measure metrics.     

Preparation and characterization of palladium-plated porous glass for hydrogen enrichment

In this study, cylindrical porous glass tablets were plated by palladium using electroless plating technique. Hypophosphite and Co(II) complexes were used as reducing agents in the prepared plating baths. Experiments were carried out in an especially designed glass vessel in which helium gas was continuously bubbled through the solution to create uniform concentration and to remove hydrogen gas from the surface for the case of hypophosphite-based procedure. XRF analysis of the upper layer of the composite membrane prepared by the hypophosphite-based bath showed a Pd/Si ratio of 4.6. SEM photographs indicated impregnation of Pd into the substrate upto 200 μm. However, the thickness of the dense Pd layer was only about 15 μm. SEM photographs and XRF results showed that hypophosphite-based bath was much more successful than the Co(II) complex-bath in Pd plating. Permeation experiments carried out at different temperatures showed that the contribution of surface diffusion to the permeation was significant at low temperatures and solution–diffusion mechanism was not important in the 40–200 °C temperature range for these membranes. The selectivity ratio for H₂/N₂ was found to be about 7 at 200 °C.     

A comparative study of electrochemical methods on Pt–Ru DMFC anode catalysts: The effect of Ru addition

In the present study comparative electrochemical study of methanol electro-oxidation reaction, the effect of ruthenium addition and experimental parameters on methanol electro-oxidation reaction at high performance carbon supported Pt and Pt–Ru catalysts have been studied by cyclic voltammetry (CV), chronoamperometry (CA), and electrochemical impedance spectroscopy (EIS) in H₂SO₄ (0.05–2.00 M) + CH₃OH (0.01–4.00 M) at 20–70 °C. Tafel plots for the methanol oxidation reaction on Pt and Pt–Ru catalysts show reasonably well-defined linear region with the slopes of 128–174 mV dec⁻¹(α = 0.34–0.46). The activation energies from Arrhenius plots have been found as 39.06–50.65 kJ mol⁻¹. As a result, methanol oxidation is enhanced by the addition of ruthenium. Furthermore, Pt–Ru (25:1) catalyst shows best electro–catalytic activity, higher resistance to CO, and better long term stability compared to Pt–Ru (3:1), Pt–Ru (1:1), and Pt. Finally, the EIS measurements on Pt–Ru (25:1) catalyst reveals that methanol electro-oxidation reaction consists of two process: methanol dehydrogenation step at low potentials (<700 mV) and the oxidation removal of CO_ads by OH_ads at higher potentials (>700 mV).