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.     

Unified winding function approach for dynamic simulation of different kinds of eccentricity faults in cage induction machines

The multiple coupled circuit model of a three-phase cage induction machine and the uses of a modified winding function theory to calculate the time-dependent inductances of the motor with a general eccentricity fault, including static, dynamic and mixed eccentricities, in a unified manner are considered. By defining the inverse air gap function of the eccentric machine, determining its indefinite integral and assuming step variations for the turn functions at the centres of the slots, precise analytic equations are obtained for the inductances. Differentiating these analytic equations against the rotor angular position also gives precise analytic equations to calculate the derivatives of the inductances, which may be used to determine the electromagnetic torque. Consequently, dynamic simulations of an induction machine with any eccentricity type and degree as well as a healthy one is made possible in the frame of a single Simulink program. At the beginning of the simulation process, the eccentricity components' degree and position are introduced and the program is then executed accordingly. A simple technique was used to make an industrial induction motor temporarily eccentric, with varying degrees, and experiments were conducted on it. By defining and applying effective air gap length and effective eccentricity components' degrees, good agreements are achieved between the simulated and the experimental results, which implies that the whole unified simulation program is effective.     

Studies on thermal,mechanical, morphological,and viscoelastic properties of polybenzimidazole fiber reinforced high density polyethylene composites

High density polyethylene (HDPE) and polybenzimidazole fiber (PBI) composites were prepared by melt blending in a twin screw extruder. The thermomechanical properties of PBI fiber reinforced HDPE composite samples (1%, 4%, and 8%) of fiber lengths 3 mm and 6 mm were investigated using differential scanning calorimeter (DSC), universal testing machine, rheometer, and scanning electron microscopy (SEM). The effects of fiber content and fiber lengths on the thermomechanical properties of the HDPE‐PBI composites were studied. The DSC analysis showed a decrease in crystallinity of HDPE‐PBI composites with an increase of fiber loading. SEM images revealed homogeneous distribution of the fibers in the polymer matrix. The thermal behavior of the composites was evaluated from thermogravimetric analysis and the thermal stability was found to increase with the addition of fibers. The evidence of homogeneous distribution was verified by the considerably high values of tensile strength and flexural strength. In the rheology study, the complex viscosities of HDPE‐PBI composites were higher than the HDPE matrix and increased with the increasing of PBI fiber loading. POLYM. COMPOS., 5–13, 2016. © 2014 Society of Plastics Engineers     

Distributed Resource Allocation for Femtocell Networks: Regret Learning with Proportional Self-belief

Femtocell networks promise improvement in network quality and performance for dense wireless networks, but will suffer from inter-cell interference if resource management is not properly employed. This paper presents distributed joint resource allocation (sub-channel and power) to address co- and cross-tier interference issues in two-tier heterogeneous femtocell networks. Due to uncoordinated nature of femtocell base stations (HeNB) deployment, the interactions among self-interested HeNBs are formulated using game-theoretical tools. Then, we designed individual utility function for every HeNB in order to enforce cooperative behaviour among HeNBs as well as to avoid cross-tier interference towards macrocell user equipments within HeNB coverage. Based on the designed utility function, we propose a fully distributed adaptive learning algorithm with a proportional self-belief concept that can lead to correlated equilibrium with fast and decisive convergence. Finally, performance analysis on the proposed algorithm done in simulated environment showed positive results indicating improvements in terms of co- and cross-tier interference mitigation as compared to generic regret-based learning scheme and utility functions.     

Characterisation of esterolytic activity from two wild mushroom species, Amanita vaginata var. vaginata and Tricholoma terreum

Characterisation of esterase activities from the edible mushroom species, Amanita vaginata var. vaginata and Tricholoma terreum, were investigated. Native electrophoresis of the crude extracts prepared from both mushroom samples showed the presence of esterolytic activities. The extracts had the greatest activity in the presence of p-nitrophenyl butyrate (pNPB) as a substrate. pH and temperature optima were found to be 8.0 and 30 °C for both enzymes, respectively. V_max and K_m values were determined as 14.2 U/l and 71 μM for A. vaginata var. vaginata and 34.6 U/l and 9.6 μM for T. terreum, respectively. The pH-stability profile showed a stationary line between 3.0 and 10.0 for both enzymes. The esterolytic activities from the extracts were maintained between 10 and 40 °C for 4 h and started to decrease at 50 °C. The effects of EDTA, NaN₃, DTT and PMSF on the enzyme activity were also investigated.     

Failure Analysis of Tractor Wheel Axle

Journal of Failure Analysis and Prevention - A number of wheel axles of MF-240 Tractors, which had broken after unusually short times in the field (~ 2–4 months), were examined to...     

Polyphenol oxidase properties,anti-urease,and anti-acetylcholinesterase activity of Diospyros lotus L. (Plum Persimmon)

Diospyros lotus fruit polyphenol oxidase was purified using affinity chromatography, resulting in a 15-fold enrichment in specific activity. The purified enzyme, having 16.5 kDa molecular weight on sodium dodecyl sulfate-polyacrylamide gel electrophoresis, exhibited the highest activity toward 4-methylcatechol. Maximum diphenolase activity was reached at pH 7.0 and 60°C in the presence of 4-methylcatechol. K_m and V_max values were calculated as 3.8 mM and 1250 U/mg protein, respectively. Ascorbic acid was a promising inhibitor with an IC₅₀ value of 0.121 µM. The activity of the purified enzyme was stimulated by Fe²⁺, Sr²⁺, Zn²⁺, and K⁺ and deeply inhibited by Hg²⁺, at 1 mM final concentration. Aqueous extract of Diospyros lotus L. fruit showed strong substantial urease and acetylcholinesterase inhibition, with IC₅₀ values of 1.55 ± 0.05 and 16.75 ± 0.11 mg/mL, respectively.