A comprehensive survey on computer vision based concepts,methodologies, analysis and applications for automatic gun/knife detection

The ability to detect gun and gun held in hand or other body parts is a typical human skill. The same problem presents an imperative task for computer vision system. Automatic observer independent detection of hand held gun or gun held in the other body part, whether it is visible or concealed, provides enhance security in vulnerable places and initiates appropriate action there. Compare to the automatic object detection systems, automatic detection of gun has very few successful attempts. In the present scope of this paper, we present an extensive survey on automatic detection of gun and define a taxonomy for this particular detection system. We also describe the inherent difficulties related with this problem. In this survey of published papers, we examine different approaches used in state-of-the-art attempts and compare performances of these approaches. Finally, this paper concludes pointing to the possible research gaps in related fields.     

Control and care: landlords and the governance of vulnerable tenants in houses in multiple occupation

Houses in multiple occupation (HMOs) in which tenants share facilities are housing an increasing proportion of vulnerable adults who have limited affordable housing options. However, knowledge about how these types of property are managed is limited. In this paper, we examine the governance function of HMO landlords from the perspective of landlords/landlord agents and the tenants that live within their properties. The landlord exercises control through formal and informal risk assessment of tenants and close surveillance of them. These control mechanisms may also involve direct or indirect provision of support and care to some tenants. This illustrates the complex relationship between care and control and the extent to which both are integral to the housing management of vulnerable tenants living in HMOs. We suggest that this dual function calls for a critical examination of what constitutes a ‘good landlord’.     

Bayesian changepoint and time-varying parameter learning in regime switching volatility models

This paper proposes a combined state and piecewise time-varying parameter learning technique in regime switching volatility models using multiple changepoint detection. This approach is a Sequential Monte Carlo method for estimating GARCH & EGARCH based volatility models with an unknown number of changepoints. Modern auxiliary particle filtering techniques are used to calculate the posterior densities and online forecasts. This approach also automatically deals with the common ancestral path dependence problem faced in these type volatility models. The model is tested on Borsa Istanbul (BIST) formerly known as Istanbul Stock Exchange (ISE) market data using daily log returns. A full structural changepoint specification is defined in which all parameters of the conditional variance of the volatility models are dynamic. Finally, it is shown with simulation experiments that the proposed approach partitions the series into several regimes and learns the parameters of each regime's volatility model in parallel with the multiple changepoint detection process.     

Simultaneous synthesis of a heat exchanger network with multiple utilities using utility substages

Heat exchanger network synthesis (HENS) has progressed by using mathematical programming-based simultaneous methodology. Although various considerations such as non-isothermal mixing and bypass streams are applied to consider real world alternatives in modeling phase, many challenges are faced because of its properties within non-convex mixed-integer nonlinear programming (MINLP). We propose a modified superstructure, which contains a utility substage for use in considering multiple utilities in a simultaneous MINLP model. To improve model size and convergence, fixed utility locations according to temperature and series connections between utilities are suggested. The numbers of constraints, discrete, and continuous variables show that overall model size decreases compared with previous research. Thus, it is possible to expand the feasible search area for reaching the nearest global solution. The model's effectiveness and applications are exemplified by several literature problems, where it is used to deduce a network superior to that of any other reported methodology.     

Cross-linked chitosan polymers as generic adsorbents for simultaneous adsorption of multiple mycotoxins

The primary objective of this study was to synthesize three types of cross-linked chitosan polymers and further investigate their adsorption capability for multiple mycotoxins, including aflatoxin B₁ (AFB₁), ochratoxin A (OTA), zearalenone (ZEN), fumonisin B₁ (FB₁), deoxynivalenol (DON) and T-2 toxin (T2). Among these synthetic adsorbents, cross-linked chitosan-glutaraldehyde complex presented the highest adsorption capability for AFB₁ (73%), OTA (97%), ZEN (94%) and FB₁ (99%), but no obvious adsorption for DON and T2 (<30%). The effect of various incubation conditions (contact time, dosage and pH) was also studied. Subsequently, the experimental data were fitted to Langmuir, Freundlich and Hill models. The best fitting model to describe AFB₁ and FB₁ adsorption was Langmuir model (R² ≥ 0.99), with the theoretical maximum adsorption amounts of 5.67 mg/g for AFB₁ and 15.7 mg/g for FB₁. The Hill model was the best model for OTA and ZEN adsorption (R² > 0.98), with the predicted maximum adsorption amounts were 24.8 mg/g for OTA and 9.18 mg/g for ZEN. In addition, the adsorption capability of adsorbent for the simultaneous presence of multiple mycotoxins was also evaluated in buffer system and simulated gastrointestinal condition. The results indicated that the coexisted multiple mycotoxins didn't affected the adsorption capability of adsorbent, whereas the adsorption amounts of toxins were decreased by some gastrointestinal components. The findings of this research suggest that chitosan–glutaraldehyde complex has the potential to be applied as multitoxin adsorbent material for reducing the combined adverse effect of multiple mycotoxins on humans and animals.     

An efficient approach for the siting and sizing problem of distributed generation

This paper deals with the subject of finding the site and size of distributed generation in distribution systems. This problem corresponds to a mixed-integer nonlinear problem which is difficult and hard to solve with classical optimization techniques. Many approaches and with different objective functions have been applied to solve it. In this paper, the problem to solve comprises multiple distributed generation sources and the objective of minimizing power losses and generation costs, both for the distributed and conventional generators. Since it is highly combinatorial, a search space reduction is needed. So, an approximate model is used to reduce the search space of the possible buses where the distributed generation has to be located. Then, for each combination of the reduced space search, a nonlinear equations system is solved by a numerical method to get the size of the distributed generation, checking the voltage limits and the lines’ capacities. To test the method, a comparison between the proposed algorithm and a force brute algorithm is performed on a 69 and 118 bus test systems. The obtained results indicate that this method find the optimal or near optimal solution in a reasonable computation time.     

Segment dependent dynamic multi-histogram equalization for image contrast enhancement

Histogram equalization (HE) method proved to be a simple and most effective technique for contrast enhancement of digital images. However it does not preserve the brightness and natural appearance of the images, which is a major drawback. To overcome this limitation, several Bi- and Multi-HE methods have been proposed. Although the Bi-HE methods significantly enhance the contrast and may preserve the brightness, the natural appearance of the images is not preserved as these methods suffer with the problem of intensity saturation. While Multi-HE methods are proposed to further maintain the brightness and natural appearance of images, but at the cost of contrast enhancement. In this paper, two novel Multi-HE methods for contrast enhancement of natural images, while preserving the brightness and natural appearance of the images, have been proposed. The technique involves decomposing the histogram of an input image into multiple segments based on mean or median values as thresholds. The narrow range segments are identified and are allocated full dynamic range before applying HE to each segment independently. Finally the combined equalized histogram is normalized to avoid the saturation of intensities and un-even distribution of bins. Simulation results show that, for the variety of test images (120 images) the proposed method enhances contrast while preserving brightness and natural appearance and outperforms contemporary methods both qualitatively and quantitatively. The statistical consistency of results has also been verified through ANOVA statistical tool.     

Synthesis and Properties of pH-Dependent Gemini Surfactants Containing Multiple Carboxyl Groups

A series of Gemini surfactants N,N″-dialkyl-N,N′,N″-tripropionate diethylenetriamine (referred as DTPDT-n, where n is the length of the hydrocarbon chain, n = 10, 12, 14) were synthesized, which have three carboxylic head-groups and two hydrophobic alkane chains. The products were characterized by means of nuclear magnetic resonance and mass spectrometry. The physicochemical properties of DTPDT-n surfactants with different hydrocarbon chain lengths were studied such as isoelectric point, surface activities, emulsifying properties, and foam properties. It is showed that these compounds exhibit pH-dependent protonation-deprotonation behavior. The isoelectric points of DTPDT-n surfactants are between 3.40 and 10.90. The critical micellar concentration (cmc) of all three surfactants are lower than the corresponding monomeric surfactants (single head group, single-chain), especially DTPDT-14, whose cmc can reach 2.29 × 10⁻⁵ mol/L. With an increase in the length of the alkyl chain, the solubility of the surfactants decreases and the surface tension of the three surfactants at cmc increases. In consideration of pH, all of three surfactants appear better emulsifying capacity and foaming property under weak alkaline conditions. DTPDT-14 has the best performance of emulsifying capacity among the three surfactants. DTPDT-10 has excellent foaming ability and foam stability.     

多间隙平面机械系统动态性能分析

在考虑多间隙的条件下,对机械系统的动态性能进行了分析。首先,建立了转动副考虑间隙时的运动学模型,其次,选取了Flores接触力模型建立了间隙关节元素之间的法向接触力,并利用修正的"Coulomb"摩擦力模型计算了摩擦力。最后,以平面曲柄滑块机构为例,在机械系统中分别考虑单个间隙和多间隙进行了对比分析,并对不同尺寸的间隙关节进行了分别仿真,数值结果证明机械系统中引入间隙关节越多且间隙尺寸越大其动力学性能越恶化。     

A hybrid multiple feature construction approach for classification using Genetic Programming

The purpose of feature construction is to create new higher-level features from original ones. Genetic Programming (GP) was usually employed to perform feature construction tasks due to its flexible representation. Filter-based approach and wrapper-based approach are two commonly used feature construction approaches according to their different evaluation functions. In this paper, we propose a hybrid feature construction approach using genetic programming (Hybrid-GPFC) that combines filter’s fitness function and wrapper’s fitness function, and propose a multiple feature construction method that stores top excellent individuals during a single GP run. Experiments on ten datasets show that our proposed multiple feature construction method (Fcm) can achieve better (or equivalent) classification performance than the single feature construction method (Fcs), and our Hybrid-GPFC can obtain better classification performance than filter-based feature construction approaches (Filter-GPFC) and wrapper-based feature construction approaches (Wrapper-GPFC) in most cases. Further investigations on combinations of constructed features and original features show that constructed features augmented with original features do not improve the classification performance comparing with constructed features only. The comparisons with three state-of-art methods show that in majority of cases, our proposed hybrid multiple feature construction approach can achieve better classification performance.