An investigation of the thermal performance of cylindrical heat pipes using nanofluids

In this work, a two-dimensional analysis is used to study the thermal performance of a cylindrical heat pipe utilizing nanofluids. Three of the most common nanoparticles, namely Al₂O₃, CuO, and TiO₂ are considered as the working fluid. A substantial change in the heat pipe thermal resistance, temperature distribution, and maximum capillary heat transfer of the heat pipe is observed when using a nanofluid. The nanoparticles within the liquid enhance the thermal performance of the heat pipe by reducing the thermal resistance while enhancing the maximum heat load it can carry. The existence of an optimum mass concentration for nanoparticles in maximizing the heat transfer limit is established. The effect of particle size on the thermal performance of the heat pipe is also investigated. It is found that smaller particles have a more pronounced effect on the temperature gradient along the heat pipe.     

A statistical approach to the optimization of cold‐adapted amylase production by Exiguobacterium sp. SH3

Cold‐adapted enzymes produced by psychrotrophic organisms are interesting from both molecular and biotechnological viewpoints. The enzymes show superior catalytic activity than their mesophilic and thermophilic counterparts at room temperature. Therefore, the enzymes seem interesting for applications where high catalytic activity at ambient temperature is required. In this study, the production of cold‐adapted amylase by Exiguobacterium sp. SH3 was optimized and modeled. In the first step, single factor experiments using shake flask cultures were conducted for primary optimization. These experiments resulted in the improvement of amylase production up to 180 U/mL. In the next step, the Plackett–Burman design was used to identify significant factors affecting the amylase production. Starch concentration, tryptone concentration, and temperature were selected as significant factors; while time, shaking, yeast extract, pH, MnCl₂, CaCl₂, MgCl₂, and KH₂PO₄ were not significant in this step. Finally, the response surface methodology based on central composite design (CCD) was used for further optimization and modeling of the significant factors. The optimization efforts resulted in the maximum amylase production of 730 U/mL, which was four times higher than that achieved by the single‐factor optimization experiments.     

Computerized optimization of bridge structures

A unified bridge design optimization theory has been developed by a research team composed of personnel from the College of Engineering, Louisiana State University working in close collaboration with engineers from the Louisiana Department of Highways. The theory, useful in the design of multispan, simply supported highway bridges considers simultaneously the structure and the geometry of the bridge. Soil and terrain conditions, construction costs, shape, and other parameters can be investigated simultaneously with dynamic programming, an optimization technique applicable to serial systems of the type to which these structures belong.A computer program developed and now completely operational provides the bridge design engineer with an efficient, flexible tool for evaluating a multitude of preliminary designs. The average Central Processing Unit time required for a typical bridge is in the order of l min per spanonanIBM360/65configuration.The program generates the lowest cost bridge and all other bridge structures that fall within any specified percentage of the minimum cost (1 or 2 per cent, for example). The range is specified by the user. In addition the program has cycling capabilities for selecting the most economical configuration (number, lengths and types of spans).Costs, soil conditions, geometry, material properties, and types of decks, girders and bents are specified by the designer such that the program can be used as a simulation tool for selecting the most economical structure quickly and efficiently. Constraints can be imposed upon the bridge geometry by specifying locations of bents or span center lines or both, to account for special conditions such as bridge crossings, existing roadways, and other physical obstructions.The optimization program is modular and operates heuristically, that is, it permits full man-machine interaction not only through the input of data but also through structural design module interehangeability allowing the user to incorporate his own procedures as subroutines.The complete program is fully documented with a users manual and a programmers manual.     

Decision support framework for risk management on sea ports and terminals using fuzzy set theory and evidential reasoning approach

As sea ports and terminals are valuable assets, in today’s uncertain and complex environment further refinements are needed to assess risks and prioritise protective measures for these critical pieces of logistics infrastructure. The major problem that port professionals (e.g. port risk managers and port auditors) are facing is the lack of an appropriate methodology and evaluation techniques to support their risk management (RM) cycle. Therefore in response to the uncertainties and to provide continuous risk control assurance in port industry, this paper uses fuzzy set theory (FST) to describe and evaluate the associated risk factors within the ports and terminals operations and management (PTOM). An evidential reasoning (ER) approach is employed to synthesise the information produced. These processes constitute a decision support framework that will be used to conduct port-to-port risk evaluations or to assess a whole port’s and terminal’s overall risk level in order to facilitate continuous improvement strategies. The proposed framework along with a generic methodology and a risk evaluation model is tested by a case study. The case study analyses pieces of three Southern Iranian ports by using an illustrative operational risk hierarchy. The sensitivity analysis carried out in this paper prove pieces of the applicability of the proposed methodology and model for risk evaluation of the sea ports and terminals in real situations.     

Learning to rank: new approach with the layered multi-population genetic programming on click-through features

Users’ click-through data is a valuable source of information about the performance of Web search engines, but it is included in few datasets for learning to rank. In this paper, inspired by the click-through data model, a novel approach is proposed for extracting the implicit user feedback from evidence embedded in benchmarking datasets. This process outputs a set of new features, named click-through features. Generated click-through features are used in a layered multi-population genetic programming framework to find the best possible ranking functions. The layered multi-population genetic programming framework is fast and provides more extensive search capability compared to the traditional genetic programming approaches. The performance of the proposed ranking generation framework is investigated both in the presence and in the absence of explicit click-through data in the utilized benchmark datasets. The experimental results show that click-through features can be efficiently extracted in both cases but that more effective ranking functions result when click-through features are generated from benchmark datasets with explicit click-through data. In either case, the most noticeable ranking improvements are achieved at the tops of the provided ranked lists of results, which are highly targeted by the Web users.     

Worst case dimensioning and modeling of reliable real-time multihop wireless sensor network

Wireless Sensor Network (WSN) should be capable of fulfilling its mission, in a timely manner and without loss of important information. In this paper, we propose a new analytical model for calculating RRT (Reliable Real-Time) degree in multihop WSNs, where RRT degree describes the percentage of real-time data that the network can reliably deliver on time from any source to its destination. Also, packet loss probability is modeled as a function of the probability of link failure when the buffer is full and the probability of node failure when node’s energy is depleted. Most of the network properties are considered as random variables and a queuing theory based model is derived. In this model, the effect of network load on the packets’ delay, RRT degree, and node’s energy depletion rate are considered. Also network calculus is tailored and extended so that a worst case analysis of the delay and queue quantities in sensor networks is possible. Simulation results are used to validate the proposed model. The simulation results agree very well with the model.     

Ultrahigh step-up DC-DC converter consisting quadratic boost converter,multiplier cell,and three windings coupled inductor

This paper presents an ultrahigh step-up converter with combination of a quadratic boost converter, a multiplier cell, and a three windings coupled inductor. Main advantages of the converter include its high voltage gain, low voltage stress on the switch and most of the diodes, continuity of input current with low ripple, and existence of a common ground between the source and load. Furthermore, requiring small inductors leads to high efficiency performance of the converter. To confirm superiority of the proposed converter, it has been compared with the converters consisting coupled inductors. Analysis of the converter has been performed for its main operation modes to validate its quality and quantity factors. A prototype is built in order to experiment its performance per different conditions and evaluate the analysis. Rated values per the experiments are 25, 500, and 200 W for the input voltage, output voltage, and output power, respectively.     

Automating image segmentation verification and validation by learning test oracles

An image segmentation algorithm delineates (an) object(s) of interest in an image. Its output is referred to as a segmentation. Developing these algorithms is a manual, iterative process involving repetitive verification and validation tasks. This process is time-consuming and depends on the availability of experts, who may be a scarce resource (e.g., medical experts). We propose a framework referred to as Image Segmentation Automated Oracle (ISAO) that uses machine learning to construct an oracle, which can then be used to automatically verify the correctness of image segmentations, thus saving substantial resources and making the image segmentation verification and validation task significantly more efficient. The framework also gives informative feedback to the developer as the segmentation algorithm evolves and provides a systematic means of testing different parametric configurations of the algorithm. During the initial learning phase, segmentations from the first few (optimally two) versions of the segmentation algorithm are manually verified by experts. The similarity of successive segmentations of the same images is also measured in various ways. This information is then fed to a machine learning algorithm to construct a classifier that distinguishes between consistent and inconsistent segmentation pairs (as determined by an expert) based on the values of the similarity measures associated with each segmentation pair. Once the accuracy of the classifier is deemed satisfactory to support a consistency determination, the classifier is then used to determine whether the segmentations that are produced by subsequent versions of the algorithm under test, are (in)consistent with already verified segmentations from previous versions. This information is then used to automatically draw conclusions about the correctness of the segmentations. We have successfully applied this approach to 3D segmentations of the cardiac left ventricle obtained from CT scans and have obtained promising results (accuracies of 95%). Even though more experiments are needed to quantify the effectiveness of the approach in real-world applications, ISAO shows promise in increasing the quality and testing efficiency of image segmentation algorithms.     

RLSP: a signal prediction algorithm for energy conservation in wireless sensor networks

In many real world applications of wireless sensor networks, it is enough for the sensors to send just an approximation of their observations. In these networks dual prediction scheme (DPS)—including two predictive models one in the sensor side and its copy in the sink side—is widely used. In DPS, the total data transmission through the network is a function of the model’s prediction power and the size of its free parameters. In this paper, a DPS using a reinforcement learning based signal predictor (RLSP) algorithm is proposed. RLSP learns the environment’s signal and builds the predictive model gradually based on its experiences. At the moment the model gets invalid, RLSP only needs to learn and transmit the environmental data of that moment. As a result, the amount of data transmission in the network and consequently energy consumption is very low. The simulation results on 16 benchmarking signals and comparison with time series-based DPSs confirm these properties of RLSP.     

Ultracompact two‐way and four‐way SIW/HMSIW power dividers loaded by complementary split‐ring resonators

In this paper, two ultracompact power dividers based on the substrate integrated waveguide (SIW) and half‐mode SIW (HMSIW) technologies loaded by complementary split‐ring resonators (CSRRs) are presented. The presented structures are designed based on the theory of evanescent mode propagation. To obtain a size reduction, the CSRR unit cells are etched on the metallic surface of the SIW and HMSIW structures. First, a two‐way HMSIW power divider is reported. In this circuit, the concept of HMSIW is utilized aiming at a further size reduction in addition to the size reduction by the CSRR unit cells. Then, a four‐way SIW power divider is designed so that the direct coaxial feed is used for the input port and microstrip transmission lines are used for the output ports. Both two‐way and four‐way SIW/HMSIW power dividers at 5.8 GHz covering WLAN are designed, fabricated, and measured. They respectively have 0.18 × 0.21 λg² and 0.38 × 0.21 λg² total size. A fair agreement between simulated and measured results is achieved. The measured insertion losses are 0.5 ± 0.5 and 0.6 ± 0.5 dB for the two‐way and four‐way SIW/HMSIW power dividers, respectively, in the operating band of interest.