Overlapping Detection Algorithm Research Articles

Overlapping complexes detection within protein interaction networks using improved genetic algorithm

BackgroundThe protein complexes detection has an important role in analyzing biological processes and understanding life at the cellular level. Although many complex detection algorithms have been proposed by employing specific topological attributes of protein interaction networks, most of them focus on detecting separated protein complexes, where each protein can belong to only one complex. Generally, many protein interaction networks have many complexes or modules that are often overlapped with each other. Developing overlapping protein complex detection algorithm thus becomes necessary. MethodsIn this respect, this paper proposes a new overlapping detection algorithm based on GA (Genetic Algorithm) to discover overlapping protein complexes. The proposed algorithm is supplied with a new local search operator based on gene ontology annotations in order to direct the search process towards discovering overlapping protein complexes that are hyper-connected and biologically related. The proposed algorithm is assessed on two gold standard and real-world datasets. ResultsThe result attained shows that the proposed algorithm can detect important overlapping protein complexes, and presents more accurate results compared to the state-of-the-art overlapping protein complexes detection algorithms. ConclusionsThe superior performance of the proposed algorithm compared with other counterpart algorithms is attributed to the fact that the proposed algorithm is a population based stochastic search algorithm which efficiently explored the search landscape to capture dense overlapping subgraphs by employing the topological properties of network, and then enhancing the obtained solution by investing gene ontology information in developing a new local search operator inside the GA framework.

Practical and privacy-preserving geo-social-based POI recommendation

With the rapid development of location-based services and online social networks, POI recommendation services considering geographic and social factors have received extensive attention. Meanwhile, the vigorous development of cloud computing has prompted service providers to outsource data to the cloud to provide POI recommendation services. However, there is a degree of distrust of the cloud by service providers. To protect digital assets, service providers encrypt data before outsourcing it. However, encryption reduces data availability, making it more challenging to provide POI recommendation services in outsourcing scenarios. Some privacy-preserving schemes for geo-social-based POI recommendation have been presented, but they have some limitations in supporting group query, considering both geographic and social factors, and query accuracy, making these schemes impractical. To solve this issue, we propose two practical and privacy-preserving geo-social-based POI recommendation schemes for single user and group users, which are named GSPR-S and GSPR-G. Specifically, we first utilize the quad tree to organize geographic data and the MinHash method to index social data. Then, we apply BGV fully homomorphic encryption to design some private algorithms, including a private max/min operation algorithm, a private rectangular set operation algorithm, and a private rectangular overlapping detection algorithm. After that, we use these algorithms as building blocks in our schemes for efficiency improvement. According to security analysis, our schemes are proven to be secure against the honest-but-curious cloud servers, and experimental results show that our schemes have good performance.

Damage visualization and vulnerability assessment of surface ship considering the 3D multihit location of air-explosion threat

Abstract The purpose of this study is to propose a method for rapidly assessing the hit locations and extent of damage to a naval ship attacked by threatening weapons. An integrated method is presented for assessing the vulnerability to the hull and systems caused by the blast or penetrating effects of a weapon at multiple possible impact locations. The proposed method enables the assessment of vulnerability changes for design alternatives during the early design phase of naval vessels. To predict the extent of damage and to visualize the damaged hull and equipment, it is first assumed that a weapon has multiple hit locations. It is shown that a set of possible hit locations was generated by assuming the trajectory for air-explosion (AIREX) weapons by dividing the trajectory into an air-to-ship trajectory and a ship-to-ship trajectory. To account for the non-deterministic nature of weapon hits, a probability distribution approach was used to generate random multiple hit locations for each trajectory while the hit locations of AIREX in all directions were predicted using a multivariate probability distribution that generates three-dimensional random hit points. The extent of damage was then calculated, taking into account the topology of the hull structure and the equipment installed within the hull compartment, along with the damage volume associated with the weapon at each hit point. To identify the damage volume, axis-aligned bounding box (AABB) components were used, which provide a simplified representation of the ship’s geometry, as well as the relative position and dimensions of the hull structure and installed equipment. The damage compartment was defined as the portion of the hull that overlapped the damaged volume. While AABB’s overlap detection algorithm was applied to the damaged hull compartments, the algorithm identified the equipment that overlapped the damaged volume of the hull. Finally, the geometric modeling module, the probabilistic multiple hit location prediction module, and the damage analysis module were developed for damage visualization and vulnerability assessment.

A novel random angular bend (RAB) algorithm and DEM modeling of thermal cracking responses of sandstone

Sandstone is a common sedimentary rock composed mainly of sand-sized mineral particles with certain extent of cement (clay or quartz etc.). Generally, the sandstone composition, as well as the shape of the mineral particles have a significant influence on the properties of sandstone. This paper presents a novel method to simulate sandstone with any shaped mineral particles and investigates the effects of external factors (confining pressure and temperature) and internal factors (particle shape and internal structure) on the thermal cracking responses of sandstone. First, a random angular bend (RAB) algorithm and overlap detection algorithm are used to simulate sandstone with irregularly shaped mineral particles. The development of crack was obtained and used to evaluate the damage characteristic of sandstone during heating process. Finally, the effects of heating temperature, confining pressure, mineral particle shape and sandstone structure on thermal cracking of sandstone are investigated based on discrete element method. Some useful conclusions for numerical simulation on sandstone are arrived at.

Three-dimensional reconstruction of realistic stone-based materials with controllable stone inclusion geometries

Stone-based materials are heterogeneous construction materials that mainly consist of coarse stones and fine matrices. When using the numerical simulation tools (FEM, DEM, FDEM) to study the mechanical properties of the tone-based materials, the irregular stone-inclusion geometries cannot be neglected. The main objective of this study is to generate three-dimensional models of realistic stone-based materials with controllable stone-inclusion geometries. To achieve this goal, two major methodologies are adopted: (1) the Spherical Harmonic Transform (SHT) method and Inverse Monte-Carlo (IMC) algorithm are employed to randomly generate the 3D particle model of irregular stones with controllable geometries at three different shape scales, including form, roundness and roughness, and (2) based on the Spherical Harmonic function, an overlapping detection algorithm is proposed to facilitate the rapid allocation of the 3D SHT-based particles. The proposed algorithm can quantitatively control several geometrical features of the generated stone-based materials in an efficient and precise manner. Finally, the application of the proposed approach is demonstrated through the discrete modelling of stone-based materials with different stone contents and stone shapes. The proposed study has the significance to pave a viable pathway for stochastic modelling of stone-based materials pertaining to various construction and manufacture processes.

LROD: An Overlap Detection Algorithm for Long Reads Based on k-mer Distribution.

Third-generation sequencing technologies can produce large numbers of long reads, which have been widely used in many fields. When using long reads for genome assembly, overlap detection between any pair of long reads is an important step. However, the sequencing error rate of third-generation sequencing technologies is very high, and obtaining accurate overlap detection results is still a challenging task. In this study, we present a long-read overlap detection (LROD) algorithm that can improve the accuracy of overlap detection results. To detect overlaps between two long reads, LROD first retains only the solid common k-mers between them. These k-mers can simplify the process of overlap detection. Second, LROD finds a chain (i.e., candidate overlap) that includes the consistent common k-mers. In this step, LROD proposes a two-stage strategy to evaluate whether two common k-mers are consistent. Finally, LROD uses a novel strategy to determine whether the candidate overlaps are true and to revise them. To verify the performance of LROD, three simulated and three real long-read datasets are used in the experiments. Compared with two other popular methods (MHAP and Minimap2), LROD can achieve good performance in terms of the F1-score, precision and recall. LROD is available from https://github.com/luojunwei/LROD.

A Random Angular Bend Algorithm for Two- Dimensional Discrete Modeling of Granular Materials.

Generation of particles with irregular shape and the overlap detection are crucial for numerical simulation of granular materials. This paper presents a systematic approach to develop a two-dimensional random particle model for numerical simulation of granular materials. Firstly, a random angular bend (RAB) algorithm is proposed and coded in Python to simulate the geometric model of individual particle with irregular shape. Three representative parameters are used to quantitatively control the shape feature of generated polygons in terms of three major aspects, respectively. Then, the generated geometrical models are implemented into particle flow code PFC2D to construct the clump library. The clumps are created via the mid-surface method. Besides, an overlap detection algorithm is developed to address the difficulties associated with spatial allocation of irregularly shaped particles. Finally, two application examples are adopted to validate the feasibility of the proposed algorithm in the numerical modeling of realistic granular materials. The study provides a solid foundation for the generation and simulation of the granular materials based on angular bend theory.

Stochastic numerical model of stone-based materials with realistic stone-inclusion features

This paper presents a systematic approach for generating a stochastic model with realistic stone-inclusion features for the numerical simulation of stone-based materials. Firstly, the inverse discrete Fourier transform method is employed to randomly generate realistic stones with predetermined particle shape features. Then, an overlapping detection algorithm is proposed to facilitate random and quick allocation of irregularly shaped stones, considering the stone contents, stone size distributions and stone orientations. Finally, several examples are provided to show that the proposed approach can generate stone-based materials with prescribed stone features in a rapid and precise manner.

Numerical modelling of ellipsoidal inclusions

Within the framework of numerical algorithms for the three-dimensional random packing of granular materials this work presents an innovative formulation for polydispersed ellipsoidal particles, including an overlapping detection algorithm for an optimized simulation of the mesostructure of geomaterials, particularly concrete.Granular composite cement-based materials can be so reconstructed with adequate precision in terms of grain size distribution. Specifically, the algorithm performance towards the assumed inclusion shape (ellipsoidal or spherical) and degree of regularity (round or irregular) is here discussed. Examples on real grading curves prove that this approach is effective.The advantages of the proposed method for computational mechanics purposes are also disclosed when properly interfaced with visualization CAD (Computer Aided Design) tools.

Teager–Kaiser Energy Operators for Overlapped Speech Detection

Overlapped speech is referred to a monophonic audio signal in which at least two speakers are present at the same time. In this study, the focus is on distinguishing overlapped from single-speaker speech, i.e., overlapped speech detection. We develop an overlap detection algorithm using an enhanced time-frequency representation, called Pyknogram, estimated directly from the input audio signal. Pyknograms use the Teager–Kaiser energy operator to detect resonant time-frequency units and thereby suppress nonharmonic structures. We show how the resulting Pyknograms provide high separability in terms of detecting the presence of interfering speech. Our proposed unsupervised Pyknogram-based detection results in over $30\%$ relative improvement in overlap detection error rates across different signal-to-interference ratios (SIR) compared to baseline systems. In addition, a case study is presented where we evaluate speaker verification performance under different overlap conditions using the GRID database and observe that speaker verification equal error rates (EER) vary from $2\%$ to $30\%$ , depending on the average SIR values introduced to train and test sets. In order to estimate the reliability of speaker verification scores across different trials, overlap detection results are interpreted as low-level information and stack ed alongside verification outputs. The resulting high-dimensional space is passed through a support vector machine classifier to find the separating hyperplane between target and imposter scores. Combining overlap detection scores with speaker verification on average yields $20\%$ relative decrease in EER. We also provide an upper bound for this approach using existing overlap labels, which yields $23\%$ relative improvement.

Sink Node Elimination to Enhance the Performance of Overlapping Detection Algorithms along with Comparison of Existing Algorithm

Objectives: To eliminate Sink nodes so that rate of detection can improve within the community overlapping detection and this also increases the modularity. It also consumes less time. Methods/Statistical Analysis: Modified k-clique Algorithm is used. Clique algorithm considers Sink nodes. Sink nodes are those which do not have any connecting edge. The proposed algorithm (MKC) does eliminate these nodes and hence consider only those nodes which are connected in nature. To detect the Sink nodes adjacency matrix is used. MATLAB is used for the simulation. Community detection toolbox is used which provides several functions for graph generations, clustering algorithms etc., Findings: This approach produces better result in terms of community finding. More community are discovered with the proposed approach and also entropy is improved greatly. Result in terms of time consumption is reduced almost by 50%. Application/Improvements: The length and complexity of the cliques found is reduced considerably. The speed is almost enhanced by 5% which can further be increased by using hop count mechanism in addition to the already used Sink node elimination.

Distance measuring equipment interference suppression based on parametric estimation and wavelet-packet transformation for global navigation satellite systems

It is well known that some global navigation satellite system (GNSS) signals operate on the aeronautical radionavigation services (ARNS) frequency band, which is also utilized by other aeronautical service signals, such as pulsed signals from the distance-measuring equipment (DME) system. Thus, the high-power DME signal, as interference, will significantly degrade the performance of the GNSS. Some algorithms, including time blanking, frequency notch filtering, and hybrid filtering, have been proposed to suppress DME interference. However, when the density of the DME pulse is high, they will bring a huge loss of the desired satellite signal. This paper proposes a hybrid approach for DME interference suppression by combining a novel parametric algorithm and the wavelet-packet-based algorithm. An overlap detection algorithm is also proposed to automatically switch between the two algorithms. When there is no overlapped pulse, the parametric method will be utilized to suppress the interference and retain more of the desired satellite signal. For overlapped DME pulses, the interferences are suppressed by the wavelet-packet transformation algorithm. The problem of parameter selection for wavelet-packet transformation is also discussed systematically. The trade-off between performance and complexity is obtained by adaptively selecting between the traditional algorithm and the proposed hybrid approach based on a pulse density detection algorithm. Numerical results are presented to demonstrate the effectiveness of the proposed approach.

Parking simulation of three-dimensional multi-sized star-shaped particles

The shape and size of particles may have a great impact on the microstructure as well as the physico-properties of particulate composites. However, it is challenging to configure a parking system of particles to a geometrical shape that is close to realistic grains in particulate composites. In this work, with the assistance of x-ray tomography and a spherical harmonic series, we present a star-shaped particle that is close to realistic arbitrary-shaped grains. To realize such a hard particle parking structure, an inter-particle overlapping detection algorithm is introduced. A serial sectioning approach is employed to visualize the particle parking structure for the purpose of justifying the reliability of the overlapping detection algorithm. Furthermore, the validity of the area and perimeter of solids in any arbitrary section of a plane calculated using a numerical method is verified by comparison with those obtained using an image analysis approach. This contribution is helpful to further understand the dependence of the micro-structure and physico-properties of star-shaped particles on the realistic geometrical shape.

Quantitative characterization of the microstructure of fresh cement paste via random packing of polydispersed Platonic cement particles

On a microscopic scale, fresh cement paste is composed of random packing of irregular cement particles, and their initial packing behavior plays an important role in microstructural evolution. The preponderance of previous works has focused on the microstructure model by random packing of three-dimensional spheroidal particles, and little is known about non-spheroidal particles. In this paper, a modified cement particle size distribution function is used to facilitate the particle size distribution of convex polyhedral cement particles. Based on an overlapping detection algorithm, the microstructure model of fresh cement paste is simulated by the random sequential packing of Platonic cement particles of various sizes. Applying stereological tools and the serial sectioning analysis technique, the modeling microstructure composed of polydispersed Platonic cement particles is characterized and compared with that of ellipsoidal cement particles with various aspect ratios. The statistical results are investigated to evaluate the influence of cement particle shape on the microstructure of fresh cement paste. Finally, with the derived experimental and numerical results of microstructural parameters, the reliability of the statistical results is verified.

An overlapping detection algorithm for random sequential packing of elliptical particles

Random sequential packing of particles is a subject of intense research in many branches of physics and engineering. The preponderance of preview work has focused on spherical particles and little is known about non-overlapping elliptical particles. In the present work, a new numerical algorithm is developed to detect overlapping of ellipses by a series of sequential coordinate transformations and a novel golden section search algorithm. The accuracy and efficiency of the numerical algorithm are tested and compared with algorithms developed in previous literature. Its stability is verified by visualizations of random sequential packing of monodispersed and polydispersed ellipses with different boundary conditions. Then, the algorithm is applied to investigate the influence of the shape of ellipses on the random packing fraction, as well as to study the influence of the shape and size of ellipses on the wall effect in the particle packing structure, and some numerical results are demonstrated. Finally, the reliability of the numerical algorithm extended to the three-dimensional space is also evaluated by checking overlapping of ellipsoidal particles.

Slice overlap-detection algorithm for process planning in multiple-material stereolithography

Recently, a multiple-material stereolithography (MMSL) system was introduced, which included the novel feature of stacking different photocurable resins to produce a multiple-material part. This process is capable of fabricating intricately detailed parts with smooth surface finish and internal structures of various colors. However, the MMSL system requires a washing process when switching materials, and this additional step increases the fabrication time and also introduces deformations in the build structure as registration errors accumulate. Consequently, reducing the number of material changeovers in this process is a priority, and the minimum number of changeovers is imposed in part by the sweep interference between layers of different materials. Fortunately, the use of low-viscosity resins permits possible fabrication without sweeping. As a result, multiple layers of one material can be continuously built without changing resins, and subsequently, a different low-viscosity material can be added without resulting in sweep obstruction with the original layers. In this paper, a stacking simulation, which determines the maximum number of layers possible to be continuously built between material changeovers, is described. This paper presents a novel interference-detection algorithm, which identifies interference by inspecting the overlap between loop segments. In addition, the algorithm successfully manages the triangulation errors that occur at a curved surface when two materials are adjacent within a layer. Finally, several practical examples are shown to verify the algorithm and provide compelling evidence that the proposed algorithm is effective and applicable in MMSL.

Global Daytime Distribution of Overlapping Cirrus Cloud from NOAA’s Advanced Very High Resolution Radiometer

Abstract Data from the National Oceanic and Atmospheric Administration’s (NOAA’s) Advanced Very High Resolution Radiometer (AVHRR) instrument are used to provide the mean July and January global daytime distributions of multilayer cloud, where multilayer cloud is defined as cirrus overlapping one or more lower layers. The AVHRR data were taken from multiple years that were chosen to provide data with a constant local equator crossing time of 1430–1500 local time. The cloud overlap detection algorithm is used in NOAA’s Extended Clouds from AVHRR (CLAVR-x) processing system. The results between 60°N and 60°S indicated that roughly 20% of all clouds and roughly 40% of all ice clouds were classified as cirrus overlapping lower cloud (cirrus overlap). The results show a strong July–January pattern that is consistent with the seasonal cycle in convection. In some regions, cirrus overlap is found to be the dominant type of cloud observed. The distributions of overlapping cirrus cloud presented here are compared with results from other studies based on rawinsondes and manual surface observations. Comparisons are also made with another satellite-derived study that used coincident infrared and microwave observations over the tropical oceans during a 6-month period