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Aims: The aim of this article was to find functional (or disease-relevant) modules using gene expression data. Background: Biotechnological developments are leading to a rapid increase in the volume of transcriptome data and thus driving the growth of interactome data. This has made it possible to perform transcriptomic analysis by integrating interactome data. Considering that genes do not exist nor operate in isolation, and instead participate in biological networks, interactomics is equally important to expression profiles. Objective: We constructed a network-based method based on gene expression data in order to identify functional (or disease-relevant) modules. Methods: We used the energy minimization with graph cuts method by integrating gene interaction networks under the assumption of the ‘guilt by association’ principle. Result: Our method performs well in an independent simulation experiment and has the ability to identify strongly disease-relevant modules in real experiments. Our method is able to find important functional modules associated with two subtypes of lymphoma in a lymphoma microarray dataset. Moreover, the method can identify the biological subnetworks and most of the genes associated with Duchenne muscular dystrophy. Conclusion: We successfully adapted the energy minimization with the graph cuts method to identify functionally important genes from genomic data by integrating gene interaction networks. Other: This study can help us to identify disease-relevant modules which can not be identified by different expression analysis.

Nonlinear reformulations of the spectral clustering method have gained a lot of recent attention due to their increased numerical benefits and their solid mathematical background. We present a novel direct multiway spectral clustering algorithm in the p-norm, for $$p\in (1,2]$$ p ∈ ( 1 , 2 ] . The problem of computing multiple eigenvectors of the graph p-Laplacian, a nonlinear generalization of the standard graph Laplacian, is recasted as an unconstrained minimization problem on a Grassmann manifold. The value of p is reduced in a pseudocontinuous manner, promoting sparser solution vectors that correspond to optimal graph cuts as p approaches one. Monitoring the monotonic decrease of the balanced graph cuts guarantees that we obtain the best available solution from the p-levels considered. We demonstrate the effectiveness and accuracy of our algorithm in various artificial test-cases. Our numerical examples and comparative results with various state-of-the-art clustering methods indicate that the proposed method obtains high quality clusters both in terms of balanced graph cut metrics and in terms of the accuracy of the labelling assignment. Furthermore, we conduct studies for the classification of facial images and handwritten characters to demonstrate the applicability in real-world datasets.

An important task in the analysis of graphs is separating nodes into densely connected groups with little interaction between each other. Prominent methods here include flow based graph cutting procedures as well as statistical network modeling approaches. However, adequately accounting for this, the so-called community structure, in complex networks remains a major challenge. We present a novel generic Bayesian probabilistic model for graph cutting in which we derive an analytical solution to the marginalization of nuisance parameters under constraints enforcing community structure. As a part of the solution a large scale approximation for integrals involving multiple incomplete gamma functions is derived. Our multiple cluster solution presents a generic tool for Bayesian inference on Poisson weighted graphs across different domains. Applied on three real world social networks as well as three image segmentation problems our approach shows on par or better performance to existing spectral graph cutting and community detection methods, while learning the underlying parameter space. The developed procedure provides a principled statistical framework for graph cutting and the Bayesian Cut source code provided enables easy adoption of the procedure as an alternative to existing graph cutting methods.

We propose a novel hub location model that jointly eliminates some of the traditional assumptions on the structure of the network and on the discount as a result of economies of scale in an effort to better reflect real-world logistics and transportation systems. Our model extends the hub literature in various facets: instead of connecting nonhub nodes directly to hub nodes, we consider routes with stopovers; instead of connecting pairs of hubs directly, we design routes that can visit several hub nodes; rather than dimensioning pairwise connections, we dimension routes of vehicles; and rather than working with a homogeneous fleet, we use intermodal transportation. Decisions pertinent to strategic and tactical hub location and transportation network design are concurrently made through the proposed optimization scheme. An effective branch-and-cut algorithm is developed to solve realistically sized problem instances and to provide managerial insights., Science Academy of Turkey (TÜBA) BAGEP Program

One key challenge in the point cloud segmentation is the detection and split of overlapping regions between different planes. The existing methods depend on the similarity and the dissimilarity in neighbor regions without a global constraint, which brings the 'over-' and 'under-' segmentation in the results. Hence, this paper presents a pipeline of the accurate plane segmentation for point clouds to address the shortcoming in the local optimization. There are two phases included in the proposed segmentation process. One is a local phase to calculate connectivity scores between different planes based on local variations of surface normals. In this phase, a new optimal-vector-field is formulated to detect the plane intersections. The optimal-vector-field is large in magnitude at plane intersections and vanishing at other regions. The other one is a global phase to smooth local segmentation cues to mimic leading eigenvector computation in the graph-cut. Evaluation of two datasets shows that the achieved precision and recall is 94.50 percent and 90.81 percent on the collected mobile LiDAR data and obtains an average accuracy of 75.4 percent on an open benchmark, which outperforms the state-of-the-art methods in terms of completeness and correctness.

The paper presents the results of experimental studies of the strength of cross-shaped welded joints of types К1-Кт and К3-Рр [1] of thermomechanically hardened reinforcement of class A500C [2] for hardening during welding of the main bar [3]. Experimental studies included testing three series of samples of welded joints and reinforcement samples in the initial state. The first series included testing welded joints of type К1-Кт to strengthen the main bar during welding. Reinforcement Ø14A500С was accepted as the main (working) bar, and reinforcement Ø12A500С and Ø8A240С were accepted as the transverse bar. For each joints, 6 samples were tested on the lower and upper limits of the carbon equivalent values of the grade steel class Ст3пс, 0.245% and 0.346%, respectively. The second series included testing of welded joints of the K3-Pp type to strengthen the main bar during welding. To directly compare the test results of different types of cross-shaped welded joints, the diameter of the main and transverse rods, the number of samples and the value of equivalent were the same as in the first series. The third series included testing welded joints of type К1-Кт for cutting. Reinforcement Ø14A500С was taken as a longitudinal bar, and reinforcement Ø12A500С and Ø8A500С was taken as a transverse rod. For each joints, 12 samples were tested at the lower and upper limits of the carbon equivalent values. As a result of testing samples of welded joints of types К1-Кт and К3-Рр of reinforcement A500C for strengthening during welding, it was established that the destruction occurs along the main bar and in the zone of thermal exposure, the amount of hardening was up to 4% for the К1-Кт compound, and for the К3-Рр type connection - 4... 10%. Failure of samples of welded joints of type К1-Кт reinforcement A500C with reinforcement Ø12A500C and Ø8A240C during the cut test usually occurred in the zone of thermal exposure, or in some cases, in the presence of both rods from reinforcement of class A500C - directly at the welding site. The average value of cut strength for a sample of 24 samples of reinforcement connections of class A500C with reinforcement A240C was 356.5 MPa, or 0.89 of the temporary resistance of the reinforcement in the initial state, with a coefficient of variation - 0.08 and a span of 107.4 MPa. The average cut strength for a sample of 24 samples of reinforcement connections of class A500C with reinforcement A500C was 541.1 MPa, or 0.80 of the temporary resistance of the reinforcement in the initial state, with a coefficient of variation – 0.152 and a span of 280.0 MPa. The first series included fittings Ø12A500C, Ø14A500C, Ø16A500C from steel of the ST3ps brand and Ø18A500C, Ø20A500C, Ø22A500C from steel of the Ст3пс brand. A total of 278 samples of welded joints were tested. The second series included samples of C1-Co welded joints from the rest of the nomenclature of diameters and grades of steel reinforcement class A500C, namely Ø16A500C from steel grade St3ps, Ø20A500C, Ø22A500C from steel grade St3Gps and Ø25A500C, Ø32A500C, Ø32A500C, Ø32A500C. For each of the diameters, 6 samples were tested in the initial state and 6 samples of C1-Co welded joints. It was found that the destruction of the butt contact connection type C1-Co thermomechanically reinforced reinforcement class A500C of all diameters, from 12 to 32 mm, and steel grades, takes place in the zone of thermal impact and is plastic. Experimental data on the strength of the butt contact weld type C1-Кo reinforcement class A500C for the entire range of diameters and grades of steel produced in Ukraine. It is established that the greatest degree of hardening within 19-20% takes place at diameters of armature of 12… 16 mm from steel of the Ст3пс brand, at diameters of armature of 18… 22 mm from steel of the Ст3Гпс brand of marking made 15-17%, and at diameters of 25… 32 mm from steel of the 25Г2С brand - 2,5… 8%., В роботі наведені результати експериментальних досліджень міцності хрестоподібних зварних з’єднань типів К1-Кт і К3-Рр за [1] термомеханічнозміцненої арматури класу А500С [2] на зріз і розміцнення при зварюванні основного стержня за [3]. Експериментальні дослідження включали в себе випробування трьох серій зразків зварних з’єднань і зразків арматури у вихідному стані. Перша серія включала в себе випробування зварних з’єднань типу К1-Кт на розміцнення основного стержня при зварюванні. У якості основного (робочого) стержня приймалася арматура Ø14А500С, а у якості поперечних – арматура Ø12А500С і Ø8А240С. Для кожного з'єднання випробовувалось по 6 зразків на нижній і верхній границях значень вуглецевого еквіваленту марочного класу сталі Ст3пс, відповідно 0,245% і 0,346%. Друга серія включала в себе випробування зварних з’єднань типу К3-Рр на розміцнення основного стержня при зварюванні. Для прямого співставлення результатів випробувань різних типів хрестоподібних зварних з’єднань, діаметр основного і поперечного стержнів, кількість зразків і значення вуглецевого еквіваленту були такими, як і у першій серії. Третя серія включала в себе випробування зварних з’єднань типу К1-Кт на зріз. У якості поздовжнього стержня приймалася арматура Ø14А500С, а якості поперечних – арматура Ø12А500С і Ø8А500С. Для кожного з'єднання випробовувалось по 12 зразків на нижній і верхній границях значень вуглецевого еквіваленту. В результаті випробувань зразків зварних з'єднань типів К1-Кт і К3-Рр арматури А500С на розміцнення при зварюванні встановлено, що руйнування відбувається по основному стержню і у зоні теплового впливу, величина розміцнення при цьому складала для з'єднання К1-Кт до 4%, а для з'єднання типу К3-Рр - 4…10%. Руйнування зразків зварних з’єднань типу К1-Кт арматури A500C з арматурою Ø12А500С і Ø8А240С при випробуванні на зріз, зазвичай, відбувалося у зоні теплового впливу, або ряді випадків, при наявності обох стержнів з арматури класу А500С - безпосередньо по місцю зварювання. Середнєє значення міцності на зріз для вибірки з 24 зразків з’єднань арматури класу А500С з арматурою А240С склало 356,5 МПа, або 0,89 від тимчасового опору арматури у вихідному стані, при коефіцієнті варіації- 0,08 і розмаху - 107,4 МПа. Середнє значення міцності на зріз для вибірки з 24 зразків з’єднань арматури класу А500С з арматурою А500С склало 541,1 МПа, або 0,80 від тимчасового опору арматури у вихідному стані, при коефіцієнті варіації – 0,152 і розмаху – 280,0 МПа.

Dissertação de mestrado integrado em Engenharia Mecânica, A presente dissertação baseia-se no desenvolvimento de um projeto que pretende otimizar a troca de tiras no processo de construção de pneus OTR (Off the Road). O objetivo deste projeto prende-se com a alteração do método de troca de tiras, evitando que seja necessário recorrer ao operador para o fazer. Para se conceber uma solução exequível, foi necessário realizar previamente uma série de tarefas. Inicialmente, procedeu-se a um estudo aprofundado da empresa e dos pneus que esta produz, de forma a contextualizar o problema existente. Posteriormente, realizou-se uma análise de todos os tipos de componentes que poderiam ser envolvidos no sistema. Além disso, traçou-se a metodologia a seguir, servindo como linha de pensamento para o trabalho a realizar. Numa terceira fase, através de diversas modelações e simulações, conceberam-se diversas soluções que foram devidamente analisadas. Com a evolução dos sistemas desenvolvidos, foi possível obter um conjunto final que cumpria com todos os requisitos exigidos. Com isto, realizou-se uma lista dos componentes necessários e a sua orçamentação. Por fim, efetuou-se a validação do funcionamento do sistema através de uma simulação de análise estática do conjunto., The present dissertation is based on the development of a project that aims to optimize the OTR (Off the Road) tire strip exchange. The project’s main goal is to adjust the tire exchange method, avoiding the necessity to resort to manual labor. It was necessary to go through a series of tasks to contextualize the existing problem and design a feasible solution. For this, an exploratory study was carried out to fully comprehend the company, as well as the respective tire production. Subsequently, an in-depth analysis of every possibly involved component in the system was conducted. Moreover, the methodology was traced to guide the project’s train of thought. In the third stage, through multiple modelings and simulations, various solutions were thoroughly analyzed. As the systems developed, it was possible to obtain a final ensemble that complied with requirements; therefore, enabling the construction of a list with the necessary components, and respective cost evaluation. Lastly, a series of simulations were conducted, to verify the proper performance of the system.

Purpose The purpose of this paper is to perform a comparative assessment on working of the existed subassembly identification (SI) methods, which are widely practiced during the product development stage and to propose an improved method for solving the SI problem in assembly sequence planning (ASP). Design/methodology/approach The cut-set method is found as a suitable method among various knowledge-based methods such as the theory of loops, theory of connectors and theory of clusters for the workability enhancement to meet the current requirements. Necessary product information is represented in the matrix format by replacing the traditional AND/OR graphs and the advanced predicates are included in the evaluation criteria. Findings The prominent methods in SI are followed a few of the predicates to avoid complexity in solution generation. The predicate consideration is found as the most influencing factor in eliminating the infeasible part combinations at SI. However, the quality of identified subassemblies without advanced predicates is not influencing the solution generation phase but practical applicability is affecting adversely. Originality/value The capability of performing SI by the cut-set method is improved to deal with the complex assembly configurations. The improved method is tested by applying on different assembly configurations and the effectiveness is compared with other existent methods of ASP along with the conventional method.

For a poset P=(X,≤P), the strict-double-bound graph of P is the graph sDB(P) on V(sDB(P))=X for which vertices u and v of sDB(P) are adjacent if and only if u ≠ v and there exist elements x,y ∈ X distinct from u and v such that x ≤P u ≤P y and x ≤P v ≤P y. The strict-double-bound number ζ(G) of a graph G is defined as min{ n ; sDB(P) ≅ G ∪ Ǩn {for some poset P}. We obtain an upper bound of strict-double-bound numbers of graphs with a cut-set generating a complete subgraph. We also estimate upper bounds of strict-double-bound numbers of chordal graphs.

Aiming at the problem of complex method and low efficiency of fuzzy numbers in classification processing, a parallel Fuzzy CMeans (FCM) clustering method based on cut set is proposed. Firstly, according to the decomposition theorem, the fuzzy numbers are divided horizontally into the form of the union of interval numbers, and then the interval numbers are transformed into the determined “real” data, and the parallel FCM clustering algorithm is used to classify the fuzzy numbers. The theoretical analysis and application show that the method has good classification accuracy and efficiency for fuzzy data clustering.

Detection as well as classification of different object for machine vision application is a challenging task. Similar to the other object detection and classification task, human detection concept provides a major role for the ad- vancement in the design of an automatic visual surveillance system (AVSS). For the future automation system if it is possible to include human detection and tracking, human action recognition, usual as well as unusual event recognition etc. concept for future AVSS, it will be a greater success in the transformable world. In this paper we have proposed a proper human detection and tracking technique for human action recognition toward the design of AVSS. Here we use median filter for noise removal, graph cut for segment the human images, mathematical morphology to refine the segmentation mask, extract selective feature points by sing HOG, classify human objects by using SVM with polynomial ker- nel and finally particle filter for tracking those of detected human. Due to the above mentioned combinations our system can independent to the variations of lightening conditions, color, shape, size, clothing etc. and can handle the occlusion. Our system can easily detect and track human in different indoor as well as outdoor environ- ment with a automatic multiple human detection rate of 97:61% and total multiple human detection and tracking accuracy is about 92% for AVSS. Due to the use of HOG to extract features af- ter graph cut segmentation operation, our system requires less memory for store the trained data therefore processing speed as well as accuracy of detection and tracking will be better than other techniques which can be suitable for action classification task.

Transmission line failure in power systems prop-agate non-locally, making the control of the resulting outages extremely difficult. In Part II of this paper, we continue the study of line failure localizability in transmission networks and characterize the impact of cut set outages. We establish a Simple Path Criterion, showing that the propagation pattern due to bridge outages, a special case of cut set failures, are fully determined by the positions in the network of the buses that participate in load balancing. We then extend our results to general cut set outages. In contrast to non-cut outages discussed in Part I whose subsequent line failures are contained within the original blocks, cut set outages typically impact the whole network, affecting the power flows on all remaining lines. We corroborate our analytical results in both parts using the IEEE 118-bus test system, in which the failure propagation patterns exhibit a clear block-diagonal structure predicted by our theory, even when using full AC power flow equations.

Airborne light detection and ranging (LiDAR) point clouds have become growingly popular as a reliable data source for 3-D digital building model reconstruction. Therefore, we develop a label-constraint approach for automatically detecting building roofs using airborne LiDAR point clouds and multispectral images, where the label information is introduced in both the discriminative feature space generation and the detection procedure. To obtain a robust and highly discriminative descriptor, a supervised sparse coding-enhanced bag of visual word (SC-BOVW) model based on a learned discriminative dictionary is used to encode local geometric and spectral information within each super-voxel into high-level semantic representation, which is then fed into a support vector machine (SVM) classifier for distinguishing buildings from others. Additionally, a graph cut-based procedure is used as a postprocessing step to guarantee the spatial consistency in detection results. Experiments were conducted on the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark data sets. Results indicate that the proposed method is accurate and efficient in terms of building roof region detection. Moreover, the proposed method is superior to other existing methods with average differences in recall of 2.23%, precision of 0.28% and quality of 1.99%.

We introduce a novel learning-based, visibility-aware, surface reconstruction method for large-scale, defect-laden point clouds. Our approach can cope with the scale and variety of point cloud defects encountered in real-life Multi-View Stereo (MVS) acquisitions. Our method relies on a 3D Delaunay tetrahedralization whose cells are classified as inside or outside the surface by a graph neural network and an energy model solvable with a graph cut. Our model, making use of both local geometric attributes and line-of-sight visibility information, is able to learn a visibility model from a small amount of synthetic training data and generalizes to real-life acquisitions. Combining the efficiency of deep learning methods and the scalability of energy based models, our approach outperforms both learning and non learning-based reconstruction algorithms on two publicly available reconstruction benchmarks. Our code and data is available at https://github.com/raphaelsulzer/dgnn., Comment: The presentation of this work at SGP 2021 is available at https://youtu.be/KIrCDGhS10o

Classification, segmentation, and recognition techniques based on deep-learning algorithms are used for smart farming. It is an important and challenging task to reduce the time, burden, and cost of annotation procedures for collected datasets from fields and crops that are changing in a wide variety of ways according to growing, weather patterns, and seasons. This study was conducted to generate crop image datasets for semantic segmentation based on an image style transfer using generative adversarial networks (GANs). To assess data-augmented performance and calculation burdens, our proposed framework comprises contrastive unpaired translation (CUT) for a conditional GAN, pix2pixHD for an unconditional GAN, and DeepLabV3+ for semantic segmentation. Using these networks, the proposed framework provides not only image generation for data augmentation, but also automatic labeling based on distinctive feature learning among domains. The Fréchet inception distance (FID) and mean intersection over union (mIoU) were used, respectively, as evaluation metrics for GANs and semantic segmentation. We used a public benchmark dataset and two original benchmark datasets to evaluate our framework of four image-augmentation types compared with the baseline without using GANs. The experimentally obtained results showed the efficacy of using augmented images, which we evaluated using FID and mIoU. The mIoU scores for the public benchmark dataset improved by 0.03 for the training subset, while remaining similar on the test subset. For the first original benchmark dataset, the mIoU scores improved by 0.01 for the test subset, while they dropped by 0.03 for the training subset. Finally, the mIoU scores for the second original benchmark dataset improved by 0.18 for the training subset and 0.03 for the test subset.

The presented research is concerned to obtain an efficient solution for bi-level multi-objective fully quadratic fractional optimization model (BLMOFQFOM). It is based on the idea of transforming the fractional model into non-fractional one for the initial guess of variables. Trapezoidal fuzzy numbers (TrFN) are being taken as the parameters in the fuzzy model to deal with vague and incomplete data and $$ \alpha $$ -cut set is used to get a deterministic model from the fuzzy model. Concept of fuzzy goals is implemented to set aspiration level as unity for all objective functions. An attractive flowchart, algorithm and a computational problem are also illustrated to show the validity of the designed approach.

Fault tree analysis (FTA) is a prominent reliability analysis method, which is widely used in safety-critical industries. Computing the minimal cut sets (MCSs) of a fault tree, i.e., finding all the smallest combinations of the basic events that cause system failures, is a fundamental step in FTA. Since coherent fault trees are the most common in industrial systems in practice, they are the focus of this article. Computing MCSs is a computationally hard problem. Classical methods have been proposed based on manipulation of Boolean expressions and binary decision diagrams. However, given the inherent intractability of computing MCSs in practice, there are still limitations on time and memory in these methods. Therefore, developing new methods over different paradigms remains to be an interesting research direction. In this article, motivated by recent progress on modern Boolean satisfiability problem (SAT) solvers, we present a new method for computing MCSs based on SAT, namely SATMCS . Specifically, given a fault tree, we iteratively search for a cut set based on the conflict-driven clause learning framework. By exploiting local propagation graph, which characterizes the partial failure propagation based on the cut set, we provide efficient algorithms for extracting an MCS. The new MCS is learned as a block clause for SAT solving, and the conflict clauses in iterations are incrementally recorded, which helps to prune search space and ensures completeness of the results. Moreover, we adopt a jump-chronological backtracking strategy to prepare the next iteration, which allows for reusing the same search steps in SAT solving. We compare SATMCS with state-of-the-art commercial tools on practical fault trees. Although SATMCS is only a prototype, it shows comparable performance in time consumption with one tool ( XFTA ), and in various cases, it outperforms the others ( FaultTree + and Commander ). Besides, SATMCS exhibits much better performance on memory usage than these tools. Specifically, SATMCS consumes about one order of magnitude less memory usage in most instances.

In this paper, we propose a fully automatic mesh segmentation method, which divides meshes into sub-meshes recursively through spectral analysis. A common problem in the spectral analysis of geometric processing is how to choose the specific eigenvectors and the number of these vectors for analysis and processing. This method tackles this problem with only one eigenvector, i.e. Fiedler vector. In addition, using only one eigenvector drastically reduces the cost of computing. Different from the Fiedler vector commonly used in the bipartition of graphs and meshes, this method finds multiple parts in only one iteration, vastly reducing the number of iterations and thus the time of operation, because each iteration produces as many correct boundaries as possible, instead of only one. We have tested this method on many 3D models, the results of which suggest the proposed method performs better than many advanced methods of recent years.

Single-cell gene expression data with positional information is critical to dissect mechanisms and architectures of multicellular organisms, but the potential is limited by the scalability of current data analysis strategies. Here, we present scGCO, a method based on fast optimization of hidden Markov Random Fields with graph cuts to identify spatially variable genes. Comparing to existing methods, scGCO delivers a superior performance with lower false positive rate and improved specificity, while demonstrates a more robust performance in the presence of noises. Critically, scGCO scales near linearly with inputs and demonstrates orders of magnitude better running time and memory requirement than existing methods, and could represent a valuable solution when spatial transcriptomics data grows into millions of data points and beyond.

The topic of this paper includes graph cuts based computed tomography reconstruction methods in binary and multi-gray level cases. This approach combines the graph cuts and a gradient based method. The present paper introduces and analyses the shape circularity as a new regularization and incorporates it in a graph cuts based computed tomography reconstruction approach, thus introducing a new energy-minimization based reconstruction algorithm for binary tomography. Proposed method is capable for reconstructions in cases of limited projection view availability. Results of experimental evaluation of the considered graph cuts type reconstruction methods for both binary and multi-level tomography are presented.