Merge Operations Research Articles

Optimizing development and operations from the project success perspective using the analytic hierarchy process

By merging development and operation disciplines, the approach known as development and operations (DevOps) can significantly improve the efficiency and effectiveness of software development. Despite its potential benefits, successfully implementing DevOps within traditional project management frameworks presents significant challenges. This study explores the critical factors influencing the implementation of DevOps practices from the project management perspective, specifically focusing on software development projects in the Ministry of Finance. This study utilizes the analytic hierarchy process (AHP) to prioritize the critical elements of project success criteria and DevOps factors necessary for effective implementation. The findings indicate that stakeholder satisfaction, quality, and value creation are the primary criteria for project success. Moreover, knowledge and skills, collaboration and communication, and robust infrastructure are pivotal factors for facilitating DevOps within project management. The study provides actionable insights for organizations aiming to improve their project outcomes by incorporating DevOps and offers a systematic approach to decision-making using AHP. This study recognizes limitations due to its focus on specific contexts and emphasizes the need for future research in diverse organizational environments to validate and expand these findings.

The merging operation and \((d-i)\)-simplicial \(i\)-simple \(d\)-polytopes

The merging operation and \((d-i)\)-simplicial \(i\)-simple \(d\)-polytopes

NCNet: Deformable medical image registration network based on neighborhood cross-attention combined with multi-resolution constraints

Objective. Existing registration networks based on cross-attention design usually divide the image pairs to be registered into patches for input. The division and merging operations of a series of patches are difficult to maintain the topology of the deformation field and reduce the interpretability of the network. Therefore, our goal is to develop a new network architecture based on a cross-attention mechanism combined with a multi-resolution strategy to improve the accuracy and interpretability of medical image registration. Approach. We propose a new deformable image registration network NCNet based on neighborhood cross-attention combined with multi-resolution strategy. The network structure mainly consists of a multi-resolution feature encoder, a multi-head neighborhood cross-attention module and a registration decoder. The hierarchical feature extraction capability of our encoder is improved by introducing large kernel parallel convolution blocks; the cross-attention module based on neighborhood calculation is used to reduce the impact on the topology of the deformation field and double normalization is used to reduce its computational complexity. Main result. We performed atlas-based registration and inter-subject registration tasks on the public 3D brain magnetic resonance imaging datasets LPBA40 and IXI respectively. Compared with the popular VoxelMorph method, our method improves the average DSC value by 7.9% and 3.6% on LPBA40 and IXI. Compared with the popular TransMorph method, our method improves the average DSC value by 4.9% and 1.3% on LPBA40 and IXI. Significance. We proved the advantages of the neighborhood attention calculation method compared to the window attention calculation method based on partitioning patches, and analyzed the impact of the pyramid feature encoder and double normalization on network performance. This has made a valuable contribution to promoting the further development of medical image registration methods.

Ideal Integration Approaches for Enhancing ASDP Group's Performance Post-Subsidiary Acquisition

Transportation plays a crucial role in the development of civilizations by enabling the movement of people, goods, and cultural exchanges. Among various transportation modes, water-based transport, especially ferries, is vital for linking communities, boosting trade, and promoting tourism in regions that land or air transport cannot reach. PT ASDP Indonesia Ferry (Persero), the sole state-owned enterprise managing ferry services in Indonesia, holds a leading position in the national ferry industry. To sustain its dominance, ASDP undertook a corporate action by acquiring a competitor. The acquisition process, dating back to the 1970s, involves stages of strategy formulation, investigation and selection, negotiation, and integration. According to Mroczynski, integration involves merging operations, cultures, and systems to achieve synergies and strategic goals. This study examines the optimal integration approach used by ASDP to enhance group performance post-acquisition. Utilizing a qualitative research method, focus group discussions were conducted with stakeholders from both ASDP and the acquired company. The study applies Pitkethly’s integration framework, which categorizes integration levels from minimal to extensive. Results show that ASDP has established significant financial management control over the acquired company, but gaps still exist between the desired and current conditions. Therefore, a detailed analysis of the targets ASDP needs to achieve to optimize the integration process is crucial for meeting post-acquisition requirements.

Alleviating bus bunching via modular vehicles

The notorious phenomenon of bus bunching prevailing in uncontrolled bus systems produces irregular headways and downgrades the level of service by increasing passengers’ expected waiting time. Modular autonomous vehicles (MAVs), due to their ability to split and merge en route, have the potential to help both late and early buses recover from schedule deviation while providing continuous service. In this paper, we propose a novel bus bunching alleviation strategy for MAV-aided transit systems. We first consider a soft vehicle capacity constraint and establish a continuum approximation (CA) model (Model I) to capture the system dynamics intertwined with the MAV splitting and merging operations, and then establish an infinite-horizon stochastic optimization model to determine the optimal splitting and merging strategy. To capture the reality that passengers may fail to board an overcrowded bus, we propose a second model (Model II) by extending Model I to accommodate a hard vehicle capacity constraint. Based on the characteristics of the problem, we develop a customized deep Q-network (DQN) algorithm with multiple relay buffers and a penalized ruin state applicable for both models to optimize the strategy for each MAV. Numerical results show that the strategy obtained via the DQN algorithm is an effective bunch-proof strategy and has a better performance than the myopic strategy for MAV-aided systems and the two-way-looking strategy for conventional bus systems. Sensitivity analyses are also conducted to examine the effectiveness and benefits of the proposed strategy across different operation scenarios.

A reconfigurable Buck-Boost Cross-Coupled charge Pump

This paper presents a reconfigurable buck-boost Charge Pump (CP) based on the cross-coupled topology. The proposed CP is a switched capacitor (SC) converter merging different ratios and operation modes in one topology. The converter is able to step-up or step-down the input voltage at the output only by controlling the external connections of the circuit. By achieving various conversion ratios and operation modes, higher efficiency levels are performed maximizing the power that is delivered to the load. The reconfigurable buck-boost CP has been designed in 65 nm TSMC CMOS technology and covers an input voltage range from 1.6 V to 3.2 V and an output voltage range also from 1.6 V to 3.2 V in the boost mode and an input voltage range from 2.6 V to 3.6 V and an output voltage range from 1.4 to 1.8 V in the buck mode. In addition, efficiency estimation analysis is presented and proves how parasitic capacitances of flying capacitors affect the overall efficiency of a multi-ratio converter. The converter indicates peak efficiency equal to 77 % and delivers up to 4 mA load current.

DCoMA: A dynamic coordinative merging assistant strategy for on-ramp vehicles with mixed traffic conditions

Merging sections on highways are identified as traffic bottlenecks, leading to congestion and accidents. The emergence of Connected and Autonomous Vehicles (CAVs) technology promises an optimized solution to on-ramp merging issues. Existing cooperative merging strategies typically focus on determining the merging sequence of vehicles in specific merging areas, overlooking the influence of the macroscopic traffic flow conditions on the merging process. In this paper, we innovatively introduce the Dynamic Cooperative Merging Assistance (DCoMA) strategy, a traffic management approach designed to enhance merging operations under variable traffic demands. At the macroscopic level, DCoMA employs the fundamental diagram of traffic flow to develop a platoon formation algorithm for mainline vehicles, tailored to the dynamic macroscopic states of traffic. The algorithm adaptively adjusts the size of platoons based on the volumes of both the mainline and the on-ramp. Subsequently, the spatio-temporal dynamics of these platoons function as the ’red phase’ of traffic signals, with the intervals between platoons analogous to the ’green phase’. This information is then converted into a time-series format and transmitted to all vehicles on the on-ramp. At the microscopic level, vehicles on the on-ramp alter their driving strategies in response to this time-series data, ensuring a seamless merging into the mainline flow without causing halting. Through simulations and comparative analysis with three existing strategies (i.e., CoopMA, ALINEA, and X-ALINEAQ), the proposed DCoMA strategy exhibits considerable potential for application across diverse traffic volumes and CAV penetration rates. The results indicate that the proposed approach can significantly improve the efficiency of the mainline and on-ramp, achieving a maximum improvement of 18.32%. More importantly, the DCoMA strategy effectively enlarges merging gaps and, coupled with the speed control capabilities of CAVs, significantly mitigates the risk of accidents and reduces exhaust emissions.

On Expectation-Maximization Algorithm with Split and Merge in R

This paper presents a comprehensive study on the implementation of the Expectation-Maximization (EM) algorithm for a 4-component bivariate Gaussian Mixture Model (GMM) with a focus on incorporating the split and merge techniques. The bivariate GMM is widely utilized in various fields, such as pattern recognition, image processing, and data clustering, due to its flexibility in capturing complex data distributions. Our proposed implementation leverages the versatility of the R programming language to create a robust and efficient framework for modeling and optimizing the parameters of the 4-component GMM. The EM algorithm is employed as a powerful tool for iteratively estimating the model parameters, ensuring convergence to a local maximum of the likelihood function. To enhance the model’s flexibility, we introduce the split and merge strategies, enabling the algorithm to adapt to diverse data structures and efficiently manage the complexity of the mixture components. The split operation allows for the subdivision of components when needed, while the merge operation facilitates the combination of components that represent similar patterns. This adaptive approach contributes to the model’s ability to capture intricate data patterns and improve convergence during the optimization process. The implementation is validated through extensive experimentation on synthetic, demonstrating its effectiveness in accurately estimating the parameters of the 4-component bivariate GMM. The proposed methodology proves to be a valuable addition to the existing tools available for GMM based modeling, providing researchers and practitioners with a flexible and powerful framework for analyzing complex data structures in diverse applications

Explanations for combinatorial optimization problems

We introduce a representation for generating explanations for the outcomes of combinatorial optimization algorithms. The two key ideas are (A) to maintain fine-grained representations of the values manipulated by these algorithms and (B) to derive explanations from these representations through merge, filter, and aggregation operations. An explanation in our model presents essentially a high-level comparison of the solution to a problem with a hypothesized alternative, illuminating why the solution is better than the alternative. Our value representation results in explanations smaller than other dynamic program representations, such as traces. Based on a measure for the conciseness of explanations we demonstrate through a number of experiments that the explanations produced by our approach are small and scale well with problem size across a number of different applications.

Using of Fuzzy Logic Matching Algorithm for Hindi Dataset

Various new computing methods based on Fuzzy Logic can be used in various systems. Numerous researchers and developers are working with Fuzzy Logic and its Applications. One such application is applied on the dataset which contains the values in Hindi Language; the major problem is that data come from different sources and when do the merge operation it will create a lot of problem; since same names are represented by different ways of writing it. Here, we apply a technique of fuzzy matching algorithm for hindi dataset. We are applying for the project of sharing bike concept which comes from different data sources. Fuzzy Logic is a very promising mathematical approach for training the dataset which being characterized by subjectivity & imprecision. More emphasis is put on the fuzzy string matching and its criteria for the performing the merge operation. This paper presents an analysis of the results achieved using fuzzy logic to model the dataset. Assessing the performance of datasets after performing matching algorithm at different levels using traditional methods.

Coordination and binary branching

AbstractIn “Subordination and Binary Branching”, a recent (2023) Syntax paper, Ad Neeleman and colleagues proposed a new analysis of subordination. The main aim of this paper is to refute that analysis, using data from the coordination of unlike categories and unlike grammatical functions. Additionally, building on Neeleman et al.'s observations about the arbitrarily ‐ary—not just binary—nature of coordination, I sketch a more Minimalist approach to subordination and coordination that is devoid of the problems that Neeleman et al.'s analysis faces, but otherwise covers a similar range of data. On this approach, “subordination” is a synonym of “result of PairMerge” and “coordination” is a synonym of “result of SetMerge”, where SetMerge is understood as an operation creating an arbitrary set, as opposed to the usual more specialized Merge operation, which creates a binary set.

A practical algorithm for the design of multiple-sized porous scaffolds with triply periodic structures

In this study, we present a practical volume-merging method for generating multiple-sized porous structures that exhibit geometries with triply periodic minimal surface (TPMS) lattice structures. The proposed method consists of three stages: (1) designing the physical models with a signed distance field, (2) performing a merging operation for the porous scaffolds, and (3) assembling different units into a composite structure. The significant advantages of the proposed algorithm can be summarized as follows: Our method is independent of the model shape; the designed structures maintain a smooth surface with a constant mean curvature, and the mathematical computational complexity is low. We can join two different-sized triply periodic minimal surface lattices in the radial direction, where the transition region is obtained by smooth interpolation between the two lattice structures with different cell sizes or types. However, constructing large-sized models is only conceptually possible due to computational cost and memory storage constraints. To overcome these limitations, we present a practical method that can efficiently assemble large-scaled models at a low computational cost. The proposed method is based on a Boolean union operation of basic units of TPMS. Thus, it is simple to generate large-scale three-dimensional multiple-sized porous volumes based on our proposed method, which can be applied to many applications in mechanical and electrical engineering. The produced multi-scale compound scaffolds have smooth surfaces without fractures, making them suitable for straightforward application in additive manufacturing. Several numerical tests are conducted to validate the efficiency of the proposed algorithm.

Q-Learning Model for Blockchain Security in Internet of Medical Things Networks

Secure Internet of Medical Things (IoMT) installations must take a long time to complete, which leaves them vulnerable to low energy performance, low throughput, and low packet delivery rates. This limits performance in real-time deployments, which has an effect on healthcare efficiency. Many optimization approaches have been proposed by researchers to deal with these issues, but most of them increase the complexity of mining and thus reduce its scalability. In order to improve the temporal QoS performance of IoMT deployments, this paper proposes the creation of a novel bio-inspired model. In order to evaluate the temporal mining performance (of miners) in terms of mining throughput, block mining ratio, latency, and energy, the proposed model makes use of the Mayfly Optimization (MO) Method. A fitness function based on these indices is then used to decide whether or not the underlying blockchains should be combined or split up. During a split operation, a blockchain is divided into two equal parts, and during a merge operation, the blockchain is saved for later retrieval. The proposed model improves block mining performance by 3.2%, compared to the average QoS optimization model under similar conditions, increases mining speed by 3.5%, decreases energy consumption by 4.9%, increases throughput by 2.8%, and improves performance by 3.2%.

Delineation Errors Caused by Replication and Expansion Operations in Monaco.

This study aims to investigate the errors in structure volume and shape caused by the replication, expansion, and merging operations of the Monaco system and analyze their influence on dosimetry evaluation. A retrospective collection of 30 patients undergoing radiotherapy was utilized. Cylinders with radii of 5, 10, and 30 mm were delineated in computerized tomography (CT) images from 10 patients with thoracic and abdominal issues, and the Margins function in Monaco was used to expand the margins by 0, 3, 5, and 10 mm in 2D mode. In 10 patients with vertebral metastases, the Margins function was utilized to replicate and merge targets, and the Copy Structure function was employed to replicate targets. Cross-CT replication was performed for the targets of 10 patients with nasopharyngeal carcinoma. The deviation between the processed structure volume and the ideal value was compared. The difference in the maximum dose (Dmax) before and after lens replication was evaluated in 10 patients undergoing whole-brain radiotherapy. Monaco's Margins function increased the volume of the processed structure during the copying procedure. The margin error was equivalent to expanding the structure by 0.3-0.4 mm, and a margin error of 0.3-0.4 mm was produced in each expansion instance. The volume deviation for a cylinder with a radius of 5 mm was 12.99%. The Merge function of Margins copied substructures and merged them. The Copy Structure function did not alter the structure during copying, but the volume was reduced by less than 1% after copying across CT. Dmax after lens replication was higher than that before replication, with a median difference of 31.3 cGy for the left lenses. Monaco's Margins function introduces errors in organ replication, expansion, and merging, resulting in incorrect dose assessment. Physicians should be mindful of the potential effects when utilizing them.

Collective Belief Revision

In this article, we study the dynamics of collective beliefs. As a first step, we formulate David Westlund’s Principle of Collective Change (PCC) —a criterion that characterizes the evolution of collective knowledge— in the realm of belief revision. Thereafter, we establish a number of unsatisfiability results pointing out that the widely-accepted revision operators of Alchourrón, Gärdenfors and Makinson, combined with fundamental types of merging operations —including the ones proposed by Konieczny and Pino Pérez as well as Baral et al.— collide with the PCC. These impossibility results essentially extend in the context of belief revision the negative results established by Westlund for the operations of contraction and expansion. At the opposite of the impossibility results, we also establish a number of satisfiability results, proving that, under certain (rather strict) requirements, the PCC is indeed respected for specific merging operators. Overall, it is argued that the PCC is a rather unsuitable property for characterizing the process of collective change. Last but not least, mainly in response to the unsatisfactory situation related to the PCC, we explore some alternative criteria of collective change, and evaluate their compliance with belief revision and belief merging.

Time Series Manufacturing Data Edge Monitoring and Visualization to Support Industrial Maintenance Teams

Traditional manufacturing control systems such as Manufacturing Execution Systems (MES) or SCADA (Supervisory Control And Data Acquisition) were not designed for Industry 4.0 paradigm. Industry 4.0 implies that more data variables must be automatically monitored and data must be captured at a higher frequency: from one value of a few key variables to values of several variables captured at frequencies of seconds. Thus, new architectures and tools are required to merge Information Technology (IT) and Operation Technology (OT) fields and to meet Industry 4.0 requirements. This paper proposes a lightweight architecture based on micro-services and time series data requirements to connect to manufacturing process controllers, and to capture, store, monitor and visualize relevant data about the process. Moreover, a reference implementation based on Open Source tools is presented. This implementation has been validated by members of the maintenance team of a factory from Lecta, a paper manufacturer. The implementation has proven to be a new valuable tool providing further insights and customized alarms of the manufacturing process.

Developable Quad Meshes and Contact Element Nets

The property of a surface being developable can be expressed in different equivalent ways, by vanishing Gauss curvature, or by the existence of isometric mappings to planar domains. Computational contributions to this topic range from special parametrizations to discrete-isometric mappings. However, so far a local criterion expressing developability of general quad meshes has been lacking. In this paper, we propose a new and efficient discrete developability criterion that is applied to quad meshes equipped with vertex weights, and which is motivated by a well-known characterization in differential geometry, namely a rank-deficient second fundamental form. We assign contact elements to the faces of meshes and ruling vectors to the edges, which in combination yield a developability condition per face. Using standard optimization procedures, we are able to perform interactive design and developable lofting. The meshes we employ are combinatorially regular quad meshes with isolated singularities but are otherwise not required to follow any special curves on a developable surface. They are thus easily embedded into a design workflow involving standard operations like remeshing, trimming, and merging operations. An important feature is that we can directly derive a watertight, rational bi-quadratic spline surface from our meshes. Remarkably, it occurs as the limit of weighted Doo-Sabin subdivision, which acts in an interpolatory manner on contact elements.

DLSDHMS: Design of a deep learning-based analysis model for secure and distributed hospital management using context-aware sidechains

Designing an efficient hospital management solution requires the integration of multidomain operations that include secure storage, alert system modelling, infrastructure management, staff management, report analysis, and feedback-based learning tasks. Existing hospital management models are either highly complex or do not incorporate comprehensive deep learning analysis, which limits their deployment capabilities. Moreover, most of these models use mutable storage solutions, which restricts their trust levels under multi-patient to multi-doctor mapping scenarios. To overcome these issues, this article proposes the design of a novel deep Learning-based analysis model for secure and distributed hospital management via context-aware sidechains. The model initially collects large-scale information sets from different hospital entities via an IoT-based network and stores the information on context-sensitive sidechains. These context-sensitive sidechains store information sets related to Medicine Management, Doctor Management, Insurance and Billing Management, and Appointment Management operations. These chains are optimized via an Iterative Genetic Algorithm (IGA) that assists in improving storage and retrieval performance via intelligent merging and splitting operations. Information stored on these chains is processed via a combination of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), that assist in identifying patient-level diseases and issues. The information obtained from these classifiers is updated on the central repository and assists in the pre-emption of diseases for other patients. Due to these integrations, the proposed model is capable of reducing computational delay by 3.5 % and reducing storage cost by 8.3 % when compared to other blockchain-based deployments. The model is also able to pre-empt patient issues with 9.3 % higher accuracy and 4.8 % higher precision, which makes it useful for real-time clinical deployments.

RMAU-Net: Breast Tumor Segmentation Network Based on Residual Depthwise Separable Convolution and Multiscale Channel Attention Gates

Breast cancer is one of the most common female diseases, posing a great threat to women’s health, and breast ultrasound imaging is a common method for breast cancer diagnosis. In recent years, U-Net and its variants have dominated the medical image segmentation field with their excellent performance. However, the existing U-type segmentation networks have the following problems: (1) the design of the feature extractor is complicated, and the calculation difficulty is increased; (2) the skip connection operation simply combines the features of the encoder and the decoder, without considering both spatial and channel dimensions; (3) during the downsampling phase, the pooling operation results in the loss of feature information. To address the above deficiencies, this paper proposes a breast tumor segmentation network, RMAU-Net, that combines residual depthwise separable convolution and a multi-scale channel attention gate. Specifically, we designed the RDw block, which has a simple structure and a larger sensory field, to overcome the localization problem of convolutional operations. Meanwhile, the MCAG module is designed to correct the low-level features in both spatial and channel dimensions and assist the high-level features to recover the up-sampling and pinpoint non-regular breast tumor features. In addition, this paper used the Patch Merging operation instead of the pooling method to prevent the loss of breast ultrasound image information. Experiments were conducted on two breast ultrasound datasets, Dataset B and BUSI, and the results show that the method in this paper has superior segmentation performance and better generalization.

A Reconfigurable Architecture for Real-time Event-based Multi-Object Tracking

Although advances in event-based machine vision algorithms have demonstrated unparalleled capabilities in performing some of the most demanding tasks, their implementations under stringent real-time and power constraints in edge systems remain a major challenge. In this work, a reconfigurable hardware-software architecture called REMOT, which performs real-time event-based multi-object tracking on FPGAs, is presented. REMOT performs vision tasks by defining a set of actions over attention units (AUs). These actions allow AUs to track an object candidate autonomously by adjusting its region of attention and allow information gathered by each AU to be used for making algorithmic-level decisions. Taking advantage of this modular structure, algorithm-architecture codesign can be performed by implementing different parts of the algorithm in either hardware or software for different tradeoffs. Results show that REMOT can process 0.43–2.91 million events per second at 1.75–5.45 W. Compared with the software baseline, our implementation achieves up to 44 times higher throughput and 35.4 times higher power efficiency. Migrating the Merge operation to hardware further reduces the worst-case latency to be 95 times shorter than the software baseline. By varying the AU configuration and operation, a reduction of 0.59–0.77 mW per AU on the programmable logic has also been demonstrated.