Chain Segmentation Research Articles

Role of terminal beads in fracture of topological gels: A coarse-grained molecular dynamics study

Topological gels feature movable cross-linking sites which yield great deformation capability, but we show that their long-term performance would be undesirable. Because a long backbone chain has only 2 terminal beads to restrict sliding of rings, the entire chain would fail by just a small debonding. Chain segmentation by adding terminal beads is a promising strategy to improve durability. In this work, we develop a coarse-grained (CG) model of topological gels based on MARTINI force field, to simulate tensile behaviors of the gel. The developed CG model accurately predicts elastic modulus of typical topological gels with different crosslinking densities. Based on the model, we show that adding terminal beads to the chain can effectively improve mechanical properties of topological gels after initial debonding. With analyses on distribution of ring molecules along the chain and effective load-bearing chains during tensile deformation, mechanism of the improvement on durability is discussed.

Optimized Mask-RCNN model for particle chain segmentation based on improved online ferrograph sensor

AbstractFerrograph-based wear debris analysis (WDA) provides significant information for wear fault analysis of mechanical equipment. After decades of offline application, this conventional technology is being driven by the online ferrograph sensor for real-time wear state monitoring. However, online ferrography has been greatly limited by the low imaging quality and segmentation accuracy of particle chains when analyzing degraded lubricant oils in practical applications. To address this issue, an integrated optimization method is developed that focuses on two aspects: the structural re-design of the online ferrograph sensor and the intelligent segmentation of particle chains. For enhancing the imaging quality of wear particles, the magnetic pole of the online ferrograph sensor is optimized to enable the imaging system directly observe wear particles without penetrating oils. Furthermore, a light source simulation model is established based on the light intensity distribution theory, and the LED installation parameters are determined for particle illumination uniformity in the online ferrograph sensor. On this basis, a Mask-RCNN-based segmentation model of particle chains is constructed by specifically establishing the region of interest (ROI) generation layer and the ROI align layer for the irregular particle morphology. With these measures, a new online ferrograph sensor is designed to enhance the image acquisition and information extraction of wear particles. For verification, the developed sensor is tested to collect particle images from different degraded oils, and the images are further handled with the Mask-RCNN-based model for particle feature extraction. Experimental results reveal that the optimized online ferrography can capture clear particle images even in highly-degraded lubricant oils, and the illumination uniformity reaches 90% in its imaging field. Most importantly, the statistical accuracy of wear particles has been improved from 67.2% to 94.1%.

Temporal Data Scheduling in Internet of Vehicles Using an Improved Decomposition-Based Multi-Objective Evolutionary Algorithm

Nowadays, Internet of Vehicles plays an important role in the emerging intelligent transportation systems. In Internet of Vehicles, it is crucial to make appropriate temporal data scheduling to ensure the service quality and the high service ratio to meet the requests from vehicles. However, service quality and service ratio are two conflict goals because of the limited bandwidth and the vehicle mobility in Internet of Vehicles. In order to optimize these two conflict objectives simultaneously, we present an improved decomposition based multi-objective evolutionary algorithm for the temporal data scheduling (I-MOEA/D-TDS) in Internet of Vehicles. Based on the MOEA/D framework, we integrate a self-adaptive weight vector adjustment method based on chain segmentation to improve the performances of temporal data scheduling. For verifying the availability of presented algorithm, under the hybrid Vehicle-to-Infrastructure / Vehicle-to-Vehicle (V2I/V2V) communications and multiple Roadside Units (RSUs) scenario, we compare the proposed algorithm with several related algorithms under the effects of data valid periods, service workloads, maximum tolerated delay, and traffic workloads. Experimental results suggest that the presented algorithm can achieve better data service quality and service ratio.

A computation offloading algorithm based on multi-objective evolutionary optimization in mobile edge computing

For computation offloading problem (COP) in mobile edge computing (MEC), the energy consumption of terminal equipments(TEs) and the delay of mobile equipment applications are two optimization goals. In real life, terminal equipment is dynamic, and their number, mobility, and continuous changes in wireless channels will affect the balance between the mentioned energy consumption and delay. Different from available works, we model the COP in MEC as a dynamic multi-objective problem (DMOP) in this paper, and propose an improved dynamic multi-objective evolutionary optimization based on decomposition (DMOEA/D) to solve it, namely DMOEA/D-COPMEC. In the proposed algorithm, the environmental change is detected by a fixed detector, and whether the current change is similar to the historical change is determined. If so, the difference prediction is used to re-locate the population individual in the new MEC environment, otherwise, the memory-based strategy is used to response environmental change. In MOEA/D, an adaptive weight adjustment strategy based on chain segmentation (CS) is adopted to generate a set of uniformly distributed weight vectors. The simulation results show that the proposed algorithm can better balance the application delay and the terminal energy consumption if there is environment change. The solution set is closer to reality and better than the related algorithms.

Quantitative supply chain segmentation model for dynamic alignment

Companies deal with different customer groups, requirements differ among them, which makes it important to define the service level precisely and improve customer service through different supply chain strategies for each group. An alternative to deal with imprecision related to the segmentation processes suggested by either the Leagile or the Dynamic Alignment Schools is the application of fuzzy set theory. The objective of this work is to develop a quantitative model that uses the fuzzy set theory and, based on sales data, assess the company’s supply chain(s). The model's aim is to facilitate managers' decision-making processes to achieve the dynamic alignment. It was possible to identify the supply chains that serve the client groups evaluated, providing answers faster than the analysis proposed by the models found in the literature. The application in two real situations validated the model since the results obtained were consistent with the reality pointed out by the experts of the companies assessed. The model indicates possible actions for the realignment of the supply chain by their managers. Results obtained should improve practice, preparing managers to cope with the organizations` multiple supply chains. This study is the first one that aims to segment quantitatively supply chains on a company applying fuzzy set theory, providing a novel approach to align operations and supply chain strategy dynamically.

Influence of stator segmentation on iron losses in PMSMs for traction applications

PurposeThis paper presents a comparative study of different stator-segmentation topologies of a permanent magnet synchronous machine (PMSM) used in traction drives and their effect on iron losses. Using stator segmentation allows one to achieve more significant copper fill factors, resulting in increased power densities and efficiencies. The segmentation of the stators creates additional air gaps and changes the soft magnetic material’s material properties due to the cut edge effect. The aim of this paper is to present an in-depth analysis of the influence of stator segmentation on iron losses. The main goal was to compare various segmentation methods under equal excitation conditions in terms of their influence on iron loss.Design/methodology/approachA transient finite element method analysis combined with an extended iron-loss model was used to evaluate discussed effects on the stator’s iron losses. The workflow to obtain a homogenized airgap length accounting for cut edge effects was established.FindingsThe paper concludes that the segmentation in most cases slightly decreases the iron losses in the stator because of the overall reduced magnetic flux density B due to the additional air gaps in the magnetic circuit. An increase of the individual components, as well as total power loss, was observed in the Pole Chain segmentation design. In general, segmentation did not change the total iron losses significantly. However, different segmentation methods resulted in the different distortion of the magnetic field and, consequently, in different iron loss compositions. The analysed segmentation methods exhibited different iron loss behaviour with respect to the operation points of the machine. The final finding is that analysed stator segmentations had a negligible influence on the total iron loss. Therefore, applying segmentation is an adequate measure to improve PMSMs as it enables, e.g. increase of the winding fill factor or simplifying the assembly processes, etc.Originality/valueThe influence of stator segmentation on iron losses was analysed. An in-depth evaluation was performed to determine how the discussed changes influence the individual iron loss components. A workflow was developed to achieve a computationally cheap homogenized model.

Tailoring inventory classification to industry applications: the benefits of understandable machine learning

Supply chain segmentation and inventory classification, specifically, are considered a competitive advantage in many industries. Approaches like the ABC-XYZ analysis are commonly used in practice to classify SKUs based on simple rules for ranking even though simplified rules-of-thumb may lead to sub-optimal decisions and higher costs. We thus propose a cost-based, multi-dimensional inventory classification scheme for assigning SKUs to classes of replenishment policies that prescribe a group service level, a demand distribution, and an inventory control rule. We further provide an extension for classification under an overall service constraint. Our methodological approach is based on machine learning classifiers and we employ a genetic algorithm to train cost-minimising decision trees which allow for easy understanding and reproduction of classification decisions. Cost- and operational focus, simple application, and interpretability are our main contributions to the inventory classification literature. We evaluate the approach on three industry data sets and show that the classification trees result in an average cost increase of only 1.01% (3.70% with an overall service constraint) over the cost-optimal classification, where no tree structure is enforced. Once trees are constructed, unseen data can be classified out-of-sample with an average cost increase of 1.85% (7.68%) over the optimal cost of classification.

Assessment of supply chain segmentation from an interoperability perspective

ABSTRACT Based on an integrative literature review, this study aims to verify the qualifying attributes of supply chain segmentation, that is, the requirements or competencies crucial for companies to maximise effectiveness of supply chain segmentation, and evaluate them from the perspective of interoperability (business, process, service, and data). Based on the definition of segmentation attributes and the perspective of interoperability, the attributes were evaluated by the analytic hierarchy process (AHP) to set priorities among the target attributes. The applicability of the proposed model is demonstrated through a case study of a company in the consumer goods industry. Generally, this study aims to assist managers and professionals to identify elements of relevance, as well as to support decision-making in terms of managing problems involving segmentation and enterprise interoperability in the supply chain.

Differentiation vs. standardisation in supply chain segmentation: a quantitative study

The key value proposition of supply chain segmentation is to differentiate supply chains through a reasonable number of segments in order to gain a level of standardisation and avoid managerial complexity incurred in fully customised supply chains. The decision on how products are grouped into segments is at the core of a successful implementation. A fundamental trade-off in this decision-making process is between higher differentiation by having small group sizes and higher standardisation from a smaller number of groups. In this manuscript, we implement segmentation on supply chain configurations and investigate the trade-off by analysing several network scenarios. We use optimisation models for each scenario to align decisions of segment formation and supply chain configurations. We show that divergences in demand characteristics, geographic difference, and cost synergy such as pooling effect have impacts on the balance of standardisation and differentiation.

MOEA/D with a self-adaptive weight vector adjustment strategy based on chain segmentation

MOEA/D (multi-objective evolutionary algorithm based on decomposition) decomposes a multi-objective optimization problem (MOP) into a series of single-objective sub-problems through a scalarizing function and a set of uniformly distributed weight vectors, and optimizes these sub-problems simultaneously in a collaborative way. However, when the shape of the true Pareto front (PF) of the multi-objective problem has the characteristic of long tail and sharp peak, the performance of MOEA/D will be greatly affected, that is, the performance of the decomposition-based multi-objective evolutionary algorithm depends heavily on the shape of the true PF. In order to efficiently deal with this situation, a self-adaptive weight vector adjustment strategy based on chain segmentation strategy (CS) is proposed. More specifically, a chain structure is firstly derived from the current population distribution to approximate the shape of the true PF. Then each chain is evenly segmented, and the direction vector from the origin to each segment point is used as the new weight vector. Finally, a set of reasonably distributed weight vectors are obtained to improve the performance of the algorithm. In the experimental section, we integrate CS strategy with three variants of MOEA/D, and the results demonstrate the effectiveness of the proposed strategy. Furthermore, we use MOEA/D-DE (a variant of MOEA/D, which is based on differential evolution operator) as a paradigm to integrate the CS strategy, and compare it with five state-of-the-art algorithms to illustrate that the algorithm integrating the CS strategy is very competitive.

Developing a framework to support strategic supply chain segmentation decisions: a case study

There is a huge opportunity in mining operational data in the supply chain (SC) to support strategic segmentation decisions. This research has the objective of developing a framework to support strategic SC segmentation decisions. This research is exploratory in nature, with the methodology based on action research combined with a single empirical study in a large Portuguese multinational company. A data mining project, based on the CRISP-DM methodology, is adopted to develop the framework. The company had the strategic objective to move beyond a single make to order strategy towards a segmented SC strategy. By applying the framework, the most relevant criteria were identified (demand volume, demand variability, order corrections, delivery time window and delivery frequency). These were then used to identify four relevant segments each with a tailored SC strategy.

Is There a Segmentation inside Global Value Chain of the Automobile Industry in China?

Since German Volkswagen accessed to Chinese market in building a joint venture in the mid-1980s, the automobile industry in China has been engaged in global value chain (GVC) activities for more than three decades. China went through three stages of start-up, large-scale expansion and overseas M&A, and very quickly established a comprehensive manufacturing system, remaining the world’s first place after 2009 in terms of output and sales. However, in line with the rapid expansion of production scale, automobile industry in China, due to the lack of some value-added links in the global value chain of automobiles, has failed to form a closed-loop reflux pattern in the GVC of automobile industry, resulting in a significant segmentation phenomenon. It mainly manifested in: 1) In the production process, there is the break between the upstream design and key parts against the mass local vehicle manufacturing, and the high-added value chain links are held by foreign operators. 2) In the sales process, there exists a fault zone between large-scale sales of vehicles and very few export volumes. And the value chain of automobile industry in China has not been effectively extended to overseas market. This paper further investigates main reasons behind the segmentation of the automobile industry value chain in China: 1) the business strategy of multinational automobile companies aim to seize of China’s local automobile market, which blocks the overseas extension of the value chain; 2) The control of domestic automobile sales channels by the foreign stake in JVs leads to the monopoly of supply chain and post-market of automobile industry, which indirectly leads to the segmentation of value chain. 3) Low value acquisition mode of independent brand automobile enterprises is isolated in the high value-added link of global value chain, which leads to the segmentation of value chain directly. 4) Some of the industrial and trade policies lead to the abnormal high price in automobile market and the difficulty of extending the overseas value chain. Furthermore, this paper puts forward the research prospect of whether the value chain segmentation of automobile industry in China exists for a long time, and the alike problem of value chain segmentation. The new energy vehicles will confront.

Value chain segmentation: A pathway to improved profitability, service and performance

This paper analyses why a Value Chain Segmentation (VCS) process is necessary to address the escalating complexity in the marketplace and successfully translate company business strategy for both its products and customers into the right supply chain designs and corresponding capabilities. A summary of the overall methodology and steps in the process are presented. The paper sets out how to get started, the role of leadership, critical success factors and how to build capability and sustain the process. Value can be delivered across multiple vectors using this approach and are reviewed. And finally, concludes that having tailored supply chains with capabilities matched to product, customer and business strategy is the only effective means for delivering a company’s strategic objectives.

Using project demand profiling to improve the effectiveness and efficiency of infrastructure projects

PurposeThe purpose of this paper is to explore the applicability and utility of supply chain (SC) segmentation through demand profiling to improve the effectiveness and efficiency of infrastructure projects by identifying different types of project demand profiles.Design/methodology/approachA three-stage abductive research design was adopted. Stage 1 explored the applicability of SC segmentation, through demand profiling, to the portfolio of infrastructure projects in a utility company. Stage 2 was an iterative process of “theory matching”, to the portfolio, programme and project management literature. In stage 3, theoretical saturation was reached and “theory suggestions” were made through four propositions.FindingsFour propositions outline how SC segmentation through project demand profiling could improve the effectiveness and efficiency of infrastructure projects. P1: the ability to recognise the different demand profiles of individual projects, and groups thereof, is a portfolio management necessity. P2: projects that contribute to the strategic upgrade of a capital asset should be considered a potential programme of inter-related repeatable projects whose delivery would benefit from economies of repetition. P3: the greater the ability to identify different demand profiles of individual/groups of projects, the greater the delivery efficiency. P4: economies of repetition developed through efficient delivery of programmes of repeatable projects can foster greater efficiency in the delivery of innovative projects through economies of recombination.Originality/valueThis work fills a gap in the portfolio management literature, suggesting that the initial screening, selection and prioritisation of project proposals should be expanded to recognise not only the project type, but also each project’s demand profile.

Effect of different in-chain impurities on the magnetic properties of the spin chain compound SrCuO2 probed by NMR

The S=1/2 Heisenberg spin chain compound SrCuO2 doped with different amounts of nickel (Ni), palladium (Pd), zinc (Zn) and cobalt (Co) has been studied by means of Cu nuclear magnetic resonance (NMR). Replacing only a few of the S=1/2 Cu ions with Ni, Pd, Zn or Co has a major impact on the magnetic properties of the spin chain system. In the case of Ni, Pd and Zn an unusual line broadening in the low temperature NMR spectra reveals the existence of an impurity-induced local alternating magnetization (LAM), while exponentially decaying spin-lattice relaxation rates $T_1^{-1}$ towards low temperatures indicate the opening of spin gaps. A distribution of gap magnitudes is proven by a stretched spin-lattice relaxation and a variation of $T_1^{-1}$ within the broad resonance lines. These observations depend strongly on the impurity concentration and therefore can be understood using the model of finite segments of the spin 1/2 antiferromagnetic Heisenberg chain, i.e. pure chain segmentation due to S = 0 impurities. This is surprising for Ni as it was previously assumed to be a magnetic impurity with S = 1 which is screened by the neighboring copper spins. In order to confirm the S = 0 state of the Ni, we performed x-ray absorption spectroscopy (XAS) and compared the measurements to simulated XAS spectra based on multiplet ligand-field theory. Furthermore, Zn doping leads to much smaller effects on both the NMR spectra and the spin-lattice relaxation rates, indicating that Zn avoids occupying Cu sites. For magnetic Co impurities, $T_1^{-1}$ does not obey the gap like decrease, and the low-temperature spectra get very broad. This could be related to the increase of the Neel temperature which was observed by recent muSR and susceptibility measurements, and is most likely an effect of the impurity spin $S\neq0$.

A joint network design and multi-echelon inventory optimisation approach for supply chain segmentation

Segmenting large supply chains into lean and agile segments has become a powerful strategy allowing companies to manage different market demands effectively. A current stream of research into supply chain segmentation proposes demand volume and variability as the key segmentation criteria. This literature adequately justifies these criteria and analyses the benefits of segmentation. However, current work fails to provide approaches for allocating products to segments which go beyond simple rules of thumb, such as 80-20 Pareto rules. We propose a joint network and safety stock optimisation model which optimally allocates Stock Keeping Units (SKUs) to segments. We use this model, populated both with synthetic data and data from a real case study and demonstrate that this approach significantly improves cost when compared to using simple rules of thumb alone.

Vertebra segmentation based on two-step refinement.

Knowledge of vertebra location, shape, and orientation is crucial in many medical applications such as orthopedics or interventional procedures. Computed tomography (CT) offers a high contrast between bone and soft tissues, but automatic vertebra segmentation remains difficult. Hence, the wide range of shapes, aging, and degenerative joint disease alterations as well as the variety of pathological cases encountered in an aging population make automatic segmentation sometimes challenging. Besides, daily practice implies a need for affordable computation time.This paper aims to present a new automated vertebra segmentation method (using a first bounding box for initialization) for CT 3D data which tackles these problems. This method is based on two consecutive steps. The first one is a new coarse-to-fine method efficiently reducing the data amount to obtain a coarse shape of the vertebra. The second step consists in a hidden Markov chain (HMC) segmentation using a specific volume transformation within a Bayesian framework. Our method does not introduce any prior on the expected shape of the vertebra within the bounding box and thus deals with the most frequent pathological cases encountered in daily practice.We experiment this method on a set of standard lumbar, thoracic, and cervical vertebrae and on a public dataset, on pathological cases, and in a simple integration example. Quantitative and qualitative results show that our method is robust to changes in shapes and luminance and provides correct segmentation with respect to pathological cases.

Effect of Cobalt Fillers on Polyurethane Segmentations Investigated by Synchrotron Small Angle X-Ray Scattering

The segmentation between rigid and rubbery chains in polyurethanes (PUs) influences polymeric properties and implementations. Several models have successfully been proposed to visualize the configuration between the hard segment (HS) and soft segment (SS). For particulate PU composites, the arrangement of HS and SS is more complicated because the fillers tend to disrupt the chain formation and segmentation. In this work, the effect of ferromagnetic cobalt (Co) powders (average diameter 2 μm) on PU synthesized from a reaction between polyether polyol (soft segment) and diphenylmethane-4,4′-diisocyanate (hard segment) was studied with varying loadings (0, 20, 40, and 60 wt.%). The 300 μm thick PU/Co samples were tape-casted and then received heat treatment at 80°C for 180 min. From synchrotron small angle X-ray scattering (SAXS), the plot of the X-ray scattering intensity (I) against the scattering vector (q) exhibited a typical single peak of PU whose intensity was reduced by the increase in the Co loading. Characteristic SAXS peaks in the case of 0-20 wt.% Co agreed well with the scattering by globular hard segment domains according to Zernike-Prins and Percus-Yevick models. The higher Co loadings led to larger deviations from all theoretical models.

P008 Intérêt d’un questionnaire calcique court en pratique clinique quotidienne

MOEA/D (multi-objective evolutionary algorithm based on decomposition) decomposes a multi-objective optimization problem (MOP) into a series of single-objective sub-problems through a scalarizing function and a set of uniformly distributed weight vectors, and optimizes these sub-problems simultaneously in a collaborative way. However, when the shape of the true Pareto front (PF) of the multi-objective problem has the characteristic of long tail and sharp peak, the performance of MOEA/D will be greatly affected, that is, the performance of the decomposition-based multi-objective evolutionary algorithm depends heavily on the shape of the true PF. In order to efficiently deal with this situation, a self-adaptive weight vector adjustment strategy based on chain segmentation strategy (CS) is proposed. More specifically, a chain structure is firstly derived from the current population distribution to approximate the shape of the true PF. Then each chain is evenly segmented, and the direction vector from the origin to each segment point is used as the new weight vector. Finally, a set of reasonably distributed weight vectors are obtained to improve the performance of the algorithm. In the experimental section, we integrate CS strategy with three variants of MOEA/D, and the results demonstrate the effectiveness of the proposed strategy. Furthermore, we use MOEA/D-DE (a variant of MOEA/D, which is based on differential evolution operator) as a paradigm to integrate the CS strategy, and compare it with five state-of-the-art algorithms to illustrate that the algorithm integrating the CS strategy is very competitive.

Enabling supply chain segmentation through demand profiling

PurposeWhilst there are a plethora of theoretical ideas regarding a segmented approach to supply chain strategy, there is paucity of empirical data. This paper aims to present the results of a field‐based case study in a fast moving consumer goods company (FMCGCo) that sought to identify the most relevant basis for supply chain segmentation.Design/methodology/approachResearch carried out on a single instrumental case, FMCGCo. A four‐stage research design was employed to develop and test an approach to developing a segmented supply chain strategy for FMCGCo supply chain Western Europe.FindingsThe study identified demand profiling at an item level (using volume and variability classifiers) as the primary driver of supply chain strategy. It also developed a novel profiling tool. This not only segmented demand but identified through the introduction of the concept of filters a method for turning the results of the demand profiling analysis into a segmented supply chain strategy.Research limitations/implicationsThe results presented are based on a single instrumental case. It is the approach that is transferable to other supply chain contexts not the specific solution per se.Practical implicationsThe paper presents a practical and pragmatic approach for companies to develop a segmented supply chain strategy based on demand profiling.Originality/valueThe introduction of a new tool as a practical and relatively quick means of developing a segmented supply chain strategy, that considers both market and product characteristics.