Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: Operational modeling and simulation of an inter-bay AMHS in semiconductor wafer fabrication
Abstract: This paper studies the operational logic in an inter-bay automated material handling system (AMHS) in semiconductor wafer fabrication. This system consists of stockers located in a two-floor layout. Automated moving devices transfer lots between stockers within the same floor (intra-floor lot transfer) or between different floors (inter-floor lot transfer). Intra-floor lot-transferring transports use a two-rail one-directional system, whereas inter-floor lot-transferring transports use lifters. The decision problem consists of selecting rails and lifters that minimize average lot-delivery time. Several operation rules to deliver lots from source stocker to destination stocker are proposed and their performance is evaluated by discrete event simulation.
Extracted statement — Objective function(s): Delivery time
Research field: Science
Title: Simulation based comparison of semiconductor AMHS alternatives: continuous flow vs. overhead monorail
Abstract: Automation is an essential component in today's semiconductor manufacturing. As factories migrate to 300 mm technology, automated handling becomes increasingly important for variety of ergonomic, safety, and yield considerations. Traditional semiconductor AMHS systems, such as the Overhead Monorail Vehicles (OMV) or Overhead Hoist, can be overly expensive. Cost projections for a 300 mm inter/intrabay AMHS installation are in the range of $50 M-$100 M. As an alternative, a lower cost alternative AMHS, called Continuous Flow Transport has been proposed. The CFT system is similar to what has historically been identified as a conveyor based movement system. The CFT system provides cost savings at reduced flexibility and longer delivery time. This study compares the CFT to Overhead Monorail transport, determining a cumulative delivery time distribution. As expected, the CFT system requires a longer average delivery time interval than OMV, but may provide total savings through reduced transport variability.
Extracted statement — Performance measures: Delivery time distribution
Research field: Science
Title: Dental erosion among children in an Istanbul public school
Abstract: The aim of this study was to evaluate the prevalence, clinical manifestations, and etiology of dental erosion among children. A total of 153 healthy, 11-year-old children were sampled from a downtown public school in Istanbul, Turkey comprised of middle-class children. Data were obtained via: (1) dinical examination; (2) questionnaire; and (3) standardized data records. A new dental erosion index for children designed by O'Sullivan (2000) was used. Twenty-eight percent (N=43) of the children exhibited dental erosion. Of children who consumed orange juice, 32% showed erosion, while 40% who consumed carbonated beverages showed erosion. Of children who consumed fruit yogurt, 36% showed erosion. Of children who swam professionally in swimming pools, 60% showed erosion. Multiple regression analysis revealed no relationship between dental erosion and related erosive sources (P > .05).
Extracted statement — Aim of the study: Dental erosion
Research field: Science
Title: Ship detection and classification in high-resolution remote sensing imagery using shape-driven segmentation method
Abstract: High-resolution remote sensing imagery provides an important data source for ship detection and classification. However, due to shadow effect, noise and low-contrast between objects and background existing in this kind of data, traditional segmentation approaches have much difficulty in separating ship targets from complex sea-surface background. In this paper, we propose a novel coarse-to-fine segmentation strategy for identifying ships in 1-meter resolution imagery. This approach starts from a coarse segmentation by selecting local intensity variance as detection feature to segment ship objects from background. After roughly obtaining the regions containing ship candidates, a shape-driven level-set segmentation is used to extract precise boundary of each object which is good for the following stages such as detection and classification. Experimental results show that the proposed approach outperforms other algorithms in terms of recognition accuracy.
Extracted statement — Main purpose: Detection and classification
Research field: Science
Title: An Effective Multirestart Deterministic Annealing Metaheuristic for the Fleet Size and Mix Vehicle-Routing Problem with Time Windows
Abstract: This paper presents a new deterministic annealing metaheuristic for the fleet size and mix vehicle-routing problem with time windows. The objective is to service, at minimal total cost, a set of customers within their time windows by a heterogeneous capacitated vehicle fleet. First, we motivate and define the problem. We then give a mathematical formulation of the most studied variant in the literature in the form of a mixed-integer linear program. We also suggest an industrially relevant, alternative definition that leads to a linear mixed-integer formulation. The suggested metaheuristic solution method solves both problem variants and comprises three phases. In Phase 1, high-quality initial solutions are generated by means of a savings-based heuristic that combines diversification strategies with learning mechanisms. In Phase 2, an attempt is made to reduce the number of routes in the initial solution with a new local search procedure. In Phase 3, the solution from Phase 2 is further improved by a set of four local search operators that are embedded in a deterministic annealing framework to guide the improvement process. Some new implementation strategies are also suggested for efficient time window feasibility checks. Extensive computational experiments on the 168 benchmark instances have shown that the suggested method outperforms the previously published results and found 167 best-known solutions. Experimental results are also given for the new problem variant.
Extracted statement — Method: Deterministic annealing
Research field: Science
Title: Environmental Kuznets curve: evidences from developed and developing economies
Abstract: Previous studies show that the environmental quality and economic growth can be represented by the inverted U curve called Environmental Kuznets Curve (EKC). In this study, we conduct empirical analyses on detecting the existence of EKC using the five common pollutants emissions (i.e. CO2, SO2, BOD, SPM10, and GHG) as proxy for environmental quality. The data spanning from year 1961 to 2009 and cover 40 countries. We seek to investigate if the EKC hypothesis holds in two groups of economies, i.e. developed versus developing economies. Applying panel data approach, our results show that the EKC does not hold in all countries. We also detect the existence of U shape and increasing trend in other cases. The results reveal that CO2 and SPM10 are good data to proxy for environmental pollutant and they can be explained well by GDP. Also, it is observed that the developed countries have higher turning points than the developing countries. Higher economic growth may lead to different impacts on environmental quality in different economies.
Extracted statement — Shape of EKC: Developing countries
Research field: Science
Title: Evaluating the critical success factors of supplier development: a case study
Abstract: Purpose – The purpose of this paper is to identify and evaluate the critical success factors (CSFs) responsible for supplier development (SD) in a manufacturing supply chain environment.Design/methodology/approach – In total, 13 CSFs for SD are identified (i.e. long‐term strategic goal; top management commitment; incentives; supplier's supplier condition; proximity to manufacturing base; supplier certification; innovation capability; information sharing; environmental readiness; external environment; project completion experience; supplier status and direct involvement) through extensive literature review and discussion held with managers/engineers in different Indian manufacturing companies. A fuzzy analytic hierarchy process (FAHP) is proposed and developed to evaluate the degree of impact of each CSF on SD.Findings – The degree of impact for each CSF on SD is established for an Indian company. The results are discussed in detail with managerial implications. The long‐term strategic goal is found to be ...
Extracted statement — Critical success factors: direct involvement
Research field: Science
Title: Distributed Group
Support Systems
Abstract: Distributed group support systems are likely to be widely used in the future as a means for dispersed groups of people to work together through computer networks. They combine characteristics of computer-mediated communication systems with the specialized tools and processes developed in the context of group decision support systems, to provide communications, a group memory, and tools and structures to coordinate the group process and analyze data. These tools and structures can take a wide variety of forms in order to best support computer-mediated interaction for different types of tasks and groups. This article summarizes five case studies of different distributed group support systems developed by the authors and their colleagues over the last decade to support different types of tasks and to accommodate fairly large numbers of participants (tens to hundreds). The case studies are placed within conceptual frameworks that aid in classifying and comparing such systems. The results of the case studies demonstrate that design requirements and the associated research issues for group support systems an be very different in the distributed environment compared to the decision room approach.
Extracted statement — Technology: Distributed group support systems
Research field: Science
Title: Domain Transformation-Based Efficient Cost Aggregation for Local Stereo Matching
Abstract: Binocular stereo matching is one of the most important algorithms in the field of computer vision. Adaptive support-weight approaches, the current state-of-the-art local methods, produce results comparable to those generated by global methods. However, excessive time consumption is the main problem of these algorithms since the computational complexity is proportionally related to the support window size. In this paper, we present a novel cost aggregation method inspired by domain transformation, a recently proposed dimensionality reduction technique. This transformation enables the aggregation of 2-D cost data to be performed using a sequence of 1-D filters, which lowers computation and memory costs compared to conventional 2-D filters. Experiments show that the proposed method outperforms the state-of-the-art local methods in terms of computational performance, since its computational complexity is independent of the input parameters. Furthermore, according to the experimental results with the Middlebury dataset and real-world images, our algorithm is currently one of the most accurate and efficient local algorithms.
Extracted statement — Algorithm: Domain transformation
Research field: Science
Title: A dynamic model and algorithm for fleet planning
Abstract: By analysing the merits and demerits of the existing linear model for fleet planning, this paper presents an algorithm which combines the linear programming technique with that of dynamic programming to improve the solution to linear model for fleet planning. This new approach has not only the merits that the linear model for fleet planning has, but also the merit of saving computing time. The numbers of ships newly added into the fleet every year are always integers in the final optimal solution. The last feature of the solution directly meets the requirements of practical application. Both the mathematical model of the dynamic fleet planning and its algorithm are put forward in this paper. A calculating example is also given.
Extracted statement — Method: dynamic programming
Research field: Science
Title: Detecting pathogen exposure during the non-symptomatic incubation period using physiological data
Abstract: Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with high-resolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.
Extracted statement — Objective: Early warning
Research field: Science
Title: Detecting pathogen exposure during the non-symptomatic incubation period using physiological data
Abstract: Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with high-resolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.
Extracted statement — Setting: Early warning
Research field: Science
Title: Model-driven development of user interfaces for educational games
Abstract: The main topic of this paper is the problem of developing user interfaces for educational games. Focus of educational games is usually on the knowledge while it should be evenly distributed to the user interface as well. Our proposed solution is based on the model-driven approach, thus we created a framework that incorporates meta-models, models, transformations and software tools. We demonstrated practical application of the mentioned framework by developing user interface for educational adventure game.
Extracted statement — Game Genres: Educational Games
Research field: Science
Title: Logical structure analysis of document images based on emergent computation
Abstract: A new method for logical structure analysis of document images is proposed in this paper as the basis for a document reader which can extract logical information from various printed documents. The proposed system consists of five basic modules: typography analysis, object recognition, object segmentation, object grouping and object modification. Emergent computation, which is a key concept of artificial life, is adopted for the cooperative interaction among the modules in the system in order to achieve an effective and flexible behavior of the whole system. It has two principal advantages over other methods: adaptive system configuration for various and complex logical structures, and robust document analysis that is tolerant of erroneous feature detection.
Extracted statement — Key Idea: emergent computation
Research field: Science
Title: Study on a Liner Shipping Network Design Considering Empty Container Reposition
Abstract: Empty container allocation problems arise due to imbalance on trades. Imbalanced trade is a common fact in the liner shipping,creating the necessity of repositioning empty containers from import-dominant ports to export-dominant ports in an economic and efficient way. The present work configures a liner shipping network, by performing the routes assignment and their integration to maximize the profit for a liner shipping company. The empty container repositioning problem is expressly taken into account in whole process. By considering the empty container repositioning problem in the network design, the choice of routes will be also influenced by the empty container flow, resulting in an optimum network, both for loaded and empty cargo. The Liner Shipping Network Design Program (LS-NET program) will define the best set of routes among a set of candidate routes, the best composition of the fleet for the network and configure the empty container repositioning network. Further, a network of Asian ports was studied and the results obtained show that considering the empty container allocation problem in the designing process can influence the final configuration of the network.
Extracted statement — emarkable factor: Empty container repositioning
Research field: Science
Title: Energy efficiency estimation based on data fusion strategy: Case study of ethylene product industry
Abstract: Data fusion is an emerging technology to fuse data from multiple data or information of the environment through measurement and detection to make a more accurate and reliable estimation or decision. In this Article, energy consumption data are collected from ethylene plants with the high temperature steam cracking process technology. An integrated framework of the energy efficiency estimation is proposed on the basis of data fusion strategy. A Hierarchical Variable Variance Fusion (HVVF) algorithm and a Fuzzy Analytic Hierarchy Process (FAHP) method are proposed to estimate energy efficiencies of ethylene equipments. For different equipment scales with the same process technology, the HVVF algorithm is used to estimate energy efficiency ranks among different equipments. For different technologies based on HVVF results, the FAHP method based on the approximate fuzzy eigenvector is used to get energy efficiency indices (EEI) of total ethylene industries. The comparisons are used to assess energy utilization...
Extracted statement — Objective/estimate(s) process systems: Energy efficiencies of ethylene
Research field: Science
Title: Evaluating the critical success factors of supplier development: a case study
Abstract: Purpose – The purpose of this paper is to identify and evaluate the critical success factors (CSFs) responsible for supplier development (SD) in a manufacturing supply chain environment.Design/methodology/approach – In total, 13 CSFs for SD are identified (i.e. long‐term strategic goal; top management commitment; incentives; supplier's supplier condition; proximity to manufacturing base; supplier certification; innovation capability; information sharing; environmental readiness; external environment; project completion experience; supplier status and direct involvement) through extensive literature review and discussion held with managers/engineers in different Indian manufacturing companies. A fuzzy analytic hierarchy process (FAHP) is proposed and developed to evaluate the degree of impact of each CSF on SD.Findings – The degree of impact for each CSF on SD is established for an Indian company. The results are discussed in detail with managerial implications. The long‐term strategic goal is found to be ...
Extracted statement — Critical success factors: environmental readiness
Research field: Science
Title: E-procurement, the golden key to optimizing the supply chains system
Abstract: Procurement is an important component in the field of operating resource management and e-procurement is the golden key to optimizing the supply chains system. Global firms are optimistic on the level of savings that can be achieved through full implementation of e-procurement strategies. E-procurement is an Internet-based business process for obtaining materials and services and managing their inflow into the organization. In this paper, the subjects of supply chains and e-procurement and its benefits to organizations have been studied. Also, e-procurement in construction and its drivers and barriers have been discussed and a framework of supplier selection in an e-procurement environment has been demonstrated. This paper also has addressed critical success factors in adopting e-procurement in supply chains. Keywords—E-Procurement, Supply Chain, Benefits, Construction, Drivers, Barriers, Supplier Selection, CFSs.
Extracted statement — SCM field: e-Procurement in supply chain