2,477 research outputs found

    Organizing sustainable development

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    The role and meaning of sustainable development have been recognized in the scientific literature for decades. However, there has recently been a dynamic increase in interest in the subject, which results in numerous, in-depth scientific research and publications with an interdisciplinary dimension. This edited volume is a compendium of theoretical knowledge on sustainable development. The context analysed in the publication includes a multi-level and multi-aspect analysis starting from the historical and legal conditions, through elements of the macro level and the micro level, inside the organization. Organizing Sustainable Development offers a systematic and comprehensive theoretical analysis of sustainable development supplemented with practical examples, which will allow obtaining comprehensive knowledge about the meaning and its multi-context application in practice. It shows the latest state of knowledge on the topic and will be of interest to students at an advanced level, academics and reflective practitioners in the fields of sustainable development, management studies, organizational studies and corporate social responsibility

    Optimal Design of Steel Structures Using Innovative Black Widow Algorithm Hybridized with Greedy Sensitivity-Based Particle Swarm Optimization Technique

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    This paper presents a Greedy Sensitivity-based analysis implemented on the Particle Swarm Optimization search engine (GSPSO). The effectiveness of the method focuses mainly on providing an intelligent population to enter meta-heuristic algorithms. As a meta-heuristic method in the second stage, the recently introduced Black Widow Optimization (BWO) algorithm was selected and improved by the authors. It is based on three operators: cannibalism, crossover, and mutation, whose main stage is Cannibalism. The advantage of this stage is that those designs that do not match the solutions close to the global optimal are eliminated, and the more effective solutions remain. To examine the proposed approach, five optimization examples, including three two-dimensional benchmark frames and two three-dimensional structures, have been used. The results show that the greedy sensitivity-based PSO technique can improve computational efficiency in solving discrete variable structural optimization problems. The hybridized BWO (BGP) with this technique was able to obtain very good results in terms of convergence speed and performance accuracy. Overall, compared to the performance of BWO, between 50 and 75% improvement in the total number of analyzes was achieved. In addition, a slight improvement in the weight of the evaluated structures was also reported. Compared to other hybrid algorithms, very competitive and promising results were obtained

    A swarm intelligence-based ensemble learning model for optimizing customer churn prediction in the telecommunications sector

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    In today's competitive market, predicting clients' behavior is crucial for businesses to meet their needs and prevent them from being attracted by competitors. This is especially important in industries like telecommunications, where the cost of acquiring new customers exceeds retaining existing ones. To achieve this, companies employ Customer Churn Prediction approaches to identify potential customer attrition and develop retention plans. Machine learning models are highly effective in identifying such customers; however, there is a need for more effective techniques to handle class imbalance in churn datasets and enhance prediction accuracy in complex churn prediction datasets. To address these challenges, we propose a novel two-level stacking-mode ensemble learning model that utilizes the Whale Optimization Algorithm for feature selection and hyper-parameter optimization. We also introduce a method combining K-member clustering and Whale Optimization to effectively handle class imbalance in churn datasets. Extensive experiments conducted on well-known datasets, along with comparisons to other machine learning models and existing churn prediction methods, demonstrate the superiority of the proposed approach

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Optimization for Energy Management in the Community Microgrids

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    This thesis focuses on improving the energy management strategies for Community Microgrids (CMGs), which are expected to play a crucial role in the future smart grid. CMGs bring many benefits, including increased use of renewable energy, improved reliability, resiliency, and energy efficiency. An Energy Management System (EMS) is a key tool that helps in monitoring, controlling, and optimizing the operations of the CMG in a cost-effective manner. The EMS can include various functionalities like day-ahead generation scheduling, real-time scheduling, uncertainty management, and demand response programs. Generation scheduling in a microgrid is a challenging optimization problem, especially due to the intermittent nature of renewable energy. The power balance constraint, which is the balance between energy demand and generation, is difficult to satisfy due to prediction errors in energy demand and generation. Real-time scheduling, which is based on a shorter prediction horizon, reduces these errors, but the impact of uncertainties cannot be completely eliminated. In regards to demand response programs, it is challenging to design an effective model that motivates customers to voluntarily participate while benefiting the system operator. Mathematical optimization techniques have been widely used to solve power system problems, but their application is limited by the need for specific mathematical properties. Metaheuristic techniques, particularly Evolutionary Algorithms (EAs), have gained popularity for their ability to solve complex and non-linear problems. However, the traditional form of EAs may require significant computational effort for complex energy management problems in the CMG. This thesis aims to enhance the existing methods of EMS in CMGs. Improved techniques are developed for day-ahead generation scheduling, multi-stage real-time scheduling, and demand response implementation. For generation scheduling, the performance of conventional EAs is improved through an efficient heuristic. A new multi-stage scheduling framework is proposed to minimize the impact of uncertainties in real-time operations. In regards to demand response, a memetic algorithm is proposed to solve an incentive-based scheme from the perspective of an aggregator, and a price-based demand response driven by dynamic price optimization is proposed to enhance the electric vehicle hosting capacity. The proposed methods are validated through extensive numerical experiments and comparison with state-of-the-art approaches. The results confirm the effectiveness of the proposed methods in improving energy management in CMGs

    Enhancing the forensic comparison process of common trace materials through the development of practical and systematic methods

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    An ongoing advancement in forensic trace evidence has driven the development of new and objective methods for comparing various materials. While many standard guides have been published for use in trace laboratories, different areas require a more comprehensive understanding of error rates and an urgent need for harmonizing methods of examination and interpretation. Two critical areas are the forensic examination of physical fits and the comparison of spectral data, which depend highly on the examiner’s judgment. The long-term goal of this study is to advance and modernize the comparative process of physical fit examinations and spectral interpretation. This goal is fulfilled through several avenues: 1) improvement of quantitative-based methods for various trace materials, 2) scrutiny of the methods through interlaboratory exercises, and 3) addressing fundamental aspects of the discipline using large experimental datasets, computational algorithms, and statistical analysis. A substantial new body of knowledge has been established by analyzing population sets of nearly 4,000 items representative of casework evidence. First, this research identifies material-specific relevant features for duct tapes and automotive polymers. Then, this study develops reporting templates to facilitate thorough and systematic documentation of an analyst’s decision-making process and minimize risks of bias. It also establishes criteria for utilizing a quantitative edge similarity score (ESS) for tapes and automotive polymers that yield relatively high accuracy (85% to 100%) and, notably, no false positives. Finally, the practicality and performance of the ESS method for duct tape physical fits are evaluated by forensic practitioners through two interlaboratory exercises. Across these studies, accuracy using the ESS method ranges between 95-99%, and again no false positives are reported. The practitioners’ feedback demonstrates the method’s potential to assist in training and improve peer verifications. This research also develops and trains computational algorithms to support analysts making decisions on sample comparisons. The automated algorithms in this research show the potential to provide objective and probabilistic support for determining a physical fit and demonstrate comparative accuracy to the analyst. Furthermore, additional models are developed to extract feature edge information from the systematic comparison templates of tapes and textiles to provide insight into the relative importance of each comparison feature. A decision tree model is developed to assist physical fit examinations of duct tapes and textiles and demonstrates comparative performance to the trained analysts. The computational tools also evaluate the suitability of partial sample comparisons that simulate situations where portions of the item are lost or damaged. Finally, an objective approach to interpreting complex spectral data is presented. A comparison metric consisting of spectral angle contrast ratios (SCAR) is used as a model to assess more than 94 different-source and 20 same-source electrical tape backings. The SCAR metric results in a discrimination power of 96% and demonstrates the capacity to capture information on the variability between different-source samples and the variability within same-source samples. Application of the random-forest model allows for the automatic detection of primary differences between samples. The developed threshold could assist analysts with making decisions on the spectral comparison of chemically similar samples. This research provides the forensic science community with novel approaches to comparing materials commonly seen in forensic laboratories. The outcomes of this study are anticipated to offer forensic practitioners new and accessible tools for incorporation into current workflows to facilitate systematic and objective analysis and interpretation of forensic materials and support analysts’ opinions

    Particle swarm optimization with state-based adaptive velocity limit strategy

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    Velocity limit (VL) has been widely adopted in many variants of particle swarm optimization (PSO) to prevent particles from searching outside the solution space. Several adaptive VL strategies have been introduced with which the performance of PSO can be improved. However, the existing adaptive VL strategies simply adjust their VL based on iterations, leading to unsatisfactory optimization results because of the incompatibility between VL and the current searching state of particles. To deal with this problem, a novel PSO variant with state-based adaptive velocity limit strategy (PSO-SAVL) is proposed. In the proposed PSO-SAVL, VL is adaptively adjusted based on the evolutionary state estimation (ESE) in which a high value of VL is set for global searching state and a low value of VL is set for local searching state. Besides that, limit handling strategies have been modified and adopted to improve the capability of avoiding local optima. The good performance of PSO-SAVL has been experimentally validated on a wide range of benchmark functions with 50 dimensions. The satisfactory scalability of PSO-SAVL in high-dimension and large-scale problems is also verified. Besides, the merits of the strategies in PSO-SAVL are verified in experiments. Sensitivity analysis for the relevant hyper-parameters in state-based adaptive VL strategy is conducted, and insights in how to select these hyper-parameters are also discussed.Comment: 33 pages, 8 figure

    Proceedings of SIRM 2023 - The 15th European Conference on Rotordynamics

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    It was our great honor and pleasure to host the SIRM Conference after 2003 and 2011 for the third time in Darmstadt. Rotordynamics covers a huge variety of different applications and challenges which are all in the scope of this conference. The conference was opened with a keynote lecture given by Rainer Nordmann, one of the three founders of SIRM “Schwingungen in rotierenden Maschinen”. In total 53 papers passed our strict review process and were presented. This impressively shows that rotordynamics is relevant as ever. These contributions cover a very wide spectrum of session topics: fluid bearings and seals; air foil bearings; magnetic bearings; rotor blade interaction; rotor fluid interactions; unbalance and balancing; vibrations in turbomachines; vibration control; instability; electrical machines; monitoring, identification and diagnosis; advanced numerical tools and nonlinearities as well as general rotordynamics. The international character of the conference has been significantly enhanced by the Scientific Board since the 14th SIRM resulting on one hand in an expanded Scientific Committee which meanwhile consists of 31 members from 13 different European countries and on the other hand in the new name “European Conference on Rotordynamics”. This new international profile has also been emphasized by participants of the 15th SIRM coming from 17 different countries out of three continents. We experienced a vital discussion and dialogue between industry and academia at the conference where roughly one third of the papers were presented by industry and two thirds by academia being an excellent basis to follow a bidirectional transfer what we call xchange at Technical University of Darmstadt. At this point we also want to give our special thanks to the eleven industry sponsors for their great support of the conference. On behalf of the Darmstadt Local Committee I welcome you to read the papers of the 15th SIRM giving you further insight into the topics and presentations
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