Exact Penalization and Necessary Optimality Conditions for Multiobjective Optimization Problems with Equilibrium Constraints

A calmness condition for a general multiobjective optimization problem with equilibrium constraints is proposed. Some exact penalization properties for two classes of multiobjective penalty problems are established and shown to be equivalent to the calmness condition. Subsequently, a Mordukhovich stationary necessary optimality condition based on the exact penalization results is obtained. Moreover, some applications to a multiobjective optimization problem with complementarity constraints and a multiobjective optimization problem with weak vector variational inequality constraints are given

OMAE2004-51016 RISK BASED INSPECTION AND REPAIR OPTIMIZATION OF SHIP STRUCTURES CONSIDERING CORROSION EFFECTS

ABSTRACT A theoretical framework of risk based optimal inspection and repair planning is proposed for the ship structures subjected to corrosion deterioration. The planning problem is formulated as an optimization problem where the expected lifetime costs were minimized with a constraint on the minimum acceptable reliability index. The safety margins are established for the inspection events, the repair events and the failure events for ship structures. Moreover, the formulae are derived to calculate failure probabilities and repair probabilities. Based on them, a component subjected to pitting corrosion is investigated to illustrate the process of selecting the optimal inspection and repair strategy. Furthermore, some sensitivity studies were provided. The results show that the optimal inspection instants should take place before the reliability index reaches the minimum acceptable reliability index. The optimal target failure probability is . In addition, a balance can be achieved between the risk cost and total expected inspection and repair costs by using the risk based optimal inspection and repair method, which is very effective in selecting the optimal inspection and repair strategy

Analisis Adopsi Inovasi Teknologi Pertanian Berbasis Padi di Sumatera Selatan dalam Perspektif Komunikasi

Analysis of Adoption of Agricultural Technology Innovation Rice-based Farming in Sumatra inthe perspective of communications. Assessment Institute of Agricultural Technology (AIAT) South Sumatrahas produced innovative rice-based farming technology in various agroecosystem. However, adoption ratesare still relatively low. Evaluation of four assessments aimed to identify the factors that predominantly affectthe adoption of technological innovation based local-specific farming rice and to know the level of adoption.This activity is carried out in OKI, East OKU and Banyuasin regencies with 67 respondents interviewedin July-September 2007. The results of this assessment showed that the factors that influence the adoption oftechnological innovations such as the level of selective exposure of technology innovation, cosmopolite,triability, complexity of technology and agricultural extension intensity. The average adoption index for thepacket of rice cultivation technology was 50.32%. As many as 93.02% of respondents have positive perceptionsof the researcher-extension AIAT South Sumatra as the communicator in delivering information technology.Most respondents (80%) expressed a desire to obtain agricultural information generated AIAT South Sumatra.Key words: Adoption, innovation, rice, communication Balai Pengkajian Teknologi Pertanian (BPTP) Sumatera Selatan sudah menghasilkan inovasi teknologipertanian berbasis padi di berbagai agroekosistem. Namun tingkat adopsinya masih relatif rendah. Evaluasi terhadapempat pengkajian ini bertujuan untuk mengidentifikasi faktor-faktor yang dominan mempengaruhi proses adopsiinovasi teknologi pertanian spesifik lokasi berbasis padi dan mengetahui tingkat adopsinya. Kegiatan ini dilakukan diKabupaten OKI, OKU Timur dan Banyuasin dengan mewawancarai 67 orang responden pada bulan Juli – September2007. Berdasarkan hasil analisis deskriptif kualitatif diketahui bahwa (1) adopsi inovasi teknologi budidaya tanamanpadi di Sumatera Selatan dipengaruhi oleh tingkat kebutuhan petani terhadap inovasi teknologi, sifat kekosmopolitanpetani, triabilitas dan kompleksitas teknologi dan intensitas pembinaan, (2) indeks adopsi inovasi petani terhadappaket teknologi budidaya padi kondisinya beragam tergantung pada jenis kegiatan, (3) petani di Sumatera Selatanumumnya memberikan apresiasi positif terhadap peneliti-penyuluh BPTP Sumatera Selatan, terlihat dari tingginyaminat petani untuk mendapatkan berbagai media informasi pertanian BPTP Sumatera Selatan, dan (4) temuankajian ini mengindikasikan faktor komunikasi memegang peran utama yang dapat mempengaruhi adopsi teknologi

You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model

Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with increasing model size, resulting in slow inference speed and increased cost for severing. While some certain predictions benefit from the full complexity of the large-scale model, not all of inputs need the same amount of computation to conduct, potentially leading to computation resource waste. To handle this challenge, early exiting is proposed to adaptively allocate computational power in term of input complexity to improve inference efficiency. The existing early exiting strategies usually adopt output confidence based on intermediate layers as a proxy of input complexity to incur the decision of skipping following layers. However, such strategies cannot apply to encoder in the widely-used unified architecture with both encoder and decoder due to difficulty of output confidence estimation in the encoder. It is suboptimal in term of saving computation power to ignore the early exiting in encoder component. To handle this challenge, we propose a novel early exiting strategy for unified visual language models, which allows dynamically skip the layers in encoder and decoder simultaneously in term of input layer-wise similarities with multiple times of early exiting, namely \textbf{MuE}. By decomposing the image and text modalities in the encoder, MuE is flexible and can skip different layers in term of modalities, advancing the inference efficiency while minimizing performance drop. Experiments on the SNLI-VE and MS COCO datasets show that the proposed approach MuE can reduce expected inference time by up to 50\% and 40\% while maintaining 99\% and 96\% performance respectively

Analyzing Size of Loss Frequency Distribution Patterns: Uncovering the Impact of the COVID-19 Pandemic

This study delves into a critical examination of the Size of Loss distribution patterns in the context of auto insurance during pre- and post-pandemics, emphasizing their profound influence on insurance pricing and regulatory frameworks. Through a comprehensive analysis of the historical Size of Loss data, insurers and regulators gain essential insights into the probabilities and magnitudes of insurance claims, informing the determination of precise insurance premiums and the management of case reserving. This approach aids in fostering fair competition, ensuring equitable premium rates, and preventing discriminatory pricing practices, thereby promoting a balanced insurance landscape. The research further investigates the impact of the COVID-19 pandemic on these Size of Loss patterns, given the substantial shifts in driving behaviours and risk landscapes. Also, the research contributes to the literature by addressing the need for more studies focusing on the implications of the COVID-19 pandemic on pre- and post-pandemic auto insurance loss patterns, thus offering a holistic perspective encompassing both insurance pricing and regulatory dimensions

Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data

The study of actuarial fairness in auto insurance has been an important issue in the decision making of rate regulation. Risk classification and estimating risk relativities through statistical modeling become essential to help achieve fairness in premium rates. However, because of minor adjustments to risk relativities allowed by regulation rules, the rates charged eventually may not align with the empirical risk relativities calculated from insurance loss data. Therefore, investigating the relationship between the premium rates and loss costs at different risk factor levels becomes important for studying insurance fairness, particularly from rate regulation perspectives. This work applies statistical models to rate and classification data from the automobile statistical plan to investigate the disparities between insurance premiums and loss costs. The focus is on major risk factors used in the rate regulation, as our goal is to address fairness at the industry level. Various statistical models have been constructed to validate the suitableness of the proposed methods that determine a fixed effect. The fixed effect caused by the disparity of loss cost and premium rates is estimated by those statistical models. Using Canadian data, we found that there are no significant excessive premiums charged at the industry level, but the disparity between loss cost and premiums is high for urban drivers at the industry level. This study will help better understand the extent of auto insurance fairness at the industry level across different insured groups characterized by risk factor levels. The proposed fixed-effect models can also reveal the overall average loss ratio, which can tell us the fairness at the industry level when compared to loss ratios by the regulation rules

Exploring Industry-Level Fairness of Auto Insurance Premiums by Statistical Modeling of Automobile Rate and Classification Data

The study of actuarial fairness in auto insurance has been an important issue in the decision making of rate regulation. Risk classification and estimating risk relativities through statistical modeling become essential to help achieve fairness in premium rates. However, because of minor adjustments to risk relativities allowed by regulation rules, the rates charged eventually may not align with the empirical risk relativities calculated from insurance loss data. Therefore, investigating the relationship between the premium rates and loss costs at different risk factor levels becomes important for studying insurance fairness, particularly from rate regulation perspectives. This work applies statistical models to rate and classification data from the automobile statistical plan to investigate the disparities between insurance premiums and loss costs. The focus is on major risk factors used in the rate regulation, as our goal is to address fairness at the industry level. Various statistical models have been constructed to validate the suitableness of the proposed methods that determine a fixed effect. The fixed effect caused by the disparity of loss cost and premium rates is estimated by those statistical models. Using Canadian data, we found that there are no significant excessive premiums charged at the industry level, but the disparity between loss cost and premiums is high for urban drivers at the industry level. This study will help better understand the extent of auto insurance fairness at the industry level across different insured groups characterized by risk factor levels. The proposed fixed-effect models can also reveal the overall average loss ratio, which can tell us the fairness at the industry level when compared to loss ratios by the regulation rules

How to Detect Scale Effect of Ecosystem Services Supply? A Comprehensive Insight from Xilinhot in Inner Mongolia, China

Spatial scale plays a crucial role in the assessment and management of ecosystem services (ES), yet explicit information for identifying and understanding the scale effect on ES supply remains limited. In an attempt to detect scale effect on ES supply from a comprehensive perspective, this study developed a framework for integrating scale effect in three aspects, including individual ES patterns, pairwise ES interactions, and ecosystem service bundles (ESB). The framework was tested in Xilinhot, a prairie landscape city of Inner Mongolia, at four different levels of spatial scale. The results indicated that, most ES showed a decreasing clustering at coarser scales in terms of spatial pattern. At the same time, coarser scales resulted in fewer trade-offs and stronger synergies between pairwise ES. The identification of ESB varied greatly with scale, and this change reflected in the composition of ES variables and spatial distribution of bundles. We attributed the scale effect of the above three aspects to differences in social-ecological factors and their driving mechanisms at different scales. This comprehensive framework could support local managers to coordinate the management of multiple ES at different scales

Ecosystem Services and Their Relationships in the Grain-for-Green Programme—A Case Study of Duolun County in Inner Mongolia, China

Grassland restoration projects are currently being implemented to mitigate human disturbance to the natural environment and reduce grassland degradation. China’s Grain-for-Green Programme (GFGP), including one project implemented in Duolun County, China, in 2000, has significantly improved the overall ecological health of this region. Using a modeling approach, this study quantified changes in four ecosystem services (ESs), including Net Primary Production (NPP), soil conservation (SC), water yield (WY), and sandstorm prevention (SP), in Duolun County between 2000 and 2016. We found the total NPP, water yield, and soil conservation increased by 80.44%, 248.2%, and 12.2%, respectively, during this period, while the sandstorm prevention decreased by 55.9%. Unlike other areas of GFGP implementation, the improvement of the ecological environment in Duolun County is largely attributed to the increased of vegetation coverage (88%) instead of land use circulation (12%). We found the grassland is a factor that reduces the trade-off while this effect was related with the grassland coverage. Future policies should be based on the mechanisms of vegetation underlying the ESs change and the relationships of ESs in order to achieve sustainable provision of ESs