Optimal Projection Research Articles

Maxillary reconstruction with horizontally oriented scapular tip free flap: outcomes in orbital support and palate closure

BackgroundCurrent indications of maxillary reconstruction with scapular tip free flap are palatoalveolar defects associated with zygomaticomaxillary buttress and/or orbital floor defects. The scapular tip free flap can be placed either horizontally or vertically. Horizontal placement usually allows ideal palatal conformation preventing oronasal communication but has been argued to compromise orbital support and projection of the midface, while vertical placement is advocated for midface support but may be insufficient for complete closure of the palate. The present work focuses on the horizontal placing of scapular tip free flap to allow complete palate reconstruction and fistulae prevention while still obtaining optimal midface projection and orbital support. Material and methodsWe share our experience of twenty-one case complex maxillary reconstructions with this flap, in which the horizontally placed scapular tip component replaced the palate, a muscular flap component was included for midface volume restoration and an alloplastic implant utilized for supporting the orbital content when needed. Observation and resultsNone of our patients presented palatal fistulas or alterations in the orbital support. ConclusionA multi-level approach is proposed according to the maxillectomy defect. Our experience supports the horizontal insetting of the scapular tip free flap to allow palatal fistulae prevention while still obtaining an optimal midface projection and orbital floor support.

Innovative projection acquisition algorithm for optimizing portable LNDCT in oil and gas pipeline imaging

Fluid pipelines, commonly utilized in the oil industry, often face efficiency and reliability issues due to sediment buildup causing erosion, corrosion, and pipe wall thinning. Traditional assessment methods involve disruptive measures like cutting or creating holes and temporarily taking pipelines out of service. A non-destructive alternative, Limited-Number-Detector Computed Tomography (LNDCT), proves cost-effective and superior. Our proposed algorithm enhances data acquisition and projections using discrete detectors, employing Co-60 as a gamma-ray source and thallium-doped sodium iodide, NaI(Tl), detectors in an arc configuration. Monte Carlo simulations aligned closely with experimental data. Optimization involved adjusting the detector aperture angle based on a primary-to-scatter ratio of gamma-ray photons. We investigated the utility of various isotopes (Co-60, Cs-137, Am-241, Ir-192) to determine optimal projection signal amplitude. The algorithm generates a large sinogram matrix, and a filtered back-projection algorithm with a Hamming filter maximizes image quality while ensuring acceptable calculation volume and time. Using four phantoms, including pipelines filled to different scales, our study evaluates LNDCT configuration, performance, and validation. The results highlight its potential for efficiently evaluating sediment in pipelines, confirming the correctness and accuracy of our proposed algorithm.

Cross-condition bearing fault detection based on online drift detection and domain adaptation

Aiming at the problem that the data distribution of bearings across operating conditions generates offset resulting in insufficient diagnostic accuracy of the original model for new data, a cross-condition bearing fault detection method based on online drift detection and domain adaptation is proposed. First, the original one-dimensional vibration signals collected are transformed by a two-dimensional wavelet transform to convert the time-frequency image dataset. Second, the drift detection of the data across operating conditions is carried out using Random Forest (RF), and the 3σ criterion as well as the drift detection judgment criteria are set. Next, the source domain model based on Googlenet is used to extract features from the target domain data, and the Whale Optimization Algorithm to Improve Local Preserving Projection Algorithm (WOA-LPP) algorithm is combined to construct a brand-new projection space to align the features of the source and target domains. Then, the source and target domain features are reconstructed by combining the LPP optimal projection matrix to construct a fully connected network trained by the source domain features. Finally, probabilistic label-based decision fusion is proposed to integrate multiple classifiers to reduce the effects of model training randomness and strong noise interference. Validated by the publicly available Western Reserve University bearing data, the method proposed in this paper has good detection accuracy as well as robustness across operating conditions, which can effectively improve the defects of shifting data distribution and degradation of model accuracy under variable speed.

Strategic Integration of Drone Technology and Digital Twins for Optimal Construction Project Management

Strategic Integration of Drone Technology and Digital Twins for Optimal Construction Project Management

효율성 향상을 위한 방안으로 최적조합을 찾는 휴리스틱 방법에 관한 연구

This paper explores the optimal combination as a way to improve efficiency. Nonlinear optimization, random search, and greedy algorithm methods were used to find an optimal project combination of projects for government research institutes. According to our numerical experiments, the nonlinear programming method is not computationally efficient (i.e., requiring long computational time) to find an optimal combination. However, the greedy algorithm can produce multiple optimal project combinations in a short time window. Random search can also generates multiple optimal combinations, but it has disadvantage that computational time increases in the number of random generations. By using the proposed greedy method, multiple optimal combination can be quickly found in a situation where the number of R&D projects increases. Therefore, a research institute has a greater flexibility in selecting one among multiple optimal combinations.

NUMERICAL SIMULATION OF THE SPATIAL MODEL OF THE DAMAGED NINE-STORY BUILDING FOR STRENGTH AND RIGIDITY ASSESSMENT BASED ON SURVEY RESULTS

The paper presents the main ideas and possibilities of creating a complex spatial numerical model, research and analysis of the stress-strain state, strength and stiffness of a damaged nine-story building with the help of our own software, using the modified method of straight lines, and the calculation software complex LIRA from the action of a complex load based on the results of an engineering survey by experimental methods. The created complex model of the building based on BIM technologies is used for further adoption of optimal project decisions and significantly accelerates project work. The results of a numerical study of a complex spatial model and conclusions about the strength characteristics of structures and the building as a whole significantly expand the possibilities in making project decisions and creating promising developments based on them.

Ad Optimization Via Machine Learning: A Focus on Upper Confidence Bound and Thompson Sampling Algorithms

The objective of this project is to improve the effectiveness and efficiency of advertising on various platforms by utilizing advanced algorithms, namely the Upper Confidence Bound and Thompson Sampling Algorithm. The project aims to find a balance between exploring new advertising strategies and exploiting proven high-performing approaches. By implementing these bandit algorithms, the project aims to dynamically optimize ad placements, formats, and targeting to maximize user engagement and ad revenue. The methodology involves an iterative process of data collection, analysis, and adaptation. The initial phases include defining project objectives, understanding the target audience, and reviewing the current ad strategy. The Upper Confidence Bound algorithm enables intelligent decision-making by assigning confidence bounds to different ad strategies, allowing for efficient exploration and exploitation. On the other hand, the Thompson Sampling algorithm, rooted in Bayesian principles, dynamically adapts based on observed outcomes, striking a balance between exploration and exploitation through probabilistic reasoning. In summary, this Ads Optimization Project utilizes the power of the Upper Confidence Bound and Thompson Sampling algorithms to create a data-driven, adaptive, and user-centric approach to advertising. The ultimate goal is to achieve optimal user engagement and ad revenue.

Manajemen Pengendalian Waktu Dan Biaya Pada Proyek Daerah Irigasi Saluran Primer Ketonggo Dengan Menggunakan Metode Pert

This thesis presents the results of research about time and cost control management in Ketonggo Primary Canal Irrigation Area project. Time and cost in the best planning is needed in project implementation to ensure that project implementation can achieve maximum results by utilizing existing resources. This study uses the PERT (Program Evaluation and Review Technique) method. By implementing Network planning which is one of the management techniques to plan and control the implementation of a project. Optimization needs to shorten the duration of the project with minimal expenditure. Total of this contract is Rp. 4,706,045,953.00 with 90 days of completion. The results of this analysis showed that the most likely project completion time was 90 days, with the fastest being 75 days and the slowest being 105 days. Based on the critical path analysis, obtained 9 work items that are very influential in the smooth running of the project. The optimal project cost at normal time is Rp. 4,249,989,942.19 and the total cost required for project acceleration is Rp. 4,710,928,986. Additional costs are used for additional manpower, additional working hours and additional hours of heavy equipment.

Поиск оптимальной модели (прототипа), предназначенной для управления проектами в медицинских организациях региона.

Building a system for measuring the value of a model of a medical organization implementing a project involves choosing a mutually appropriate model of the optimal prototype, by assessing and preparing tools based on it for effective project management. The purpose of the study is to formalize the requirements for searching and visualizing the optimal project prototype using the principles (adequacy, value, abstraction and simplicity) applied to determining the model of a medical organization implementing a regional project. Materials and methods. Using the analytical research method, an attempt was made to determine the correspondence between the required accuracy and the initial data of the object (MO implementing the project) by formalizing the requirements of the stages of constructing a mathematical model to find the optimal prototype. Results. Determining the adequacy of the goals when the properties of the model coincide with similar indicators of the modeled object is a necessary condition for assessing the value of the model, giving an understanding of the degree of reliability and approximation of the calculation results to experimental (pragmatic) data. The totality of data from the model of a medical organization implementing a project in conjunction with its elements that interact with the environment as a single whole is the principle of adequacy (value) of the project model for finding the initial parameters of the prototype. The principle of abstraction from minor details of the model, or simplification, while maintaining the essential properties of the system, is an important condition for its optimization. Structuring the interconnections of relationships, functions of objects and subjects of relationships during the empirical deployment of the initial model to a prototype (working model of the project) for a subset of possible stakeholders (customer, developer, performer, patient) provides feedback and transformation of the model at an early stage. The principles of abstraction from minor details of the model (simplification) and simplicity are important conditions for the formation of assumptions for the search for a prototype. Conclusions. Thus, finding a compromise between the desire to create a complete description and obtaining the required results when constructing a simple project model is possible by taking into account the essential parameters specified in the framework of assessing their impact on the efficiency of the system.

Governance Framework Trajectory for Major Public Projects: The Case of Canada

The aim of this article is to understand the dynamic evolution of a governance framework for major public projects. Drawing on Ralf Müller’s contributions in the field of project studies regarding governance, combined with a theoretical anchoring in neo-institutionalism, we seek to shed light on the evolutive aspects of a governance framework, also framed as a public policy. Based on the case of Canada, relying on secondary sources and ethnographic sensitivity, we trace the trajectory of the policy (the governance framework) and highlight the main evolutions and turning points. Five iterations are documented, resulting in increasing complexity over time and institutional layering. The theoretical contribution is to uncover the dynamic and evolving nature of governance frameworks, whereas the practical benefit is to enable decision makers to understand the implications of layering and find the right balance between novelty and overlap for optimal project governance.

Erosion behaviour of cold sprayed stainless-steel coatings for civil infrastructures: An energetic approach

Along with corrosion, erosion is one of the main causes of damage to civil infrastructures. The continuous impact of fine particles carried by the air produces a deterioration in the elements of these structures. When this damage is significant, these elements are replaced by new ones. In this work, the possibility of extending the useful life of these components by spraying coatings on the damaged areas is proposed. In this sense, repair techniques such as cold spray deposition are possibilities for this purpose. Therefore, this study focuses on evaluating the erosion behaviour of stainless-steel coatings deposited on structural steel. In addition, the erosion results have been correlated with the model proposed by Hutchings, and an energetic interpretation of the erosion process through this model has been proposed. To carry out this study, stainless steel coatings have been deposited on carbon steel substrates to improve their resistance to corrosion. Different spraying temperatures, from 800 °C to 1100 °C, combined with different pressures of the carrier gas, from 40 to 70 bar, have been analysed. The elastic moduli of the coatings, and their stress-strain curve, have been determined by depth sensing indentation tests with Berkovich and spherical tips, respectively. Erosion tests with normal incidence have been programmed to obtain the corresponding erosion rates. From the results of this work, the optimal projection conditions of stainless steel on carbon steel substrates for the repair of civil infrastructures subjected to erosion processes could be identified.

Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data

BackgroundNumerous transcriptomic-based models have been developed to predict or understand the fundamental mechanisms driving biological phenotypes. However, few models have successfully transitioned into clinical practice due to challenges associated with generalizability and interpretability. To address these issues, researchers have turned to dimensionality reduction methods and have begun implementing transfer learning approaches.MethodsIn this study, we aimed to determine the optimal combination of dimensionality reduction and regularization methods for predictive modeling. We applied seven dimensionality reduction methods to various datasets, including two supervised methods (linear optimal low-rank projection and low-rank canonical correlation analysis), two unsupervised methods [principal component analysis and consensus independent component analysis (c-ICA)], and three methods [autoencoder (AE), adversarial variational autoencoder, and c-ICA] within a transfer learning framework, trained on > 140,000 transcriptomic profiles. To assess the performance of the different combinations, we used a cross-validation setup encapsulated within a permutation testing framework, analyzing 30 different transcriptomic datasets with binary phenotypes. Furthermore, we included datasets with small sample sizes and phenotypes of varying degrees of predictability, and we employed independent datasets for validation.ResultsOur findings revealed that regularized models without dimensionality reduction achieved the highest predictive performance, challenging the necessity of dimensionality reduction when the primary goal is to achieve optimal predictive performance. However, models using AE and c-ICA with transfer learning for dimensionality reduction showed comparable performance, with enhanced interpretability and robustness of predictors, compared to models using non-dimensionality-reduced data.ConclusionThese findings offer valuable insights into the optimal combination of strategies for enhancing the predictive performance, interpretability, and generalizability of transcriptomic-based models.

HerzMobil Tirol PreOp - A Multidisciplinary Telemonitoring Project for Heart Failure Patients Prior to Cardiac Surgery.

Patients with heart failure are at risk of perioperative complications with elective cardiac surgery. Conception of a multidisciplinary telemedicine-assisted optimisation project for high-risk patients prior to elective cardiac surgery. Multidisciplinary concept design. A pilot-project for 30 patients was developed. Design of the first preoperative telemonitoring-assisted optimisation project for high-risk patients undergoing cardiac surgery.

Study on agricultural drought disaster risk assessment in Heilongjiang reclamation area based on SSAPSO optimization projection pursuit model.

Heilongjiang reclamation area serves as a crucial hub for commodity grain production and strategic reserves in China, playing a vital role in maintaining national food security. Investigating the assessment of agricultural drought risk in this region can yield valuable insights into spatial and temporal variations in drought risk. Such insights can aid in formulating effective strategies for disaster prevention and mitigation, thereby minimizing food losses caused by drought disasters. This study employs a comprehensive indicator system comprising 17 indicators categorized into hazard, exposure, vulnerability, and resistance capacity. The projection pursuit model is applied to evaluate regional drought risk, while the PSO algorithm, optimized by the SSA algorithm, addresses the limitations of low local search ability and search accuracy during the large-scale search process of the PSO optimization algorithm. This study examines and compares the optimization and convergence capabilities of three algorithms: real number encoding-based genetic algorithm (RAGA), particle swarm optimization algorithm (PSO), and sparrow algorithm-based improved particle swarm optimization algorithm (SSAPSO). The analysis demonstrates that SSAPSO exhibits superior optimization performance and convergence properties, establishing it as a highly effective algorithm for optimization tasks. The findings reveal the following trends: over time, agricultural drought risk in Heilongjiang reclamation area has generally declined, with fluctuations observed in hazard and vulnerability, an increase in exposure, and a continuous enhancement of resistance capacity. Spatially, the western region exhibits significantly higher agricultural drought risk compared to the eastern region, primarily due to elevated hazard and vulnerability, coupled with lower resistance capacity. As the agricultural economy grows and agricultural expertise accumulates, the risk of agricultural drought decreases. However, variations in economic growth among different regions lead to diverse spatial distributions of risk.

A joint learning framework for optimal feature extraction and multi-class SVM

In high-dimensional data classification, effectively extracting discriminative features while eliminating redundancy is crucial for enhancing the performances of classifiers, such as Support Vector Machine (SVM). However, previous studies have decoupled the process of feature extraction from the development of SVM, leading to suboptimal classification accuracy. To address this problem, we propose a novel joint learning framework that combines optimal feature extraction and multi-class SVM, incorporating a generalized regression form to learn a discriminative latent subspace. The projected data in this subspace are more likely to have a larger margin between different classes and align with the properties of the SVM classification mechanism, enhancing the overall classification performance. Three iterative algorithms were presented to obtain optimal solutions with guaranteed convergence, and theoretical analyses were also conducted to reveal their fundamental nature. The optimal linear projection subspace is equivalent to that obtained from Linear Discriminant Analysis (LDA) in some special cases. We conducted extensive experiments using diverse datasets to evaluate the performances of the proposed algorithms. Our algorithms achieved an accuracy improvement of up to 7.55% compared to other conventional methods.

Dose optimization for cancer treatments with considerations for late-onset toxicities.

Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.

Analysis of The Causes of Delays and Overbudget And Mitigation To Overcome Them In PT.WER Construction Projects

This study discusses variables that affect delays and overbudget in construction projects at PT. WER. Taking the approach of multiple linear regression analysis and the Relative Important Index (RII), this study shows that the most influential variables for delay and overbudget involve unavailability of manpower, changes in the behavior of the surrounding community, lack of project team knowledge about work sites, lack of knowledge transfer between project teams, and lack of maturity of project team competencies. The results of the study provide an in-depth understanding of the condition of PT. WER, which at certain periods experienced delays and overbudgets on a number of its projects. The proposed mitigation measures involve partnerships with educational institutions to increase the availability of skilled labor, improve communication with local communities, increase project team knowledge through training and guidance, encourage knowledge transfer between project teams, and implement continuous training programs to improve project team competencies. The conclusion of this study provides a foundation for companies to develop more effective mitigation strategies to achieve optimal project success.

Discriminative sparse subspace learning with manifold regularization

Common subspace learning methods only utilize local or global structure in feature extraction, and cannot obtain the global optimal discriminative projection matrix. For this reason, this paper proposes a discriminative sparse subspace learning method based on the manifold regularization framework (DSSL-MR), which introduces the graph Laplacian matrix that reflects the intrinsic geometric structure of the sample as a penalty term. DSSL-MR simultaneously uses both sub-manifold and multi-manifold information of samples for obtaining optimal projection to enhance the discriminability of different classes in subspace. DSSL-MR uses the sparse property of the L2,1-norm to constrain the projection matrix, which can eliminate redundant features and select features that are significant for classification. It is a linear supervised method, which belongs to the Fisher discriminant analysis framework. Experimental results on multiple real-world datasets show that the algorithm is very effective in classification and has high recognition rates.

Project-Fair and Truthful Mechanisms for Budget Aggregation

We study the budget aggregation problem in which a set of strategic voters must split a finite divisible resource (such as money or time) among a set of competing projects. Our goal is twofold: We seek truthful mechanisms that provide fairness guarantees to the projects. For the first objective, we focus on the class of moving phantom mechanisms, which are -- to this day -- essentially the only known truthful mechanisms in this setting. For project fairness, we consider the mean division as a fair baseline, and bound the maximum difference between the funding received by any project and this baseline. We propose a novel and simple moving phantom mechanism that provides optimal project fairness guarantees. As a corollary of our results, we show that our new mechanism minimizes the L1 distance to the mean for three projects and gives the first non-trivial bounds on this quantity for more than three projects.

Efficiency of investment activity in the dairy subcomplex

The article presents the results related to the evaluation of the effectiveness of investment activities in the dairy subcomplex. It is established that investments are an important component of the economic development of the dairy subcomplex, the effectiveness of which depends on a set of factors of the macro- and microeconomic environment, its organizational and economic characteristics, the investment strategy of enterprises, objects and sources of investment, the period for which capital is invested, the certainty of its future effect and the associated risk level. It was found out that in the conditions of low competitiveness of dairy subcomplex enterprises, there is a need to use effective investment projects, which significantly strengthened the role of investment efficiency analysis as a tool within which it is possible to justify, evaluate and select priority investment projects. It is determined that the task of analyzing the effectiveness of investments is the calculation of profits and cash flows, the assessment of their effectiveness, the identification of the influence of uncertainty factors on their profitability, the selection of optimal investment projects. It is advisable to organize the analysis of the effectiveness of investments in the following areas: the definition of subjects and objects of analysis; the choice of organizational forms of analysis, depending on the organizational structure and the distribution of responsibilities between individual performers; the preparation of the analysis program and its information support; analytical data processing, design and generalization of the analysis results. The proposed methodological approach to the analysis of investment efficiency takes into account the conditions of an unstable market environment (variable macroeconomic conditions, insufficient supply of dairy raw materials, uncertainty about wholesale purchase and sale prices), makes it possible to determine the effectiveness of investments, conduct risk analysis by simulation and predict data for risk analysis using regression methods.