Статті в журналах: "Optimized settings"

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Li, Xinyi, Jackie Wu, Manisha Palta, You Zhang, Yang Sheng, Jiahan Zhang, and Chunhao Wang. "A Collimator Setting Optimization Algorithm for Dual-Arc Volumetric Modulated Arc Therapy in Pancreas Stereotactic Body Radiation Therapy." Technology in Cancer Research & Treatment 18 (January 1, 2019): 153303381987076. http://dx.doi.org/10.1177/1533033819870767.

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Purpose: To optimize collimator setting to improve dosimetric quality of pancreas volumetric modulated arc therapy plan for stereotactic body radiation therapy. Materials and Methods: Fifty-five volumetric modulated arc therapy cases in stereotactic body radiation therapy of pancreas were retrospectively included in this study with internal review board approval. Different from the routine practice of initializing collimator settings with a template, the proposed algorithm simultaneously optimizes the collimator angles and jaw positions that are customized to the patient geometry. Specifically, this algorithm includes 2 key steps: (1) an iterative optimization algorithm via simulated annealing that generates a set of potential collimator settings from 39 cases with pancreas stereotactic body radiation therapy, and (2) a multi-leaf collimator modulation scoring system that makes the final decision of the optimal collimator settings (collimator angles and jaw positions) based on organs at risk sparing criteria. For validation, the other 16 cases with pancreas stereotactic body radiation therapy were analyzed. Two plans were generated for each validation case, with one plan optimized using the proposed algorithm ( Planopt) and the other plan with the template setting ( Planconv). Each plan was optimized with 2 full arcs and the same set of constraints for the same case. Dosimetric results were analyzed and compared, including target dose coverage, conformity, organs at risk maximum dose, and modulation complexity score. All results were tested by Wilcoxon signed rank tests, and the statistical significance level was set to .05. Results: Both plan groups had comparable target dose coverage and mean doses of all organs at risk. However, organs at risk (stomach, duodenum, large/small bowel) maximum dose sparing ( D0.1 cc and D0.03 cc) was improved in Planopt compared to Planconv. Planopt also showed lower modulation complexity score, which suggests better capability of handling complex shape and sparing organs at risk . Conclusions: The proposed collimator settings optimization algorithm successfully improved dosimetric performance for dual-arc pancreas volumetric modulated arc therapy plans in stereotactic body radiation therapy of pancreas. This algorithm has the capability of immediate clinical application.

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Lamonds, Lucas, Bryan Orf, Michael Frachel, Xaver Thrun, Georg Erley, Philip Groeger, Alexander Muehle, and Boris Habets. "Focus leveling improvement using optimized wafer edge settings." Journal of Micro/Nanolithography, MEMS, and MOEMS 18, no. 04 (November 12, 2019): 1. http://dx.doi.org/10.1117/1.jmm.18.4.043505.

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Christiansen, Kaare, Michael Drosner, and Peter Bjerring. "Optimized settings for Nd:YAG laser treatments of leg telangiectasias." Journal of Cosmetic and Laser Therapy 17, no. 2 (December 22, 2014): 69–76. http://dx.doi.org/10.3109/14764172.2014.988729.

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Valdes, Marcelo, Steve Hansen, and Peter Sutherland. "Optimized Instantaneous Protection Settings: Improving Selectivity and Arc-Flash Protection." IEEE Industry Applications Magazine 18, no. 3 (May 2012): 66–73. http://dx.doi.org/10.1109/mias.2012.2186008.

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Togashi, Kazutomo, Keijiro Sunada, Naohisa Yoshida, David G. Hewett, and Hironori Yamamoto. "Flexible spectral-imaging color enhancement: optimized settings for polyp detection?" Gastrointestinal Endoscopy 74, no. 4 (October 2011): 940. http://dx.doi.org/10.1016/j.gie.2011.05.034.

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Ricketts, Todd A., Erin M. Picou, and Jason Galster. "Directional Microphone Hearing Aids in School Environments: Working Toward Optimization." Journal of Speech, Language, and Hearing Research 60, no. 1 (January 2017): 263–75. http://dx.doi.org/10.1044/2016_jslhr-h-16-0097.

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PurposeThe hearing aid microphone setting (omnidirectional or directional) can be selected manually or automatically. This study examined the percentage of time the microphone setting selected using each method was judged to provide the best signal-to-noise ratio (SNR) for the talkers of interest in school environments.MethodA total of 26 children (aged 6–17 years) with hearing loss were fitted with study hearing aids and evaluated during 2 typical school days. Time-stamped hearing aid settings were compared with observer judgments of the microphone setting that provided the best SNR on the basis of the specific listening environment.ResultsDespite training for appropriate use, school-age children were unlikely to consistently manually switch to the microphone setting that optimized SNR. Furthermore, there was only fair agreement between the observer judgments and the hearing aid setting chosen by the automatic switching algorithm. Factors contributing to disagreement included the hearing aid algorithm choosing the directional setting when the talker was not in front of the listener or when noise arrived only from the front quadrant and choosing the omnidirectional setting when the noise level was low.ConclusionConsideration of listener preferences, talker position, sound level, and other factors in the classroom may be necessary to optimize microphone settings.

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Baldovino, Fritzie Hannah B., Nathaniel P. Dugos, Susan A. Roces, Armando T. Quitain, and Tetsuya Kida. "Process Optimization of Carbon Dioxide Adsorption using Nitrogen-Functionalized Graphene Oxide via Response Surface Methodology Approach." ASEAN Journal of Chemical Engineering 17, no. 2 (July 7, 2018): 106. http://dx.doi.org/10.22146/ajche.49559.

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This paper presents a response surface methodology approach in the optimizationof the carbon dioxide temperature-programmed adsorption process using a new materialreferred as nitrogen-functionalized graphene oxide. This material was synthesized byloading nitrogen groups to graphene oxide using aqueous ammonia in supercriticalcondition. Later on, it was utilized as a sorbent for carbon dioxide adsorption. This process was optimized by implementing a response surface methodology coupled with a Box- Behnken design for the effects of three factors: adsorption temperature, carbon dioxide flow rate, and the amount of adsorbent. In analyzing the response surface, a model equation was generated based on the experimental data by regression analysis. This model equation was then utilized to predict optimum values of response. Furthermore, response optimizer was also conducted in identifying factor combination settings that jointly optimize the best response.

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Martens, Thomas W., Janet S. Lima, Elizabeth A. Johnson, Jessica A. Conry, Jennifer J. Hoppe, Richard M. Bergenstal, Anders L. Carlson, and Janet L. Davidson. "Trial of a New Diabetes Education Model: Closing the Gap in Health Disparity for People with Diabetes." Journal of Diabetes Science and Technology 15, no. 3 (March 24, 2021): 568–74. http://dx.doi.org/10.1177/19322968211001444.

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Background: Quality measures relating to diabetes care in America have not improved between 2005 and 2016, and have plateaued even in areas that outperform national statistics. New approaches to diabetes care and education are needed and are especially important in reaching populations with significant barriers to optimized care. Methods: A pilot quality improvement study was created to optimize diabetes education in a clinic setting with a patient population with significant healthcare barriers. Certified Diabetes Care and Education Specialists (CDCES) were deployed in a team-based model with flexible scheduling and same-day education visits, outside of the traditional framework of diabetes education, specifically targeting practices with underperforming diabetes quality measures, in a clinic setting significantly impacted by social determinants of health. Results: A team-based and flexible diabetes education model decreased hemoglobin A1C for individuals participating in the project (and having a second A1C measured) by an average of −2.3%, improved Minnesota Diabetes Quality Measures (D5) for clinicians participating in the project by 5.8%, optimized use of CDCES, and reduced a high visit fail rate for diabetes education. Conclusions: Diabetes education provided in a team-based and flexible model may better meet patient needs and improve diabetes care metrics, in settings with a patient population with significant barriers.

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Gu, Hyun-Sil, and Seong-kyu Hwang. "Labeling network applicaion study policy settings for optimized transmission of multimedia internet." Journal of the Korea Institute of Information and Communication Engineering 19, no. 8 (August 31, 2015): 1780–84. http://dx.doi.org/10.6109/jkiice.2015.19.8.1780.

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Calmy, Alexandra, Cecilia Pizzocolo, Louis Pizarro, Gilles Brücker, Robert Murphy, and Christine Katlama. "The marriage of science and optimized HIV care in resource-limited settings." AIDS 22, no. 17 (November 2008): 2227–30. http://dx.doi.org/10.1097/qad.0b013e32830f4c4d.

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Ma, Xiao-Xu, and Jie-Sheng Wang. "Optimized Parameter Settings of Binary Bat Algorithm for Solving Function Optimization Problems." Journal of Electrical and Computer Engineering 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/3847951.

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The bat algorithm (BA) is a new bionic intelligent optimization algorithm to simulate the foraging behavior and the echolocation principle of the bats. The parameter initialization of the discussed binary bat algorithm (BBA) has important influence on the convergence speed, convergence precision, and good global searching ability of the BBA. The convergence speed and algorithm searching precision are determined by the pulse of loudness and pulse rate. The simulation experiments are carried out by using the six typical test functions to discuss this influence. The simulation results show that the convergence speed of the BBA is relatively sensitive to the setting of the algorithm parameters. The convergence precision reduces when increasing the rate of bat transmitted pulse alone and the convergence speed increases the launch loudness alone. The proper combination of BBA parameters (the rate of bat transmitted pulse and the launch loudness) can flexibly improve the algorithm’s convergence velocity and improve the accuracy of the searched solutions.

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Wong, Chi-kwong, and Yiu-yin Lee. "Lane-Based Traffic Signal Simulation and Optimization for Preventing Overflow." Mathematics 8, no. 8 (August 15, 2020): 1368. http://dx.doi.org/10.3390/math8081368.

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In the lane-based signal optimization model, permitted turn directions in the form of lane markings that guide road users to turn at an intersection are optimized with traffic signal settings. The spatial queue requirements of approach lanes should be considered to avoid the overdesigning of the cycle, effective red, and effective green durations. The point-queue system employed in the conventional modeling approach is unrealistic in many practical situations. Overflow conditions cannot be modeled accurately, while vehicle queues are accumulated that block back upstream intersections. In a previous study, a method was developed to manually refine the traffic signal settings by using the results of lane-based optimization. However, the method was inefficient. In the present study, new design constraint sets are proposed to control the effective red and effective green durations, such that traffic enters the road lanes without overflow. The reduced cycle times discharge the accumulated vehicles more frequently. Moreover, queue spillback and residual queues can be avoided. One of the most complicated four-arm intersections in Hong Kong is considered as a case study for demonstration. The existing traffic signal settings are ineffective for controlling the observed traffic demand, and overflow occurs in short lanes. The optimized traffic signal settings applied to the proposed optimization algorithm effectively avoided traffic overflow. The resultant queuing dynamics are simulated using TRANSYT 15 Cell Transmission Model (CTM) to verify the proposed model. The model application is extended to handle the difficult residual queue scenario. It is found that the proposed model can optimize the traffic signal settings in cases where there are short initial residual queues.

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Trierweiler Ribeiro, Gabriel, João Guilherme Sauer, Naylene Fraccanabbia, Viviana Cocco Mariani, and Leandro dos Santos Coelho. "Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting." Energies 13, no. 9 (May 11, 2020): 2390. http://dx.doi.org/10.3390/en13092390.

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Load forecasting impacts directly financial returns and information in electrical systems planning. A promising approach to load forecasting is the Echo State Network (ESN), a recurrent neural network for the processing of temporal dependencies. The low computational cost and powerful performance of ESN make it widely used in a range of applications including forecasting tasks and nonlinear modeling. This paper presents a Bayesian optimization algorithm (BOA) of ESN hyperparameters in load forecasting with its main contributions including helping the selection of optimization algorithms for tuning ESN to solve real-world forecasting problems, as well as the evaluation of the performance of Bayesian optimization with different acquisition function settings. For this purpose, the ESN hyperparameters were set as variables to be optimized. Then, the adopted BOA employs a probabilist model using Gaussian process to find the best set of ESN hyperparameters using three different options of acquisition function and a surrogate utility function. Finally, the optimized hyperparameters are used by the ESN for predictions. Two datasets have been used to test the effectiveness of the proposed forecasting ESN model using BOA approaches, one from Poland and another from Brazil. The results of optimization statistics, convergence curves, execution time profile, and the hyperparameters’ best solution frequencies indicate that each problem requires a different setting for the BOA. Simulation results are promising in terms of short-term load forecasting quality and low error predictions may be achieved, given the correct options settings are used. Furthermore, since there is not an optimal global optimization solution known for real-world problems, correlations among certain values of hyperparameters are useful to guide the selection of such a solution.

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Senarathna, Thiramuni Sisitha Sameera, and Kullappu Thantrige Manjula Udayanga Hemapala. "Optimized Adaptive Overcurrent Protection Using Hybridized Nature-Inspired Algorithm and Clustering in Microgrids." Energies 13, no. 13 (June 30, 2020): 3324. http://dx.doi.org/10.3390/en13133324.

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Microgrids have been popularized in the past decade because of their ability to add distributed generation into the classic distribution systems. Protection problems are among several other problems that need solutions in order to extract the full capability of these novel networks. This research follows the branches of two major solutions, namely adaptive protection and protection optimization. Adaptive protection implementation with a novel concept of clustering is considered, and protection setting optimization is done using a novel hybrid nature-inspired algorithm. Adaptive protection is utilized to cope with the topology variations, while optimization techniques are used to calculate the protection settings that provide faster fault clearances in coordination with backup protection. A modified IEEE 14 bus system is used as the test system. Validation was done for the fault clearing performance. The selected algorithm was effective than most other algorithms, and the clustering approach for adaptive overcurrent protection was able to reduce the number of relays’ setting groups.

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Premkumar, K., M. Vishnupriya, Thanikanti Sudhakar Babu, B. V. Manikandan, T. Thamizhselvan, A. Nazar Ali, Md Rabiul Islam, Abbas Z. Kouzani, and M. A. Parvez Mahmud. "Black Widow Optimization-Based Optimal PI-Controlled Wind Turbine Emulator." Sustainability 12, no. 24 (December 11, 2020): 10357. http://dx.doi.org/10.3390/su122410357.

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In this article, the parameters of the proportional-integral (PI) controller of the wind turbine (WT) emulator, i.e., proportional and integral gain of the PI controller, are optimized using a black widow optimization algorithm (BWOA). The proposed system is developed and analyzed using MATLAB/Simulink environment. The performance of the BWOA optimized PI controller is compared with a BAT algorithm, particle swarm optimization, and genetic algorithm optimized PI controller to measure the effectiveness of the proposed control system. The developed system is tested for different operating conditions such as static wind speed settings, static pitch angle conditions, step-change in wind speed settings, and step-change in pitch angle settings. Finally, the proposed system is realized in real-time by hardware experimentations. The results of the experimentation are compared with simulation results as well. The presented simulation and hardware result shows good agreement, which confirms the effectiveness of the proposed method. Thereby, the proposed optimization-based PI-controlled wind emulator can be recommended for emulating the characteristics of any type of WT with a low-cost system.

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Shahid, Syed Maaz, Sunghoon Ko, and Sungoh Kwon. "Real-Time Classification of Diesel Marine Engine Loads Using Machine Learning." Sensors 19, no. 14 (July 18, 2019): 3172. http://dx.doi.org/10.3390/s19143172.

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An engine control system is responsible for controlling the combustion parameters of an internal combustion engine to increase the efficiency of the engine. An optimized parameter setting of an engine control system is highly influenced by the engine load. Therefore, with a change in engine load, the parameter settings need to be updated for higher engine efficiency. Hence, to optimize parameter settings during operation, engine load information is necessary. In this paper, we propose a real-time engine load classification from sensed signals. For the classification, an artificial neural network is used and trained using processed, real, measured data. To that end, a magnetic pickup sensor extracts the rotational speed of the prime mover of a four-stroke V12 marine diesel engine. The measured signal is then converted into a crank angle degree (CAD) signal that shows the behavior of the combustion strokes of firing cylinders at a particular engine load. The CAD signals are considered an input feature to the designed network for classification of engine loads. For verification, we considered five classes of engine load, and the trained network classifies these classes with an accuracy of 99.4%.

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Achtmann, J., and G. Ba¨r. "Optimized Bearing Ellipses of Hypoid Gears1." Journal of Mechanical Design 125, no. 4 (December 1, 2003): 739–45. http://dx.doi.org/10.1115/1.1625403.

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For given machine tool settings of a universal hypoid gear generator, the tooth contact patterns are computed for the coast and drive side of a hypoid gear drive. Each contact pattern is replaced by a determined tooth-bearing ellipse. The position, shape, and inclination of each bearing ellipse is calculated. By the help of these data, an influence function is designed that describes the influence of supplemental kinematic flank correction motions (modified motions) on the gear-tooth contact. Examples show the influence of helical motion and modified roll. An evaluation function permits the calculation of modified motions which improve the tooth contact either at coast and drive side simultaneously, or only at one of the sides. For a given pair of start-bearing ellipses at coast and drive side, and for given importance weights to the sides, we describe how modified motions can be computed that best fit a given target pair of bearing ellipses.

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Imai, Shungo, Yoh Takekuma, Takayuki Miyai, and Mitsuru Sugawara. "A New Algorithm Optimized for Initial Dose Settings of Vancomycin Using Machine Learning." Biological and Pharmaceutical Bulletin 43, no. 1 (January 1, 2020): 188–93. http://dx.doi.org/10.1248/bpb.b19-00729.

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Kawaler, Miłosz, and Andrzej Czyżewski. "Database of speech and facial expressions recorded with optimized face motion capture settings." Journal of Intelligent Information Systems 53, no. 2 (February 21, 2019): 381–404. http://dx.doi.org/10.1007/s10844-019-00547-y.

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Ondra, Thomas, Sebastian Jobjörnsson, Robert A. Beckman, Carl-Fredrik Burman, Franz König, Nigel Stallard, and Martin Posch. "Optimized adaptive enrichment designs." Statistical Methods in Medical Research 28, no. 7 (December 18, 2017): 2096–111. http://dx.doi.org/10.1177/0962280217747312.

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Based on a Bayesian decision theoretic approach, we optimize frequentist single- and adaptive two-stage trial designs for the development of targeted therapies, where in addition to an overall population, a pre-defined subgroup is investigated. In such settings, the losses and gains of decisions can be quantified by utility functions that account for the preferences of different stakeholders. In particular, we optimize expected utilities from the perspectives both of a commercial sponsor, maximizing the net present value, and also of the society, maximizing cost-adjusted expected health benefits of a new treatment for a specific population. We consider single-stage and adaptive two-stage designs with partial enrichment, where the proportion of patients recruited from the subgroup is a design parameter. For the adaptive designs, we use a dynamic programming approach to derive optimal adaptation rules. The proposed designs are compared to trials which are non-enriched (i.e. the proportion of patients in the subgroup corresponds to the prevalence in the underlying population). We show that partial enrichment designs can substantially improve the expected utilities. Furthermore, adaptive partial enrichment designs are more robust than single-stage designs and retain high expected utilities even if the expected utilities are evaluated under a different prior than the one used in the optimization. In addition, we find that trials optimized for the sponsor utility function have smaller sample sizes compared to trials optimized under the societal view and may include the overall population (with patients from the complement of the subgroup) even if there is substantial evidence that the therapy is only effective in the subgroup.

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Yang, Xiuzhi, Yu Wu, Jiqing Li, Wuliang Yin, Yang An, Yanfen Wang, Man Wang, et al. "A Pilot Study of Parameter-Optimized Low-Intensity Pulsed Ultrasound Stimulation for the Bone Marrow Mesenchymal Stem Cells Viability Improvement." Computational and Mathematical Methods in Medicine 2019 (October 1, 2019): 1–11. http://dx.doi.org/10.1155/2019/8386024.

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To investigate how a back propagation neural network based on genetic algorithm (GA-BPNN) optimizes the low-intensity pulsed ultrasound (LIPUS) stimulation parameters to improve the bone marrow mesenchymal stem cells (BMSCs) viability further. The LIPUS parameters were set at various frequencies (0.6, 0.8, 1.0, and 1.2 MHz), voltages (5, 6, 7, and 8 V), and stimulation durations (3, 6, and 9 minutes). As only some discrete points can be set up in the experiments, the optimal LIPUS stimulation parameter may not be in the value of these settings. The GA-BPNN algorithm is used to optimize parameters of LIPUS to increase the BMSCs viability further. The BMSCs viability of the LIPUS-treated group was improved up to 19.57% (P<0.01). With the optimization via the GA-BPNN algorithm, the viability of BMSCs was further improved by about 5.36% (P<0.01) under the optimized condition of 6.92 V, 1.02 MHz, and 7.3 min. LIPUS is able to improve the BMSCs viability, which can be improved further by LIPUS with parameter optimization via GA-BPNN algorithm.

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Kraska, Tim. "Towards instance-optimized data systems." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 3222–32. http://dx.doi.org/10.14778/3476311.3476392.

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In recent years, we have seen increased interest in applying machine learning to system problems. For example, there has been work on applying machine learning to improve query optimization, indexing, storage layouts, scheduling, log-structured merge trees, sorting, compression, and sketches, among many other data management tasks. Arguably, the ideas behind these techniques are similar: machine learning is used to model the data and/or workload in order to derive a more efficient algorithm or data structure. Ultimately, these techniques will allow us to build "instance-optimized" systems: that is, systems that self-adjust to a given workload and data distribution to provide unprecedented performance without the need for tuning by an administrator. While many of these techniques promise orders-of-magnitude better performance in lab settings, there is still general skepticism about how practical the current techniques really are. The following is intended as a progress report on ML for Systems and its readiness for real-world deployments, with a focus on our projects done as part of the Data Systems and AI Lab (DSAIL) at MIT By no means is it a comprehensive overview of all existing work, which has been steadily growing over the past several years not only in the database community but also in the systems, networking, theory, PL, and many other adjacent communities.

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Aghniaey, Sama, Thomas M. Lawrence, Javad Mohammadpour, WenZhan Song, Richard T. Watson, and Marie C. Boudreau. "Optimizing thermal comfort considerations with electrical demand response program implementation." Building Services Engineering Research and Technology 39, no. 2 (February 5, 2018): 219–31. http://dx.doi.org/10.1177/0143624417752645.

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Maintaining thermal comfort within an occupied building requires energy; thus, optimized solution methods for balancing energy use with indoor environmental quality (adequate thermal comfort, lighting, etc.) are needed. Current building temperature control systems do not adequately take in account the adaptive capability of the occupants, but this concept can be used advantageously during implementation of demand response. Demand response programs can affect both the occupants’ thermal comfort from temporary adjustments to space temperature settings. This paper describes ongoing research and field testing of methods to optimize building energy consumption for heating, ventilation and air conditioning applications accounting for human factors such as the thermal comfort by the occupants. Model predictive controllers could serve as powerful tools to optimize the operation of smart buildings and improve human comfort perceptions while helping to better integrate renewable energy systems with increased grid stability. Practical application: This work outlines how the operation of cooling systems can be optimized with respect to reducing peak demand while still maintaining thermal comfort within acceptable ranges.

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Pashankar, Dinesh S., Troy Brown, Paul Votto, Marie Follo, Richard N. Formica, Michael L. Schilsky, David C. Mulligan, and Babar Khokhar. "Sustained Improvement in Patient Experience by Optimizing Patient Flow in Ambulatory Settings." Journal of Patient Experience 9 (January 2022): 237437352210926. http://dx.doi.org/10.1177/23743735221092610.

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Patient experience has become a priority for healthcare institutions as it affects clinical quality of care, financial reimbursement, provider, and patient satisfaction. We report our experience of improving patient experience measured by Press Ganey surveys in a busy multidisciplinary clinic over 65 months. We optimized patient flow in the clinic by technology-facilitated communication among the clinic staff and by a modest space redesign. We noted a significant improvement in “clinic visit” scores from baseline of 82.1 to 84.6 at year 1, 86.1 at year 2, 88.7 at year 3, and 88.9 at year 4 ( P < .001). In comparison with previous short-term studies, we were able to sustain improvement in patient experience scores over 4 years due to optimized patient flow and monitoring of clinic operations. A similar approach can be implemented in other ambulatory settings and is likely to cause a long-term positive impact on patient experience.

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Li, J.-G., S.-M. Mao, J.-L. He, and X.-T. Wu. "Optimization of Pinion Roughing of Spiral Bevel and Hypoid Gear." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 220, no. 4 (April 1, 2006): 483–88. http://dx.doi.org/10.1243/09544062c04105.

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Roughing plays a very important role in spiral bevel and hypoid gear manufacturing. The roughing machine settings and cutter blade profile are optimized in this article on the basis of three considerations: the transition between the roughing root and the finishing fillet is smoothened, which causes the gear to obtain minimum possible bending stress and maximum bending strength; the finishing stock is distributed evenly to improve the residual stress, which causes the distortion of pinion during the process of heat treatment; and the working load of finishing cutter tip is minimized, and the maximum cutter life is obtained. The complex shape method is successfully used to optimize the roughing machine settings and cutter blade profile. The advantages and benefits of the newly developed roughing process are verified in the manufacture of hypoid gears for a heavy truck axle in a Chinese vehicle company.

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Hu, Jia, and Wentao Chen. "Design of Active Disturbance Rejection Controller for Dynamic Positioning Based on Improved Particle Swarm Optimization." Mathematical Problems in Engineering 2022 (April 20, 2022): 1–11. http://dx.doi.org/10.1155/2022/5991281.

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Intelligent control methods play an important role in the dynamic positioning system (DPS). To improve the control accuracy of dynamic positioning systems, an improved active disturbance rejection controller (IADRC) is designed in this study, which can optimize the steady-state performance of the system and improve the tracking accuracy of the system. For nonlinear active disturbance rejection controllers, their internal parameters are complex and numerous, with difficult settings. Proper parameters cannot be found with the trial and error method, and traditional optimization algorithms showcase some problems, such as slow convergence speed, leading to frequent failures in local optimal solutions. An optimized particle swarm optimization (IPSO) algorithm is applied to the IADRC parameter setting to boost the global search ability and the local development function. Simulation analyses demonstrate that, compared with other intelligent control methods, the IPSO-based IADRC dynamic positioning system has advantages such as fast response speed and strong anti-interference ability.

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Cingoz, Fatih, Ali Elrayyah, and Yilmaz Sozer. "Optimized Settings of Droop Parameters Using Stochastic Load Modeling for Effective DC Microgrids Operation." IEEE Transactions on Industry Applications 53, no. 2 (March 2017): 1358–71. http://dx.doi.org/10.1109/tia.2016.2633538.

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Guo, Li Ping, Sheng Gui Fu, and Xiao Juan Liu. "Numerical Aperture and Partial Coherence Optimization in Optical Li-Thography under Off-Axis Illumination." Advanced Materials Research 383-390 (November 2011): 2437–42. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2437.

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Numerical aperture NA and partial coherence sigma optimization in optical lithography is investigated for 100 nm dense lines under annular illumination and quadrupole illumination. Depth of focus (DOF) is calculated at different NA and sigma settings using PROLITH software. Based on the calculations of DOF, the NA and sigma settings are optimized by finding the values to maximize the DOF. At the optimum NA and sigma settings for 100 nm dense lines, the imaging performance is analyzed for semi-dense and isolated lines.

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Hwang, You Mi, Hong Euy Lim, Dae In Lee, Hee Tae Yu, Yae Min Park, and Boyoung Joung. "2021 Korean Heart Rhythm Society Guidelines: Management of Atrial Fibrillation in Specific Clinical Settings." Korean Journal of Medicine 96, no. 4 (August 1, 2021): 264–95. http://dx.doi.org/10.3904/kjm.2021.96.4.264.

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Optimized management of atrial fibrillation requires patient-oriented decision making with a multidisciplinary approach. This report incorporates recent authoritative studies to provide detailed recommendations for managing atrial fibrillation in specific clinical settings. The principles of the Atrial fibrillation Better Care (ABC) pathway apply in these clinical settings. In addition, specific considerations are discussed for each of these conditions and populations.

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Meyer, Markus R., Frank T. Peters, and Hans H. Maurer. "Automated Mass Spectral Deconvolution and Identification System for GC-MS Screening for Drugs, Poisons, and Metabolites in Urine." Clinical Chemistry 56, no. 4 (April 1, 2010): 575–84. http://dx.doi.org/10.1373/clinchem.2009.135517.

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Abstract Background: The challenge in systematic toxicological analysis using gas chromatography and/or liquid chromatography coupled to mass spectrometry is to identify compounds of interest from background noise. The large amount of spectral information collected in one full-scan MS run demands the use of automated evaluation of recorded data files. We evaluated the applicability of the freeware deconvolution software AMDIS (Automated Mass Spectral Deconvolution and Identification System) for GC-MS–based systematic toxicological analysis in urine for increasing the speed of evaluation and automating the daily routine workload. Methods: We prepared a set of 111 urine samples for GC-MS analysis by acidic hydrolysis, liquid-liquid extraction, and acetylation. After analysis, the resulting data files were evaluated manually by an experienced toxicologist and automatically using AMDIS with deconvolution and identification settings previously optimized for this type of analysis. The results by manual and AMDIS evaluation were then compared. Results: The deconvolution settings for the AMDIS evaluation were successfully optimized to obtain the highest possible number of components. Identification settings were evaluated and chosen for a compromise between most identified targets and general number of hits. With the use of these optimized settings, AMDIS-based data analysis was comparable or even superior to manual evaluation and reduced by half the overall analysis time. Conclusions: AMDIS proved to be a reliable and powerful tool for daily routine and emergency toxicology. Nevertheless, AMDIS can identify only targets present in the user-defined target library and may therefore not indicate unknown compounds that might be relevant in clinical and forensic toxicology.

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Ummels, Darcy, Wouter Bijnens, Jos Aarts, Kenneth Meijer, Anna J. Beurskens, and Emmylou Beekman. "The Validation of a Pocket Worn Activity Tracker for Step Count and Physical Behavior in Older Adults during Simulated Activities of Daily Living." Gerontology and Geriatric Medicine 6 (January 2020): 233372142095173. http://dx.doi.org/10.1177/2333721420951732.

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Purpose: The purpose of this study was to validate optimized algorithm parameter settings for step count and physical behavior for a pocket worn activity tracker in older adults during ADL. Secondly, for a more relevant interpretation of the results, the performance of the optimized algorithm was compared to three reference applications Methods: In a cross-sectional validation study, 20 older adults performed an activity protocol based on ADL with MOXMissActivity versus MOXAnnegarn, activPAL, and Fitbit. The protocol was video recorded and analyzed for step count and dynamic, standing, and sedentary time. Validity was assessed by percentage error (PE), absolute percentage error (APE), Bland-Altman plots and correlation coefficients. Results: For step count, the optimized algorithm had a mean APE of 9.3% and a correlation coefficient of 0.88. The mean APE values of dynamic, standing, and sedentary time were 15.9%, 19.9%, and 9.6%, respectively. The correlation coefficients were 0.55, 0.91, and 0.92, respectively. Three reference applications showed higher errors and lower correlations for all outcome variables. Conclusion: This study showed that the optimized algorithm parameter settings can more validly estimate step count and physical behavior in older adults wearing an activity tracker in the trouser pocket during ADL compared to reference applications.

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Benekohal, Rahim F., Yoassry M. Elzohairy, and Joshua E. Saak. "Comparison of Delays from Highway Capacity Software, Synchro, PASSER II and IV, and CORSIM for Urban Arterials." Transportation Research Record: Journal of the Transportation Research Board 1802, no. 1 (January 2002): 133–44. http://dx.doi.org/10.3141/1802-16.

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The delays for signalized intersections from the Highway Capacity Manual [using the Highway Capacity Software (HCS)], PASSER II-90, PASSER IV-96, Synchro, and CORSIM software were compared. An examination was made of how much delay reduction materialized when optimized signal settings were implemented. Synchro should not be compared with HCS unless certain precautions are taken to make the conditions analyzed in Synchro comparable with those analyzed in HCS. For both pretimed and actuated signals, if simultaneous phases exist in which one of them terminates ahead of the others, the results of HCS and Synchro are not comparable. For both pretimed and actuated signals, when the phases terminate simultaneously, whether the data are imported from Synchro or manually entered in HCS, the condition analyzed will not be comparable unless precautions are taken. Once these precautions were taken, control delays from Synchro and HCS were not significantly different for pretimed, uncoordinated signals. For actuated—uncoordinated and for actuated—coordinated controllers, the comparisons are not meaningful. Delays for optimized conditions for PASSER II and PASSER IV were not significantly different from the delays before optimization. However, for Synchro, the delays for optimized conditions were significantly lower than the delays before optimization. CORSIM was used to assess the impacts of the optimization. If optimized signal settings suggested by PASSER II, PASSER IV, or Synchro are implemented, the delays for the optimized condition would not be significantly lower than the delays before optimization.

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Wong, C. K., and Yi Liu. "Lane-Based Optimization for Macroscopic Network Configuration Designs." Discrete Dynamics in Nature and Society 2017 (2017): 1–18. http://dx.doi.org/10.1155/2017/1257569.

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Lane markings (arrows) at individual intersections serve as interfaces to connect upstream and downstream intersections in signal-controlled networks. Demand flows from origins to destinations may need to pass through a series of intersections. If lane markings are not well established to ban turns at intersections, then paths connecting origin and destination (OD) pairs could be inefficient. Due to indirect connections, road users need to take longer paths to reach their destinations. Conventionally, network configurations are fixed inputs for network analysis. In the present study, concepts of the lane-based designs for individual signalized intersections are extended for signal-controlled network designs. Taking OD demand flows as inputs, the proposed algorithm will optimize all lane markings and assigned lane flows on approach lanes. Paths (flows) will then be optimized by linking up the optimized lane markings across upstream and downstream intersections. Traffic signal settings at individual intersections will be optimized simultaneously by maximizing the reserve capacity for the entire OD demand flow matrix. The problem is formulated as a Binary-Mixed-Integer-Linear-Program (BMILP) and a standard branch-and-bound routine is applied to solve for global optimum solutions. A numerical example using a 4-intersection network will be given to demonstrate the effectiveness of the proposed design methodology.

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Pang, Zhiqiang, Jasmine Chong, Shuzhao Li, and Jianguo Xia. "MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics." Metabolites 10, no. 5 (May 7, 2020): 186. http://dx.doi.org/10.3390/metabo10050186.

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Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and (3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100× faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

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Khoshdel, Vahab, and Alireza Akbarzadeh. "An optimized artificial neural network for human-force estimation: consequences for rehabilitation robotics." Industrial Robot: An International Journal 45, no. 3 (May 21, 2018): 416–23. http://dx.doi.org/10.1108/ir-10-2017-0190.

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Purpose This paper aims to present an application of design of experiments techniques to determine the optimized parameters of artificial neural networks (ANNs), which are used to estimate human force from Electromyogram (sEMG) signals for rehabilitation robotics. Physiotherapists believe, to make a precise therapeutic exercise, we need to design and perform therapeutic exercise base on patient muscle activity. Therefore, sEMG signals are the best tool for using in therapeutic robots because they are related to the muscle activity. Using sEMG signals as input for therapeutic robots need precise human force estimation from sEMG. Furthermore, the ANN estimator performance is highly dependent on the accuracy of the target date and setting parameters. Design/methodology/approach In the previous studies, the force data, which are collected from the force sensors or dynameters, has widely been used as target data in the training phase of learning ANN. However, force sensors or dynameters could measure only contact force. Therefore, the authors consider the contact force, limb’s dynamic and time in target data to increase the accuracy of target data. Findings There are plenty of algorithms that are used to obtain optimal ANN settings. However, to the best of our knowledge, they do not use regression analysis to model the effect of each parameter, as well as present the contribution percentage and significance level of the ANN parameters for force estimation. Originality/value In this paper, a new model to estimate the force from sEMG signals is presented. In this method, the sum of the limb’s dynamics and the contact force is used as target data in the training phase. To determine the limb’s dynamics, the patient’s body and the rehabilitation robot are modeled in OpenSim. Furthermore, in this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi is used to find the optimal parameters settings. Next, analysis of variance technique is used to obtain significance level, as well as contribution percentage of each parameter, to optimize ANN’s modeling in human force estimation. The results indicate that the presented model can precisely estimate human force from sEMG signals.

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El-Fergany, Attia A., and Hany M. Hasanien. "Optimized settings of directional overcurrent relays in meshed power networks using stochastic fractal search algorithm." International Transactions on Electrical Energy Systems 27, no. 11 (July 17, 2017): e2395. http://dx.doi.org/10.1002/etep.2395.

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Miller, Keith, Andrew Merliss, Michelle Khoo, William Sauer, Gregg Yamada, Vinay Thohan, Rhonda Olmsted, Jason R. Brown, and Michelle M. Fedewa. "Cardiac Resynchronization Therapy Responders Have Echo Doppler Optimized AV Intervals Closest to Nominal Device Settings." Journal of Cardiac Failure 15, no. 6 (August 2009): S115. http://dx.doi.org/10.1016/j.cardfail.2009.06.039.

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Huang, Jingxiu, Qingtang Liu, Yunxiang Zheng, and Linjing Wu. "Chinese Comma Disambiguation in Math Word Problems Using SMOTE and Random Forests." AI 2, no. 4 (December 20, 2021): 738–55. http://dx.doi.org/10.3390/ai2040044.

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Natural language understanding technologies play an essential role in automatically solving math word problems. In the process of machine understanding Chinese math word problems, comma disambiguation, which is associated with a class imbalance binary learning problem, is addressed as a valuable instrument to transform the problem statement of math word problems into structured representation. Aiming to resolve this problem, we employed the synthetic minority oversampling technique (SMOTE) and random forests to comma classification after their hyperparameters were jointly optimized. We propose a strict measure to evaluate the performance of deployed comma classification models on comma disambiguation in math word problems. To verify the effectiveness of random forest classifiers with SMOTE on comma disambiguation, we conducted two-stage experiments on two datasets with a collection of evaluation measures. Experimental results showed that random forest classifiers were significantly superior to baseline methods in Chinese comma disambiguation. The SMOTE algorithm with optimized hyperparameter settings based on the categorical distribution of different datasets is preferable, instead of with its default values. For practitioners, we suggest that hyperparameters of a classification models be optimized again after parameter settings of SMOTE have been changed.

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Mocholí Belenguer, Ferran, Antonio Martínez-Millana, Antonio Mocholí Salcedo, Víctor Milián Sánchez, and María Josefa Palomo Anaya. "Disdrometer Performance Optimization for Use in Urban Settings Based on the Parameters that Affect the Measurements." Symmetry 12, no. 2 (February 20, 2020): 303. http://dx.doi.org/10.3390/sym12020303.

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There are currently different types of commercial optical disdrometers to measure the rainfall intensity, of which many are commonly used for monitoring road conditions. Having information about the amount of rain, the composition of the precipitation particles and visibility are essential to avoid accidents, which requires intelligent systems that warn drivers and redirect traffic. However, few studies related to Intelligent Transport Systems (ITS) have been performed regarding why these devices are not optimized for this type of applications. Therefore, this paper analyzes and evaluates the operating mode of these equipment through their theoretical model, which will allow the design of prototypes of disdrometers with different characteristics. In addition, this model will be implemented in a simulation program, through which an exhaustive study analyzing how the type of precipitation and its intensity affect the measures provided by the model will be conducted. In this way, the results will help optimize its operation to be thus used in urban settings, which will allow obtaining more accurate real-time information, better traffic management, and a reduction in the number of accidents.

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Parbat, Dibyangana, Avijit Das, Kousik Maji, and Uttam Manna. "Hydrophobicity or superhydrophobicity—which is the right choice for stabilizing underwater superoleophilicity?" Journal of Materials Chemistry A 8, no. 1 (2020): 97–106. http://dx.doi.org/10.1039/c9ta09877a.

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Hydrophobic multilayers chemically optimized with discontinuous metastable trapped air displayed highly unusual and superior oil wettability underwater in comparison to the superhydrophobic interface and were unprecedentedly extended for filtration-based oil-in-water emulsion separation at diverse and severe settings.

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Širůček, Martin, and Karel Šíma. "Optimized Indicators of Technical Analysis on the New York Stock Exchange." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 64, no. 6 (2016): 2123–31. http://dx.doi.org/10.11118/actaun201664062123.

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The article is focused on the use of technical analysis and it’s indicators. The main aim is the evaluation of technical analysis for selected index instruments which are traded on NYSE. The secondary objective is the optimization of indicator’s parameters of technical analysis and subsequent comparison of profitability of business strategies based on these optimized parameters. The empirical analysis includes the backtesting of optimized indicators and comparison with the default settings of these indicators. The optimization and backtesting were performed on cyclical stocks, represented by stock index S&P 500 Financial from 11/1/2014 to 10/31/2015.

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Jackson, Charles S., Mrinal K. Sen, Gabriel Huerta, Yi Deng, and Kenneth P. Bowman. "Error Reduction and Convergence in Climate Prediction." Journal of Climate 21, no. 24 (December 15, 2008): 6698–709. http://dx.doi.org/10.1175/2008jcli2112.1.

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Abstract Although climate models have steadily improved their ability to reproduce the observed climate, over the years there has been little change to the wide range of sensitivities exhibited by different models to a doubling of atmospheric CO2 concentrations. Stochastic optimization is used to mimic how six independent climate model development efforts might use the same atmospheric general circulation model, set of observational constraints, and model skill criteria to choose different settings for parameters thought to be important sources of uncertainty related to clouds and convection. Each optimized model improved its skill with respect to observations selected as targets of model development. Of particular note were the improvements seen in reproducing observed extreme rainfall rates over the tropical Pacific, which was not specifically targeted during the optimization process. As compared to the default model sensitivity of 2.4°C, the ensemble of optimized model configurations had a larger and narrower range of sensitivities around 3°C but with different regional responses related to the uncertain choice in optimized parameter settings. These results suggest current generation models, if similarly optimized, may become more convergent in their measure of global sensitivity to greenhouse gas forcing. However, this exploration of the possible sources of modeling and observational uncertainty is not exhaustive. The optimization process illustrates an objective means for selecting an ensemble of plausible climate model configurations that quantify a portion of the uncertainty in the climate model development process.

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Maruyama, Shun, Shintaro Yamasaki, Kentaro Yaji, and Kikuo Fujita. "Topology optimization incorporating external variables with metamodeling." Structural and Multidisciplinary Optimization 62, no. 5 (June 11, 2020): 2455–66. http://dx.doi.org/10.1007/s00158-020-02616-1.

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Abstract The objective of conventional topology optimization is to optimize the material distribution for a prescribed design domain. However, solving the topology optimization problem strongly depends on the settings specified by the designer for the shape of the design domain or their specification of the boundary conditions. This contradiction indicates that the improvement of structures should be achieved by optimizing not only the material distribution but also the additional design variables that specify the above settings. We refer to the additional design variables as external variables. This paper presents our work relating to solving the design problem of topology optimization incorporating external variables. The approach we follow is to formulate the design problem as a multi-level optimization problem by focusing on the dominance-dependence relationship between external variables and material distribution. We propose a framework to solve the optimization problem utilizing the multi-level formulation and metamodeling. The metamodel approximates the relationship between the external variables and the performance of the corresponding optimized material distribution. The effectiveness of the framework is demonstrated by presenting three examples.

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Simoni, Filippo, Andrea Huxol, and Franz-Josef Villmer. "Improving surface quality in selective laser melting based tool making." Journal of Intelligent Manufacturing 32, no. 7 (April 2, 2021): 1927–38. http://dx.doi.org/10.1007/s10845-021-01744-9.

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AbstractIn the last years, Additive Manufacturing, thanks to its capability of continuous improvements in performance and cost-efficiency, was able to partly replace and redefine well-established manufacturing processes. This research is based on the idea to achieve great cost and operational benefits especially in the field of tool making for injection molding by combining traditional and additive manufacturing in one process chain. Special attention is given to the surface quality in terms of surface roughness and its optimization directly in the Selective Laser Melting process. This article presents the possibility for a remelting process of the SLM parts as a way to optimize the surfaces of the produced parts. The influence of laser remelting on the surface roughness of the parts is analyzed while varying machine parameters like laser power and scan settings. Laser remelting with optimized parameter settings considerably improves the surface quality of SLM parts and is a great starting point for further post-processing techniques, which require a low initial value of surface roughness.

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Brero, Gianluca, Sébastien Lahaie, and Sven Seuken. "Fast Iterative Combinatorial Auctions via Bayesian Learning." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1820–28. http://dx.doi.org/10.1609/aaai.v33i01.33011820.

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Iterative combinatorial auctions (CAs) are often used in multibillion dollar domains like spectrum auctions, and speed of convergence is one of the crucial factors behind the choice of a specific design for practical applications. To achieve fast convergence, current CAs require careful tuning of the price update rule to balance convergence speed and allocative efficiency. Brero and Lahaie (2018) recently introduced a Bayesian iterative auction design for settings with singleminded bidders. The Bayesian approach allowed them to incorporate prior knowledge into the price update algorithm, reducing the number of rounds to convergence with minimal parameter tuning. In this paper, we generalize their work to settings with no restrictions on bidder valuations. We introduce a new Bayesian CA design for this general setting which uses Monte Carlo Expectation Maximization to update prices at each round of the auction. We evaluate our approach via simulations on CATS instances. Our results show that our Bayesian CA outperforms even a highly optimized benchmark in terms of clearing percentage and convergence speed.

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Rahman and Siddiqui. "An Optimized Abstractive Text Summarization Model Using Peephole Convolutional LSTM." Symmetry 11, no. 10 (October 14, 2019): 1290. http://dx.doi.org/10.3390/sym11101290.

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Abstractive text summarization that generates a summary by paraphrasing a long text remains an open significant problem for natural language processing. In this paper, we present an abstractive text summarization model, multi-layered attentional peephole convolutional LSTM (long short-term memory) (MAPCoL) that automatically generates a summary from a long text. We optimize parameters of MAPCoL using central composite design (CCD) in combination with the response surface methodology (RSM), which gives the highest accuracy in terms of summary generation. We record the accuracy of our model (MAPCoL) on a CNN/DailyMail dataset. We perform a comparative analysis of the accuracy of MAPCoL with that of the state-of-the-art models in different experimental settings. The MAPCoL also outperforms the traditional LSTM-based models in respect of semantic coherence in the output summary.

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SENTHIL KUMAR, M., B. DHANASEKAR, G. RANGA JANARDHANA, S. PARAMASIVAM, and K. S. JAYA KUMAR. "COMPARISON OF MODELING TECHNIQUES FOR SELECTING OPTIMIZED AND AUTOMATED PLASMA CUTTING PROCESS PARAMETERS." International Journal of Modeling, Simulation, and Scientific Computing 03, no. 03 (May 17, 2012): 1250011. http://dx.doi.org/10.1142/s1793962312500110.

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The advancement in technologies made the entire manufacturing system, to be operated with more efficient, flexible, user friendly, more productive and cost effective. One such a system to be focused for advancement is plasma cutting system, which has wider industrial applications. There are many researches pursuing at various area of plasma cutting technology, still the automated and optimized parameters value selection is challenging. The work is aimed to eliminate the manual mode of feeding the input parameters for cutting operation. At present, cutting parameters are fed by referring the past cut data information or with the assistance of experienced employers. The cutting process parameters selections will have direct impact on the quality of the material being cut, and life of the consumables. This paper is intended to automate the process parameters selection by developing the mathematical model with existing cutting process parameters database. In this, three different approaches, multiple regression, multiple polynomial regression and AI technique, are selected and analyzed with the mathematical relations developed between the different cutting process parameters. The accuracy and reliability of those methods are detailed. The advantage and disadvantage of those methods for optimal setting conditions are discussed. The appropriate method that can be preferred for automated and optimal settings are elucidated. Finally, the selected technique is checked for accuracy and reliability for the existing cut data.

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Lee, Christopher M., Gordon A. Watson, and Dennis D. Leavitt. "Dynamic collimator optimization compared with fixed collimator angle in arc-based stereotactic radiotherapy: a dosimetric analysis." Neurosurgical Focus 19, no. 1 (July 2005): 1–5. http://dx.doi.org/10.3171/foc.2005.19.1.13.

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Object The purpose of this study was to determine the effect of static and dynamic collimator optimization when using a micromultileaf collimator (mMLC) in dynamic-arc stereotactic radiosurgery (SRS) by evaluating the dose to healthy peritumoral tissue. Methods Thirty patients previously treated for intracranial lesions with the BrainLAB mMLC underwent retrospective replanning. Three collimator optimization strategies were compared for a simulated SRS treatment plan, as follows: Strategy 1, static collimation fixed at 90° throughout arcs; Strategy 2, static collimator settings optimized for each arc; and Strategy 3, dynamic collimator settings optimized every 10° throughout treatment arcs. Dose–volume histograms for a 0.7-cm shell of healthy peritumoral tissue were quantitatively compared. Collimator optimization schemes (Strategies 2 and 3) significantly decreased the volume of peritumoral tissue that is irradiated when compared with static collimation at 90° (Strategy 1). The volume was reduced by 40.6% for Strategy 2 (95% confidence interval [CI] ± 11) and by 47.1% for Strategy 3 (95% CI ± 8.1) at the 95% isodose; by 28.4% for Strategy 2 (95% CI ± 4.9) and 39.1% for Strategy 3 (95% CI ± 6) at the 90% isodose; and by 18.2% for Strategy 2 (95% CI ± 8.1) and 25.4% for Strategy 3 (95% CI ± 7.1) at the 80% isodose. Serial collimator optimization throughout the treatment arcs (Strategy 3) reduced the mean volume of peritumoral tissue irradiated when compared with static collimator optimization (Strategy 2), by 16.1% (95% CI ± 1.5) at 95% isodose, by 11.7% (95% CI ± 1) at 90% isodose, and by 8.2% (95% CI ± 1.2) at 80% isodose regions. In specific cases, linear or polynomial functions were formulated to optimize collimator settings dynamically throughout treatment arcs. Conclusions Dynamic collimator optimization during arc-based SRS decreases the volume of healthy peritumoral tissue treated with high doses of radiation and appears to be an effective method of improving target conformality. This study is the first step toward determination of a smoothing function algorithm to allow for true dynamic collimation during SRS.

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Yamamoto, Takehito, Nobuhiro Yasuno, Shoichi Katada, Akihiro Hisaka, Norio Hanafusa, Eisei Noiri, Naoki Yahagi, Toshiro Fujita, and Hiroshi Suzuki. "Proposal of a Pharmacokinetically Optimized Dosage Regimen of Antibiotics in Patients Receiving Continuous Hemodiafiltration." Antimicrobial Agents and Chemotherapy 55, no. 12 (September 12, 2011): 5804–12. http://dx.doi.org/10.1128/aac.01758-10.

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ABSTRACTThe aim of the study was to quantitatively predict the clearance of three antibiotics, amikacin, vancomycin, and teicoplanin, during continuous hemodiafiltration (CHDF) and to propose their optimal dosage in patients receiving CHDF. For this goal,in vitroCHDF experiments with a polyacrylonitrile (PAN) membrane were first performed using these antibiotics, and then the clearances were compared within vivoCHDF situations determined in 16 critically ill patients. Thein vitroCHDF clearances were described as the product of the outflow rate of a drain (Qoutflow) and the drug unbound fraction in artificial plasma, indicating that drug adsorption to the PAN membrane has minor effect on drug clearance in our settings. The observedin vivoclearances also agreed very well with the predicted values, with a product ofQoutflowand plasma unbound fraction, when residual creatinine clearance (CLCR) was taken into account (within a range of 0.67- to 1.5-fold for 15 of 16 patients). Based on these results, a nomogram of the optimized dosages of amikacin, vancomycin, and teicoplanin was proposed, and it was evident thatQoutflowand residual CLCRare major determinants of the dosage and dosing interval for these antibiotics. Although the applicability needs to be confirmed with another type of membrane or higherQoutflow, our nomogram can help determine the dosage setting in critically ill patients receiving CHDF.

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Ren, Bin, and Huanfei Ma. "Global optimization of hyper-parameters in reservoir computing." Electronic Research Archive 30, no. 7 (2022): 2719–29. http://dx.doi.org/10.3934/era.2022139.

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<abstract><p>Reservoir computing has emerged as a powerful and efficient machine learning tool especially in the reconstruction of many complex systems even for chaotic systems only based on the observational data. Though fruitful advances have been extensively studied, how to capture the art of hyper-parameter settings to construct efficient RC is still a long-standing and urgent problem. In contrast to the local manner of many works which aim to optimize one hyper-parameter while keeping others constant, in this work, we propose a global optimization framework using simulated annealing technique to find the optimal architecture of the randomly generated networks for a successful RC. Based on the optimized results, we further study several important properties of some hyper-parameters. Particularly, we find that the globally optimized reservoir network has a largest singular value significantly larger than one, which is contrary to the sufficient condition reported in the literature to guarantee the echo state property. We further reveal the mechanism of this phenomenon with a simplified model and the theory of nonlinear dynamical systems.</p></abstract>

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