Internal Tibial Forces and Moments During Graded Running

This note is to correct errata in the paper "Internal Tibial Forces and Moments During Graded Running" published in Journal of Biomechanical Engineering, Vol. 144, p. 011009 (2021), DOI: 10.1115/1.4051924. In the Data Analysis section, it was stated that, "The joint moments used in the optimization were the flexion-extension and abduction-adduction moments at the hip and ankle, and the flexion-extension moment at the knee." However, it has come to our attention that this is incorrect, and instead the joint moments used in the static optimization routine were the flexion-extension and abduction-adduction moments at the hip, and the flexion-extension moment at the knee and ankle. Please accept our apologies for the error.

A Framework for Multi-Objective Optimization of Plate-Fin Heat Exchangers Using a Detailed Three-Dimensional Simulation Model

The design of a multi-stream plate-fin heat exchanger is a highly integrated task with multiple opposing objectives and many degrees of freedom. This work shows how it can be fully or partially automated by the combination of a detailed three-dimensional simulation model and an optimization routine. The desired task is formulated as the target of a multi-objective optimization and solved using a genetic algorithm. The workflow is presented using a cryogenic plate-fin heat exchanger with four process streams. The design is optimized towards high efficiency, low pressure drop, and low unit weight by adjusting the outer geometry, the inlet and outlet distributor configuration, and the detailed stream geometry. A detailed analysis of the Pareto-set gives a good overview of possible solutions, and the optimization routine can automatically find a feasible design with a reasonable tradeoff between the objectives. All elements of the framework are implemented in open source software. A highlight of this research is that a very comprehensive and detailed simulation model is employed in the optimization framework. Thus, the presented method can be easily adjusted to fit the needs of other engineering tasks.

Design Optimization of Wearable Multiband Antenna Using Evolutionary Algorithm Tuned with Dipole Benchmark Problem

In this paper we present the optimal design of wearable four band antenna that is suitable to work in the fifth-generation wireless systems as well as in cellular systems and in unlicensed bands. The design of the antenna relies on a careful study of optimization algorithms that are suitable for antenna design. We have proposed a benchmark problem to compare different optimization algorithms. It is the space of voltage standing wave ratio and the gain of dipole antenna that was identified for wide range of dipole length and radius. Using this pre-calculated data, we have tuned the parameters of optimization routine for optimal performance with our benchmark. After this, we optimized the geometry of four-band wearable antenna. In the optimization process, we used finite-difference time-domain method together with simplified model of human body. The antenna design was assessed with a fabricated prototype.

Choice-Based Dynamic Pricing for Vacation Rentals

In this paper, we propose a new dynamic pricing approach for the vacation rental revenue management problem. The proposed approach is based on a conditional logistic regression that predicts the purchasing probability for rental units as a function of various factors, such as lead time, availability, property features, and market selling prices. In order to estimate the price sensitivity throughout the booking horizon, a rolling window technique is provided to smooth the impact over time and build a consistent estimation. We apply a nonlinear optimization algorithm to determine optimal prices to maximize the revenue, considering current demand, availability from both the rental company and its competitors, and the price sensitivity of the rental guest. A booking curve heuristic is used to align the booking pace with business targets and feed the adjustments back into the optimization routine. We illustrate the proposed approach by successfully applying it to the revenue management problem of Wyndham Destinations vacation rentals. Model performance is evaluated by pricing two regions within the Wyndham network for part of the 2018 vacation season, indicating revenue per unit growth of 3.5% and 5.2% (for the two regions) through model use.

Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor

This paper proposes a new surrogate optimization routine for optimal design of a direct on line (DOL) squirrel cage induction motor. The geometry of the motor is optimized to maximize its electromagnetic efficiency while respecting the constraints, such as output power and power factor. The routine uses the methodologies of Latin-hypercube sampling, a clustering technique and a Box–Behnken design for improving the accuracy of the surrogate model while efficiently utilizing the computational resources. The global search-based particle swarm optimization (PSO) algorithm is used for optimizing the surrogate model and the pattern search algorithm is used for fine-tuning the surrogate optimal solution. The proposed surrogate optimization routine achieved an optimal design with an electromagnetic efficiency of 93.90%, for a 7.5 kW motor. To benchmark the performance of the surrogate optimization routine, a comparative analysis was carried out with a direct optimization routine that uses a finite element method (FEM)-based machine model as a cost function.

An Automatic and Simplified Approach to Muscle Path Modelling

The current paper aims at proposing an automatic method to design and adjust simplified muscle paths of a musculoskeletal model. These muscle paths are composed of a limited set of via points and an optimization routine is developed to place these via points on the model in order to fit moment arms and musculotendon lengths input data. The method has been applied to a forearm musculoskeletal model extracted from the literature, using theoretical input data as an example. Results showed that for $75\%$ of the muscle set, the relative root mean square error was under $29.23\%$ for moment arms and of $1.09\%$ for musculotendon lengths with regard to the input data. These results confirm the ability of the method to automatically generate computationally efficient muscle paths for musculoskeletal simulations. Using only via points lowers computational expense compared to paths exhibiting wrapping objects. A proper balance between computational time and anatomical realism should be found to help those models being interpreted by practitioners.

OPTIMALISASI PENGAJIAN RUTIN MASJID AL-AQOBAH 7 PALEMBANG SEBAGAI UPAYA PEMBERDAYAAN MASYARAKAT

This research is entitled Optimizing the Routine Recitation of Masjid Al-Aqobah 7 Palembang as Community Empowerment Effort in the Field of Islamic Insights. This study aims to determine: the background of holding routine recitation, optimization of routine recitation, constraints of routine recitation, opportunities to advance routine recitation, and implementation of routine recitation management at Masjid Al-Aqobah 7 Palembang. This research is a field research with data used in the form of qualitative data. The data collection techniques in this study were through interviews, observation, and documentation. Meanwhile, the data analysis technique uses three kinds of analysis stages: data reduction, data display, verification and conclusion drawing. This analysis is commonly called the Interactive Model Analysis based on Miles and Huberman's theory. The results of the study found that the background of holding routine recitation at Masjid Al-Aqobah 7 Palembang is: lack of Islamic knowledge in the mosque administrators and the surrounding community, as an effort to manage mosque management, the mosque as a place of Islamic civilization. Efforts to optimize the routine recitation of Masjid Al-Aqobah 7 Palembang can be done by: utilizing existing facilities, using thin books, a combination of study methods, consumption, attendance lists, congregational social media groups, inviting well-known preachers, inviting the public direct, and gradual delivery of material. Some of the obstacles to the routine recitation of the Al-Aqobah 7 Palembang mosque are: short time, lack of public interest in learning, financial problems, busy management, technical constraints, and differences in schools. Then there are several opportunities to advance the routine recitation of the Al-Aqobah 7 Palembang mosque: domicile of the mosque on the side of the main road, the number of housing around the mosque, the strong commitment of the speakers, the strong commitment of the board, optimization of youth and mothers. Meanwhile, the implementation of routine recitation management at Masjid Al-Aqobah 7 Palembang through four management functions: planning, organizing, implementing, and evaluating. Keywords: Optimization, Routine Studies, Community Empowerment.

Bayesian optimization with evolutionary and structure-based regularization for directed protein evolution

Background Directed evolution (DE) is a technique for protein engineering that involves iterative rounds of mutagenesis and screening to search for sequences that optimize a given property, such as binding affinity to a specified target. Unfortunately, the underlying optimization problem is under-determined, and so mutations introduced to improve the specified property may come at the expense of unmeasured, but nevertheless important properties (ex. solubility, thermostability, etc). We address this issue by formulating DE as a regularized Bayesian optimization problem where the regularization term reflects evolutionary or structure-based constraints. Results We applied our approach to DE to three representative proteins, GB1, BRCA1, and SARS-CoV-2 Spike, and evaluated both evolutionary and structure-based regularization terms. The results of these experiments demonstrate that: (i) structure-based regularization usually leads to better designs (and never hurts), compared to the unregularized setting; (ii) evolutionary-based regularization tends to be least effective; and (iii) regularization leads to better designs because it effectively focuses the search in certain areas of sequence space, making better use of the experimental budget. Additionally, like previous work in Machine learning assisted DE, we find that our approach significantly reduces the experimental burden of DE, relative to model-free methods. Conclusion Introducing regularization into a Bayesian ML-assisted DE framework alters the exploratory patterns of the underlying optimization routine, and can shift variant selections towards those with a range of targeted and desirable properties. In particular, we find that structure-based regularization often improves variant selection compared to unregularized approaches, and never hurts.

Towards Enhancing the Potential of Injection Molding Tools through Optimized Close-Contour Cooling and Additive Manufacturing

This work deals with the parametric optimization of the position and form of a conformal cooling used in the injection molding industry. Based on a literature survey, an optimization routine concerning the parameter optimization of cooling system designs was developed and implemented with the help of the software package Moldflow. The main objective of the optimization is to reduce the cooling time; the second is to obtain an optimized homogeneous temperature distribution over the complete tool surface. To enable a comparison of the new close-contour solution with a classical manufacturing process, an optimized cooling system simulation, based on a conventional manufacturing solution, was established. It can be shown that the optimized close-contour cooling design offers significant advantages that cannot be exploited using classical manufacturing. Finally, the additive manufacturing of a prototype in the framework of powder bed fusion is documented as a proof of concept.

EM-Driven Multi-Objective Optimization of a Generic Monopole Antenna by Means of a Nested Trust-Region Algorithm

Antenna structures for modern applications are characterized by complex and unintuitive topologies that are difficult to develop when conventional, experience-driven techniques are of use. In this work, a method for the automatic generation of antenna geometries in a multi-objective setup has been proposed. The approach involves optimization of a generic spline-based radiator with an adjustable number of parameters using a nested, trust region-based algorithm. The latter iteratively increases the dimensionality of the radiator in order to gradually improve its performance. The method has been used to generate a set of nine antenna designs, representing a trade-off between minimization of reflection within 3.1 GHz to 10.6 GHz and a reduction of size. The properties of the optimized designs vary along the Pareto set from −10 dB to −20 dB and from 230 mm2 to 757 mm2 for the first and second objectives, respectively. The presented design approach has been validated against a genuine, population-based optimization routine. Furthermore, the smallest Pareto-optimal design has been compared to the antennas from the literature.