Optimal Choice Research Articles

When the Going Gets Tough, Do the Tough Really Get Going? Identity Reconsideration in the Face of Activity Participation Threat

ABSTRACT Eudaimonic identity theory (EIT) suggests that engaging in activities aligned with personal beliefs, goals, and values fosters flourishing. EIT posits that the subjective experience of eudaimonia, termed personal expressiveness (PE), guides optimal identity-related choices. Two studies of U.S. adults, employing correlational and experimental designs respectively, examined links between feelings of personal expressiveness and identity. Study 1 (N = 78) explored the relationships between feelings of personal expressiveness and identity-related constructs, identity commitment, identity reconsideration, and passion. Study 2 (N = 270) aimed to investigate the impact on identity reconsideration when participation in these types of activities is threatened and if psychological flexibility acts as a buffer in the face of participation threat. Study 1 found that personal expressiveness was positively associated with identity commitment and both harmonious and obsessive passion and unrelated to identity reconsideration. Study 2 replicated the relationships between personal expressiveness and identity commitment and reconsideration found in Study 1. However no moderating effect of severity of threat to participation or psychological flexibility was found. Results revealed a consistent and robust effect of high threat on reconsideration when compared to low threat. Theoretical and practical implications of these results, as well as opportunities for future research are discussed.

Operation of a hybrid heating system based on heat pumps using a photovoltaic installation

In Poland, heating systems using renewable energy sources have gained importance in construction projects, especially in newly designed buildings. This is mainly due to the Regulation of the Minister of Infrastructure, which sets technical conditions for buildings. As of December 31, 2020, the primary energy index for newly designed single-family buildings should not exceed 70 kWh/(m2year). This requires efficient energy sources in building design. Renewable energy installations have significantly lower primary energy utilization rates than fossil fuel systems, making them the preferred choice. In a facility in Batowice near Krakow, a hybrid energy system with ground-source and air-source heat pumps has been installed. These pumps are powered by electricity from a photovoltaic installation connected to the grid. The study aims to determine the optimal heat pump choice based on the facility’s conditions and optimize electricity consumption from the photovoltaic installation. Both heat pumps showed similar efficiencies during the heating season from December 2022 to March 2023: the ground-source heat pump achieved an annual coefficient of performance of 2.69, and the air-source heat pump achieved a seasonal coefficient of perfor-mance of 2.63. Given the high non-renewable primary energy factor for grid electricity, the feasibility of replacing gas boilers with heat pumps requires careful evaluation. The results indicate that integrating a heat pump with a photovoltaic installation substantially reduces the primary energy utilization index, supporting climate protection and the advancement of renewable energy sources. However, heat pumps alone may not be sufficiently efficient without the support of a PV installation.

Robustness-Based Evaluation of GHG Emissions and Energy Use at Neighborhood Level

Evaluating neighborhood performance is crucial for achieving long-term zero-carbon goals, enabling efficient energy, cost, and resource sharing among buildings. This task requires balancing multiple criteria and managing uncertainties, emphasizing the importance of performance robustness alongside high performance. This article introduces a flexible multi-criteria approach for evaluating neighborhood performance, focusing on greenhouse gas (GHG) emissions and energy use across different life cycle stages. Flytårnet, a Norwegian neighborhood with zero-emission ambitions, serves as a case study. The methodology incorporates the T-robust method, an established robustness-based approach, to select high-performance, resilient neighborhood designs under various uncertainties. Results indicate that when assessing buildings as key components and considering energy delivered during the operational phase, including photovoltaic generation, the supplied energy ranges from 25 to 80 kWh/m2/year. Over a 60-year period, life cycle GHG emissions span from 4 to 12 kg CO2-eq./m2/year, accounting for uncertainties and encompassing material production and replacement, as well as energy consumption and generation. However, the optimal design choice varies based on whether life cycle stages beyond the use stage are considered. This research provides valuable insights for decision-makers and designers seeking effective neighborhood designs in early-stage planning, considering diverse and conflicting performance criteria to achieve zero-emission goals.

Energy Absorption Behavior of Elastomeric Matrix Composites Reinforced with Hollow Glass Microspheres

Hollow glass microsphere (HGM) reinforced composites are a suitable alternative to energy absorption materials in the automotive and aerospace industries, because of their high crush efficiency and energy absorption characteristics. In this study, a polyurethane elastomeric matrix was reinforced with HGMs for HGM loadings ranging from 0 to 70 vol% (volume fraction). Quasi-static uniaxial compression tests were performed, subjecting the composite to compressive strains of up to 65%, to assess stress vs. strain and energy absorption characteristics. The results reveal that samples with a higher concentration of spheres generally exhibit better crush efficiency. Specifically, the highest crush efficiency was observed in samples with a 70 vol% HGM loading. A similar relationship was reflected in the energy absorption efficiency results, with the highest energy absorption observed in the 65 vol% sample. A correlation exists between the concentration of HGMs and important metrics such as mean crush stress and energy absorption efficiency. However, it is crucial to note that the optimal choice of sphere concentration varies depending on the desired performance characteristics of the material.

Screening Oxidoreductase Activities of Nanozymes: Toward Activity‐Tailored Guidelines

Nanozymes (NZs) are nanomaterials able to mimic natural enzymes, offering the advantages of better durability, wider range of operational conditions, and easier functionalization. In addition, some NZs exhibit multiple enzyme‐like activities and effective tandem/self‐cascade reactions. In this context, metallic and metal–oxide nanoparticles are among the most promising candidates for biomedical/biotechnological applications, thanks to their efficient oxidoreductase activities. However, characterizing NZs is challenging because the experimental set‐up of different assays strongly influence their performances. Consequently, currently available literature provides limited understanding of the characteristics of the various NZs, especially in terms of activity‐related performance, hindering their optimal selection and applicative potential. Here, leveraging on accurate characterization methodology, we report a direct comparison of several NZs (gold‐Au, platinum‐Pt, palladium‐Pd, ceria, iron oxide) possessing multi‐enzymatic activities. Our main findings indicate that 1) PtNZs outperform the other tested NZs in catalase‐like activity; 2) Pt and PdNZs present superior superoxide dismutase‐like activity; 3) for peroxidase‐ and oxidase‐like activity, the best‐performing NZ is substrate‐dependent; 4) competitive reactions in multifunctional NZs play a key role in their specific performance, and AuNZs can be the optimal choice in some peroxidase/oxidase configurations; 5) metallic NZs are more efficient than metal‐oxide ones, but 6) ceria presents unique phosphatase‐like activity.

Nematic host and alignment layer dependence on monodomain formation in the liquid crystal blue phase

Blue phases (BPs) of liquid crystals are highly valued in electro-optics for their fast, polarization-independent response and the advantage of not requiring alignment layers. While BP lattices do not need to be aligned to substrates, doing so can improve their electro-optic performance. Traditionally, rubbed polyimide films have been the preferred method for inducing monodomains in BPs. This study, using polarized optical microscopy, explores various BP compositions with different nematic hosts, chiral dopant concentrations, and alignment layer combinations, including polyimide, surfactants, and obliquely evaporated SiOx. The results show that while rubbed polyimide is effective for many BP compositions, it is not always the optimal choice for inducing monodomains. The research highlights that inorganic alignment methods can be more effective for BP compositions consisting of specific nematic hosts.

Energy-Resolved Mass Spectrometry and Mid-Infrared Spectroscopy for Purity Assessment of a Synthetic Peptide Cyclised by Intramolecular Huisgen Click Chemistry

Cyclic peptides have higher stability and better properties as therapeutic agents than their linear peptide analogues. Consequently, intramolecular click chemistry is becoming an increasingly popular method for the synthesis of cyclic peptides from their isomeric linear peptides. However, assessing the purity of these cyclic peptides by mass spectrometry is a significant challenge, as the linear and cyclic peptides have identical masses. In this paper, we have evaluated the analytical capabilities of energy-resolved mass spectrometry (ER MS) and mid-infrared microscopy (IR) to address this challenge. On the one hand, mixtures of both peptides were subjected to collision-induced dissociation tandem mass spectrometry (CID MS/MS) experiments in an ion trap mass spectrometer at several excitation energies. Two different calibration models were used: a univariate model (at a single excitation voltage) and a multivariate model (using multiple excitation voltages). The multivariate model demonstrated slightly enhanced analytical performance, which can be attributed to more effective signal averaging when multiple excitation voltages are considered. On the other hand, IR microscopy was used for the quantification of the relative amount of linear peptide. This was achieved through univariate calibration, based on the absorbance of an alkyne band specific to the linear peptide, and through Partial Least Squares (PLS) multivariate calibration. The PLS calibration model demonstrated superior performance in comparison to univariate calibration, indicating that consideration of the full IR spectrum is preferable to focusing on the specific peak of the linear peptide. The advantage of IR microscopy is that it is linear across the entire working interval, from linear peptide molar ratios of 0 (equivalent to pure cyclic peptide) up to 1 (pure linear peptide). In contrast, the ER MS calibration models exhibited linearity only up to 0.3 linear peptide molar ratio. However, ER MS showed better performances in terms of the limit of detection, intermediate precision and the root-mean-square-error of calibration. Therefore, ER MS is the optimal choice for the detection and quantification of the lowest relative amounts of linear peptides.

Least-cost light-duty vehicle fleet decarbonization and the electric vehicle conundrum

This paper evaluates the greenhouse gas (GHG) emissions and financial performance of the U.S. light-duty vehicle (LDV) fleet. It develops dynamic models for conventional (ICEV), hybrid (HEV), plugin hybrid (PHEV) and battery electric (BEV) LDVs in four vehicle classes (compact, sedan, SUV and pickup truck), estimating their component sizes, purchase prices, embodied emissions, fuel consumption, life cycle GHG emissions and total cost of ownership (TCO) as a function of vehicle miles travelled (VMT). These dynamic models are applied to the US LDV fleet with its wide range of VMT per vehicle, revealing that ICEVs have the lowest TCO at lower VMT, but HEVs and BEVs become more cost-effective as VMT increases. Financially optimal powertrain choice can then reduce fleet-wide GHG emissions by one-third with current technologies, without any subsidies or increased costs. Technological advances, particularly in battery costs and electricity emissions, are then powerful drivers of further fleet decarbonization.

Hydropneumatic Storage Methodology Towards a New Era of Hybrid Energy System's Efficiency and Flexibility

This research explores the link between hydropneumatic energy storage capacity and the efficiency and flexibility of hybrid energy systems in water-energy solutions. A new methodology is introduced, featuring mathematical models, experimental data collection, and hydraulic simulations using 1D and 2D CFD models for hydropneumatics modeling. A promised energy storage efficiency of around 30-50% was obtained on a small lab scale. The optimization of hybrid systems through Solver and Python algorithms with various objective functions enables optimal design choices tailored to specific needs such as drinking water supply, irrigation, or industrial processes. Hydropneumatic vessels emerge as an effective storage solution, combining pumped storage and hydropower in one circuit. When integrated with renewable sources, such as solar (PV) and wind energy, they offer a flexible, long-lasting energy management system, applicable across different water-energy sectors to support Sustainable Development Goals. A case study with 4.8 Mm³/year water allocation, producing 1000 MWh of hydropower and 13500 MWh of solar energy, achieved 100% water reliability and a 25-year cash flow of 2.5 million euros.

Cognitive ability in matching with strategic uncertainty: An experimental study

Strategyproof mechanisms have become the predominant choice for educational institutions. However, both laboratory experiments and empirical evidence highlight the persistent occurrence of strategic misreporting, leading to adverse consequences. This underscores the need to examine the reporting strategies of students, particularly those with varying cognitive abilities, across different decision environments. We present an experimental comprehension test of reporting strategies using computerized opponents to precisely control the levels of strategic uncertainty. The results reveal that removing strategic uncertainty does not significantly affect truth-telling rates. However, subjects with median cognitive ability are more truthful under strategic uncertainty, especially when information is incomplete. Additionally, providing information about priorities has a negative and significant impact on truth-telling rates. The findings of this research demonstrate that constructing a simplified market environment using computerized agents and providing relevant feedback to students can effectively enhance their understanding of the matching mechanism and nudge them to make optimal strategy choices.

A three-way decision with prospect-regret theory under Pythagorean fuzzy environments

As science and technology continue to advance at a rapid pace, people are facing increasingly complex data, and research on uncertain information technology has become a hot field. Compared to intuitionistic fuzzy sets, Pythagorean fuzzy sets are more tolerant and flexible in representing uncertain information. Therefore, this article mainly focuses on the study of three-way decisions based on Pythagorean fuzzy β covering. First, considering the disadvantages of other methods in comparing the magnitudes of Pythagorean fuzzy numbers, we propose the concept of the ideal positive degree in a Pythagorean fuzzy environment and determine a method for calculating conditional probabilities based on this newly defined ideal positive degree. Second, regret theory and prospect theory are combined to obtain a relative utility function. Then, based on the aforementioned conditional probabilities and relative utility function, a three-way multi-attribute decision-making method based on the Pythagorean fuzzy environment is established. Finally, the three-way multi-attribute decision-making method is utilized to solve the problem of selecting the optimal choice for rural revitalization industrial projects. Through experimental comparison and analysis, the effectiveness and reliability of this method are verified.

An integrated flux-symmetric spectrometer-magnet system for the SND@LHC experiment upgrade

The proposed upgrade of the SND@LHC experiment for the High Luminosity phase of the LHC (HL-LHC) will strongly benefit from the presence of a magnetized region, allowing for muon momentum and charge measurement. In this paper we describe an iron core magnet system that is partly integrated with the calorimeter and that is designed to respect the strict constraints from the available space in the experimental cavern, power consumption, and field requirements. Semi-analytical tools are introduced to explore the parameter space, in order to define the primary design options. A full 3-D analysis is then performed in order to validate the optimal choice, and to propose a conceptual design, including sizing of the components, detector performances and stray fields. Several technical options are also discussed, anticipating the design phase.

Performance analysis of CKF algorithm for battery SoC estimation with its aging effect

The penetration of electric vehicle (EV) in automobile market is very much dependent on the battery technology. Its size, weight, and cost are issues of concern. To effectively utilize the battery expertise, precise estimate of state of charge (SoC) is vital which greatly depends on the battery model. Current models lack consideration for variations in battery capacity over their lifespan. This paper develops a battery model which depicts the depletion of battery capacity with its life. Subsequently, this model has been utilized for estimation using advanced Kalman filtering (KF) algorithms. For the developed model, the design and effectiveness of the cubature Kalman filter (CKF) is applied as a proposed robust state-estimator for this problem. Moreover, a comparative analysis was undertaken with existing non-linear KFs based on performance metrics. The optimal choice of estimator is identified, through the results obtained from the Octave/MATLAB simulation. The outcomes show CKF algorithm based SoC estimator is superior to others in ensuring high accuracy, strong robustness even under changes in initial conditions (i.e., initial SoC, process and sensor noise levels), system's ability to converge quickly while ensuring that the maximum error in state of charge (SoC) estimation remains within 1% after convergence.

Exoskeleton Robot Based on Brain-Computer Interface

Brain-computer interface (BCI) technology represents a means of facilitating human-computer interaction. One of the most widely accepted paradigms of brain-computer interface is motor imagery, which enables the recognition of electroencephalogram (EEG) signals generated in a specific brain region by imagining the movement of a limb. Following the acquisition, preprocessing, feature processing, and signal classification of the EEG signals, the complex signals are accurately recognized. Therefore, by creating a control system that translates the recognized EEG signals into movement commands for the robot and transmits them to the robot, it is possible to control the robot's movements by motor imagery. The convolutional neural network is the most popular signal processing algorithm due to its high EEG recognition accuracy, excellent performance in feature extraction, and superior performance in end-to-end learning. The convolutional neural network is an optimal method for signal processing in robot control. This makes CNN an optimal choice for processing EEG signals in robot control, enhancing both the effectiveness and user experience of BCI systems by enabling more intuitive and responsive interactions with robotic devices.

Moving Horizon Fault Estimation of a Hybrid System using the Switched ARX-Laguerre Model

Using moving horizon Fault estimation, we propose in this article a fault estimation of a linear hybrid system using a reduced complexity SARX-Laguerre model. The linear hybrid systems are approximated by a SARX-Laguerre model (Switched ARX-Laguerre).This latter is obtained by expanding a discrete time SARX model parameters on Laguerre orthonormal bases. The resulting model ensures an efficient complexity reduction with respect to the classical SARX model. This parametric complexity reduction is still subject to an optimal choice of the Laguerre poles defining Laguerre bases. Therefore, we propose in this paper to identify the parameters of the SARX-Laguerre model by using a recursive algorithm to identify the Fourier coefficients and a metaheuristic algorithm to identify the poles. The proposed model is built from the system input / output observations and is used to develop a fault estimation scheme. The scheme of the fault estimation based on the Moving Horizon fault Estimation (MHE). The proposed fault estimation using moving horizon procedure is tested on numerical simulation.

An Integrated Multi-Product Biodiesel and Bioethanol Supply Chain Model with Torrefaction Under Uncertainty

This study presents an integrated supply chain network model for biodiesel and bioethanol production, incorporating torrefaction under uncertain conditions related to the establishment of new facilities. The proposed mixed-integer linear programming model aims to minimize the total cost of the supply chain while maximizing social objectives such as reducing unemployment. To solve the bi-objective model, a three-stage approach is employed: first, uncertain parameters are defuzzified; second, the augmented epsilon-constraint method is applied to generate a set of efficient Pareto-optimal solutions; and third, robust optimization is used to handle real-world uncertainties, such as disruptions caused by natural disasters and sanctions, ensuring feasibility under different scenarios. The study considers various stages of the supply chain, from feedstock cultivation to processing, transportation, and distribution. A real-life case study in Iran is used to evaluate the effectiveness of the proposed model, highlighting that biodiesel and bioethanol supply chains are interrelated, particularly at the cultivation stage, where each crop impacts the other. In this regard, Kermanshah, Isfahan, Chahar Mahal & Bakhtiari, Khorasan North, Kohgiluyeh & Boyer-Ahmad, and Lorestan are identified as the most suitable provinces for second-generation plant cultivation. Additionally, Azerbaijan East is identified as the best location for a bioethanol refinery, while Tehran and Markazi are the optimal choices for biodiesel refineries. This integrated approach offers a novel solution that prevents impractical overlaps in land use, providing a comprehensive, sustainable, and socially beneficial framework for bioenergy supply chain management.

LEVERAGING EPILEPSY DETECTION ACCURACY THROUGH BURST ENERGY INTEGRATION IN CWT AND DECISION TREE CLASSIFICATION OF EEG SIGNALS

The biomedical field plays a pivotal role in advancing healthcare by leveraging technological innovations to enhance diagnostics and treatment strategies. In the context of neurological disorders, particularly epilepsy, automated EEG signal processing stands out as a critical facet of biomedical research. The ability to analyze and interpret vast amounts of electroencephalogram (EEG) data using sophisticated techniques, such as Continuous Wavelet Transform (CWT), contributes significantly to the timely and accurate detection of epileptic events. Automated EEG signal processing, which uses advanced algorithms and pattern recognition methods, enables the identification of subtle yet crucial patterns indicative of epileptic activity. This paper presents an in-depth exploration of epilepsy identification using the CWT on the CHB-MIT Scalp EEG database. The study uses nine complex mother wavelets from the Gaussian and Morlet families to look at 8920 EEG segments, including 197 seizure events. The performance of each wavelet in detecting epileptic convulsions within EEG signals is rigorously evaluated. Our study shows that the complex Gaussian wavelet of order 5 (cgau5) emerged as the optimal choice, with a sensitivity of 97.58% and a precision of 97.93%. To improve epilepsy detection, we introduce burst energy, a novel engineered feature. This method gets accurate information about brain activity from the CWT scalogram by detecting ictal and ictal-free EEGs at different energy levels. The use of burst energy has a significant impact on classification performance, highlighting its potential for improved accuracy in epilepsy identification. This comprehensive study contributes valuable insights into selecting appropriate wavelets and introduces an innovative feature for more effective EEG-based epilepsy detection.

A minimally invasive modified revolving-door flap for surgical reconstruction of concha and antihelix defects

Skin cancers affecting the concha and antihelix are quite common, because of anterior auricular projection from the head and subsequent actinic exposure, leading to the need for effective ear reconstruction post-surgery. Various methods such as skin grafts, free tissue transplantation, and local flaps have been used. This study introduces a refined technique for concha-antihelix defect reconstruction, based on a minimally invasive modification of the revolving-door flap procedure. Here, a fasciocutaneous flap technique involving minimal posterior auricular muscle contribution is used, particularly suitable for anterior skin and cartilage defects. The study included 75 patients with nonmelanocytic skin carcinoma on the ear's anterior surface, in which we evaluated patient's and surgeon's satisfaction with a visual analogue scale and discussed the surgical technique and its advantages and indications. The technique demonstrated excellent cosmetic and functional outcomes, effectively hiding donor-site deformities and ensuring patient and surgeon contentment. This approach allows for one-stage reconstruction under local anesthesia, reducing costs, surgery time, and recovery period. The procedure provides high-quality tissue for reconstruction with low complication risks, making it an optimal choice for wide conchal and antihelix defect repairs due to its proximity to the affected area and the reliable, swift nature of the method.

Mimicking Scaffolds for Osteogenesis Based on Poly(vinyl alcohol) Hydrogel with Hard Calcium Phosphate and Soft Silk Fibroin Particle for Bone Regeneration: Molecular Organization, Morphology, Properties, and In Vitro Evaluation.

In this study, poly(vinyl alcohol) (PVA)/silk fibroin particle (SFP) hydrogel scaffolds are prepared by mixing compound calcium phosphate (CCP) in different weight ratios (0, 4, 8, and 16%) via the repeated freeze-thawing method. The physicochemical characteristics and biological behavior of the hydrogel are evaluated, and the results show a decreased porous structure of the hydrogel composite with a swelling ability upon CCP addition; however, the mechanical strength and degradation rate increase. Cell attachment and growth analyses demonstrate PVA/SFP/CCP as nontoxic to cells. Furthermore, osteogenesis evaluation shows that the group with 8% CCP in PVA/SFP hydrogel exhibits higher cell adhesion and proliferation than other groups. Additionally, the group exhibits better osteogenic performance than the other groups when alkaline phosphatase activity, total protein content, and calcium deposition are compared. Considered as a mixture, the PVA/SFP/CCP hydrogel is promising for bone tissue engineering applications; particularly, PVA/SFP/8% CCP serves as an optimal choice.

Optimal Prototyping with Noisy Measurements

Problem definition: Prototyping and testing are an integral part of almost any new product development process, helping firms navigate the inherent uncertainties of creating new products. Recent developments in rapid prototyping, including technologies that enable cheaper low-fidelity tests, have opened up the possibilities for firms in reconfiguring their product development processes. Firms can, by choosing the level of evaluation fidelity, alter the traditional cost-quality trade-offs inherent in sequential prototyping. Methodology/results: The current article formulates a general model of sequential search where firms can proceed by obtaining noisy low-fidelity evaluations of their prototypes. Our results demonstrate that the imperfect fidelity of evaluations alters the firm’s optimal experimentation, with the starkest difference being that it may make it optimal for the firm to select and launch a prototype that did not yield the best evaluation. In addition, our analysis of optimal measurement technology reveals that the focal firm should demand the most precise measurements when their ex-ante uncertainty is moderate (not too high or low). We also consider extensions analyzing how the optimal choice of evaluation fidelity is affected by the number of available prototypes, by operational flexibility (to dynamically change measurement technology), and by the ability to outsource evaluations to an experimentation platform. Managerial implications: We develop managerial insights for how the optimal choice of fidelity and the optimal length of the evaluation cycle should be planned depending on the evaluation costs and the firm’s ex-ante uncertainty. The resulting framework offers guidance to product and software development firms to successfully leverage imperfect fidelity experiments. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1133 .