Technology Benchmarking Research Articles

AN ADAPTIVE DIFFERENTIAL EVOLUTION ALGORITHM WITH A BOUND ADJUSTMENT STRATEGY FOR SOLVING NONLINEAR PARAMETER IDENTIFICATION PROBLEMS

Real-world parameter identification problems require determining the bounds that cover the unknown solutions. This paper presents an adaptive differential evolution algorithm with a bound adjustment strategy (ADEBAS) for solving nonlinear parameter identification problems. The adjustment strategy detects the parameter-bound violations of mutant vectors during the evolution process and gradually extends the bounds. The algorithm adaptively uses two mutation strategies and two ranges of crossover rate to balance the population diversity and convergence speed. Experimental results show that ADEBAS can solve 24 nonlinear regression tasks from the National Institute of Standards and Technology benchmark with accurate estimation and reliability. It also outperforms the compared methods on real-world parameter identification problems.

X-ray computed tomography analysis of calcium chloride hexahydrate solidification

Latent Thermal Energy Storages (LTESs) are receiving increasing interest, since they offer many advantages to the field of Thermal Energy Storages (TESs), such as a nearly isothermal solid–liquid phase transition and a high energy storage density. To boost the transition towards a climate-neutral future, a lot of work is surely needed on Phase Change Materials (PCMs) which are used inside LTESs. Salt hydrates, a particular class of PCMs, are very attractive in terms of cost and volumetric latent enthalpy but present drawbacks that still limit their effective deployment in real applications, such as supercooling and phase separation (or segregation). The current work assesses the suitability of X-ray Computed Tomography (XCT) as a novel technique in the field of LTESs to study the solidification of phase-change materials. In this study, calcium chloride hexahydrate (CaCl2·6H2O) is chosen as a representative PCM and the results derived are shown to be repeatable. For the first time, the direct measurement of the volumetric liquid fraction is correlated to temperature measurements allowing for the visualisation of supercooling and the validation of an enthalpy-porosity numerical model, which could be useful in the design of LTES. XCT methodology offers unmatchable features as compared to the other method to measure the volumetric liquid fraction. Given its accuracy and the rigorous protocol, it can be considered as a benchmark for new liquid fraction measurement technologies but it also permits to study how the different PCMs melt and solidify, giving new insights on the mechanisms at the basis of the most crucial challenges, among those: subcooling, segregation, and metastable phase change.

Reducing Energy Consumption and Greenhouse Gas Emissions of Industrial Drying Processes in Lithium-Ion Battery Cell Production: A Qualitative Technology Benchmark

As the world’s automotive battery cell production capacity expands, so too does the demand for sustainable production. Much of the industry’s efforts are aimed at reducing the high energy consumption in battery cell production. A key driver is electrode drying, which is currently performed in long ovens using large volumes of hot air. Several drying technologies from other industries could reduce energy consumption and greenhouse gas emissions if successfully applied to battery cell production. High process and quality requirements must be met when adapting these technologies for battery cell production. Evaluating the technologies against these requirements is difficult due to the technological novelty of this industry and the associated lack of data. Furthermore, the significant differences in drying technologies render a comparison even more challenging. One objective of this study was to evaluate drying technologies and identify those that could be best adapted to lithium-ion battery cell production. Near-infrared and laser drying were found to be the best in terms of energy efficiency, cost savings and other parameters. Another aim was to analyse, in more detail, the technological challenges and the advantages and disadvantages of the top-ranked drying technologies. Finally, the saving potential for greenhouse gas emissions of near-infrared and laser drying was calculated for a global production scenario of LIB cells in 2030. The saving potential in this scenario would amount to 2.63 million metric tonnes (Mt) CO2eq per year if near-infrared drying was applied in all global LIB cell production facilities within the mentioned scenario and 1.47 million Mt CO2eq per year for laser drying.

Future in Battery Production: An Extensive Benchmarking of Novel Production Technologies as Guidance for Decision Making in Engineering

Due to the rising interest in electric vehicles, the demand for more efficient battery cells is increasing rapidly. To support this trend, battery cells must become much cheaper and “greener.” Energy consumption during production is a major driver of cost and CO <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$_2$</tex></formula> emissions. The drying production step is one of the major energy consumers and cost drivers. The technological approach of “dry coating” allows the energy-intensive drying step to be eliminated for significant energy and cost savings. However, there are numerous emerging dry coating technologies that differ significantly in physics, chemistry, and readiness levels. Moreover, typical methodological procedures for technology selection remain less applicable to the early stages of technological development. Both issues raise the questions, “What is the most promising dry coating technology?” and “How do we identify it?” To answer these questions, a comprehensive, systematic technology benchmark was conducted. Following a four-step analytical approach, based on the nominal group technique, qualitative content analysis, and multicriteria decision analysis, different dry coating technologies were identified, analyzed, and cross-compared. This was performed qualitatively and quantitatively. We also forecast which factor will impact the application of the most promising technologies for CO <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$_2$</tex></formula> emission rate reductions and cost savings in 2030. In summary, four different technologies were identified with a high chance of technological breakthrough within the next 3–5 years. By applying these technologies, 4.76 million tons of CO <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$_2$</tex></formula> could be saved per year by 2030.

Evaluation Metrics for Augmented Reality in Neurosurgical Preoperative Planning, Surgical Navigation, and Surgical Treatment Guidance: A Systematic Review.

Recent years have shown an advancement in the development of augmented reality (AR) technologies for preoperative visualization, surgical navigation, and intraoperative guidance for neurosurgery. However, proving added value for AR in clinical practice is challenging, partly because of a lack of standardized evaluation metrics. We performed a systematic review to provide an overview of the reported evaluation metrics for AR technologies in neurosurgical practice and to establish a foundation for assessment and comparison of such technologies. PubMed, Embase, and Cochrane were searched systematically for publications on assessment of AR for cranial neurosurgery on September 22, 2022. The findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The systematic search yielded 830 publications; 114 were screened full text, and 80 were included for analysis. Among the included studies, 5% dealt with preoperative visualization using AR, with user perception as the most frequently reported metric. The majority (75%) researched AR technology for surgical navigation, with registration accuracy, clinical outcome, and time measurements as the most frequently reported metrics. In addition, 20% studied the use of AR for intraoperative guidance, with registration accuracy, task outcome, and user perception as the most frequently reported metrics. For quality benchmarking of AR technologies in neurosurgery, evaluation metrics should be specific to the risk profile and clinical objectives of the technology. A key focus should be on using validated questionnaires to assess user perception; ensuring clear and unambiguous reporting of registration accuracy, precision, robustness, and system stability; and accurately measuring task performance in clinical studies. We provided an overview suggesting which evaluation metrics to use per AR application and innovation phase, aiming to improve the assessment of added value of AR for neurosurgical practice and to facilitate the integration in the clinical workflow.

Comprehensive benchmarking of laser welding technologies including novel beam shapes and wavelengths for e-drive copper hairpins

Laser welding is the industrially accepted method for the joining of Cu hairpin windings in the production of electric drives. High brilliance laser beams are scanned over the bare ends of the Cu wires producing a rapid connection through deep penetration remote welding. Despite being an accepted manufacturing method, laser welding of Cu hairpins still requires detailed studies concerning manufacturing productivity and quality. As the availability of novel laser sources with higher power levels, new wavelengths, and beam shaping capabilities increase, the need for benchmarking studies emerges. In this work, six different laser welding systems were compared in terms of process productivity and quality during the welding of Cu hairpins used for automotive traction. The different solutions presented power levels from 3 to 6 kW, with wavelengths from near infrared (NIR) to visible, including in source dynamic beam shaping. The weld bead formation was observed through high-speed imaging. The welds were analyzed in terms of their geometry, internal defects, and most relevantly for their mechanical strength. The results showed advantages of each of the employed system while the laser systems providing the highest irradiance profile produced the fastest weld with more elevated mechanical strength independently from the wavelength.

Techno-economic modelling of AEM electrolysis systems to identify ideal current density and aspects requiring further research

Hydrogen produced by water electrolysis using renewable energy is a sustainable alternative to steam reformation. As a nascent commercial technology, performance, and economic comparisons of anion exchange membrane water electrolyzers (AEMWE) to other electrolyzer technology benchmarks are not available. We present a techno-economic model estimating AEMWE's baseline levelized cost of hydrogen (LCOH) at $5.79/kg, considering trade-offs between current density, efficiency, stability, capital, and operating costs. The optimal current density is 1.38 A cm−2, balancing stability and performance for the lowest LCOH. Using low-cost electricity and larger stack sizes, AEMWE could achieve $2/kg low-carbon hydrogen. Technical improvements targeting system efficiency, particularly reducing overpotentials in hydrogen and oxygen evolution reactions, could further reduce LCOH to $1.29/kg, approaching U.S. Department of Energy cost targets. There are hopes this model could raise the profile of AEMWE's economic potential to produce green hydrogen and highlight its suitability for decarbonizing the energy sector.

Long term outcome after surgical treatment for hypothalamic hamartoma

PurposeTo determine long-term outcome for seizure control and clinical predictors for seizure freedom in patients undergoing surgical treatment for epilepsy associated with hypothalamic hamartoma (HH). Methods155 patients underwent surgical treatment for HHs and treatment-resistant epilepsy at one center (Barrow Neurological Institute at St. Joseph’s Hospital and Medical Center, Phoenix, Arizona, USA) between February 2003 and June 2010. Data collection included medical record review and direct follow-up interviews to determine seizure outcome. Statistical analysis included descriptive summaries of patient characteristics and time-to-event analysis for seizure freedom. ResultsLong-term survival with follow-up of at least five years since first surgical treatment was available for 108 patients (69.7% of the treatment cohort). The surgical approach for first HH intervention consisted of transventricular endoscopic resection (n = 57; 52.8%), transcallosal interforniceal resection (n = 35; 32.4%), pterional resection (n = 7; 6.5%), and gamma knife radiosurgery (n = 9; 8.3%). Multiple surgical procedures were required for 39 patients (36.1%). There were 10 known deaths from all causes in the treatment cohort (6.5%). Of these, one (0.6%) was related to immediate complications of HH surgery, three (1.9%) were attributed to Sudden Unexpected Death in Epileptic Persons (SUDEP), and one (0.6%) to complications of status epilepticus. For surviving patients with long-term follow-up, 55 (50.9%) were seizure-free for all seizure types. Univariable analysis showed that seizure-freedom was related to 1) absence of a pre-operative history for central precocious puberty (p = 0.01), and 2) higher percentage of HH lesion disconnection after surgery (p = 0.047). Kaplan-Meier survival analysis shows that long-term seizure outcome following HH surgery is comparable to short-term results. SummaryThese uncontrolled observational results show that long-term seizure control following HH surgical treatment is comparable to other forms of epilepsy surgery. Late relapse (at least one year after surgery) and SUDEP do occur, but in a relatively small number of treated patients. These results inform clinical practice and serve as a comparable benchmark for newer technologies for HH surgery, such as magnetic resonance imaging-guided laser interstitial thermal therapy, where long-term outcome results are not yet available.

Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors

Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.

Multi-Objective Optimization of Ultra-Low Energy Housing in Hot Summer Cold Winter Climate Zone of China Based on a Probabilistic Behavioral Model

Occupant behavior has an important impact on building energy consumption, and the accuracy of an occupant behavior model directly affects the reliability of energy consumption simulation results. Ultra-low energy buildings are crucial to achieving building energy conservation and carbon dioxide reduction in China. In order to effectively promote the development of ultra-low energy buildings in Hot Summer and Cold Winter Climate Zones. where most residents adopt a “part-time, part-space” pattern of intermittent energy use behavior, and to solve the problem of poor indoor thermal environments and the high incremental cost of ultra-low energy, the study described in this paper takes Changsha as an example to carry out a multi-objective optimization study on ultra-low energy housing using a probabilistic behavioral model. On the basis of a probability model representing the residents’ actual behavior in Changsha, the optimization objective indicators, key variables and the technology benchmarks for ultra-low energy building were determined, then multi-objective optimization was carried out for a range of energy efficient technologies to obtain the Pareto optimal solutions. The results showed that the set of optimal solutions could reduce energy demand by 50.2 to 60.2% and reduce indoor thermal discomfort time by 3.52–11.09% compared with those of a reference base case, which just meets the requirements of the current design standard for energy efficient domestic buildings. An optimum solution for energy savings and indoor thermal comfort, along with economic costs, was identified, which can assist in decision-making by providing different preferences and provide useful reference for the design of ultra-low energy buildings in Hot Summer and Cold Winter Climate regions.

A comparison of five Illumina, Ion Torrent, and nanopore sequencing technology-based approaches for whole genome sequencing of SARS-CoV-2

Rapid identification of the rise and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern remains critical for monitoring of the efficacy of diagnostics, therapeutics, vaccines, and control strategies. A wide range of SARS-CoV-2 next-generation sequencing (NGS) methods have been developed over the last years, but cross-sequence technology benchmarking studies have been scarce. In the current study, 26 clinical samples were sequenced using five protocols: AmpliSeq SARS-CoV-2 (Illumina), EasySeq RC-PCR SARS-CoV-2 (Illumina/NimaGen), Ion AmpliSeq SARS-CoV-2 (Thermo Fisher), custom primer sets (Oxford Nanopore Technologies (ONT)), and capture probe-based viral metagenomics (Roche/Illumina). Studied parameters included genome coverage, depth of coverage, amplicon distribution, and variant calling. The median SARS-CoV-2 genome coverage of samples with cycle threshold (Ct) values of 30 and lower ranged from 81.6 to 99.8% for, respectively, the ONT protocol and Illumina AmpliSeq protocol. Correlation of coverage with PCR Ct values varied per protocol. Amplicon distribution signatures differed across the methods, with peak differences of up to 4 log10 at disbalanced positions in samples with high viral loads (Ct values ≤ 23). Phylogenetic analyses of consensus sequences showed clustering independent of the workflow used. The proportion of SARS-CoV-2 reads in relation to background sequences, as a (cost-)efficiency metric, was the highest for the EasySeq protocol. The hands-on time was the lowest when using EasySeq and ONT protocols, with the latter additionally having the shortest sequence runtime. In conclusion, the studied protocols differed on a variety of the studied metrics. This study provides data that assist laboratories when selecting protocols for their specific setting.

Network externalities in the long-term performance of ICOs

This study examines the impact of technology and network externalities on exchange-listed Initial Coin Offerings (ICOs). Utilising an online database comprising of self-reported ICO characteristics, measures of post-ICO performance, along with information on business social networks, higher fundraising figures are found to contribute positively to the ICOs long-term success. This positive impact is multiplied by six times when fundraising is conducted to an existing, proprietary blockchain. This is explained by the network effect. The modified information ratio measure is used to approximate the quality signalling of ICO organisations using price time series and benchmarking these to already functioning blockchain technology, e.g. ethereum in the long term. The ICO sample's mean trading period on an exchange is 1.5 years and is used for long-period asset analysis. Additionally, the cointegration to the market technology benchmark is found to have a large, significant negative effect on long-term ICO organisational success as this indicates lower ICO intrinsic value.

Market Timing, Selectivity, and Performance of Technology Exchange-Traded Funds and Mutual Funds

This study compares the risk-adjusted performance of technology mutual funds and exchange-traded funds with several benchmark indexes from January 2010 to July 2021. It is discovered that the average monthly returns on technology mutual funds and exchange-traded funds were highly correlated with the DJIA US Technology, NASDAQ 100 Tech, NYSE Arca Tech 100 Index, and S&amp;P 500 Information Technology benchmark indexes. Furthermore, technology mutual funds outperformed exchange-traded funds and benchmark indexes in absolute and risk-adjusted performance. In addition, it is discovered that institutional technology funds’ risk-adjusted performance was marginally greater than noninstitutional funds’ risk-adjusted performance, and index technology mutual funds outperformed nonindex funds in risk-adjusted performance. Unconditional models indicated that technology mutual fund managers may have some market timing ability but no security selection skill. Conditional performance evaluation models indicated that fund managers do not have superior security selection techniques or the ability to time the market.

МОДЕЛЬ ЭФФЕКТИВНОГО ФУНКЦИОНИРОВАНИЯ ПРЕДПРИЯТИЯ НА ОСНОВЕ ТЕХНОЛОГИЧЕСКОГО БЕНЧМАРКИНГА

The development of domestic enterprises is significantly affected by the constantly im-posed sanctions by Western countries, which leads to changes in logistics flows, the need to promptly search for new suppliers and markets, the comprehension of lagging behind in a number of industries due to the lack of components, spare parts, equipment, etc. Another major problem of low functioning efficiency of domestic industrial enterprises is the wear and tear and the lack of necessary equipment. Limited technological resources act as a significant constraint to the effective innovative development of enterprises. The solution to the problem can be the application of the best available technologies, covering all aspects of enterprise activity such as production, technological, economic, environmental, social and in-novative ones. This requires combining the best available technologies for the enterprise into a single interrelated set of technologies, taking into account the enterprise mission, its strategic goals, the inter-dependence of the all business process components, the presence of limitations and forecasts for over-coming them. A conceptual effective functioning model of the domestic enterprise is presented in the article based on implementation of the best available technologies through technological benchmarking, which involves the application of the program-target and system-synergetic approaches principles. The combi-nation of program-target and system-synergetic approaches to the industrial enterprise effective func-tioning allows to specify its components, which are formed in relation to a set of objectives that ensure effective functioning when calculating composite indices of all the most significant components. The implementation of the best available technologies comprehensively influences the enterprise's capabilities components, directing the enterprise to the effective functioning trajectory. Technological benchmarking makes it possible to establish the most accurate areas of limited resources application in order to achieve peak effect and allows for a systematic approach to identifying key benchmarks for en-terprise development.

Potential Benefits of Pila ampullacea, Tempeh, Moringa oleifera Leaves as Nutritional Support for Hemodialysis

Background: Hemodialysis patients can experience problems, including proteinenergy malnutrition, infection, disorders of the immune system, and inflammation. One etiology of malnutrition in hemodialysis is inadequate energy and protein intake, making patients need nutritional support, which can be fulfilled by local Indonesian foodstuffs. Objective: This paper aimed to review the potential health benefits of snails (Pila ampullacea), tempeh, and Moringa oleifera leaves as nutritional support for hemodialysis patients. Methods: In this review, the methodology used was based on comprehensive data searched from PubMed for literature review and technology benchmarking in making nutritional support for hemodialysis patients. An in-depth discussion, including the advantages and drawbacks of each foodstuff, is presented and outlined. Furthermore, key solutions are proposed and presented to overcome hemodialysis issues. Results: The mixture of snail, tempeh, and moringa leaves as nutritional support for hemodialysis patients shows a good combination of natural ingredients from animals and plants. The nutritional content of the three mixed ingredients is found to meet the dietary requirements of hemodialysis, which are high protein, calcium and antioxidants, low phosphorus, and a phosphorus-toprotein ratio of &lt;16. Conclusion: The combination of snails, tempeh, and moringa leaves provides several potential health benefits in overcoming nutritional problems, decreased immune status, and inflammation of hemodialysis patients.

A Technology Development Matrix for Carbon Capture: Technology Status and R&amp;D Gap Assessment

Numerous carbon capture, utilization, and storage (CCUS) technologies are under development to reduce CO2 emissions. To evaluate the status of a CCUS technology under development and identify potential gaps for further advancement, we have established a new technology assessment framework and are developing a decision-making tool, the technology development matrix (TDM), starting with available carbon capture technology (CCT) data. TDM is a data inventory system and screening tool. As a screening tool, it can be used for resource allocation decisions in research, development, and deployment (RD&amp;amp;D) by academia, government, and industry. It shares data with techno-economic analysis (TEA) and life-cycle assessment (LCA) tools as an inventory system. By using available data, this TDM framework has been demonstrated on amine-based (monoethanolamine) absorption post-combustion CO2 capture, for pulverized coal (PC) power plant flue gas, as the best available technology (BAT) for comparison. Three groups of promising post-combustion CCTs under development are presented as Alternative Technology (Alt Tech) case studies, including membrane, solid adsorption, and calcium-based chemical looping. By using available data, preliminary analysis enabled technology benchmarking and highlighted knowledge, data, and technology gaps, all providing potential future RD&amp;amp;D focus.

Valuable resources in water: Why and how to recover?

Water is a key element of the global water-energy nexus and a crucial foundation of economic globalization. Water, as an essential resource for humans, carries diverse valuable and scarce resources. Mining these resources from the water will be of significance for material flows of resources, which will substantially accelerate a transition to sustainable and circular economies. Compared with conventional water sources, various unconventional water sources such as seawater, brines, and industrial waste streams are considered economically nonviable, yet still worthy of extraction. Mining water, energy, valuable nutrients, minerals, metals, and chemical products from these sources is valuable and viable. There is a growing need for introducing innovative technologies, processes, models, and systems to effectively extract, reclaim, and reuse resources from diverse types of water sources. There also demands a comprehensive techno-economic analysis, assessment of life-cycle impacts, and benchmarking of these new technologies against their traditional counterparts.

Multiscale Contrasts Between the Right and Left Ventricle Biomechanics in Healthy Adult Sheep and Translational Implications.

Cardiac biomechanics play a significant role in the progression of structural heart diseases (SHDs). SHDs alter baseline myocardial biomechanics leading to single or bi-ventricular dysfunction. But therapies for left ventricle (LV) failure patients do not always work well for right ventricle (RV) failure patients. This is partly because the basic knowledge of baseline contrasts between the RV and LV biomechanics remains elusive with limited discrepant findings. The aim of the study was to investigate the multiscale contrasts between LV and RV biomechanics in large animal species. We hypothesize that the adult healthy LV and RV have distinct passive anisotropic biomechanical properties. Ex vivo biaxial tests were performed in fresh sheep hearts. Histology and immunohistochemistry were performed to measure tissue collagen. The experimental data were then fitted to a Fung type model and a structurally informed model, separately. We found that the LV was stiffer in the longitudinal (outflow tract) than circumferential direction, whereas the RV showed the opposite anisotropic behavior. The anisotropic parameter K from the Fung type model accurately captured contrasting anisotropic behaviors in the LV and RV. When comparing the elasticity in the same direction, the LV was stiffer than the RV longitudinally and the RV was stiffer than the LV circumferentially, suggesting different filling patterns of these ventricles during diastole. Results from the structurally informed model suggest potentially stiffer collagen fibers in the LV than RV, demanding further investigation. Finally, type III collagen content was correlated with the low-strain elastic moduli in both ventricles. In summary, our findings provide fundamental biomechanical differences between the chambers. These results provide valuable insights for guiding cardiac tissue engineering and regenerative studies to implement chamber-specific matrix mechanics, which is particularly critical for identifying biomechanical mechanisms of diseases or mechanical regulation of therapeutic responses. In addition, our results serve as a benchmark for image-based inverse modeling technologies to non-invasively estimate myocardial properties in the RV and LV.

Stability Enhancement of Zinc‐Ion Batteries Using Non‐Aqueous Electrolytes

AbstractOwing to their high energy density and low cost, zinc‐ion batteries (ZIBs) are gaining much in popularity. However, in practice, issues with hydrogen evolution, zinc dendrite development, corrosion, and passivation persist. Such drawbacks prove difficult to eradicate completely. To address these difficulties, many techniques have been proposed including inhibitor addition, artificial SEI, and Zn electrode modification. As a result, some researchers believe that using non‐proton donor electrolytes or nonaqueous electrolytes can fundamentally solve these problems. Herein, the efforts to apply nonaqueous electrolytes such as organic electrolytes, room‐temperature ionic liquids, and deep‐eutectic solvents to ZIBs are described. An understanding of the mechanisms of nonaqueous ZIBs (NZIBs) regarding zinc plating/stripping and intercalation/deintercalation is also highlighted. Importantly, research gaps are identified in order to pave the way for future study. In addition, an attempt is made to offer a viewpoint on critical topics as well as a benchmarking and enhancement of NZIB technologies.

A Feature Selection Approach Hybrid Grey Wolf and Heap-Based Optimizer Applied in Bearing Fault Diagnosis

An effective bearing fault diagnosis model based on machine learning is proposed in this study. The model can separate into three stages: feature extraction, feature selection, and classification. In the stage of feature extraction, multiresolution analysis (MRA) and fast Fourier transform (FFT) are applied to extract the features from the raw signal measured from the rotating machine. In the second stage, a powerful feature selection method is proposed and utilized in the stage of feature selection. The new feature selection method is based on grey wolf optimization (GWO) and heap-based optimizer (HBO) with new strategies combined. Finally, support vector machine (SVM) and linear discriminant analysis (LDA) are used as the classifier independently. To verify the capability of the proposed model, four different datasets are applied to test the model in this study, respectively University of California Irvine (UCI) benchmark dataset, bearing dataset, Case Western Reserve University (CWRU) benchmark dataset, and Machinery Failure Prevention Technology (MFPT) benchmark dataset. The proposed method is compared with the existing methods and can certify the robustness with the experiment results.