Multiple Performance Research Articles

Elevating Lithium-Sulfur Battery Durability through Samarium Oxide/Ketjen Black Modified Separator.

Lithium-sulfur batteries have garnered significant attention as a promising next-generation battery technology due to their potential for high energy density. However, their practical application is hampered by slow reaction kinetics and the shuttle effect of lithium polysulfide intermediates. In this context, the authors introduce a pioneering solution in the form of a novel porous carbon nanostructure modified with samarium oxide, denoted as Sm2O3/KB. The material has a highly polar surface, allowing lithium polysulfide to be chemisorbed efficiently. The unsaturated sites provided by the oxygen vacancies of Sm2O3 promote Li2S nucleation, lowering the reaction energy barrier and accelerating Li2S dissolution. The porous structure of Ketjen Black provides a highly conductive channel for electron transport and effectively traps polysulfides. Meanwhile, the batteries with Sm2O3/KB/PP spacers exhibited remarkable electrochemical performances, including a low-capacity decay rate of only 0.046 % for 1000 cycles at 2 C and an excellent multiplicative performance of 624 mAh g-1 at 3 C. This work opens up a new avenue for the potential use of rare-earth-based materials in lithium-sulfur batteries.

Niobium-Based Oxide for Anode Materials for Lithium-Ion Batteries.

Recently, it has become imperative to develop high energy density as well as high safety lithium-ion batteries (LIBS) to meet the growing energy demand. Among the anode materials used in LIBs, the currently used commercial graphite has low capacity and is a safety hazard due to the formation of lithium dendrites during the reaction. Among the transition metal oxide (TMO) anode materials, TMO based on the intercalation reaction mechanism has a more stable structure and is less prone to volume expansion than TMO based on the conversion reaction mechanism, especially the niobium-based oxide in it has attracted much attention. Niobium-based oxides have a high operating potential to inhibit the formation of lithium dendrites and lithium deposits to ensure safety, and have stable and fast lithium ion transport channels with excellent multiplicative performance. This review summarizes the recent developments of niobium-based oxides as anode materials for lithium-ion batteries, discusses the special structure and electrochemical reaction mechanism of the materials, the synthesis methods and morphology of nanostructures, deficiencies and improvement strategies, and looks into the future developments and challenges of niobium-based oxide anode materials.

Single versus multiple project teams and individual performance: Do they ask for different leadership behaviors?

Multiple project team membership is a prevalent phenomenon in modern organizational life. However, is any leadership behavior in such a setting beneficial to individual team members' performance? Our study suggests that working in a multiple project team setting requires particular types of leadership. In an experimental design, we manipulated charismatic and boundary-spanning leadership behaviors in single and multiple team project settings and we studied their effects on project members’ performance. When workers are part of a single team, charismatic leadership enhances their performance to a greater extent than a boundary spanning leader. When members are part of two project teams concurrently, boundary-spanning leadership behavior becomes more beneficial for individual performance compared to charismatic leadership. The main theoretical contribution lies in the insight that different organizational project forms ask for different leadership behaviors to nurture individual performance. Practically, (future) project leaders must be prepared for operating in different project settings.

Bismuth Nanoparticles Encapsulated in a Porous Carbon Skeleton as Stable Chloride-Storage Electrodes for Seawater Desalination

Cost-effective bismuth (Bi) boasts a high theoretical capacity and exceptional selectivity towards Cl- ion storage, making it a promising material for desalination batteries (DBs). However, the substantial volume expansion and low conductivity severely hinder the cycling performance of Bi-based DBs. In this study, a carbon-layer-coated Bi nanocomposite (Bi@C) was synthesized by pyrolyzing a metal–organic framework (Bi-MOF) containing Bi using a straightforward method. The results show that the Bi@C synthesized under the condition of annealing at 700 °C for 2 h has the optimum properties. The Bi@C has good multiplication performance, and the desalination capacity is 106.1 mg/g at a high current density of 1000 mA/g. And the material exhibited a high desalination capacity of 141.9 mg/g at a current density of 500 mA/g and retained 66.9% of its capacity after 200 cycles. In addition, the Bi@C can operate at a wide range of NaCl concentrations from 0.05 to 2 mol/L. The desalination mechanism analysis of the Bi@C revealed that the carbon coating provides space for Bi particles to expand in volume, thereby mitigating the issues of electrode material powdering and shedding. Meanwhile, the porous carbon skeleton establishes electron and ion channels to enhance the electrode material’s conductivity. This research offers a promising strategy for the application of chloride-storage electrode materials in electrochemical desalination systems.

Multi-objective compression for CNNs via evolutionary algorithm

Filter pruning has been successfully applied in convolutional neural networks (CNN) to reduce model redundancy and speed up inference. Current pruning algorithms require setting a target value for a pruned model, which is not conducive to the rapid implementation in practical application. Firstly, for a target model, it may be necessary to pay attention to multiple performances at the same time, but it is hard to keep the best trade-off among indicators. Secondly, it is time-consuming to find a suitable model by pruning in a trial-and-error way. Therefore, this paper attempts to combine the most concerning indicators into a search target, rather than producing a model that only contains some pre-defined constraint. Specifically, the multi-object differential evolution algorithm is introduced into the search process, which is used to obtain a set of sparse structures that have taken all useful indicators into account. Then select proper sub-network to retrain from search results according to actual requirements. Experiments on multiple advanced CNN networks show that our algorithm can compress different indicators while keeping comparable or better performance than prior works. On ImageNet, pruning based on ResNet50 reduces FLOPs by 60.4%, while the accuracy of top-1 and top-5 only loses 1.09% and 0.57%, respectively.

Calibration of Heterogeneous Treatment Effects in Randomized Experiments

Machine learning is commonly used to estimate the heterogeneous treatment effects (HTEs) in randomized experiments. Using large-scale randomized experiments on Facebook and Criteo platforms, we observe substantial discrepancies between machine learning-based treatment effect estimates and difference-in-means estimates directly from the randomized experiment. This paper provides a two-step framework for practitioners and researchers to diagnose and rectify this discrepancy. We first introduce a diagnostic tool to assess whether bias exists in the model-based estimates from machine learning. If bias exists, we then offer a model-agnostic method to calibrate any HTE estimates to known, unbiased, subgroup difference-in-means estimates, ensuring that the sign and magnitude of the subgroup estimates approximate the model-free benchmarks. This calibration method requires no additional data and can be scaled for large data sets. To highlight potential sources of bias, we theoretically show that this bias can result from regularization, and further use synthetic simulation to show biases result from misspecification and high-dimensional features. We demonstrate the efficacy of our calibration method using extensive synthetic simulations and two real-world randomized experiments. We further demonstrate the practical value of this calibration in three typical policy-making settings: a prescriptive, budget-constrained optimization framework; a setting seeking to maximize multiple performance indicators; and a multitreatment uplift modeling setting.

DPIM-based global reliability analysis method for the cable-stayed bridges and the determination of global target reliability index

This study presents an approach to analyzing the dynamic global reliability of cable-stayed bridges using the direct probability integral method. The analysis considers the degradation of resistance in the concrete main beam, tower, and cables, as well as the time-varying nature of the load effect. Six types of limit state functions for failure modes are established, including bending failure of the beam, static torsional failure of the beam, wind vibration failure of the beam, transverse buckling failure of the tower, longitudinal strength failure of the tower, and strength failure of the stay cable due to fretting fatigue and corrosion. A time-dependent extreme value mapping is constructed in the time domain, then the joint probability density integral equation of time-varying extreme value mapping of multiple performance functions is established. According to the mean and variance of random variables in the evaluation reference period, the direct probability integral method with Heaviside function (DPIM-H) is employed to solve the equation, and the dynamic global reliability index of cable-stayed bridge is obtained. As an example, the Brotonne cable-stayed bridge’s global reliability is analyzed, and the reasonable value of the global target reliability index is studied considering the structural performance degradation.

Dual-process modeling of sequential decision making in the balloon analogue risk task

People are often faced with repeated risky decisions that involve uncertainty. In sequential risk-taking tasks, like the Balloon Analogue Risk Task (BART), the underlying decision process is not yet fully understood. Dual-process theory proposes that human cognition involves two main families of processes, often referred to as System 1 (fast and automatic) and System 2 (slow and conscious). We cross models of the BART with different architectures of the two systems to yield a pool of computational dual-process models that are evaluated on multiple performance measures (e.g., parameter identifiability, model recovery, and predictive accuracy). Results show that the best-performing model configuration assumes the two systems are competitively connected, an evaluation process based on the Scaled Target Learning model of the BART, and an assessment rate that incorporates sensitivity to the trial number, pumping opportunity, and bias to engage in System 1. Findings also shed light on how modeling choices and response times in a dual-process framework can benefit our understanding of sequential risk-taking behavior.

Intra-Day and Inter-Day Reliability and Usefulness of Performance, Kinetic and Kinematic Variables during Drop Jumping in Hurling Players

The aim of this study was to estimate the intra-day and inter-day reliability and usefulness of performance (Jump height (JH), ground contact time (GCT) and reactive strength index (RSI)), kinetic (force, power, eccentric rate of force development [E-RFD] and leg stiffness [LS]) and kinematic (velocity) variables during drop jumping (DJ) in hurling players. Seventeen (n = 17; mean ± SD; age = 23.35 ± 5.78 years, height = 178.35 ± 6.30 cm, body mass = 78.62 ± 8.06 kg) male club-level hurling players completed two maximal DJs from 0.20, 0.30, 0.40, 0.50 and 0.60 m drop heights on three testing days separated by 5–9 days of rest. Reliability was assessed using the coefficient of variation percentage (CV% ≤ 15%) and intraclass correlation coefficient (ICC > 0.70). For intra-day reliability, GCT (0.40 m, 0.50 m and 0.60 m), peak force (absolute and relative) (0.40 m and 0.50 m) and leg stiffness (0.40 m and 0.50 m) were found to be unreliable (ICC = 0.32–0.68 and CV% = 3.67–11.83%) from those specific drop heights. All other variables were found to be reliable (ICC = 0.72–0.98 and CV% = 1.07–14.02%) intra-day. All variables were found to be reliable (ICC = 0.72–0.96 and CV% = 2.57–14.68%) inter-day except for relative peak force and absolute and relative eccentric RFD (0.30 m and 0.40 m) (ICC = 0.68–0.90 and CV% = 7.76–16.47%). Practitioners have multiple reliable DJ performance, kinetic and kinematic variables for performance testing and training purposes.

Dynamic performance of supercritical CO2 Brayton cycle and its relationship to the correction of turbomachinery performance maps: A comparative analysis

Due to its numerous advantages, including the capability to achieve high thermal efficiencies and compactness, the supercritical carbon dioxide Brayton cycle (SCO2BC) has recently been considered as an attractive next-generation heat-to-power conversion cycle. Dynamic modelling of the SCO2BC is essential to simply grasp and analyze its performance in either design or off-design conditions. Turbomachines are key components in the SCO2BC, which are typically modelled by adopting their design-point performance map. However, during off-design operations, the performance maps become inaccurate, and correction relations must be integrated into these models to enhance their validity. Despite the development of multiple performance map correction, reduction, and normalization methods, there is a scarce literature that implemented highly accurate correction relations in SCO2BC transient models, which impacts the validity of the obtained results. Therefore, there is a need to assess the accuracy of these results compared to the results of a SCO2BC model adopting an accurate correction method. However, some information needs to be provided first, which include how turbomachinery correction impacts the dynamic behavior of SCO2BC compared to using uncorrected turbomachinery models, and if the correction of one turbomachine (i.e., the compressor) is more significant than the other. This work aims to provide this data and more by developing dynamic SCO2BC models (CPP models), comprehensively evaluating their transient performance, and comparing them to SCO2BCs with uncorrected turbomachinery models (FPP models). This work possesses novelties of utilizing the most accurate turbomachinery correction model found for SCO2BC and building the model in Simcenter Amesim. It is found that FPP models may be viable when the dynamic responses of the components near the fluctuation source are desired. Otherwise, at least a compressor model correction is required to obtain reliable results. Moreover, all turbomachinery models should be corrected for accurate mass flow rate and power responses.

A dual-population multiobjective co-evolutionary matching ensemble learning for product multi-indicator prediction in continuous annealing

Achieving simultaneous and precise prediction of multiple product performance indicators is crucial to ensure stable production in the continuous annealing process. However, the majority of existing quality prediction modeling methods concentrate on developing single-output regression models that are limited to single-indicator prediction scenarios. When dealing with the multi-indicator prediction scenarios, these methods usually require the construction of a separate model for each performance indicator, which leads to more computational and storage resources and more complex maintenance in practical applications. To address this issue, a dual-population multiobjective co-evolutionary matching ensemble learning method called DP-CEMEL is developed. It utilizes dual-population co-optimization to enable the joint learning of the algorithm on input–output relationships and inter-indicator correlations. More specifically, a two-stage learning based individual encoding and decoding method and a performance boosting degree (PBD) based individual evaluation strategy are designed to achieve co-optimization of the dual populations to evolve a set of individuals (i.e., base learners) with good accuracy and low complexity. Then, a PBD based matching ensemble method is applied to get the final multi-indicator prediction model by the optimal matching and ensemble of the evolved individuals. Experimental results on both benchmark data and practical strip production data demonstrate that the proposed DP-CEMEL attains competitive or better performance compared to other state-of-the-art learning methods.

Proposed hybrid Model in Online Education

The advancement of technology powering e-learning has brought numerous benefits, including consistency, scalability, cost reduction, and improved usability. However, there are also challenges that need to be addressed. Here are some key considerations for enhancing the technology powering e-learning. Artificial intelligence has revolutionized the field of e-learning and created tremendous opportunities for education Storage, servers, software systems, databases, online management systems, and apps are examples of such resources. This paper aims to forecast students' adaptability to online education using predictive machine learning (ML) models, including Logistic Regression, Decision tree, Random Forest, AdaBoost, ANN. The dataset utilized for this study was sourced from Kaggle and comprised 1205 high school to college students. The research encompasses several stages of data analysis, including data preprocessing, model training, testing, and validation. Multiple performance metrics such as accuracy, specificity, sensitivity, F1 score, and precision were employed to assess the effectiveness of each model. The findings demonstrate that all five models exhibit considerable predictive capabilities. Notably, decision tree and hybrid models outperformed the others, achieving an impressive accuracy rate of 92%. Consequently, it is recommended to utilize these two models, RF and XGB, for predicting students' adaptability levels in online education due to their superior predictive accuracy. Additionally, the Logistic regression, KNN, and AdaBoost, ANN models also yielded respectable performance levels, achieving accuracy rates of 77.48%, 83.77 ,74.17% and 91.06% respectively. In summary, this study underscores the superiority of RF and XGB models in delivering higher prediction accuracy, aligning with similar research endeavours employing ML techniques to forecast adaptability levels.

Performance investigation and evaluation of a low-temperature solar thermal energy storage system under dynamic weather conditions

Designed and operational parameters matter greatly in how successful an energy storage system performs. However, a current challenge in matching suitable these parameters lies in the poor understanding of such systems’ performance under dynamic weather conditions. In this paper, we investigated a phase change material (PCM) storage unit that is particularly aimed for poor-solar areas, and connected the unit to a flat plate solar collector to establish a complete solar thermal energy storage system (STESS). To optimize the system performance, different grades of solar radiation and outdoor ambient temperatures are introduced, and the effects of heat transfer fluid (HTF) flow rates and solar collecting areas are investigated numerically through multiple performance evaluation parameters. The results indicate that the solar collecting area has a significant effect on the performance of the STESS compared to that of the water flow rate. And different collecting areas of 1 m2, 1.5 m2, and 3 m2 with an optimal water flow rate of 0.06 m3/h are matched to different average daily solar radiation of 203.4 W/m2, 155.2 W/m2, 92.7 W/m2, respectively. Further for the heat release process, different heat release efficiencies from 88.32 % to 97.12 % with different energy consumption of the fan from 0.003 kWh to 0.063 kWh are obtained when the air flow rate is increased from 30 m3/h to 90 m3/h. One of the air flow rates of 60 m3/h is superior for the STESS considering higher heat release efficiency and lower energy consumption of the fan. The findings of this paper will contribute to optimizing the performance of a STESS in practical applications.

Design of S-box multi-objective optimization algorithm based on combined chaotic system

S-box is the only nonlinear cryptographic component that determines the security of the block cipher. The higher the security of the S-box, the higher the security of the block cipher. Therefore, this paper proposes an S-box multi-objective optimization algorithm based on the combined chaotic system. Firstly, designing an integrated chaotic system based on a fractional index and its dynamic behavior is studied; it shows incredibly high-performance stability and chaotic parameter range coverage in the entire parameter space. A novel chaotic S-box construction algorithm is proposed based on the combined chaotic system. It introduces a linear congruential pseudo-random number generator to extend the sequence period and scramble the chaotic S-box through Henon mapping to improve the nonlinearity of the s-box. Finally, introducing a teaching and learning multi-objective optimization model and the performance evaluation criteria of the S-box are incorporated into the design of the objective function; this design enables the resulting S-box to perform well under multiple performance indicators, and then the approximate optimal S-box in the population is obtained. Through the performance test of the approximate optimal S-box, the comparative analysis shows that the S-box has good cryptographic performance, can resist linear analysis and differential analysis, and has a good application prospect in lightweight cipher.

Benchmark and performance progression: Examining the roles of market competition and focus

AbstractAs the US healthcare system transitions from volume to value, various value‐based programs tie medical reimbursements to hospital performance relative to national top performers (i.e., benchmarks). However, prior studies report very limited results on how such benchmarks affect care delivery and patient outcomes across multiple performance fronts. This study examines how general acute care hospitals progress toward benchmarks measured by performance frontiers in technical efficiency, clinical quality, and patient experience over time, subjecting to external market conditions and internal focuses. Based on a panel dataset comprising hospitals in California from 2012 and 2019, our results find support for competitive‐distance‐driven progression rates, suggesting that hospitals' competitive positions measured by their distances to benchmarks drive performance improvements. Yet, the effect diminishes as they move closer to performance frontiers. In addition, we find that market competition reduces the progression rate of technical efficiency. Finally, our results also suggest that focus improves performance progression rates, yet its effects are curvilinear.

Logic mining with hybridized 3-satisfiability fuzzy logic and harmony search algorithm in Hopfield neural network for Covid-19 death cases

<abstract> <p>Since the beginning of the Covid-19 infections in December 2019, the virus has emerged as the most lethally contagious in world history. In this study, the Hopfield neural network and logic mining technique merged to extract data from a model to provide insight into the link between factors influencing the Covid-19 datasets. The suggested technique uses a 3-satisfiability-based reverse analysis (3SATRA) and a hybridized Hopfield neural network to identify the relationships relating to the variables in a set of Covid-19 data. The list of data is to identify the relationships between the key characteristics that lead to a more prolonged time of death of the patients. The learning phase of the hybridized 3-satisfiability (3SAT) Hopfield neural network and the reverse analysis (RA) method has been optimized using a new method of fuzzy logic and two metaheuristic algorithms: Genetic and harmony search algorithms. The performance assessment metrics, such as energy analysis, error analysis, computational time, and accuracy, were computed at the end of the algorithms. The multiple performance metrics demonstrated that the 3SATRA with the fuzzy logic metaheuristic algorithm model outperforms other logic mining models. Furthermore, the experimental findings have demonstrated that the best-induced logic identifies important variables to detect critical patients that need more attention. In conclusion, the results validate the efficiency of the suggested approach, which occurs from the fact that the new version has a positive effect.</p> </abstract>

Survival outcomes according to the tumor location and prognostic factor in metastatic rectal cancer: a multicenter retrospective cohort study.

Prognostic factors of metastatic rectal cancer are not well known. We aim to determine prognostic factors affecting survival for metastatic rectal cancer patients and also to investigate the effect of tumor localization on overall survival. Metastatic rectal cancer patients who received treatment in 5 different centers between 2012 and 2022 were included. Prognostic factors for survival were evaluated using univariate and multivariate analysis. The statistical methods included Pearson's chi-square test, Fisher exact test, Log-rank test, and Cox regression model. A total of 283 patients with metastatic rectal cancer were included in the study. The median OS was not significantly different among the three groups (upper rectum 30.1 months, middle rectum 28.3 months, and low rectum cancer 24.8 months; log-rank p = 0.25). In univariate analysis, Grade 3, ECOG performance status 2, the presence of multiple metastatic sites, the presence of KRAS mutation, the presence of liver metastases, the presence of nonregional lymph node metastases, and the presence of bone metastases were significant predictors of poor survival. In multivariate analysis, Grade 3, ECOG performance status 2, and the presence of multiple metastatic sites were determined as indicators of worse prognosis. Our findings, primary tumor location did not affect survival in metastatic rectal cancer. The most important factors affecting survival were multiple metastatic sites, tumor grade, and ECOG performance status.

Design of Non-Imaging Condenser Based on Critical Interception to Eliminate Multiple Reflections and Performance Analysis

Design of Non-Imaging Condenser Based on Critical Interception to Eliminate Multiple Reflections and Performance Analysis

Thermal constraints on exercise and metabolic performance do not explain the use of dormancy as an overwintering strategy in the cunner (Tautogolabrus adspersus).

Winter cold slows ectotherm physiology, potentially constraining activities and ecological opportunities at poleward latitudes. Yet, many fishes are winter-active, facilitated by thermal compensation that improves cold performance. Conversely, winter-dormant fishes (e.g. cunner, Tautogolabrus adspersus) become inactive and non-feeding overwinter. Why are certain fishes winter-dormant? We hypothesized that winter dormancy is an adaptive behavioural response arising in poleward species that tolerate severe, uncompensated constraints of cold on their physiological performance. We predicted that below their dormancy threshold of 7--8°C, exercise and metabolic performance of cunner are greatly decreased, even after acclimation (i.e. shows above-normal, uncompensated thermal sensitivity, Q10>1-3). We measured multiple key performance metrics (e.g. C-start maximum velocity, chase swimming speed, aerobic scope) in cunner after acute exposure to 26-2°C (3°C intervals using 14°C-acclimated fish) or acclimation (5-8 weeks) to 14-2°C (3°C intervals bracketing the dormancy threshold). Performance declined with cooling, and the acute Q10 of all six performance rate metrics was significantly greater below the dormancy threshold temperature (Q10,acute8-2°C=1.5-4.9, mean=3.3) than above (Q10,acute14-8°C=1.1-1.9, mean=1.5), inferring a cold constraint. However, 2°C acclimation (temporally more relevant to seasonal cooling) improved performance, abolishing the acute constraint (Q10,acclimated8-2°C=1.4-3.0, mean=2.0; also cf. Q10,acclimated14-8°C=1.2-2.9, mean=1.7). Thus, dormant cunner show partial cold-compensation of exercise and metabolic performance, similar to winter-active species. However, responsiveness to C-start stimuli was greatly cold-constrained even following acclimation, suggesting dormancy involves sensory limitation. Thermal constraints on metabolic and exercise physiology are not significant drivers of winter dormancy in cunner. In fact, compensatory plasticity at frigid temperatures is retained even in a dormant fish.

Approaches for Enhancing Pre-hospital EMS Response during the COVID-19 Pandemic Machine Learning

Background: Coronavirus disease 2019 (COVID-19) caused an unprecedented healthcare crisis and warranted a need to use artificial intelligence (AI) and machine learning (ML) for enhancing caller screening and triage within pre-hospital Emergency Medical Services (EMS) specifically tailored to COVID-19 cases. This study aimed to analyze existing AI and ML models and assess their accuracy and precision. Methods: A comprehensive assessment of artificial intelligence (AI) applications used to improve EMS responses in the context of COVID-19 instances was done. The dataset produced by Mexican government was used. This dataset was assessed over different models encompassing logistic regression, random forest, gradient boosting, neural networks, k-nearest neighbors (KNN), Naive Bayes, and clustering (K-means). Results: Multiple models performance evaluation was done employing metrics such as accuracy, precision, recall, and F1-score to comprehensively assess the strengths and limitations of these models. Conclusion: The study\'s findings underline the complexities inherent in caller screening and triage for COVID-19 cases, showcasing diverse strengths and limitations within the deployed machine learning models. The discourse underscores the necessity for a multifaceted approach to effectively manage the intricate challenges associated with caller classification and triage, offering invaluable insights for future research endeavors and guiding the enhancement of emergency healthcare systems.