Severe reactive infectious mucocutaneous eruption mimicking drug-induced epidermal necrolysis triggered by norovirus.

Reactive infectious mucocutaneous eruption (RIME) is an eruptive mucositis with varying degrees of cutaneous involvement presumed to be due to an immunologic response to various infectious pathogens. Most reported cases occur after a prodromal upper respiratory illness. We present a patient with a particularly severe case mimicking drug-induced epidermal necrolysis found to be triggered by asymptomatic norovirus infection, a virus not previously reported in association with RIME.

PRIME-nurse practitioner competency model validation and criterion based OSCE rubric interrater reliability.

The PRIME-NP (Professional-Reporter-Interpreter-Manager-Educator/Evaluation-Nurse Practitioner) Model is adapted from the RIME (Reporter-Interpreter-Manager-Educator) model used in medical education to guide medical student and resident education. The Delphi technique was used to validate the PRIME-NP Model. After two rounds of review by a group of experts in NP curriculum, the model was determined to be valid based on expert consensus. Agreement percent increase from the first round to the second round in all categories. Interrater reliability (IRR) was assessed using interclass correlation after instrument validation was completed for each of the five levels of the PRIME-NP model. Overall, the IRR of the instrument was found to be acceptable with some notable exceptions. No variance was noted in professional behaviors at any level. Variance was increased in management and educator/evaluator behaviors in higher/later course levels. The PRIME-NP Model and PRIME-NP OSCE Rubric is a valid and reliable instrument to assess NP student progression in objective structured clinical examinations. This instrument has the potential for adaptation for use in other types of health sciences education and settings.

An Enhanced RIME Optimizer with Horizontal and Vertical Crossover for Discriminating Microseismic and Blasting Signals in Deep Mines.

Real-time monitoring of rock stability during the mining process is critical. This paper first proposed a RIME algorithm (CCRIME) based on vertical and horizontal crossover search strategies to improve the quality of the solutions obtained by the RIME algorithm and further enhance its search capabilities. Then, by constructing a binary version of CCRIME, the key parameters of FKNN were optimized using a binary conversion method. Finally, a discrete CCRIME-based BCCRIME was developed, which uses an S-shaped function transformation approach to address the feature selection issue by converting the search result into a real number that can only be zero or one. The performance of CCRIME was examined in this study from various perspectives, utilizing 30 benchmark functions from IEEE CEC2017. Basic algorithm comparison tests and sophisticated variant algorithm comparison experiments were also carried out. In addition, this paper also used collected microseismic and blasting data for classification prediction to verify the ability of the BCCRIME-FKNN model to process real data. This paper provides new ideas and methods for real-time monitoring of rock mass stability during deep well mineral resource mining.

Aligning Medical Student Workplace-Based Assessments with Entrustable Professional Activities and the RIME Model in a Psychiatry Clerkship.

OBJECTIVE: The authors piloted use of workplace-based assessments of students during the psychiatry clerkship utilizing both entrustable professional activities (EPAs) and the reporter, interpreter, manager, and educator (RIME) model. METHODS: After supervising clinicians conducted assessments of medical students (N=109) during the psychiatry clerkship using a supervisory scale aligned with both EPA and RIME models, each student received individualized formative feedback. Students were then surveyed on the usefulness of this feedback, and participating faculty/residents were surveyed on the ease of completion of the supervisory scale. RESULTS: Students' mean skill profile suggested they no longer needed direct supervision on EPA1 and EPA6. Mean scores on other studied EPAs suggested students were well on their way toward performing these EPAs without direct supervision. Students had mean RIME scores that exceeded the suggested levels identified for a Reporter to start clerkships, for an Interpreter to start clerkships, and for a Manager to transition to the fourth year. Close to half of the students found the feedback helpful in their development as a clinician but most felt their performance should not be shared with residency program directors, either before or after the Match. Almost all responding preceptors felt the supervisory ratings were easy to complete. CONCLUSIONS: This pilot RIME/EPA framework served as a successful step toward a more competency-based medical education in the psychiatry clerkship with relatively little additional faculty time commitment by using workplace-based assessments already in place and a supervisory scale based on EPAs and RIME.

Clinical features of recurrent Mycoplasma pneumoniae-induced rash and mucositis.

BACKGROUND/OBJECTIVES: The characteristics of patients experiencing recurrent Mycoplasma pneumoniae-induced rash and mucositis (MIRM) are not well understood. We aimed to characterize patients with recurrent disease by comparing the demographics, skin and mucosal involvement, seasonality, and treatment to those with single episodes (isolated MIRM). METHODS: This retrospective case series screened all patients seen by our dermatology inpatient consult service from September 2014 to March 2020. Cases were selected based on laboratory and clinical criteria that confirmed a diagnosis of MIRM. RESULTS: We identified 13 patients with MIRM: 5 who experienced recurrence (38%) and 8 with isolated, single episodes without recurrence. Mean age was 13.6 years for initial episodes in the recurrent patients compared to 11.7 in patients with isolated episodes. All 5 recurrent MIRM patients were male (compared with 75% of isolated MIRM patients) and predominantly Black (60%, compared with 25%). Most episodes overall (isolated and recurrent) occurred from the months of October to February. Recurrences after initial MIRM episode had less severe skin and mucosal findings, often involving only one mucous membrane, less frequent need for hospital admission, and shorter duration of hospital stay. Prophylactic treatments and treatments beyond supportive care were of unclear value. CONCLUSIONS: Some characteristics of MIRM differ between patients with recurrent and isolated disease. Clinicians should be aware of the potential for recurrence, which occurred in 38% of our cohort. Although recurrences after initial MIRM episode tended to be less severe, there is still potential for prolonged hospitalizations with recurrent episodes. More evidence is needed regarding effective preventive and treatment regimens in patients with recurrent MIRM.

A rime ice-inspired bismuth-based flexible sensor for zinc ion detection in human perspiration.

A nature-inspired special structure of bismuth is newly presented as Zn ion sensing layer for high-performance electrochemical heavy metal detection sensor applications. The rime ice-like bismuth (RIBi) has been synthesized using an easy ex situ electrodeposition method on the surface of a flexible graphene-based electrode. The flexible graphene-based electrode was fabricated via simple laser-writing and substrate-transfer techniques. The Zn ion sensing performance of the proposed heavy metal sensor was evaluated by square wave anodic stripping voltammetry after investigating the effects of several parameters, such as preconcentration potential, preconcentration time, and pH of acetate buffer. The proposed RIBi-based heavy metal sensor demonstrated a good linear relationship between concentration and current in the range 100-1600 ppb Zn ions with an acceptable sensitivity of 106 nA/ppb·cm2. The result met the requirements in terms of common human perspiration levels (the average Zn ion concentration in perspiration is 800 ppb). In addition, the heavy metal sensor response to Zn ions was successfully performed in human perspiration samples as well, and the results were consistent with those measured by atomic absorption spectroscopy. Besides, the fabricated Zn ion sensor exhibited excellent selectivity, repeatability, and flexibility. Finally, a PANI-LIG-based pH sensor (measurement range: pH 4-7) was also integrated with the Zn ion sensor to form a single chip hybrid sensor. These results may provide a great possibility for the use of the proposed flexible sensor to realize wearable perspiration-based healthcare systems. Graphical abstract.

Analysis of 4 imaging features in patients with COVID-19.

BACKGROUND: The aim of this was to analyze 4 chest CT imaging features of patients with coronavirus disease 2019 (COVID-19) in Shenzhen, China so as to improve the diagnosis of COVID-19. METHODS: Chest CT of 34 patients with COVID-19 confirmed by the nucleic acid test (NAT) were retrospectively analyzed. Analyses were performed to investigate the pathological basis of four imaging features("feather sign","dandelion sign","pomegranate sign", and "rime sign") and to summarize the follow-up results. RESULTS: There were 22 patients (65.2%) with typical "feather sign"and 18 (52.9%) with "dandelion sign", while few patients had "pomegranate sign" and "rime sign". The "feather sign" and "dandelion sign" were composed of stripe or round ground-glass opacity (GGO), thickened blood vessels, and small-thickened interlobular septa. The "pomegranate sign" was characterized as follows: the increased range of GGO, the significant thickening of the interlobular septum, complicated with a small amount of punctate alveolar hemorrhage. The "rime sign" was characterized by numerous alveolar edemas. Microscopically, the wall thickening, small vascular proliferation, luminal stenosis, and occlusion, accompanied by interstitial infiltration of inflammatory cells, as well as numerous pulmonary interstitial fibrosis and partial hyaline degeneration were observed. Repeated chest CT revealed the mediastinal lymphadenectasis in one patient. Re-examination of the NAT showed another positive anal swab in two patients. CONCLUSION: "Feather sign" and "dandelion sign" were typical chest CT features in patients withCOVID-19; "pomegranate sign" was an atypical feature, and "rime sign" was a severe feature. In clinical work, accurate identification of various chest CT signs can help to improve the diagnostic accuracy of COVID-19 and reduce the misdiagnosis or missed diagnosis rate.

Identification of a chromatin-bound ERRα interactome network in mouse liver.

OBJECTIVES: Estrogen-related-receptor α (ERRα) plays a critical role in the transcriptional regulation of cellular bioenergetics and metabolism, and perturbations in its activity have been associated with metabolic diseases. While several coactivators and corepressors of ERRα have been identified to date, a knowledge gap remains in understanding the extent to which ERRα cooperates with coregulators in the control of gene expression. Herein, we mapped the primary chromatin-bound ERRα interactome in mouse liver. METHODS: RIME (Rapid Immuno-precipitation Mass spectrometry of Endogenous proteins) analysis using mouse liver samples from two circadian time points was used to catalog ERRα-interacting proteins on chromatin. The genomic crosstalk between ERRα and its identified cofactors in the transcriptional control of precise gene programs was explored through cross-examination of genome-wide binding profiles from chromatin immunoprecipitation-sequencing (ChIP-seq) studies. The dynamic interplay between ERRα and its newly uncovered cofactor Host cell factor C1 (HCFC1) was further investigated by loss-of-function studies in hepatocytes. RESULTS: Characterization of the hepatic ERRα chromatin interactome led to the identification of 48 transcriptional interactors of which 42 were previously unknown including HCFC1. Interrogation of available ChIP-seq binding profiles highlighted oxidative phosphorylation (OXPHOS) under the control of a complex regulatory network between ERRα and multiple cofactors. While ERRα and HCFC1 were found to bind to a large set of common genes, only a small fraction showed their colocalization, found predominately near the transcriptional start sites of genes particularly enriched for components of the mitochondrial respiratory chain. Knockdown studies demonstrated inverse regulatory actions of ERRα and HCFC1 on OXPHOS gene expression ultimately dictating the impact of their loss-of-function on mitochondrial respiration. CONCLUSIONS: Our work unveils a repertoire of previously unknown transcriptional partners of ERRα comprised of chromatin modifiers and transcription factors thus advancing our knowledge of how ERRα regulates metabolic transcriptional programs.

Chaotic RIME optimization algorithm with adaptive mutualism for feature selection problems.

The RIME optimization algorithm is a newly developed physics-based optimization algorithm used for solving optimization problems. The RIME algorithm proved high-performing in various fields and domains, providing a high-performance solution. Nevertheless, like many swarm-based optimization algorithms, RIME suffers from many limitations, including the exploration-exploitation balance not being well balanced. In addition, the likelihood of falling into local optimal solutions is high, and the convergence speed still needs some work. Hence, there is room for enhancement in the search mechanism so that various search agents can discover new solutions. The authors suggest an adaptive chaotic version of the RIME algorithm named ACRIME, which incorporates four main improvements, including an intelligent population initialization using chaotic maps, a novel adaptive modified Symbiotic Organism Search (SOS) mutualism phase, a novel mixed mutation strategy, and the utilization of restart strategy. The main goal of these improvements is to improve the variety of the population, achieve a better balance between exploration and exploitation, and improve RIME's local and global search abilities. The study assesses the effectiveness of ACRIME by using the standard benchmark functions of the CEC2005 and CEC2019 benchmarks. The proposed ACRIME is also applied as a feature selection to fourteen various datasets to test its applicability to real-world problems. Besides, the ACRIME algorithm is applied to the COVID-19 classification real problem to test its applicability and performance further. The suggested algorithm is compared to other sophisticated classical and advanced metaheuristics, and its performance is assessed using statistical tests such as Wilcoxon rank-sum and Friedman rank tests. The study demonstrates that ACRIME exhibits a high level of competitiveness and often outperforms competing algorithms. It discovers the optimal subset of features, enhancing the accuracy of classification and minimizing the number of features employed. This study primarily focuses on enhancing the equilibrium between exploration and exploitation, extending the scope of local search.

R.I.M.E. and reason: multi-station OSCE enhancement to neutralize grade inflation.

To offset grade inflation, many clerkships combine faculty evaluations with objective assessments including the Medical Examiners Subject Examination (NBME-SE) or Objective Structured Clinical Examination (OSCE), however, standardized methods are not established. Following a curriculum transition removing faculty clinical evaluations from summative grading, final clerkship designations of fail (F), pass (P), and pass-with-distinction (PD) were determined by combined NBME-SE and OSCE performance, with overall PD for the clerkship requiring meeting this threshold in both. At the time, 90% of students achieved PD on the Internal Medicine (IM) OSCE resulting in overall clerkship grades primarily determined by the NBME-SE. The clerkship sought to enhance the OSCE to provide a more thorough objective clinical skills assessment, offset grade inflation, and reduce the NBME-SE primary determination of the final clerkship grade. The single-station 43-point OSCE was enhanced to a three-station 75-point OSCE using the Reporter-Interpreter-Manager-Educator (RIME) framework to align patient encounters with targeted assessments of progressive skills and competencies related to the clerkship rotation. Student performances were evaluated pre- and post-OSCE enhancement. Student surveys provided feedback about the clinical realism of the OSCE and the difficulty. Pre-intervention OSCE scores were more tightly clustered (SD = 5.65%) around a high average performance with scores being highly negatively skewed. Post-intervention OSCE scores were more dispersed (SD = 6.88%) around a lower average with scores being far less skewed resulting in an approximately normal distribution. This lowered the total number of students achieving PD on the OSCE and PD in the clerkship, thus reducing the relative weight of the NMBE-SE in the overall clerkship grade. Student response was positive, indicating the examination was fair and reflective of their clinical experiences. Through structured development, OSCE assessment can provide a realistic and objective measurement of clinical performance as part of the summative evaluation of students.

A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization.

The RIME optimization algorithm (RIME) represents an advanced optimization technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. In response to these shortcomings, we propose a multi-strategy enhanced version known as the multi-strategy improved RIME optimization algorithm (MIRIME). Firstly, the Tent chaotic map is utilized to initialize the population, laying the groundwork for global optimization. Secondly, we introduce an adaptive update strategy based on leadership and the dynamic centroid, facilitating the swarm's exploitation in a more favorable direction. To address the problem of population scarcity in later iterations, the lens imaging opposition-based learning control strategy is introduced to enhance population diversity and ensure convergence accuracy. The proposed centroid boundary control strategy not only limits the search boundaries of individuals but also effectively enhances the algorithm's search focus and efficiency. Finally, to demonstrate the performance of MIRIME, we employ CEC 2017 and CEC 2022 test suites to compare it with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, to assess the method's practical feasibility, we apply MIRIME to solve the three-dimensional path planning problem for unmanned surface vehicles. Experimental results indicate that MIRIME outperforms other competing algorithms in terms of solution quality and stability, highlighting its superior application potential.

Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models.

As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm's efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.

Multiple elite strategy enhanced RIME algorithm for 3D UAV path planning.

With the wave of artificial intelligence sweeping the world in recent years, UAVs is widely used in various fields. UAV path planning has attracted much attention from scientists as an essential part of UAV work. In order to design an efficient and reasonable 3D UAV path planning program, recent researchers have invented and improved many algorithms. This paper proposes an elite RIME algorithm for 3D UAV path planning. First, we propose an elite reverse learning population selection strategy based on piecewise mapping to enhance the population diversity of the algorithm for better exploration. Second, this paper proposes a stochastic factor-controlled elite pool exploration strategy so that the algorithm is difficult to enter the local optimum and can better explore the global optimum. Then, this paper proposes a hard frost puncture exploitation strategy based on the sine-cosine function so that the algorithm can find the global optimum faster during the exploitation process. Meanwhile, in order to test the performance of the algorithm proposed in this paper, we compare it with 13 other intelligent optimization algorithms that are classical and popular nowadays on 52 test functions in three test sets, CEC2017, CEC2020, and CEC2022, and obtain competitive results. Finally, we applied it to the 3D UAV path planning problem in three different terrain scenarios, and the ELRIME algorithm achieved good results in all of them. Especially in the 7-peak model, the ELRIME algorithm improves the performance of the RIME algorithm by a factor of two. In the 9-peak model, the average value aspect also reduce the cost by 91 compared to the RIME algorithm, and more importantly, it has the smallest fluctuation in 30 runs, which is among the most stable of all the compared algorithms. In the 12-peak model, its stability is also significantly enhanced, and in terms of worst-case cost, it improves the cost by 340 compared to RIME.

Efficient bladder cancer diagnosis using an improved RIME algorithm with Orthogonal Learning.

Bladder cancer (BC) diagnosis presents a critical challenge in biomedical research, necessitating accurate tumor classification from diverse datasets for effective treatment planning. This paper introduces a novel wrapper feature selection (FS) method that leverages a hybrid optimization algorithm combining Orthogonal Learning (OL) with a rime optimization algorithm (RIME), termed mRIME. The mRIME algorithm is designed to avoid local optima, streamline the search process, and select the most relevant features without compromising classifier performance. It also introduces mRIME-SVM, a novel hybrid model integrating modified mRIME for FS with Support Vector Machine (SVM) for classification. The mRIME algorithm is employed as an FS method and is also utilized to fine-tune the hyperparameters of it the It SVM, enhancing the overall classification accuracy. Specifically, mRIME navigates complex search spaces to optimize FS without compromising classifier performance. Evaluated on eight diverse BC datasets, mRIME-SVM outperforms popular metaheuristic algorithms, ensuring precise and reliable diagnostic outcomes. Moreover, the proposed mRIME was employed for tackling global optimization problems. It has been thoroughly assessed using the IEEE Congress on Evolutionary Computation 2022 (CEC'2022) test suite. Comparative analyzes with Gray wolf optimization (GWO), Whale optimization algorithm (WOA), Harris hawks optimization (HHO), Golden Jackal Optimization (GJO), Hunger Game optimization algorithm (HGS), Sinh Cosh Optimizer (SCHO), and the original RIME highlight mRIME's competitiveness and efficacy across diverse optimization tasks. Leveraging mRIME's success, mRIME-SVM achieves high classification accuracy on nine BC datasets, surpassing existing models. Results underscore mRIME's competitiveness and applicability across diverse optimization tasks, extending its utility to enhance BC classification. This study contributes to advancing BC diagnostics with a robust computational framework, promising broader applications in bioinformatics and AI-driven medical research.

CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images.

To improve the detection of COVID-19, this paper researches and proposes an effective swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method. First, this paper proposes a novel RIME structure integrating the Co-adaptive hunting and dispersed foraging strategies, called CDRIME. Specifically, the Co-adaptive hunting strategy works in coordination with the basic search rules of RIME at the individual level, which not only facilitates the algorithm to explore the global optimal solution but also enriches the population diversity to a certain extent. The dispersed foraging strategy further enriches the population diversity to help the algorithm break the limitation of local search and thus obtain better convergence. Then, on this basis, a new multi-threshold image segmentation method is proposed by combining the 2D non-local histogram with 2D Kapur entropy, called CDRIME-MTIS. Finally, the results of experiments based on IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 demonstrate that CDRIME has superior performance than some other basic, advanced, and state-of-the-art algorithms in terms of global search, convergence performance, and escape from local optimality. Meanwhile, the segmentation experiments on COVID-19 X-ray images demonstrate that CDRIME is more advantageous than RIME and other peers in terms of segmentation effect and adaptability to different threshold levels. In conclusion, the proposed CDRIME significantly enhances the global optimization performance and image segmentation of RIME and has great potential to improve COVID-19 diagnosis.

An improved RIME optimization algorithm for lung cancer image segmentation.

Lung cancer is a prevalent form of cancer worldwide, necessitating early and accurate diagnosis for successful treatment. Within medical imaging processing, image segmentation plays a vital role in medical diagnosis. This study applies swarm intelligence algorithms to segment lung cancer pathological images at three levels. The original algorithm incorporates the Whales' search prey mechanism and a random mutation strategy, resulting in an improved version named WDRIME, which aims to enhance convergence speed and avoid local optima (LO). Additionally, the study introduces a multilevel image segmentation method for lung cancer based on the improved algorithm. WDRIME's performance is showcased by comparing it to the state-of-the-art algorithms in IEEE CEC2014. To design a framework for lung cancer image segmentation, this paper combines the WDRIME algorithm with the multilevel segmentation method. Evaluation of the segmentation results employs metrics such as PSNR, SSIM, and FSIM. Overall, the analysis confirms that the proposed algorithm supersedes others regarding convergence speed and accuracy. This model signifies a high-quality segmentation method and offers practical support for in-depth exploration of lung cancer pathological images.

Early Outcomes of Minimally Invasive Right Anterior Thoracotomy vs. Median Full Sternotomy in Isolated Aortic Valve Replacement: A Propensity Score Analysis.

INTRODUCTION: This study aimed to compare the early postoperative outcomes of right anterior thoracotomy minimally invasive aortic valve replacement (RAT-MIAVR) surgery with those of median full sternotomy aortic valve replacement (MFS-AVR) approach with the goal of identifying potential benefits or drawbacks of each technique. METHODS: This retrospective, observational, cohort study included 476 patients who underwent RAT-MIAVR or MFS-AVR in our hospital from January 2015 to January 2023. Of these, 107 patients (22.5%) underwent RAT-MIAVR, and 369 patients (77.5%) underwent MFS-AVR. Propensity score matching was used to minimize selection bias, resulting in 95 patients per group for analysis. RESULTS: After propensity matching, two groups were comparable in preoperative characteristics. RAT-MIAVR group showed longer cardiopulmonary bypass time (130.24 ± 31.15 vs. 117.75 ± 36.29 minutes, P=0.012), aortic cross-clamping time (76.44 ± 18.00 vs. 68.49 ± 19.64 minutes, P=0.004), and longer operative time than MFS-AVR group (358.47 ± 67.11 minutes vs. 322.42 ± 63.84 minutes, P=0.000). RAT-MIAVR was associated with decreased hospitalization time after surgery, lower postoperative blood loss and drainage fluid, a reduced incidence of mediastinitis, increased left ventricular ejection fraction, and lower pacemaker use compared to MFS-AVR. However, there was no significant difference in the incidence of major complications and in-hospital mortality between the two groups. CONCLUSION: RAT-MIAVR is a feasible and safe alternative procedure to MFS-AVR, with comparable in-hospital mortality and early follow-up. This minimally invasive approach may be a suitable option for patients requiring isolated aortic valve replacement.

RV-FELM: Futures commodity price forecasting based on RIME-VMD algorithm coupled with FA-ELM.

Commodity futures are an important hedging tool in material trade, and by accurately predicting prices, countries and firms are able to make informed production and consumption decisions. This paper introduces a novel machine learning ensemble method that combines decomposition algorithms and physical optimization algorithms to predict commodity futures prices. First, the VMD(Variational mode decomposition) is optimized by the RIME algorithm (Rime optimization algorithm) to obtain the optimal modal decomposition results, and the trend and seasonal terms are predicted using the ELM (Extreme Learning Machines) and FA (Fourier Attention) models, respectively, and the results are finally synthesized. The results show that the MAPE(mean absolute percentage error) of one-step, three-step, and six-step methods for predicting crude oil prices are 0.48%, 0.66%, and 0.75%, respectively, and the MAPE of soybean prediction results are 0.22%, 0.27%, and 0.37%, respectively. The empirical results and ablation experiments show that it outperforms other benchmark models in terms of both horizontal and directional accuracy. Notably, it outperforms in predicting soybean futures prices, which demonstrates the ability of our model to better capture the characteristics of both the time and frequency domains of the series, to take sufficient consideration of the series characteristics, and to ensure robustness.