Average Rank Research Articles

Panoramic image quality assessment based on multi-viewport adaptive fusion

Existing panoramic image quality assessment models are relatively independent when extracting local features of each viewport, resulting in high computational complexity and difficulty in characterizing the correlation between viewports using an end-to-end fusion model. To address the above problems, a quality assessment method based on feature sharing and multi-viewport adaptive fusion is proposed. Using a shared backbone network, the viewport segmentation and calculation tasks that are independent of each other in the existing method are converted to the feature domain, so that the local features of the entire image can be extracted with only one feedforward calculation. On this basis, a feature domain viewport segmentation method based on spherical uniform sampling is introduced to ensure that the pixel density of the observation space and the representation space is consistent, and semantic information is used to guide the adaptive fusion of local quality features of each viewport. The linear correlation coefficient and rank correlation coefficient on the CVIQ and OIQA datasets are both above 0.96, which is the best compared with the existing mainstream evaluation methods. Compared with the traditional evaluation method SSIM, its average linear correlation coefficient and average rank correlation coefficient on the two datasets are improved by 9.52%and respectively 8.69%; compared with the latest evaluation method MPFIQA, its average linear correlation coefficient and average rank correlation coefficient are improved by 1.71%and respectively 1.44%.

Effectiveness of Isometric Handgrip Exercise on Blood Pressure in Hypertensive Patients in Tenajar Lor Village, Kertasemaya District, Indramayu Regency

Uncontrolled hypertension may result in numerous consequences; effective management is essential to regulate blood pressure in hypertensive individuals by both pharmacological and non-pharmacological interventions. Isometric handgrip exercise is a non-pharmacological therapy. This study sought to assess the impact of isometric handgrip exercise on blood pressure in hypertensive individuals in Tenajar Lor Village, Kertasemaya District, Indramayu Regency. This research design employs a quasi-experimental framework featuring a two-group pre- and post-test methodology alongside a control group design. The study sample comprised 30 respondents, categorized into intervention and control groups. The isometric handgrip exercise intervention was conducted daily for five successive days. The Wilcoxon test results indicated that the intervention group had an average systolic mean rank of 8.00 with a p-value of 0.000, and a diastolic mean rank of 7.50 with a p-value of 0.000. The control group exhibited a mean rank of 2.00 for systolic measurements with a p-value of 0.083, and a mean rank of 3.13 for diastolic measurements with a p-value of 0.157. The Mann-Whitney test yielded the average blood pressure disparity between the intervention and control groups. The p-values obtained were 0.001 for systolic blood pressure and 0.006 for diastolic blood pressure (p < 0.05). This study concludes that isometric handgrip exercise influences blood pressure in hypertensive patients. Individuals with hypertension are advised to perform isometric handgrip exercises autonomously at home. Healthcare professionals, particularly nurses, implement isometric handgrip exercises to regulate blood pressure in individuals with hypertension.

Leveraging Clinical Intuition to Improve Accuracy of Phenotype-Driven Prioritization

PurposeClinical intuition is commonly incorporated into the differential diagnosis as an assessment of the likelihood of candidate diagnoses based either on the patient population being seen in a specific clinic or on the signs and symptoms of the initial presentation. Algorithms to support diagnostic sequencing in individuals with a suspected rare genetic disease do not yet incorporate intuition and instead assume that each Mendelian disease has an equal pretest probability. MethodsThe LIRICAL algorithm calculates the likelihood ratio of clinical manifestations represented by Human Phenotype Ontology (HPO) terms to rank candidate diagnoses. The initial version of LIRICAL assumed an equal pretest probability for each disease in its calculation of the posttest probability (where the test is diagnostic exome or genome sequencing). We introduce Clinical Intuition for Likelihood Ratios (ClintLR), an extension of the LIRICAL algorithm that boosts the pretest probability of groups of related diseases deemed to be more likely. ResultsThe average rank of the correct diagnosis in simulations using ClintLR showed a statistically significant improvement over a range of adjustment factors. ConclusionClintLR successfully encodes clinical intuition to improve ranking of rare diseases in diagnostic sequencing. ClintLR is freely available at https://github.com/TheJacksonLaboratory/ClintLR.

The current attitudes and practices of dentists in Australia towards composite repair: A cross-sectional survey study.

Whether to replace or repair a composite restoration is controversial and varies among clinicians. This study was designed to collect information on the attitudes and practices of Australian dentists towards composite repair. An electronic 16-item questionnaire was distributed online on Australian clinicians' dental forum with a URL address and instructions on completing the survey. The questionnaire remained accessible for 2 months. The data collected were analysed statistically using descriptive, average rank, Pearson chi-square and Kruskal-Wallis tests at α = 0.05. Repair of composite restorations was a widely accepted treatment modality among surveyed dentists. Despite this, approximately half of clinicians reported the prognosis of repaired restorations to be worse when compared to replacement. The most cited indications for repair were partial loss or fracture of the restoration, while the most common reason for repair was because this treatment modality was more conservative in terms of tooth structure removal. Most important patient factor influencing decision to repair composite restoration was caries risk of the patient (n = 50). Most significant situational factor to consider in decision whether to repair was previous (failed) attempts to repair (n = 74). The most important tooth level factor was the proximity of restoration to pulp (mean statistic rank 2.22). The most common composite surface treatment employed by participants bonding to old composite was acid etching (n = 87), and the main reason participants employed their chosen surface treatment was based on personal experience (n = 72). The repair of composite restorations was a commonly performed procedure that is well accepted but may still be viewed as an inferior treatment to replacement by many Australian dentists. Most dentists agreed on indications for repair restorations and surface conditioning techniques, but there was wide variation in opinions overall. Due to the lack of high-quality evidence regarding composite repair techniques, dentists tend to rely on personal experience to guide their clinical decisions.

Ranking co-change candidates suggested by FLeCCS using programmer sensitivity

Identifying co-change candidates when making changes to a particular program entity is an important software engineering task which is known as change impact analysis. A number of techniques for detecting co-change candidates currently exist. Most of these techniques work considering file level, class level, or method level granularity. FLeCCS is a recently introduced technique that suggests co-change candidates considering even fragment level granularity. FLeCCS ranks its co-change candidates considering file proximity. However, programmer sensitivity might be an important factor while ranking co-change candidates. Intuitively, it is more comfortable for a programmer to change her own code rather than changing someone else's code. Considering this fact, we investigate ranking co-change candidates suggested by FLeCCS considering programmer sensitivity.In our research, we have two main contributions: (1) proposing a composite ranking mechanism considering three things - file proximity, code similarity, and programmer sensitivity and (2) an empirical evaluation of our proposed mechanism by comparing it with file proximity ranking and similarity extent ranking mechanisms on thousands of revisions of five subject systems. We compare the ranking mechanisms in ranking the co-change candidates suggested by FLeCCS. According to our investigation incorporating four measures (Precision At K, Recall At K, Mean Average Precision and Mean Reciprocal Rank), composite ranking mechanism significantly outperforms the other two existing ranking mechanisms. Our findings are supported by statistical significance tests. We believe that our Proposed Composite Ranking mechanism is not only applicable to FLeCCS but also to other co-change suggestion techniques.

Systematic review of Janus kinases inhibitors for rheumatoid arthritis: methodology, reporting, and quality of evidence evaluation.

To evaluate the methodological, reporting and evidence quality of systematic reviews or meta-analyses of Janus kinases (JAK) inhibitors for the treatment of rheumatoid arthritis (RA). Our study systematically retrieved reviews from various databases, spanning from inception to June 2024. Two evaluators independently assessed the methodological, reporting, and evidence quality of each review using the AMSTAR-2 and PRIAMA2020 tools. The evidence quality was evaluated according to GRADE criteria. Six aspects were evaluated: publication year, study type, homogeneity, risk of publication bias, AMSTAR-2 methodology, and PRIAMA2020 reporting quality. Excel 2016 facilitated conversion of scores into radar plots. Following stringent selection criteria, a total of 18 relevant studies were identified. The AMSTAR-2 scores ranged from 4 to 13 points, with five studies rated as low quality and the remaining 13 as critically low quality. All studies encompassed populations, interventions, controls, and outcome measures, demonstrating commendable integrity. However, there is room for improvement in study protocol development and registration, comprehensive search strategies, inclusion and exclusion criteria, conflict of interest disclosure, and discussion of heterogeneity. PRIAMA2020 assessments ranged from 14.5 to 21 points, with two studies scoring below 15 points due to increased bias risk from data transformation and sensitivity analysis. Notably, all reviews (100%) adhered to PRIAMA2020 guidelines for certain items but none met all criteria. GRADE evaluation included 446 outcome measures, with 158 of moderate, 156 of low, and 132 of very low quality, indicating JAK inhibitors is effective in improving RA. According to radar chart, the average rank score was 13.13. One study achieved a balanced score across all dimensions, while 11 exceeded the average, five showed significant differences in PRIAMA2020 scores, and four in AMSTAR two scores. Despite summarizing the efficacy and safety of JAK inhibitors in treating RA, the included studies exhibited poor methodological and reporting quality, along with low-quality evidence overall. Therefore, caution is warranted among decision-makers regarding the use of JAK inhibitors in RA treatment. Urgent requirements include high-quality, multicenter studies investigating JAK inhibitors for RA. https://www.crd.york.ac.uk/PROSPERO, identifier 413415.

Strategic manipulation of preferences in the rank minimization mechanism

We consider one-sided matching problems, where agents are allocated items based on stated preferences. Posing this as an assignment problem, the average rank of obtained matchings can be minimized using the rank minimization (RM) mechanism. RM matchings can have significantly better rank distributions than matchings obtained by mechanisms with random priority, such as Random Serial Dictatorship. However, these matchings are sensitive to preference manipulation from strategic agents. In this work we consider a scenario where agents aim to be matched to their top-n preferred items using the RM mechanism, and strategically manipulate their preferences to achieve this. We derive a best response strategy for an agent to be assigned to their n most preferred items using the Hungarian algorithm, under a simplified cost function. This strategy is then extended to a first-order heuristic strategy for being matched to the top-n items in a setup that minimizes the average rank. Based on this finding, an empirical study is conducted examining the impact of the first-order heuristic strategy. The study utilizes data from both simulated markets and real-world matching markets in Amsterdam, taking into account variations in item popularity, fractions of strategic agents, and the preferences for the n most favored items. For most scenarios, RM yields more rank efficient matches than Random Serial Dictatorship, even when agents apply the first-order heuristic strategy. However, although highly market dependent, the matching performance can become worse when 50% of agents or more want to be matched to their top-1 or top-2 preferred items and apply the first-order heuristic strategy to achieve this.

Optimization of Cost-Based Hybrid Flowshop Scheduling Using Teaching-Learning-Based Optimization Algorithm

A cost-based hybrid flowshop scheduling (CHFS) combines flow shop and job shop elements, with cost considerations as a key indicator. CHFS is a complex combinatorial optimization challenge encountered in real-world manufacturing and production environments. This paper investigates the optimization of a CHFS problem using the Teaching Learning-Based Optimization (TLBO) algorithm. Effective CHFS is crucial for achieving production balance, reducing costs, and improving customer satisfaction. The authors formulate the CHFS scheduling problem and propose applying the TLBO algorithm to minimize total costs, including labor, energy, maintenance, and delay expenses. The performance of the TLBO technique is evaluated through computational experiments on various CHFS problem instances. The results demonstrate the effectiveness of the TLBO algorithm, which achieved the best results in 42% of the test cases, surpassing other algorithms like the Grey Wolf Optimizer and Particle Swarm Optimization. Additionally, the TLBO algorithm had the highest average performance ranking across the comparative algorithms. The study highlights the potential of the TLBO algorithm as an efficient optimization tool for complex manufacturing scheduling problems.

A nonlinear randomly reuse-based mutated whale optimization algorithm and its application for solving engineering problems

The whale optimization algorithm (WOA) is a meta-heuristic optimization algorithm inspired by the social behavior of whale hunting. However, the WOA has the defects of easily falling into local optimum and slow convergence speed. Therefore, to overcome the shortcomings of the original WOA, a novel variant of WOA called nonlinear randomly reuse-based mutated whale optimization algorithm (NRRMWOA) is proposed in this study. In detail, the proposed NRRMWOA includes three novel strategies as compared with original WOA. Firstly, a nonlinear adaptive parameter strategy is introduced to achieve nonlinear adjustment of search pattern along with the iteration time. The second improvement is the random reuse strategy, which can fully utilize the current optimal whale to improve the solution accuracy. The thirdly proposed late disturbance mutation strategy has the function of increasing population diversity in the later stage of iteration. The three proposed strategies complement each other and significantly improve the convergence efficiency and global exploration capability of WOA. To verify the superiority of the proposed NRRMWOA, a series of typical benchmark functions are utilized to conduct comprehensive numerical experiments. Compared with three sets of comparison algorithms (five variants of WOA, five advanced algorithms, and five well-known algorithms), the ARVs (average ranking values) obtained by NRRMWOA are 1.4667, 1.9167, and 2.1333, respectively. The rankings are all the best in the corresponding group. The detailed experimental and statistics results indicate that the presented NRRMWOA has higher convergence speed and better solution quality compared with the three sets of existing algorithms. In addition, 15 real-world optimization problems are employed to further test the performance of NRRMWOA for solving engineering optimization problems. For the results of 15 real-world optimization problems, the proposed NRRMWOA achieves the best results including 8 best “Mean”, 10 best “Min.”, and 7 best “Max.”. As a conclusion, the proposed NRRMWOA is a promising and excellent algorithm.

The Effect of Mindfulness-Based SEL Programs on Reducing Stress and Improving Mental Health in Adolescents in Jordan

This study employed quantitative methodologies to investigate the impact of a 12-week mindfulness-based Social and Emotional Learning (SEL) program on lowering stress and improving mental well-being in a sample of 300 adolescents from Jordan. The study employed a stratified random sampling strategy to choose participants from diverse urban and rural populations. Data was collected utilizing the Perceived Stress Scale (PSS) and the Strengths and Difficulties Questionnaire (SDQ). The data evaluation involved the use of descriptive statistics, paired-sample t-tests, regression analysis, and an Analysis of Covariance (ANCOVA). The results indicate a significant decrease in stress levels and positive changes in mental health outcomes following the intervention. The regression analysis revealed that age was a likely predictor of changes in stress levels, indicating that older persons saw more significant declines. The analysis of covariance (ANCOVA) indicated significant gender-specific discrepancies in the impacts of the intervention. Independent samples t-tests revealed statistically significant variations in the average rankings across specific age groups and school environments. Proposed measures include implementing mindfulness-based social and emotional learning (SEL) programs in school systems, tailoring treatments to suit different demographic groups, examining gender-specific factors, and advocating for policy integration. Performing a longitudinal study is typically advised to assess the long-term sustainability of intervention results.

Modeling and significance assessment of road construction participant and user benefits using expert evaluation methods

Human activities are related to obtain economic, technical, social and environmental benefits. The road construction process participants and road users have direct or indirect benefits from elaboration of new infrastructure. The number of the benefits received by individual entities is publicly discussed by politicians, lobbyists, experts and other decision-makers without having quantitative estimates of benefits, i.e., often relying on intuition or considering theoretical reasoning. The paper suggests a system of 19 benefit entities (criteria) assigned the ranks given by experts. The study involved three categories of experts, including 35 road engineers, 36 transport engineers and 61 road users. The values of the concordance coefficients obtained as a result of the conducted research were found significantly higher than critical values and showed that the opinions of the experts in each category were consistent (not contradictory). This made it possible to consider the average of the opinions of the expert group as a reliable result of solving the problem. Rank averages were replaced by the normalized weights of criteria using Average Rank Transformation into Weight-Linear (ARTIW-L) and – Nonlinear (ARTIW-N) methods. The global averages of criterion weights were used employing the Inverse Hierarchy for Assessment Main Criteria Importance (IHAMCI) method thus calculating the normalized weights of the road-related classified entities (three main criteria). The findings prove that road users benefit the most (weight 0.3485), the road construction contractor (weight 0.3325) is in the next position and the road owner (investor) takes the weight equal to 0.3190. The generated research data can be used for justifying the rationality of road investment.

Comparative analysis of salinity tolerance mechanisms in two maize genotypes: growth performance, ion regulation, and antioxidant responses

This study investigates the differential responses of two maize genotypes, SC180 and SC168, to salt stress, aiming to elucidate the mechanisms underlying salinity tolerance and identify traits associated with improved stress resilience. Salinity stress, imposed by 150 mM NaCl, adversely affected various growth parameters in both genotypes. SC180 exhibited a more pronounced reduction in shoot length (13.6%) and root length (13.6%) compared to SC168, which showed minimal reductions (3.0% and 2.3%, respectively). Additionally, dry weight losses in SC180's leaves, stems, and roots were significantly greater than those in SC168. Under salinity stress, both genotypes accumulated Na+ in all organs, with SC168 showing higher Na + concentrations. However, K+ levels decreased more significantly in SC180's leaves than in SC168's. The study also assessed physiological responses, noting that SC180 experienced a substantial reduction in relative water content (RWC) in leaves (22.7%), while SC168's RWC remained relatively stable (5.15%). Proline accumulation, a marker for osmotic adjustment, increased 2.3-fold in SC168 compared onefold in SC180. Oxidative stress indicators, such as electrolyte leakage and hydrogen peroxide levels, were elevated in both genotypes under salt stress, with SC180 showing higher increases (48.5% and 48.7%, respectively) than SC168 (35.25% and 22.0%). Moreover, antioxidant enzymes (APX, CAT, POD, SOD, GR) activities were significantly enhanced in SC168 under salinity stress, whereas SC180 showed no significant changes in these activities. Stress indices, used to quantify and compare salinity tolerance, consistently ranked SC168 as more tolerant (average rank = 1.08) compared to SC180 (average rank = 1.92). Correlation analyses further confirmed that SC168's superior tolerance was associated with better Na + regulation, maintenance of K+ levels, and a robust antioxidant defense system. In conclusion, SC168 demonstrated greater resilience to salinity stress, attributed to its efficient ion regulation, stable water status, enhanced osmotic adjustment, and strong antioxidant response. These findings provide valuable insights for breeding and developing salinity-tolerant maize varieties.

An Ideal Four-factor Components Mixture Experiment Design Based on Prominent Optimality Criteria at Level Two

Aim: Mixture experiment designs are essential tools that need to be determined prior to conducting any mixture experiment in any field of study. The primary goal of these types of experiments are optimization that is either maximizing the profit/produce or minimizing the cost of production. Study Design: Any researcher anticipating to do any research work on the mixture experiment will not evade to talk about the design that he or she is likely to use. The common of such designs being either Simplex Lattice Design (SLD) or Simplex Centroid Design (SCD). Methodology: The choice of such design is wholly based on the optimality criteria employed. The classical of these criteria include on D-Determinant criterion, A-Average variance criterion, E- Eigen value criterion and T- Trace optimality criterion usually denoted as D-, A-, E- T- criteria. They are also known as prominent criteria. Results: This paper considered four-factor components at order two. The penalized moment matrices obtained from the information matrices whose primary source was the design points gave the values of the criteria aforementioned. These values were ranked independently with the least average rank termed as the best design. Conclusion: The {4,4} Simplex Lattice Design had the lowest rank value of 1.0 as compared to the other designs. This design is therefore to be used in any research work considering four factors when only two of factors are to be employed.

MISAO: Ultra-Short-Term Photovoltaic Power Forecasting with Multi-Strategy Improved Snow Ablation Optimizer

The increase in installed PV capacity worldwide and the intermittent nature of solar resources highlight the importance of power prediction for grid integration of this technology. Therefore, there is an urgent need for an effective prediction model, but the choice of model hyperparameters greatly affects the prediction performance. In this paper, a multi-strategy improved snowmelt algorithm (MISAO) is proposed for optimizing intrinsic computing-expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and weighted least squares support vector machine for PV power forecasting. Firstly, a cyclic chaotic mapping initialization strategy is used to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain quickly. Secondly, the Gaussian diffusion strategy enhances the local exploration ability of the intelligences and extends their search in the solution space, effectively preventing them from falling into local optima. Finally, a stochastic follower search strategy is employed to reserve better candidate solutions for the next iteration, thus achieving a robust exploration–exploitation balance. With these strategies, the optimization performance of MISAO is comprehensively improved. In order to comprehensively evaluate the optimization performance of MISAO, a series of numerical optimization experiments were conducted using IEEE CEC2017 and test sets, and the effectiveness of each improvement strategy was verified. In terms of solution accuracy, convergence speed, robustness, and scalability, MISAO was compared with the basic SAO, various state-of-the-art optimizers, and some recently developed improved algorithms. The results showed that the overall optimization performance of MISAO is excellent, with Friedman average rankings of 1.80 and 1.82 in the two comparison experiments. In most of the test cases, MISAO delivered more accurate and reliable solutions than its competitors. In addition, the altered algorithm was applied to the selection of hyperparameters for the ICEEMDAN-WLSSVM PV prediction model, and seven neural network models, including WLSSVM, ICEEMDAN-WLSSVM, and MISAO-ICEEMDAN-WLSSVM, were used to predict the PV power under three different weather types. The results showed that the models have high prediction accuracy and stability. The MAPE, MAE and RMSE of the proposed model were reduced by at least 25.3%, 17.8% and 13.3%, respectively. This method is useful for predicting the output power, which is conducive to the economic dispatch of the grid and the stable operation of the power system.

Variance Feedback Drift Detection Method for Evolving Data Streams Mining

Learning from changing data streams is one of the important tasks of data mining. The phenomenon of the underlying distribution of data streams changing over time is called concept drift. In classification decision-making, the occurrence of concept drift will greatly affect the classification efficiency of the original classifier, that is, the old decision-making model is not suitable for the new data environment. Therefore, dealing with concept drift from changing data streams is crucial to guarantee classifier performance. Currently, most concept drift detection methods apply the same detection strategy to different data streams, with little attention to the uniqueness of each data stream. This limits the adaptability of drift detectors to different environments. In our research, we designed a unique solution to address this issue. First, we proposed a variance estimation strategy and a variance feedback strategy to characterize the data stream’s characteristics through variance. Based on this variance, we developed personalized drift detection schemes for different data streams, thereby enhancing the adaptability of drift detection in various environments. We conducted experiments on data streams with various types of drifts. The experimental results show that our algorithm achieves the best average ranking for accuracy on the synthetic dataset, with an overall ranking 1.12 to 1.5 higher than the next-best algorithm. In comparison with algorithms using the same tests, our method improves the ranking by 3 to 3.5 for the Hoeffding test and by 1.12 to 2.25 for the McDiarmid test. In addition, they achieve a good balance between detection delay and false positive rates. Finally, our algorithm ranks higher than existing drift detection methods across the four key metrics of accuracy, CPU time, false positives, and detection delay, meeting our expectations.

Expert Evaluation of the Significance of Criteria for Electric Vehicle Deployment: A Case Study of Lithuania

This study presents the hierarchical structure of 50 sub-criteria divided into 7 main criteria for the assessment of electric vehicle (EV) deployment. Two options, Average Rank Transformations and Analytic Hierarchy Process methods, were applied in determining the local weights of the sub-criteria. The sufficient compatibility of expert opinions was accomplished using the averages of the ranks of the main criteria and sub-criteria as the result of solving the problem. The averages of the local weights were calculated employing three Multiple Criteria Decision-Making methods that increased the reliability of the research results. Based on this, the global weights and priorities of the sub-criteria were evaluated. The experts suppose that EV deployment at the national level is mainly affected by the higher cost of manufacturing and purchasing EVs, the application of financial incentives for purchasing EVs, the lack of exhausted gasses, the installation of fast charging points, and the absence of infrastructure in the five largest cities nationwide. The obtained results demonstrate that out of 50 sub-criteria, the cumulative global weight of the 10 most important sub-criteria (mainly based in economics) amounts to more than 35%, whereas that of the 22 most important sub-criteria have a weight above the average (0.2), reaching approximately 65%. The findings can be put into practice by state decision makers of EV deployment.

An Ensemble Spectral Prediction (ESP) model for metabolite annotation.

A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have emerged to address the annotation problem: mapping candidate molecules to spectra, and mapping query spectra to molecular candidates. In essence, the candidate molecule with the spectrum that best explains the query spectrum is recommended as the target molecule. Despite candidate ranking being fundamental in both approaches, limited prior works incorporated rank learning tasks in determining the target molecule. We propose a novel machine learning model, Ensemble Spectral Prediction (ESP), for metabolite annotation. ESP takes advantage of prior neural network-based annotation models that utilize multilayer perceptron (MLP) networks and Graph Neural Networks (GNNs). Based on the ranking results of the MLP- and GNN-based models, ESP learns a weighting for the outputs of MLP and GNN spectral predictors to generate a spectral prediction for a query molecule. Importantly, training data is stratified by molecular formula to provide candidate sets during model training. Further, baseline MLP and GNN models are enhanced by considering peak dependencies through label mixing and multi-tasking on spectral topic distributions. When trained on the NIST 2020 dataset and evaluated on the relevant candidate sets from PubChem, ESP improves average rank by 23.7% and 37.2% over the MLP and GNN baselines, respectively, demonstrating performance gain over state-of-the-art neural network approaches. However, MLP approaches remain strong contenders when considering top five ranks. Importantly, we show that annotation performance is dependent on the training dataset, the number of molecules in the candidate set and candidate similarity to the target molecule. The ESP code, a trained model, and a Jupyter notebook that guide users on using the ESP tool is available at https://github.com/HassounLab/ESP.

Day 3 embryo assessment does not provide a reliable prediction for blastocyst formation and designation: a retrospective cohort study.

Although many Fertility Centers have adopted day 5 or 6 embryo transfer policy, yet, 30% of embryo transfers in the US are performed on day 3. This is mainly due to concerns related to longer embryo culture effect and higher rates of embryo transfer cancellation on day 5, with no effect on cumulative pregnancy rate. We conducted a retrospective cohort study comparing individual embryo transfer order rank, best embryo for fresh transfer and intention to freeze, of day-3 and day-5 embryos based on their morphology score. Day-3 embryos of each patient were ranked by embryologists for the order of transfer and intention to freeze, based on morphological score, blinded to actual blastulation outcome. The corresponding blastocysts were similarly ranked for the order of transfer and vitrification intention. Ranking was compared to test the predictive value of day-3 morphological assessment. Sixty patients with 784 day-3 embryos were included. There was only a moderate positive significant correlation between ranks on day-3 and ranks on day-5 [r = 0.662 95% CI (0.611-0.706, p < 0.001)]. Only 25% of the best embryos for transfer on day 3 (rank = 1) were chosen for fresh transfer on day 5. A total of 441 embryos were intended to be frozen on day 3. Of those, 201 were not transferred nor vitrified on day 5-6 (45%), 3.35 embryos per patient. No significant difference was found between average day-3 rank of embryos ranked 1, 2 (3.12 vs 4.12, p = 0.074) and 3 (3.12 vs 4.08, p = 0.082) on day-5-6. To conclude, this study brings a different perspective to the comparison of day 3 and day 5 by following each embryo's putative and actual designation. Day-3 ranking of embryo morphology did not provide a reliable prediction for blastocyst formation, transfer order and vitrification intention, and may support transfer or cryopreservation of blastocysts over cleavage stage embryos.

Gyro fireworks algorithm: A new metaheuristic algorithm

In this paper, a novel Gyro Fireworks Algorithm (GFA) is proposed by simulating the behaviors of gyro fireworks during the display process, which adopts a framework of multi-stage and multiple search strategies. At the beginning of the iteration, the gyro fireworks are full of gunpowder; they move via Lévy flight and spiral rotation, and the sprayed sparks are widely distributed and more balanced, which is an effective global exploration method. In the later iteration stages, due to the consumption of gunpowder, the gyro fireworks gradually undergo aggregation and contraction of spiral rotation, which is conducive to the search group to exploit the local area near the global optimal position. The GFA divides the iterative process into four phases, and each phase adopts a different search strategy, in order to enhance the diversity of the search of the population and to balance the exploration capability of the gyro fireworks search group in the global space and the exploitation of the local space. In order to verify the performance of the GFA, it is compared with the latest algorithms, such as the dandelion optimizer, Harris Hawks Optimization (HHO) algorithm, gray wolf optimizer, slime mold algorithm, whale optimization algorithm, artificial rabbits optimization, in 33 test functions. The experimental results show that the GFA obtains the optimal solution for all algorithms on 76% of the functions, while the second-placed HHO algorithm obtains the optimal solution for all algorithms on only 21% of the functions. Meanwhile, the GFA has an average ranking of 1.8 on the CEC2014 benchmark set and 1.4 on the CEC2019 benchmark set. It verifies that the GFA proposed in this paper has better convergence performance and better robustness than the competing algorithms. Moreover, experiments on challenging engineering optimization problems confirm the superior performance of the GFA over alternative algorithms.

Maternal Fiber Intake and Perinatal Depression and Anxiety.

(1) Background: Dietary fiber can significantly alter gut microbiota composition. The role of the gut microbiome in the Gut-Brain Axis and modulation of neuropsychiatric disease is increasingly recognized. The role of antenatal diet, particularly fiber intake, in mitigating maternal mental health disorders remains unexplored. The objective of this review is to investigate the association between maternal fiber intake and perinatal depression and anxiety (PDA). (2) Methods: A literature review of PubMed and Google Scholar was conducted using appropriate keyword/MeSH terms for pregnancy, diet, fiber, and mental health. Observational and clinical trials published between 2015 and 2021 were included and data pertaining to dietary patterns (DP), food intake, mental health, and demographic data were extracted. The top three fiber-containing food groups (FG) per study were identified using a sum rank scoring system of fiber per 100 g and fiber per serving size. The consumption of these top three fiber FGs was then ranked for each dietary pattern/group. Mental health outcomes for each study were simplified into three categories of improved, no change, and worsened. The relationship between top three fiber FGs consumed within each DP and mental health outcomes was analyzed using Spearman's correlation. (3) Results: Thirteen of fifty-two studies met the inclusion criteria. Ten (76.9%) studies assessed DPs (seven examined depression only, two examined depression and anxiety, and one examined anxiety only). Seven (53.9%) studies reported at least one significant positive relationship between mental health outcomes and DPs while three reported at least one negative outcome. Three (23.1%) studies compared intake of different food groups between depressed and non-depressed groups. In studies of DPs, the average consumption ranking of the top three fiber FGs bore a significant inverse association with mental health outcomes [r = -0.419 (95%CI: -0.672--0.078)] p = 0.015. In studies comparing the intake of different FGs between depressed and non-depressed groups, the consumption of top-ranking fiber foods was higher in the non-depressed groups, but significantly higher in four of the ten high fiber FGs. (4) Conclusions: This study reframes findings from previously published studies of maternal diet and mental health outcomes to focus on fiber intake specifically, using a fiber ranking system. A significant correlation between lower intake of fiber and poorer mental health outcomes warrants further investigation in future studies.