A novel multi-objective dung beetle optimizer for Multi-UAV cooperative path planning.

Path planning for multiple unmanned aerial vehicles (UAVs) is crucial in collaborative operations and is commonly regarded as a complicated, multi-objective optimization problem. However, traditional approaches have difficulty balancing convergence and diversity, as well as effectively handling constraints. In this study, a directional evolutionary non-dominated sorting dung beetle optimizer with adaptive stochastic ranking (DENSDBO-ASR) is developed to address these issues in collaborative multi-UAV path planning. Two objectives are initially formulated: the first one represents the total cost of length and altitude, while the second represents the total cost of threat and time. Additionally, an improved multi-objective dung beetle optimizer is introduced, which integrates a directional evolutionary strategy including directional mutation and crossover, thereby accelerating convergence and enhancing global search capability. Furthermore, an adaptive stochastic ranking mechanism is proposed to successfully handle different constraints by dynamically adjusting the comparison probability. The effectiveness and superiority of DENSDBO-ASR are demonstrated by the constrained problem functions (CF) test, the Wilcoxon rank sum test, and the Friedman test. Finally, three sets of simulated tests are carried out, each including different numbers of UAVs. In the most challenging scenario, DENSDBO-ASR successfully identifies feasible paths with average values of the two objective functions as low as 637.26 and 0. The comparative results demonstrate that DENSDBO-ASR outperforms the other five algorithms in terms of convergence accuracy and population diversity, making it an exceptional optimization approach to path planning challenges.

Innovative player evaluation: Dual-possibility Pythagorean fuzzy hypersoft sets for accurate international football rankings.

This study introduces an advanced approach for ranking international football players, addressing the inherent uncertainties in performance evaluations. By integrating dual possibility theory and Pythagorean fuzzy sets, the model accommodates varying degrees of ambiguity and imprecision in player attributes. Additionally, the use of hypersoft set theory enriches the analysis by capturing the multifaceted nature of player evaluations. The proposed aggregation operators refine the synthesis of diverse information sources, leading to a comprehensive and nuanced assessment. This research significantly enhances player evaluation methodologies, providing a more adaptable framework for a fair assessment of international football talent. A practical example illustrates the application of dual-possibility Pythagorean fuzzy hypersoft sets (DP-PFHSS). A numerical technique is proposed for solving multi-criteria decision-making (MCDM) challenges with known dual possibility information using the proposed aggregation operators. This decision-making algorithm effectively determines a football player's worth, contributing to the overall ranking and evaluation process. The approach aids in scouting and recruitment by facilitating talent identification and informed player signings. Graphical analysis, comparing existing and proposed methods using average and geometric operators, demonstrates the superiority of the proposed approach in the players evaluation, indicating that F 1 is in the top ranking.

A protocol to evaluate the taxonomic health of Neotropical species of Nasutitermes (Termitidae, Nasutitermitinae).

Herein a protocol is proposed to summarize the taxonomic situation for species, using the Neotropical Nasutitermes Dudley (Nasutitermitinae) as a test. The objective of this protocol is to allow comparisons between the available taxonomic information for species, and to provide objective criteria for assessing the information gaps for each taxon in order to prioritize topics for future investigation. Key aspects of taxonomic practice (condition of type specimens, helpfulness of descriptions and figures, compilation of distribution data, molecular data) were noted, the data were tabulated, and the taxa ranked. In addition, specific notes for each species have been included that may help to improve the solutions to the problems raised here.

A novel deep ensemble-based model with outlier removal and order-invariant ranking for carbon dioxide emission prediction.

Excessive carbon dioxide ( CO 2 ) emissions pose a formidable challenge, driving global climate change and necessitating urgent attention. Striking a balance between curbing CO 2 emissions and fostering economic growth hinges upon the ability to reliably forecast CO 2 emissions. Such forecasts are indispensable for policymakers as they endeavor to make informed decisions and proactively implement mitigation measures. In this research, we introduce an innovative deep ensemble prediction model for CO 2 emissions. This model is constructed around four parallel Long Short-Term Memory (LSTM) neural networks, complemented by a novel Multi-Layer Perception (MLP)-based ensemble framework, equipped with an outlier detection mechanism and an order-invariant ranking module. To enhance prediction accuracy and stability, a k-nearest neighbor (KNN)-based outlier detection module is employed to identify non-outliers and reasonable predictions for the ensemble models. Additionally, a novel feature ranking module is proposed to mitigate prediction fluctuations. The performance evaluation of our model is conducted using historical CO 2 emission data spanning from 1971 to 2021, encompassing six representative countries. Our findings demonstrate that the proposed methodology outperforms existing approaches across various evaluation metrics, offering considerably reduced prediction variances and greater stability. Moreover, long-term CO 2 emission predictions for the corresponding six countries have been provided, which might offer policymakers some basis for making decisions.

In silico mediated workflow for rapid development of downstream processing: Orthogonal product-related impurity removal for a Fc-containing therapeutic.

Therapeutic formats derived from the monoclonal antibody structure have been gaining significant traction in the biopharmaceutical market. Being structurally similar to mAbs, most Fc-containing therapeutics exhibit product-related impurities in the form of aggregates, charge variants, fragments, and glycoforms, which are inherently challenging to remove. In this work, we developed a workflow that employed rapid resin screening in conjunction with an in silico tool to identify and rank orthogonally selective processes for the removal of product-related impurities from a Fc-containing therapeutic product. Linear salt gradient screens were performed at various pH conditions on a set of ion-exchange, multimodal ion-exchange, and hydrophobic interaction resins. Select fractions from the screening experiments were analyzed by three different analytical techniques to characterize aggregates, charge variants, fragments, and glycoforms. The retention database generated by the resin screens and subsequent impurity characterization were then processed by an in silico tool that generated and ranked all possible two-step resin sequences for the removal of product-related impurities. A highly-ranked process was then evaluated and refined at the bench-scale to develop a completely flowthrough two-step polishing process which resulted in complete removal of the Man5 glycoform and aggregate impurities with a 73% overall yield. The successful implementation of the in silico mediated workflow suggests the possibility of a platformable workflow that could facilitate polishing process development for a wide variety of mAb-based therapeutics.

Transcriptional Dynamics and Key Regulators of Adipogenesis in Mouse Embryonic Stem Cells: Insights from Robust Rank Aggregation Analysis.

Embryonic stem cells are crucial for studying developmental biology due to their self-renewal and pluripotency capabilities. This research investigates the differentiation of mouse ESCs into adipocytes, offering insights into obesity and metabolic disorders. Using a monolayer differentiation approach over 30 days, lipid accumulation and adipogenic markers, such as Cebpb, Pparg, and Fabp4, confirmed successful differentiation. RNA sequencing revealed extensive transcriptional changes, with over 15,000 differentially expressed genes linked to transcription regulation, cell cycle, and DNA repair. This study utilized Robust Rank Aggregation to identify critical regulatory genes like PPARG, CEBPA, and EP300. Network analysis further highlighted Atf5, Ccnd1, and Nr4a1 as potential key players in adipogenesis and its mature state, validated through RT-PCR. While key adipogenic factors showed plateaued expression levels, suggesting early differentiation events, this study underscores the value of ESCs in modeling adipogenesis. These findings contribute to our understanding of adipocyte differentiation and have significant implications for therapeutic strategies targeting metabolic diseases.

Machine Learning and Wearable Technology: Monitoring Changes in Biomedical Signal Patterns during Pre-Migraine Nights.

Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology in ten individuals. Various physiological, activity-based, and signal processing metrics were examined to train predictive models and understand the relationship between specific features and migraine occurrences. Data were filtered based on specified criteria for nocturnal sleep, and analysis frames ranging from 5 to 120 min were used to improve the diversity of the training sample and investigate the impact of analysis frame duration on feature significance and migraine prediction. Several models, including XGBoost (Extreme Gradient Boosting), HistGradientBoosting (Histogram-Based Gradient Boosting), Random Forest, SVM, and KNN, were trained on unbalanced data and using cost-sensitive learning with a 5:1 ratio. To evaluate the changes in features during pre-migraine nights and nights before migraine-free days, an analysis of variance (ANOVA) was performed. The results showed that the features of electrodermal activity, skin temperature, and accelerometer exhibited the highest F-statistic values and the most significant p-values in the 5 and 10 min frames, which makes them particularly useful for the early detection of migraines. The generalized prediction model using XGBoost and a 5 min analysis frame achieved 0.806 for accuracy, 0.638 for precision, 0.595 for recall, and 0.607 for F1-score. Despite identifying distinguishing features between pre-migraine and migraine-free nights, the performance of the current model suggests the need for further improvements for clinical application.

A victim risk identification model for nature-induced urban disaster emergency response.

In recent years, nature-induced urban disasters in high-density modern cities in China have raised great concerns. The delayed and imprecise understanding of the real-time post-disaster situation made it difficult for the decision-makers to find a suitable emergency rescue plan. To this end, this study aims to facilitate the real-time performance and accuracy of on-site victim risk identification. In this article, we propose a victim identification model based on the You Only Look Once v7-W6 (YOLOv7-W6) algorithm. This model defines the "fall-down" pose as a key feature in identifying urgent victims from the perspective of disaster medicine rescue. The results demonstrate that this model performs superior accuracy (mAP@0.5, 0.960) and inference speed (5.1 ms) on the established disaster victim database compared to other state-of-the-art object detection algorithms. Finally, a case study is illustrated to show the practical utilization of this model in a real disaster rescue scenario. This study proposes an intelligent on-site victim risk identification approach, contributing significantly to government emergency decision-making and response.

Nonparametric estimation of mean residual lifetime in ranked set sampling with a concomitant variable.

The mean residual lifetime (MRL) of a unit is its expected additional lifetime provided that it has survived until time t. The MRL estimation problem has been frequently addressed in the literature since it has wide applications in statistics, reliability and survival analysis. In this paper, we consider the problem of estimating the MRL in ranked set sampling when actual quantifications of a concomitant variable are available. To exploit the additional information of the concomitant variable, we introduce several MRL estimators based on some regression techniques. We then compare them with the standard MRL estimator in simple random sampling using Monte Carlo simulation and a real dataset from the Surveillance, Epidemiology, and End Results Program. Our results indicate the superiority of the procedures that we have developed when the quality of ranking is fairly good.

A novel parallel feature rank aggregation algorithm for gene selection applied to microarray data classification.

Microarray data often comprises numerous genes, yet not all genes are relevant for predicting cancer. Feature selection becomes a crucial step to reduce the high dimensionality in these kinds of data. While no single feature selection method consistently outperforms others across diverse domains, the combination of multiple feature selectors or rankers tends to produce more effective results compared to relying on a single ranker alone. However, this approach can be computationally expensive, particularly when handling a large quantity of features. Hence, this paper presents a parallel feature rank aggregation that utilizes borda count as the rank aggregator. The concept of vertically partitioning the data along feature space was adapted to ease the parallel execution of the aggregation task. Features were selected based on the final aggregated rank list, and their classification performances were evaluated. The model's execution time was also observed across multiple worker nodes of the cluster. The experiment was conducted on six benchmark microarray datasets. The results show the capability of the proposed distributed framework compared to the sequential version in all the cases. It also illustrated the improved accuracy performance of the proposed method and its ability to select a minimal number of genes.

Novel brominated flame retardants relevant for European aquatic environments: ranking of analytes to monitor and focus on the French Eastern Mediterranean coastline.

Around a hundred of novel brominated flame retardants are currently being used to replace those regulated in the 2000s. However, data about their production, usage, and toxicity is still scarce, as well as their levels of contamination in the Mediterranean Sea and the subsequent risk. Our goal was to select the relevant novel brominated flame retardants to monitor and to apply it along the northeastern Mediterranean Sea. We proposed a ranking for novel brominated flame retardants based on their production or import, occurrence, and ecotoxicology, yielding to a selection of 21 priority molecules. From this list, 16 compounds were analyzed in ten coastal suspended matter samples, together with six related chemicals. To assess their occurrence in comparison to better documented flame retardants, eight legacy polybromodiphenyl ethers, seven polychlorobiphenyls, and short- and medium-chain chlorinated paraffins were also targeted. Novel brominated flame retardants and polychlorobiphenyls were detected in all the samples. Polybromodiphenyl ethers and chlorinated paraffins were detected in nine and seven samples, respectively. Out of the 22 novel brominated flame retardants analyzed, nine were detected, with total concentrations ranging from 0.4 to 18.5 ng.g-1 d.w., which was often higher than that of polybromodiphenyl ethers. A high risk for 2,4,6­tribromophenol and PCB 118 was assessed in two and six samples, respectively. To our knowledge, this is the first priority ranking and screening of most of the novel brominated flame retardants selected in the French Mediterranean Sea.

Rheological and sensory properties of chickpea and quinoa pastes and gels for plant-based product development.

The aim of this study was to investigate the modification of mechanical, rheological, and sensory properties of chickpea pastes and gels by incorporating other ingredients (olive oil or quinoa flour), to develop plant-based alternatives that meet consumer demands for healthy, natural, and enjoyable food products. The pastes and gels were made with different amounts of chickpea flour (9% and 12%, respectively). For each product, a first set of products with different oil content and a second set with quinoa flour (either added or replaced) were produced. The viscoelastic properties of the pastes and the mechanical properties of the gels were measured. Sensory evaluation and preference assessment were carried out with 100 participants using ranking tests. The study found remarkable differences in rheological, mechanical, and sensory properties of chickpea products upon the inclusion of oil and quinoa flour. The addition of oil increased the viscosity and decreased the elastic contribution to the viscoelasticity of the pastes, while it improved the firmness and plasticity in gels. It also increased the creaminess and preference of both pastes and gels. Replacing chickpea with quinoa flour resulted in less viscous pastes and gels with less firmness and more plasticity. In terms of sensory properties, the use of quinoa as a replacement ingredient resulted in less lumpiness in the chickpea paste and less consistency and more creaminess in both the pastes and gels, which had a positive effect on preference. The addition of quinoa increased the viscosity of pastes and the firmness and stiffness of gels. It increased the consistency and creaminess of both pastes and gels. Quinoa flour and/or olive oil are suitable ingredients in the formulation of chickpea-based products. They contribute to the structure of the system, providing different textural properties that improve acceptance.

[Trends in the choice of internal medicine (and clinical immunology) at the end of the national ranking examination since 2010]. / Évolution du choix de la médecine interne (et immunologie clinique) à l'issue des épreuves classantes nationales depuis 2010.

INTRODUCTION: The 2017 reform of the third cycle of medical studies (R3C) was accompanied by modifications in the formats and number of specialty diplomas. The aim of this study was to investigate the evolution of the choice of the internal medicine and clinical immunology specialty before and after 2017. METHODS: We used the median ranking and its evolution, as well as incoming and outgoing remorse rights, as markers of attractiveness. Data on the number of position offered, rankings and affectation were collected from decrees published in the French "Journal Officiel" each year. A survey conducted by the "Amicale des Jeunes Internistes" investigated the reasons for the outgoing or incoming rights to remorse. RESULTS: Before 2017, internal medicine was accessible to 52% of students on average, with a median rank of 1118 [339-2640]. From 2017 onwards, the internal medicine specialty was accessible to an average of 76.6% of students, with a median rank of 2772 [1039-5155]. The balance of incoming and outgoing remorse rights was -4.7% before 2017 and varied between -4.1 and -12.4% from 2017. CONCLUSION: Since 2017, the median and cut-off ranks of students choosing internal medicine specialty have increased, and balance of incoming and outgoing remorse rights was increasingly negative. A reflection on the attractiveness of the internal medicine specialty is undertaken by the National College of Internal Medicine Teachers in order to make the richness of the specialty and its different modes of practice known to future residents.

User-tailored sub-selection of climate model ensemble members for impact studies.

Climate model ensembles serve as an input to all impact studies that use sector-specific models (e.g., hydrological, ecological, crop models, fire hazard) at regional or local scales. These models require regionally scaled climate information to simulate the potential environmental and socio-economic sectoral impacts of climate change. Such simulations are based on comprehensive multi-member climate ensembles derived from the bias-adjusted and downscaled global and regional climate models. Due to limited computational resources, users of climate scenarios often can only include a small number of the ensemble members in their calculations, and therefore they often select them at random. A pre-selection of meaningful, consistent and case-specific members is therefore desired by the climate data users. In this work, we aim to fill this gap and present a novel user-tailored procedure for sub-selecting ensemble members for a variety of applications. Our method is based on the ranking of the climate change signal (CCS) calculated for a set of climate indices (e.g., mean temperature or number of hot days). Based on the CCS strength, three ensemble members representing the strongest, weakest, and median CCS are selected for each application. We also demonstrate the robustness of our approach in a specific hydrological impact model framework. Providing a systematic procedure not only assists impact modelers in selecting appropriate members, but also improves the consistency and comparability of different impact studies.

Personalized movie recommendations based on probabilistic linguistic sentiment and integrated DEMATEL-TODIM methods.

With the development of society, online reviews are increasingly becoming a crucial factor in decision-making. Especially for entertainment products such as movies, they are preferred for their affordability and high entertainment factor. Therefore, this paper proposes a movie recommendation model that considers user personalization using a probabilistic linguistic approach based on online reviews. Firstly, the method constructs a quantitative sentiment framework that transforms comments into a multi-granular probabilistic sentiment language. Secondly, we build the decision-making trial and evaluation laboratory (DEMATEL) method for probabilistic linguistic environments to explore interrelationships between product attributes, and improve the distance measure and score function to better integrate probabilistic linguistic information into DEMATEL weight calculations. Furthermore, to account for risk preferences, the model employs the extended TODIM (an acronym in Portuguese for interactive and multicriteria decision making) methodology to determine the ranking of alternatives. Finally, we design Douban movie ranking experiments to demonstrate the validity of the model. Compared with other methods, this paper incorporates the emotional tendency of movie attributes and user preference into the decision-making process leading to more reasonable results.

Spatiotemporal distribution of oxidative potential in PM2.5 and its key components across six Chinese cities.

Increasing evidence has supported that oxidative potential (OP) serves as a crucial indicator of health risk of exposure to PM2.5 over mass concentration. However, there is a lack of comparative studies across multiple cities, particularly on a fine temporal scale. In this study, we aim to investigate daily variation of ambient PM2.5 OP through simultaneous samplings in six Chinese cities for one year. Results showed that more than 60 % of the sampling days exhibited non-zero ranking difference between volume-normalized oxidative potential (OPv) and mass concentration among the six cities. Key components contributing to OPv inculde Mn, NO3-, and K+, followed by Ca2+, Al, SO42-, Cl-, Fe, and NH4+. Based on these chemical components, we developed a stepwise multivariable linear regression model (R2: 0.71) for OPv prediction. The performance of the model is comparable to both species- and sources-based ones in the literature. These findings suggest that a relatively lower daily-averaged mass concentration of PM2.5 does not necessarily indicate a lower oxidative risk. Future studies and policy developments on health benefits should also consider OPv rather than mass concentration alone. Priority could be given to sources/species that contribute significantly to oxidative potential of ambient PM2.5. SYNOPSIS: This study highlights inclusion of oxidative potential as a complementary metric for air pollution assessment and control.

The wisdom of the crowd with partial rankings: A Bayesian approach implementing the Thurstone model in JAGS.

We develop a Bayesian method for aggregating partial ranking data using the Thurstone model. Our implementation is a JAGS graphical model that allows each individual to rank any subset of items, and provides an inference about the latent true ranking of the items and the relative expertise of each individual. We demonstrate the method by analyzing data from new experiments that collected partial ranking data. In one experiment, participants were assigned subsets of items to rank; in the other experiment, participants could choose how many and which items they ranked. We show that our method works effectively for both sorts of partial ranking in applications to US city populations and the chronology of US presidents. We discuss the potential of the method for studying the wisdom of the crowd and other research problems that require aggregating incomplete or partial rankings.

Into the Jungle of Biological Agents of Foodborne Diseases: Time to Put Some Order for the French Risk Manager.

Consumers can be exposed to many foodborne biological hazards that cause diseases with varying outcomes and incidence and, therefore, represent different levels of public health burden. To help the French risk managers to rank these hazards and to prioritize food safety actions, we have developed a three-step approach. The first step was to develop a list of foodborne hazards of health concern in mainland France. From an initial list of 335 human pathogenic biological agents, the final list of "retained hazards" consists of 24 hazards, including 12 bacteria (including bacterial toxins and metabolites), 3 viruses and 9 parasites. The second step was to collect data to estimate the disease burden (incidence, Disability Adjusted Life Years) associated with these hazards through food during two time periods: 2008-2013 and 2014-2019. The ranks of the different hazards changed slightly according to the considered period. The third step was the ranking of hazards according to a multicriteria decision support model using the ELECTRE III method. Three ranking criteria were used, where two reflect the severity of the effects (Years of life lost and Years lost due to disability) and one reflects the likelihood (incidence) of the disease. The multicriteria decision analysis approach takes into account the preferences of the risk managers through different sets of weights and the uncertainties associated with the data. The method and the data collected allowed to estimate the health burden of foodborne biological hazards in mainland France and to define a prioritization list for the health authorities.

Effects of pulse shape on pitch sensitivity of cochlear implant users.

Contemporary cochlear implants (CIs) use cathodic-leading symmetric biphasic (C-BP) pulses for electrical stimulation. It remains unclear whether asymmetric pulses emphasizing the anodic or cathodic phase may improve spectral and temporal coding with CIs. This study tested place- and temporal-pitch sensitivity with C-BP, anodic-centered triphasic (A-TP), and cathodic-centered triphasic (C-TP) pulse trains on apical, middle, and basal electrodes in 10 implanted ears. Virtual channel ranking (VCR) thresholds (for place-pitch sensitivity) were measured at both a low and a high pulse rate of 99 (Experiment 1) and 1000 (Experiment 2) pulses per second (pps), and amplitude modulation frequency ranking (AMFR) thresholds (for temporal-pitch sensitivity) were measured at a 1000-pps pulse rate in Experiment 3. All stimuli were presented in monopolar mode. Results of all experiments showed that detection thresholds, most comfortable levels (MCLs), VCR thresholds, and AMFR thresholds were higher on more basal electrodes. C-BP pulses had longer active phase duration and thus lower detection thresholds and MCLs than A-TP and C-TP pulses. Compared to C-TP pulses, A-TP pulses had lower detection thresholds at the 99-pps but not the 1000-pps pulse rate, and had lower MCLs at both pulse rates. A-TP pulses led to lower VCR thresholds than C-BP pulses, and in turn than C-TP pulses, at the 1000-pps pulse rate. However, pulse shape did not affect VCR thresholds at the 99-pps pulse rate (possibly due to the fixed temporal pitch) or AMFR thresholds at the 1000-pps pulse rate (where the overall high performance may have reduced the changes with different pulse shapes). Notably, stronger polarity effect on VCR thresholds (or more improvement in VCR with A-TP than with C-TP pulses) at the 1000-pps pulse rate was associated with stronger polarity effect on detection thresholds at the 99-pps pulse rate (consistent with more degeneration of auditory nerve peripheral processes). The results suggest that A-TP pulses may improve place-pitch sensitivity or spectral coding for CI users, especially in situations with peripheral process degeneration.