Poor Convergence Research Articles

Investigating the Global Performance of the BDS-2 and BDS-3 Joint Real-Time Undifferenced and Uncombined Precise Point Positioning Using RTS Products from Different Analysis Centers

Compared to the traditional ionospheric-free (IF) precise point positioning (PPP) model, the undifferenced and uncombined (UU) PPP has the advantages of lower observation noise and the ability to obtain ionospheric information. Thanks to the IGS (International GNSS Service), real-time service (RTS) can provide RT vertical total electron content (VTEC) products, and an enhanced RT UU-PPP based on the RT-VTEC constraints can be achieved. The global performance of the BeiDou Navigation Satellite System-2 (BDS-2) and BDS-3 joint RT UU-PPP using different RTS products was investigated. There is not much difference in the RTS orbit accuracy of medium earth orbit (MEO) satellites among all analysis centers (ACs), and the optimal orbit accuracy is better than 5, 9, and 7 cm in the radial, along-track, and cross-track directions, respectively. The orbit accuracy of inclined geosynchronous orbit (IGSO) satellites is worse than that of MEO satellites. Except for CAS of 0.46 ns, the RTS clock accuracy of MEO satellites for other ACs achieves 0.2–0.27 ns, and the corresponding accuracy is about 0.4 ns for IGSO satellites. In static positioning, due to the limited accuracy of RT-VTEC, the convergence time of the enhanced RT UU-PPP is longer than that of RT IF-PPP for most ACs and can be better than 25 and 20 min in the horizontal and vertical components, respectively. After convergence, the 3D positioning accuracy of the static RT UU-PPP is improved by no more than 8.7%, and the optimal horizontal and vertical positioning accuracy reaches 3.5 and 7.0 cm, respectively. As for the kinematic mode with poor convergence performance, with the introduction of RT-VTEC constraints, the convergence time of RT UU-PPP can be slightly shorter and reaches about 55 and 60 min in the horizontal and vertical components, respectively. Both the horizontal and vertical positioning accuracies of the kinematic RT UU-PPP can be improved and achieve around 7.5 and 10 cm, respectively.

Dynamic routing optimization in software-defined networking based on a metaheuristic algorithm

Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

Next-to-next-to-leading-order QCD corrections to double J/ψ production at the B factories

In this paper, we study the next-to-next-to-leading-order (NNLO) QCD corrections for the process e+e− → J/ψ + J/ψ at the B factories. By including the NNLO corrections, the cross section turns negative due to the poor convergence of perturbative expansion. Consequently, to obtain a reasonable estimation for the cross section, the square of the amplitude up to NNLO is used. In addition, the contributions from the bottom quark and the light-by-light part, which are usually neglected, are also included. The final cross section is obtained as 1.76−1.66+2.42\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {1.76}_{-1.66}^{+2.42} $$\\end{document} fb at a center-of-mass energy of s\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\sqrt{s} $$\\end{document} = 10.58 GeV. Our result for total cross section and differential cross section could be compared with precise experimental measurement in future at the B factories.

Residential Electricity Consumption Prediction Method Based on Deep Learning and Federated Learning Under Cloud Edge Collaboration Architecture

Traditional residential electricity prediction methods have problems, such as difficulty in ensuring user privacy and poor convergence speed due to the influence of Different Residential Electricity Consumption (REC) habits. A REC prediction method based on Deep Learning (D-L) and Fed-L under the Cloud Edge Collaboration (CEC) architecture is proposed to address the above issues. First, based on the CEC architecture, combining edge computing and cloud computing center server, the overall model of REC prediction is built. Then, Federated Learning (Fed-L) and D-L model Empirical Mode Decomposition - Long Short-Term Memory (EMD-LSTM) were introduced on the edge side, and the edge side Fed-L depth model was personalized by using EMD-LSTM. Finally, aggregation of edge side models was achieved in the cloud by receiving encrypted model parameters from the edge side and updating and optimizing all edge side models. The results show that the proposed method has the highest REC prediction accuracy, reaching 96.56%, and its performance is superior to the other three comparative algorithms.

Enhancing sparrow search algorithm with OCSSA: Integrating osprey optimization and Cauchy mutation for improved convergence and precision

AbstractThis study enhances the traditional sparrow search algorithm (SSA) to address its weaknesses, such as poor convergence rates and precision due to a tendency to fall into local optima. The improved version, OCSSA, integrates the Osprey optimization algorithm (OOA) and Cauchy mutation. Logistic chaotic mapping is used for initial population generation to increase genetic diversity. OOA boosts global exploration in the producer phase, and Cauchy mutation in the scroungers phase disrupts suboptimal solutions, enhancing the algorithm's ability to avoid local optima. OCSSA's performance, validated through ten benchmark functions and fault diagnosis optimization tasks, significantly improves convergence speed and accuracy, proving its effectiveness in complex optimization challenges.

Система анализа тональности текста на телугу на основе нового пассивно-агрессивного классификатора с нечетким взвешиванием

Natural language processing (NLP) is a subset of artificial intelligence demonstrating how algorithms can interact with individuals in their unique languages. In addition, sentiment analysis in NLP is better in numerous programs, including evaluating sentiment in Telugu. Several unsupervised machine-learning algorithms, such as k-means clustering with cuckoo search, are used to detect Telugu text. However, these techniques struggle to cluster data with variable cluster sizes and densities, slow search speeds, and poor convergence accuracy. This study developed a unique ML-based sentiment analysis system for Telugu text to address the shortcomings. Initially, in the pre-processing stage, the proposed Linear Pursuit Algorithm (LPA) removes words in white spaces, punctuation, and stops. Then, for POS tagging, this research proposed a Conditional Random Field with Lexicon weighting; following that, a Contrived Passive Aggressive with Fuzzy Weighting Classifier (CPSC-FWC) is proposed to classify the sentiments in Telugu text. Consequently, the method we propose produces efficient outcomes in terms of accuracy, precision, recall, and f1-score.

Comparing maximum likelihood and Bayesian methods for fitting hidden Markov models to multi-state capture-recapture data of invasive carp in the Illinois River

BackgroundHidden Markov Models (HMMs) are often used to model multi-state capture-recapture data in ecology. However, a variety of HMM modeling approaches and software exist, including both maximum likelihood and Bayesian methods. The diversity of these methods obscures the underlying HMM and can exaggerate minor differences in parameterization.MethodsIn this paper, we describe a general framework for modelling multi-state capture-recapture data via HMMs using both maximum likelihood and Bayesian methods. We then apply an HMM to invasive silver carp telemetry data from the Illinois River and compare the results estimated by both methods.ResultsOur analysis demonstrates disadvantages of relying on a single approach and highlights insights obtained from implementing both methods together. While both methods often struggled to converge, our results show biologically informative priors for Bayesian methods and initial values for maximum likelihood methods can guide convergence toward realistic solutions. Incorporating prior knowledge of the system can successfully constrain estimation to biologically realistic movement and detection probabilities when dealing with sparse data.ConclusionsBiologically unrealistic estimates may be a sign of poor model convergence. In contrast, consistent convergence behavior across approaches can increase the credibility of a model. Estimates of movement probabilities can strongly influence the predicted population dynamics of a system. Therefore, thoroughly assessing results from HMMs is important when evaluating potential management strategies, particularly for invasive species.

An Innovational Jacobian-Split Newton-Krylov Method Combining the Advantages of the Jacobian-Free Newton-Krylov Method and the Finite Difference Jacobian-Based Newton-Krylov Method

The Jacobian-free Newton-Krylov (JFNK) method is a widely used and flexible numerical method for solving the neutronic/thermal-hydraulic coupling system. The main property of JFNK is that the Jacobian-vector product is evaluated approximately by finite difference, avoiding the forming and storage of Jacobian explicitly. However, the lack of an efficient preconditioner is a major bottleneck for the JFNK method, leading to poor convergence. The finite difference Jacobian-based Newton-Krylov (DJNK) method is another advanced numerical method, in which the Jacobian matrix is formed and stored explicitly. The DJNK method can provide a better preconditioner for Krylov iteration than JFNK. However, how to compute the Jacobian matrix efficiently and automatically is a key issue for the DJNK method. By fully utilizing the sparsity of the Jacobian matrix and graph coloring algorithm, the Jacobian can be computed efficiently. Unfortunately, when there are dense rows/blocks, a huge computational burden will emerge due to the lack of sparsity, resulting in the extremely poor efficiency of Jacobian computation. In this work, a Jacobian-split Newton-Krylov (JSNK) method is proposed to resolve the dense row/block problem by combining the advantages of JFNK and DJNK. The main feature of the JSNK method is to split the Jacobian matrix into sparse and dense parts. The sparse part of the Jacobian matrix is explicitly constructed using the graph coloring algorithm while for the dense part, the Jacobian-vector product is approximated by finite difference. The computational complexity of the JSNK method is analyzed and compared to the JFNK method and the DJNK method from theoretical and experimental aspects and under different meshes. A simplified two-dimensional (2-D) high-temperature gas-cooled reactor (HTR) model and a simplified 2-D pressurized water reactor model are utilized to demonstrate the superiority of the JSNK method. The numerical results show that the JSNK method successfully resolved the dense rows/blocks. More importantly, its efficiency significantly outperforms the JFNK method and the DJNK method.

System-of-systems approach to spatio-temporal crowdsourcing design using improved PPO algorithm based on an invalid action masking

Spatio-temporal crowdsourcing (STC) is a typical case of complex system-of-systems (SoSs) design, wherein the primary objective is to allocate real-time tasks to suitable groups of workers. Over time, the STC allocation has gradually evolved into a dynamic matching involving three distinct entities: tasks, workers, and workplaces. Aiming at addressing the problems of poor convergence, slow response and sparse actions caused by the spatial complexity and time dynamics of the STC, this paper proposes an improved proximal policy optimization algorithm based on an invalid action masking (IAM-IPPO) for the SoSs design of the STC. Initially, the ternary dynamic matching (TDM) of tasks, workers and workplaces in the STC is described. Furthermore, the STC allocation is formulated as a Markov decision process, with the corresponding definition of state space, action space, and reward mechanism. On this basis, an invalid action masking (IAM) method is mainly introduced to update the policy-based network of proximal policy optimization (PPO), realizing sampling only from valid actions to masking invalid action selection. Subsequently, the algorithmic framework of IAM-IPPO is elaborated upon, and the model is trained to generate an effective allocation scheme. Comparative experiments are conducted on authentic datasets, aiming to assess performance indicators of the presented approach. The findings demonstrate a substantial enhancement in performance for the IAM-IPPO algorithm compared to other baselines, which is helpful in exploring excellent design schemes of the crowdsourcing SoSs, especially in dynamic large-scale cases.

An enhanced Equilibrium Optimizer for solving complex optimization problems

Equilibrium Optimizer (EO), despite its acceptable performance in several complex problems, suffers from low exploitation ability with unbalanced exploration, stagnation in local optima and poor convergence rate. To enhance the performance of the EO, the authors infused the intelligence of quasi-opposite numbers and chaotic-maps into the EO and proposed a Quasi-Oppositional Chaotic Equilibrium Optimizer (QOCEO) that consists of Quasi-Opposition based learning for improving the scalability, Chaotic Time Parameter for ensuring a proper balance between exploration and exploitation, and Chaotic Local Search for reducing the local stagnation. The performance of QOCEO and several contemporary metaheuristics have been evaluated on 96 benchmark functions (including classical benchmark, CEC-2017, CEC-2019, CEC-2020 and CEC-2022) and six constrained engineering optimization problems employing several statistical tests. The average rank Friedman test confirms that for all 96 benchmark functions, QOCEO outperforms CMA-ES, OBTLEO, m-EO, EO, LSHADE-SPACMA, SPS_L_SHADE_EIG, SHADE by 88%, 83%, 49%, 35%, 27%, 18% and 5%, respectively. Comparing the results of Bonferroni-Dunn and Holm’s tests, QOCEO outperforms PSO, GWO, EO, HGSO, SSA, and GSA for classical benchmark functions with 30, 100, 300, and 500 dimensions. Further, QOCEO outperform CMA-ES, OBTLEO, m-EO, EO, LSHADE-SPACMA for all 96 benchmark functions and statistically similar to SPS_L_SHADE_EIG, SHADE and EBOwithCMAR.

Optimization Strategies for Urban Waterlogging Warning in Complex Environments: Based on Particle Swarm Optimization and Deep Neural Networks

Waterlogging warning has gradually become an important means of urban waterlogging prevention and control. However, the current urban waterlogging warning model still has problems such as low accuracy, real‐time performance, and poor model convergence. To better address these issues, this article combines particle swarm optimization (PSO) and deep neural networks (DNN) to explore the construction of early warning models in depth. First, the influencing factors of urban waterlogging were analyzed in the article, and the PSO algorithm was used to determine the influencing factors of urban waterlogging in this study; then, the selected influencing factors were used as input data to design a backpropagation (BP) neural network (NN) structure; several representative waterlogging points can be selected to construct a BP NN model and perform fitting analysis. Afterward, combined with the PSO algorithm, the constructed model was trained and optimized. In this article, the old town of Hefei City is used as the experimental object, and the building model is used to conduct early warning research on waterlogging. The study’s findings indicate that the PSO + BP model’s average accuracy in 10 early warning tests is as high as 97.95%, with a response time of only 0.022 ms; the average accuracy and response time of the BP model are 89.06% and 0.255 ms, respectively; the Bayesian network model (BN model) is 82.78% and 0.275 ms. Through the analysis of actual cases in the old urban area of Hefei City, the advantages of this model in practical application were verified, and a new intelligent warning method for urban waterlogging prevention and control was provided, demonstrating its effectiveness and potential in dealing with complex nonlinear problems.

Research on Path Planning for Unmanned Surface Vessels Based on AIS Data

To address the issues of poor endpoint convergence and suboptimal path quality in global path planning for unmanned surface vessels (USVs), this paper proposes an endpoint-convergence oriented improved genetic algorithm based on AIS data. Firstly, utilizing a genetic algorithm with an endpoint-convergence objective function, a set of paths with strong endpoint convergence is selected. Secondly, to overcome the limitations of a single optimization algorithm, a multi-objective path planning approach is employed using a genetic algorithm, thereby improving the distance and trajectory smoothness in global path planning for unmanned ships. Finally, the planned paths are evaluated by comparing them with the real paths from AIS data. The simulation results demonstrate that the proposed method outperforms other traditional algorithms in terms of average path turn count and turn angle, reducing them by an average of 35.41% and 35.72%, respectively. Moreover, the proposed method exhibits the smallest error compared to the real trajectories, with an average reduction of 18.26%. These results validate the effectiveness and rationality of the proposed approach in improving path quality, reducing path turn count, and achieving better alignment with actual trajectories.

Curious Explorer: a Provable Exploration Strategy in Policy Learning.

A coverage assumption is critical with policy gradient methods, because while the objective function is insensitive to updates in unlikely states, the agent may need improvements in those states to reach a nearly optimal payoff. However, this assumption can be unfeasible in certain environments, for instance in online learning, or when restarts are possible only from a fixed initial state. In these cases, classical policy gradient algorithms like REINFORCE can have poor convergence properties and sample efficiency. Curious Explorer is an iterative state space pure exploration strategy improving coverage of any restart distribution ρ. Using ρ and intrinsic rewards, Curious Explorer produces a sequence of policies, each one more exploratory than the previous one, and outputs a restart distribution with coverage based on the state visitation distribution of the exploratory policies. This paper main results are a theoretical upper bound on how often an optimal policy visits poorly visited states, and a bound on the error of the return obtained by REINFORCE without any coverage assumption. Finally, we conduct ablation studies with REINFORCE and TRPO in two hard-exploration tasks, to support the claim that Curious Explorer can improve the performance of very different policy gradient algorithms.

Feature selection via a multi-swarm salp swarm algorithm

<abstract><p>Feature selection (FS) is a promising pre-processing step before performing most data engineering tasks. The goal of it is to select the optimal feature subset with promising quality from the original high-dimension feature space. The Salp Swarm Algorithm (SSA) has been widely used as the optimizer for FS problems. However, with the increase of dimensionality of original feature sets, the FS problems propose significant challenges for SSA. To solve these issues that SSA is easy to fall into local optimum and have poor convergence performance, we propose a multi-swarm SSA (MSSA) to solve the FS problem. In MSSA, the salp swarm was divided into three sub-swarms, the followers updated their positions according to the optimal leader of the corresponding sub-swarm. The design of multi-swarm and multi-exemplar were beneficial to maintain the swarm diversity. Moreover, the updating models of leaders and followers were modified. The salps learn from their personal historical best positions, which significantly improves the exploration ability of the swarm. In addition, an adaptive perturbation strategy (APS) was proposed to improve the exploitation ability of MSSA. When the swarm stagnates, APS will perform the opposition-based learning with the lens imaging principle and the simulated binary crossover strategy to search for promising solutions. We evaluated the performance of MSSA by comparing it with 14 representative swarm intelligence algorithms on 10 well-known UCI datasets. The experimental results showed that the MSSA can obtain higher convergence accuracy with a smaller feature subset.</p></abstract>

Swarm intelligence technique for solving optimal power flow problem in the Nigeria power system

This paper deals on the implementation of the particle swarm optimization method for solving power flow problems in the Nigeria power system. The difficulty of solving optimal power flow (OPF) problems increases significantly with increasing network size and complexity. Some of the weakness of the conventional methods include: limited ability in solving real-world large scale optimization problems, weakness in handling constraints, poor convergence and slow computational time. The results gotten by implementing the Particle Swarm Optimization (PSO) algorithm in Nigeria 330kV 52-Bus network show that the total active power and reactive power losses were substantially reduced to 0.03MW and 0.05MVAR. The system time of convergence was faster at 0.5seconds. Also, the maximum line active and reactive power losses on line 1 to 2 were reduced greatly to 0.00MW and 0.01MVAR respectively. These show that the system was better optimized with the PSO algorithm than the conventional method.

Transient response feedback control for horizontal vibration of high-speed elevator car with prescribed performance

The high-speed elevator car is subject to violent horizontal vibration due to the wind pressure generated by the ventilation hole of the elevator rapid operation, improper adjustment of the gap and steps at the joint of the elevator car guide rail, pitting corrosion or wear on the guide surface of the guide shoe, inadequate installation and adjustment of the guide shoe or guide rail and so on. Aiming at the problems of poor transient convergence and real-time performance, low control accuracy and stability of the existing active control algorithm for the horizontal vibration control variables of the elevator car system, a transient response feedback control strategy with specified performance is proposed in this paper. Firstly, a six degree of freedom elevator car system model is established, and the state space equation is constructed based on the linear fractional transformation algorithm; Secondly, a class of preset transient performance function is introduced to construct the convergence boundary in advance to ensure that the system control variables are strictly constrained within this boundary, and the stability of the transformed variables is ensured by equivalent transformation; Finally, four different control methods including passive control, H2/H∞ control, PID control and transient response feedback control proposed in this paper are compared and analyzed by using MATLAB under two typical guideway excitation. The simulation results show that the proposed transient response feedback control strategy reduces the root mean square value and maximum value of the horizontal displacement acceleration of the elevator car system by more than 81%, effectively suppresses the horizontal vibration of the elevator car, and ensures the transient and steady-state performance of the elevator car system.

Multi‐objective Firefly algorithm for enhanced balanced exploitation and exploration capabilities

SummaryThe multi‐objective Firefly algorithm has a single strategy for finding the best in the evolutionary process, which is easy to fall into the local optimum and leads to poor distribution and convergence of the population. To address this problem, this article proposes an enhanced multi‐objective Firefly algorithm with balanced exploitation and exploration capability (MOFA‐EBE). The convergence evaluation index is introduced to divide the population into two sub‐regions according to the difference of convergence, namely, the development area and exploration area, and each sub‐region is assigned its learning strategy to maximize the utilization of population information. Since the individuals in the development region are far from the Pareto front, the Lévy flights mechanism is added to expand the search area and make them approach the Pareto front quickly under the guidance of the convergent global optimal particles to improve the convergence of the algorithm; since the individuals in the exploration region already have better convergence, they are assigned the most diverse and convergent global individuals for guidance and the Cauchy The variation mechanism is added to the Pareto frontier for continuous exploration to improve the distributivity of the algorithm. In the experimental part, the algorithm is compared with some multi‐objective optimization algorithms on 19 benchmark test functions, and the effectiveness of the added strategy of MOFA‐EBE is verified. The results show that MOFA‐EBE is significantly superior to several other algorithms in terms of improving population convergence and distributivity.

Improvement of Fuzzy Newton Power Flow Convergence

In order to address the convergence issue in fuzzy power flow calculations, this paper proposes an analytical approach based on the Levenberg–Marquardt method, aiming to improve the convergence of the fuzzy Newton power flow method. Firstly, a detailed analysis is conducted on the convergence theorem and convergence behavior of the fuzzy Newton method, revealing its poor convergence when the initial values are not properly selected. The Levenberg–Marquardt method is then selected as a means to enhance the convergence of the fuzzy Newton power flow calculations, specifically to tackle the problem of initial value deviation. Since the Jacobian matrix has a significant impact on the convergence region of the power flow, this paper reconstructs the Jacobian matrix based on the Levenberg–Marquardt method, effectively enlarging the convergence region. Through validation experiments on the IEEE 118 standard nodes and simulation comparative analysis, the results confirm the method’s effectiveness in resolving the problem of initial value deviation and notably enlarging the convergence region, thereby improving the convergence of power flow calculations.

The genomes of Scedosporium between environmental challenges and opportunism

Emerging fungal pathogens are a global challenge for humankind. Many efforts have been made to understand the mechanisms underlying pathogenicity in bacteria, and OMICs techniques are largely responsible for those advancements. By contrast, our limited understanding of opportunism and antifungal resistance is preventing us from identifying, limiting and interpreting the emergence of fungal pathogens. The genus Scedosporium (Microascaceae) includes fungi with high tolerance to environmental pollution, whilst some species can be considered major human pathogens, such as Scedosporium apiospermum and Scedosporium boydii. However, unlike other fungal pathogens, little is known about the genome evolution of these organisms. We sequenced two novel genomes of Scedosporium aurantiacum and Scedosporium minutisporum isolated from extreme, strongly anthropized environments. We compared all the available Scedosporium and Microascaceae genomes, that we systematically annotated and characterized ex novo in most cases. The genomes in this family were integrated in a Phylum-level comparison to infer the presence of putative, shared genomic traits in filamentous ascomycetes with pathogenic potential. The analysis included the genomes of 100 environmental and clinical fungi, revealing poor evolutionary convergence of putative pathogenicity traits. By contrast, several features in Microascaceae and Scedosporium were detected that might have a dual role in responding to environmental challenges and allowing colonization of the human body, including chitin, melanin and other cell wall related genes, proteases, glutaredoxins and magnesium transporters. We found these gene families to be impacted by expansions, orthologous transposon insertions, and point mutations. With RNA-seq, we demonstrated that most of these anciently impacted genomic features responded to the stress imposed by an antifungal compound (voriconazole) in the two environmental strains S. aurantiacum MUT6114 and S. minutisporum MUT6113. Therefore, the present genomics and transcriptomics investigation stands on the edge between stress resistance and pathogenic potential, to elucidate whether fungi were pre-adapted to infect humans. We highlight the strengths and limitations of genomics applied to opportunistic human pathogens, the multifactoriality of pathogenicity and resistance to drugs, and suggest a scenario where pressures other than anthropic contributed to forge filamentous human pathogens.

Cognitive functions as predictors of daily functioning among older adults

AbstractBackgroundCognitive and daily functioning are conceptually critical in distinguishing Mild Cognitive Impairments (MCI) from dementia (Petersen, 2004), but findings on the association between the two have not always been consistent (e.g., Putcha & Tremont, 2016; Teng et al., 2010). We aimed to investigate whether cognitive functions could predict daily functioning among older adults.MethodA total of 182 participants were included in this study, aged between 51 and 92 years. Cognitive functioning was measured using various neuropsychological tests which target different domains (e.g., trail‐making test, WAIS digit span, verbal fluency), while daily functioning was measured using the Functional Activity Questionnaire (FAQ) and the Instrumental Activities of Daily Living (IADL). The Geriatric Depression Scale (DGS) was used to assess depression as a possible moderator. The association between cognitive domains and daily functioning was examined using Partial Least Squares Structural Equation Modelling in R with the “seminr” package.ResultA model with three cognitive predictors and no moderator provided the best fit to the data. However, the model only explained 16.7% of the total variance. Furthermore, only memory was significantly linked to daily functioning (b = ‐0.367, 95% CI [‐0.53 ‐0.18]), while language and attention/executive functions were not. Memory, language, and daily functioning showed good construct validity (all AVE > 0.5 and HTMT < 0.9), but “attention/executive function” construct suffered from poor convergent validity (AVE = 0.43) with only two out of its seven indicators demonstrating loading larger than 0.708. Also unexpectedly, depression was not found to be a significant predictor or a moderator.ConclusionOur preliminary results suggest that cognitive function explains a small portion of the daily functioning variance and memory was found to be the best cognitive predictor. Indeed, in addition to executive function, memory has been rather consistently mentioned as crucial in the maintenance of daily functioning (Pérès et al., 2008; Royall et al., 2007). The lack of evidence for the role of executive function in this study might be due to the poor convergence of the “attention/executive function” construct in our model, suggesting that it may represent different cognitive functions and should be investigated further.