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For assessment of biosimilar drug products, if there are multiple-reference products (eg, a US-licensed product and an EU-approved product), a biosimilar bridging study with a 3-way pairwise comparison is often conducted. In our paper, two innovative methods in biosimilar bridging study are compared with the conventional method of pairwise comparisons. For parallel study design, the simultaneous confidence interval (CI) method is compared to the convention method. For crossover study design, the multiplicity-adjusted Schuirmann's two one-sided tests (MATOST) is considered. This paper conclude that the simultaneous CI method achieves the similar statistical power to the conventional approach in biosimilarity assessment. However, the MATOST method using the conservative Holm and Bonferroni approaches is not favorable since it leads to a large sample size although it controls the type I error rate. [ABSTRACT FROM AUTHOR]

Accurately determining the state of charge is crucial for efficient battery management and reliable operation in renewable energy systems. This study presents a novel approach utilizing an artificial neural network to estimate the state of charge of a battery based on key variables such as battery voltage, charging current, load current, and temperature. A photovoltaic solar energy system was installed at the American University of Ras Al Khaimah, where a comprehensive experiment was conducted over 146 hours, resulting in the collection of 3930 high-resolution data points at 2-minute intervals. The artificial neural network model was trained on 70% of the data, while the remaining 30% was used for validation and testing. The trained model achieved a mean square error of 0.000896 and an error rate of less than 2% in the state of charge estimation. The model demonstrated excellent predictive accuracy, with a determination coefficient (R ²) of 99.9%, indicating a strong correlation between the predicted and actual state of charge values. These results validate the robustness of the developed artificial neural network model and its potential for real-time state of charge estimation in renewable energy systems, providing a reliable and computationally efficient alternative to traditional state of charge estimation methods. [ABSTRACT FROM AUTHOR]

Accurate analyses and management of ion concentrations are crucial in precision agriculture. Modern technology-based methods are non-destructive and do not require sample preparation, enabling fast and accurate analysis; however, they have limitations when processing multiple samples. In this study, a multi-ion analysis system was developed for the prompt and accurate analysis of concentrations of important ions such as NO3−, HnPO4−, K+, Ca2+, and Mg2+. The RGB sensitivity control was automated through calibrations by applying a reference slot-based error rate across six slots between sample measurements, facilitating sample-to-sample comparisons and enabling accurate concentration analysis. By analyzing the correlation between each ion concentration and the proximal absorbance-based concentration prediction in the simultaneous analysis system, the accuracy was verified by achieving a coefficient of determination exceeding 0.99 for most ions. This system minimizes possible deviations between slots by using an automatic calibration algorithm, thereby facilitating the simultaneous analysis of multiple samples. This is important for saving time and cost and can help in real-time nutrient analysis and monitoring in agriculture. [ABSTRACT FROM AUTHOR]

Existing glass segmentation networks have high computational complexity and large memory occupation, leading to high hardware requirements and time overheads for model inference, which is not conducive to efficiency-seeking real-time tasks such as autonomous driving. The inefficiency of the models is mainly due to employing homogeneous modules to process features of different layers. These modules require computationally intensive convolutions and weight calculation branches with numerous parameters to accommodate the differences in information across layers. We propose an efficient glass segmentation network (EGSNet) based on multi-level heterogeneous architecture and boundary awareness to balance the model performance and efficiency. EGSNet divides the feature layers from different stages into low-level understanding, semantic-level understanding, and global understanding with boundary guidance. Based on the information differences among the different layers, we further propose the multi-angle collaborative enhancement (MCE) module, which extracts the detailed information from shallow features, and the large-scale contextual feature extraction (LCFE) module to understand semantic logic through deep features. The models are trained and evaluated on the glass segmentation datasets HSO (Home-Scene-Oriented) and Trans10k-stuff, respectively, and EGSNet achieves the best efficiency and performance compared to advanced methods. In the HSO test set results, the IoU, Fβ, MAE (Mean Absolute Error), and BER (Balance Error Rate) of EGSNet are 0.804, 0.847, 0.084, and 0.085, and the GFLOPs (Giga Floating Point Operations Per Second) are only 27.15. Experimental results show that EGSNet significantly improves the efficiency of the glass segmentation task with better performance. [ABSTRACT FROM AUTHOR]

Background/Objectives: While new methods for measuring antimicrobial susceptibility have been associated with improved patient outcomes, they should also be validated using standard protocols for error rates and other test metrics. The objective of this study was to validate a novel susceptibility assay for complicated and recurrent urinary tract infections (UTIs): pooled antibiotic susceptibility testing (P-AST). This assay was compared to broth microdilution (BMD) and disk diffusion (DD), following Clinical and Laboratory Standards Institute (CLSI) guidelines for assessment of error rates and agreement. Methods: This study analyzed consecutive fresh clinical urine specimens submitted for UTI diagnostic testing. Upon receipt, the urine samples were subjected in parallel to standard urine culture and multiplex polymerase chain reaction (M-PCR) for microbial identification and quantification. Specimens with the same monomicrobial non-fastidious bacteria detected by both M-PCR and standard urine culture (SUC) underwent standard antibiotic susceptibility testing (AST) and P-AST antibiotic susceptibility testing. Analysis was also undertaken to assess the presence of heteroresistance for specimens with P-AST-resistant and BMD/DD consensus-susceptible results. Results: The performance measures without correction for heteroresistance showed essential agreement (EA%) of ≥90%, very major errors (VMEs) of <1.5%, and major errors (MEs) of <3.0% for P-AST, all meeting the threshold guidelines established by CLSI for AST. The categorical agreement (CA%) also met acceptable criteria (>88%), as the majority of the errors were minor (mEs) with essential agreement. The very major and major error rates for P-AST decreased to <1.0% when heteroresistance was accounted for. Conclusions: The P-AST assay methodology is validated within acceptable parameters when compared to broth microdilution and disk diffusion using CLSI criteria. [ABSTRACT FROM AUTHOR]

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In this work, we introduce a novel two-point Landweber-type method to solve the non-smooth ill-posed problems in Banach spaces. The method comprises of inner solvers and two-point acceleration strategy based on iteratively regularized Landweber iteration method. With respect to penalty terms in every step, we use inner solvers to solve certain minimization problems. However, if the minimization problems are explicitly solvable, the inner solvers are same as the exact solvers. The design of the method is such that it allows various types of noises in the given data of non-smooth inverse problems. We study the convergence analysis of our method along with the convergence rates and error estimates by using the standard assumptions that hold without the reflexivity of the image space and G âteaux differentiability of the forward operator. We highlight that this paper is the first advancement in the direction of deducing the convergence rates of a two-point Landweber method. Finally, we present the numerical simulations to show the practicality of the method. [ABSTRACT FROM AUTHOR]

Traditional methods for studying microbial communities have been limited due to difficulties in culturing and sequencing all microbial species. Recent advances in third-generation sequencing technologies, particularly PacBio's high-fidelity (HiFi) sequencing, have significantly advanced metagenomics by providing accurate long-read sequences. This review explores the role of HiFi sequencing in overcoming the limitations of previous sequencing methods, including high error rates and fragmented assemblies. We discuss the benefits and applications of HiFi sequencing across various environments, such as the human gut and soil, which provides broader context for further exploration. Key studies are discussed to highlight HiFi sequencing's ability to recover complete and coherent microbial genomes from complex microbiomes, showcasing its superior accuracy and continuity compared to other sequencing technologies. Additionally, we explore the potential applications of HiFi sequencing in quantitative microbial analysis, as well as the detection of single nucleotide variations (SNVs) and structural variations (SVs). PacBio HiFi sequencing is establishing a new benchmark in metagenomics, with the potential to significantly enhance our understanding of microbial ecology and drive forward advancements in both environmental and clinical applications. [ABSTRACT FROM AUTHOR]

Stepwise multiple testing procedures have attracted several statisticians for decades and are also quite popular with statistics users because of their technical simplicity. The Bonferroni procedure has been one of the earliest and most prominent testing rules for controlling the familywise error rate (FWER). A recent article established that the FWER for the Bonferroni method asymptotically (i.e., when the number of hypotheses becomes arbitrarily large) approaches zero under any positively equicorrelated multivariate normal framework. However, similar results for the limiting behaviors of FWER of general stepwise procedures are nonexistent. The present work addresses this gap in a unified manner by elucidating that, under the multivariate normal setups with some correlation structures, the probability of rejecting one or more null hypotheses approaches zero asymptotically for any step-down procedure. Consequently, the FWER and power of the step-down procedures also tend to be asymptotically zero. We also establish similar limiting zero results on FWER of other popular multiple testing rules, e.g., Hochberg's and Hommel's procedures. It turns out that, within our chosen asymptotic framework, the Benjamini–Hochberg method can hold the FWER at a strictly positive level asymptotically under the equicorrelated normality. [ABSTRACT FROM AUTHOR]

The genomic analysis of SARS‐CoV‐2 has served as a crucial tool for generating invaluable data that fulfils both epidemiological and clinical necessities. Long‐read sequencing technology (e.g., ONT) has been widely used, providing a real‐time and faster response when necessitated. A novel nanopore‐based long‐read sequencing platform named QNome nanopore has been successfully used for bacterial genome sequencing and assembly; however, its performance in the SARS‐CoV‐2 genomic surveillance is still lacking. Synthetic SARS‐CoV‐2 controls and 120 nasopharyngeal swab (NPS) samples that tested positive by real‐time polymerase chain reaction were sequenced on both QNome and MGI platforms in parallel. The analytical performance of QNome was compared to the short‐read sequencing on MGI. For the synthetic SARS‐CoV‐2 controls, despite the increased error rates observed in QNome nanopore sequencing reads, accurate consensus‐level sequence determination was achieved with an average mapping quality score of approximately 60 (i.e., a mapping accuracy of 99.9999%). For the NPS samples, the average genomic coverage was 89.35% on the QNome nanopore platform compared with 90.39% for MGI. In addition, fewer consensus genomes from QNome were determined to be good by Nextclade compare with MGI (p < 0.05). A total of 9004 mutations were identified using QNome sequencing, with substitutions being the most prevalent, in contrast, 10 997 mutations were detected on MGI (p < 0.05). Furthermore, 23 large deletions (i.e., deletions≥ 10 bp) were identified by QNome while 19/23 were supported by evidence from short‐read sequencing. Phylogenetic analysis revealed that the Pango lineage of consensus genomes for SARS‐CoV‐2 sequenced by QNome concorded 83.04% with MGI. QNome nanopore sequencing, though challenged by read quality and accuracy compared to MGI, is overcoming these issues through bioinformatics and computational advances. The advantage of structure variation (SV) detection capabilities and real‐time data analysis renders it a promising alternative nanopore platform for the surveillance of the SARS‐CoV‐2. [ABSTRACT FROM AUTHOR]

The interconversion of a variety of entangled states can facilitate the information transmission and decline the risk of error rates. Here two faithful protocols to achieve deterministic interconversion between three‐logic‐qubit W state and three‐logic‐qubit Greenberger–Horne–Zeilinger state in decoherence‐free subspace (DFS), resorting to the state‐selective property of the quantum dot (QD)‐cavity systems and robust‐fidelity quantum control gates are presented. Moreover, the single‐photon detectors introduced can effectively herald and mitigate potential failures from imperfect interaction between the QD‐cavity system and photons, significantly enhancing experimental feasibility. Through comprehensive analysis and evaluation, the protocols demonstrate exceptional conversion efficiencies and deliver near‐perfect fidelities. Additionally, the DFS makes system coherence over extended periods to overcome the decoherence effect caused by specific environmental noise, paving the way for quantum information processing. [ABSTRACT FROM AUTHOR]

In active-matrix organic light-emitting diode (AMOLED) displays, conventional pixel circuits that compensate for the non-uniformity of the threshold voltage (VT) of low-temperature polycrystalline silicon thin-film transistors (TFTs) can hardly compensate for variations in other TFT parameters, such as carrier mobility (μ0), subthreshold swing (SS) and the various effects of parasitic capacitance. In recent high-resolution AMOLED displays, as the current required for OLED pixel driving decreases, the current error rate (CER) caused by the non-uniform TFT parameters increases. In this study, we analyzed the influence of each TFT parameter on the CER using SPICE simulation. Based on our analysis, the origin of the increased CER can be classified into two categories: the charging capability of driving TFT and the capacitive coupling effect of the switching TFT. The SS of the driving TFT and the parasitic capacitance of the switching TFT are major factors that affect the CER in terms of the charging capability and capacitive coupling effect, respectively. Our analysis results can be summarized as follows: The SS value of the driving TFT should be high, and its variation should be small to minimize the CER. The variation in the parasitic capacitance of the switching TFT possibly occurs due to long-term bias conditions, as well as process non-uniformity. Therefore, the stability of TFT should also be confirmed for the prevention of anomalous CER caused by long-term bias stress. [ABSTRACT FROM AUTHOR]

Objective. The objective of this scoping review was to identify key characteristics of primary research studies in peer-reviewed journals about reference accuracy. Characteristics of studies include the publication year, field of study, journal name and publication year of verified references, number of references verified, reference elements examined, definition of major, intermediate, and minor errors, whether punctuation or typographical errors were counted, overall error rate, major, intermediate, and minor error rates, and most common error type. Introduction. Reference accuracy has been studied across several decades and remains a persistent issue in peer-reviewed publications. Reference accuracy is typically reported as an error rate that represents the proportion of references with at least one error. Inclusion criteria for this scoping review included primary studies published in peer-reviewed journals that verified references in articles. Methods. Library, Information Science and Technology Abstracts (LISTA), Medline via EBSCOhost, and Web of Science were searched with final searches conducted in all three databases on November 26, 2022. No grey literature sources were searched given the focus of the project on peer-reviewed scholarly journals. Results. A total of 105 studies were included. The overall error rate across all studies was 32.7%. The error rates within specific fields of study were relatively similar. There is a lack of clarity about whether studies counted punctuation or typographical errors. The most common error was related to author names. Conclusions. There is a lack of studies to understand the most effective means to increase reference accuracy that are feasible to implement on a wide scale. Future research is warranted to understand how best to improve reference accuracy across fields of study. [ABSTRACT FROM AUTHOR]

Pharmacists have often been viewed as the last line of defence against prescription errors in traditional care models. Although a large number of chronic care patients are using telehealth services to increase their access to continuous care, researchers have yet to investigate prescription safety in such settings in Australia. The absence of this literature is particularly concerning in the context of the Australian Government's admission in a 2024 report that the national health system has not adequately addressed the World Health Organization's 'Medication without harm' objective. One of the report's key findings was that knowledge on digital direct-to-consumer services is insufficient. A defining feature of some of these services is their unbundling of the pharmacy component, which logically increases the risk for prescription errors. This study analyzed the frequency of which the Cloud pharmacy network intercepted prescription errors in an unbundled digital sexual dysfunction service for men. Investigators found that Cloud pharmacists were responsible for intercepting 22 (5.31%) the 414 prescribing errors observed in the Pilot Australia service in 2023, including 12 (8.05%) of the 149 prescription errors for premature ejaculation (PE) patients and 10 (3.77%) of the 265 errors for erectile dysfunction (ED) patients. Seven of the errors intercepted by Cloud pharmacists were of high or medium severity, including four drug contraindications, two cases of inadequate patient history reviews, and one case of inadequate counselling. This study also appears to be the first to provide digital prescribing error rate data in an Australian sexual healthcare setting, observing an error rate of 0.86% from 30,649 ED prescriptions, 1.13% from the 13,154 PE prescriptions, and a total prescription error rate of 0.95% (414 out of 43,792 prescriptions). These findings demonstrate the vital role of pharmacists in intercepting prescribing errors in unbundled telehealth services. Possible implications of these findings include the allocation of additional resources across the pharmacy sector and the establishment of regulatory safety standards for unbundled telehealth services. [ABSTRACT FROM AUTHOR]

With vertical stacking, 3D NAND's flash memory can achieve continuous capacity growth. However, the endurance variation between the stacked layers becomes more and more significant due to process variation, which will lead to the underutilization of many pages and seriously affect the lifetime of 3D NAND's flash memory. We investigated the endurance variation characteristics between layers and divided the stacked layers into the top, middle, and bottom layers according to the endurance characteristics. We found that the endurance of the bottom layer pages is much weaker than that of the other two layers, which is the primary factor that affects the lifetime of 3D NAND's flash memory. In response to this endurance variation feature, we proposed a new layer-aware write strategy, called LA-Write. First of all, the write–skip unit in LA-Write will reduce the wear pressure of the pages through write–skip operations. Secondly, LA-Write maintains a layer-aware table, which stores the probability of pages in different layers performing the write–skip operation. Setting the probability of the bottom pages to the highest value will result in more write–skip operations on the bottom layers, mitigating endurance variations between layers. We carried out our experiments of LA-Write on DiskSim, a popular SSD simulator. Compared to existing schemes, experimental results show that LA-Write can greatly increase SSD's lifetime. [ABSTRACT FROM AUTHOR]

A well-known person fit statistic in the item response theory (IRT) literature is the l z statistic (Drasgow et al. in Br J Math Stat Psychol 38(1):67-86, 1985). Snijders (Psychometrika 66(3):331-342, 2001) derived l z ∗ , which is the asymptotically correct version of l z when the ability parameter is estimated. However, both statistics and other extensions later developed concern either only the unidimensional IRT models or multidimensional models that require a joint estimate of latent traits across all the dimensions. Considering a marginalized maximum likelihood ability estimator, this paper proposes l zt and l zt ∗ , which are extensions of l z and l z ∗ , respectively, for the Rasch testlet model. The computation of l zt ∗ relies on several extensions of the Lord-Wingersky algorithm (1984) that are additional contributions of this paper. Simulation results show that l zt ∗ has close-to-nominal Type I error rates and satisfactory power for detecting aberrant responses. For unidimensional models, l zt and l zt ∗ reduce to l z and l z ∗ , respectively, and therefore allows for the evaluation of person fit with a wider range of IRT models. A real data application is presented to show the utility of the proposed statistics for a test with an underlying structure that consists of both the traditional unidimensional component and the Rasch testlet component. [ABSTRACT FROM AUTHOR]

Sign language, particularly Indonesian sign language (Bisindo), is vital for deaf individuals, but learning it is challenging. This study aims to develop an automated Bisindo recognition system suitable for diverse backgrounds. Previous research focused on greenscreen backgrounds and struggled with natural or complex backgrounds. To address this problem, the study proposes using Faster region-based convolutional neural networks (RCNN) and YOLOv5 for hand and face detection, MobileNetV2 for feature extraction, and long short-term memory (LSTM) for classification. The system is also designed to focus on computational efficiency. YOLOv5 model achieves the best result with a sentence accuracy (SAcc) of 49.29% and a word error rate (WER) of 16.42%, with a computational time of 0.0188 seconds, surpassing the baseline model. Additionally, the system achieved a SacreBLEU score of 67.77%, demonstrating its effectiveness in Bisindo recognition across various backgrounds. This research improves accessibility for deaf individuals by advancing automated sign language recognition technology. [ABSTRACT FROM AUTHOR]

Charge diffusion from an ion track and its collection by a biased contact in a semiconductor domain is modeled and analyzed within the framework of the so-called diffusion–collection approach. We successively examine the case of charge diffusion from a point source and from a linear distribution, introducing and discussing the concept of collection velocity at the point where the collection current is evaluated. Analytical formulations of the collected charge, collection current, and collection velocity are developed. Implications for the calculation of the soft error rate in complementary metal-oxide-semiconductor circuits exposed to ionizing particles are derived. Finally, our model provides new insights into the correct definition of the charge collection velocity in collection–diffusion models. [ABSTRACT FROM AUTHOR]

Quantiles are used as a measure of risk in many stochastic systems. We study the estimation of quantiles with the Hilbert space-filling curve (HSFC) sampling scheme that transforms specifically chosen one-dimensional points into high dimensional stratified samples while still remaining the extensibility. We study the convergence and asymptotic normality for the estimate based on HSFC. By a generalized Dvoretzky–Kiefer–Wolfowitz inequality for independent but not identically distributed samples, we establish the strong consistency for such an estimator. We find that under certain conditions, the distribution of the quantile estimator based on HSFC is asymptotically normal. The asymptotic variance is of O(n^{-1-1/d}) when using n HSFC-based quadrature points in dimension d, which is more efficient than the Monte Carlo sampling and the Latin hypercube sampling. Since the asymptotic variance does not admit an explicit form, we establish an asymptotically valid confidence interval by the batching method. We also prove a Bahadur representation for the quantile estimator based on HSFC. Numerical experiments show that the quantile estimator is asymptotically normal with a comparable mean squared error rate of randomized quasi-Monte Carlo (RQMC) sampling. Moreover, the coverage of the confidence intervals constructed with HSFC is better than that with RQMC. [ABSTRACT FROM AUTHOR]

Background and Study Purpose. The attack is a crucial technical element in scoring points in volleyball. This research aims to identify differences in attack efficiency and errors based on the value level of teams in the competitive system, considering different playing positions. Material and Methods. The analysis included 86 attacking players from the rosters of 12 teams in the men’s domestic first league. The distribution by positions was as follows: 37 outside hitters, 19 opposites, and 30 middle blockers. Statisticians from each team recorded the data using Data Volley software during three championship matches. For each player, the following parameters were interpreted: Attack Efficiency (E%), Error% (=), Blocked Attack% (/), Poor% (-), Blocked but Recovered% (!), Positive% (+), and Winning% (#). Attack efficiency was analyzed for each playing position across top teams (positions 1-4 in the final ranking), mid-level teams (positions 5-8), and lower-level teams (positions 9-12). Results. Across all three playing positions, top teams demonstrate higher percentages in attack efficiency, winning executions, and positive outcomes compared to average and lower-level teams. Top and average teams also have lower percentages of errors in attack executions. For the outside hitter position, no statistically significant superiority is observed between top and middle teams for any attack variable, indicating a balance in player performance. In the opposite hitter position, top and middle-level players show higher attack efficiency and direct point scores (Winning). They also have lower poor execution rates, despite the lack of statistical confirmation for superiority in many attack variables. Among all positions, top middle blockers exhibit a clear superiority over low-level teams across all attack variables. Conclusions. The findings of the study highlight the critical role of team value level in influencing attack efficiency and execution quality across different playing positions in volleyball. The results underscore the importance of strategic differentiation in training and game planning. This is especially crucial for teams aiming to optimize their performance. Coaches and trainers should focus on tailored approaches that address the specific strengths and weaknesses of each playing position. They should also consider the overall team value level in their strategies. This approach could lead to improved competitive outcomes and a more effective utilization of player potential across all levels of competition.

Abstract Background Recent technological advances have resulted in low-cost GPS loggers that are small enough to be used on a range of seabirds, producing accurate location estimates (± 5 m) at sampling intervals as low as 1 s. However, tradeoffs between battery life and sampling frequency result in studies using GPS loggers on flying seabirds yielding locational data at a wide range of sampling intervals. Metrics derived from these data are known to be scale-sensitive, but quantification of these errors is rarely available. Very frequent sampling, coupled with limited movement, can result in measurement error, overestimating movement, but a much more pervasive problem results from sampling at long intervals, which grossly underestimates path lengths. Methods We use fine-scale (1 Hz) GPS data from a range of albatrosses and petrels to study the effect of sampling interval on metrics derived from the data. The GPS paths were sub-sampled at increasing intervals to show the effect on path length (i.e. ground speed), turning angles, total distance travelled, as well as inferred behavioural states. Results We show that distances (and per implication ground speeds) are overestimated (4% on average, but up to 20%) at the shortest sampling intervals (1–5 s) and underestimated at longer intervals. The latter bias is greater for more sinuous flights (underestimated by on average 40% when sampling > 1-min intervals) as opposed to straight flight (11%). Although sample sizes were modest, the effect of the bias seemingly varied with species, where species with more sinuous flight modes had larger bias. Sampling intervals also played a large role when inferring behavioural states from path length and turning angles. Conclusions Location estimates from low-cost GPS loggers are appropriate to study the large-scale movements of seabirds when using coarse sampling intervals, but actual flight distances are underestimated. When inferring behavioural states from path lengths and turning angles, moderate sampling intervals (10–30 min) may provide more stable models, but the accuracy of the inferred behavioural states will depend on the time period associated with specific behaviours. Sampling rates have to be considered when comparing behaviours derived using varying sampling intervals and the use of bias-informed analyses are encouraged.

Quantum error correcting codes can enable large quantum computations provided physical error rates are sufficiently low. We combine post-selection with surface code error correction through the use of exclusive decoders, which abort on decoding instances that are deemed too difficult. For the most discriminating of exclusive decoders, we demonstrate a threshold of 50% under depolarizing noise (or 32(1)% for the fault-tolerant case), and up to a quadratic improvement in logical failure rates below threshold. Furthermore, with a modest exclusion criterion, we identify a regime at low error rates where the exclusion rate decays with code distance, providing a pathway for scalable and time-efficient quantum computing with post-selection. Our exclusive decoder applied to magic state distillation yields a 75% reduction in the number of physical qubits, and a 60% reduction in the total spacetime volume, including accounting for repetitions. Other applications include error mitigation, and in concatenated schemes. Quantum error correction produces an enormous amount of data about the quantum system, including information about whether an uncorrectable error is likely. In this work the authors analyse a new decoder that can abort when decoding is deemed too difficult, yielding improved performance overall. [ABSTRACT FROM AUTHOR]

Mixture of regression model is widely used to cluster subjects from a suspected heterogeneous population due to differential relationships between response and covariates over unobserved subpopulations. In such applications, statistical evidence pertaining to the significance of a hypothesis is important yet missing to substantiate the findings. In this case, one may wish to test hypotheses regarding the effect of a covariate such as its overall significance. If confirmed, a further test of whether its effects are different in different subpopulations might be performed. This paper is motivated by the analysis of Chiroptera dataset, in which, we are interested in knowing how forearm length development of bat species is influenced by precipitation within their habitats and living regions using finite Gaussian mixture regression (GMR) model. Since precipitation may have different effects on the evolutionary development of the forearm across the underlying subpopulations among bat species worldwide, we propose several testing procedures for hypotheses regarding the effect of precipitation on forearm length under finite GMR models. In addition to the real analysis of Chiroptera data, through simulation studies, we examine the performances of these testing procedures on their type I error rate, power, and consequently, the accuracy of clustering analysis. [ABSTRACT FROM AUTHOR]

Motivated by a small sample example in neonatal onset multisystem inflammatory disease (NOMID), we propose a method that can be used when the interest is testing for an association between a changes in disease progression with start of treatment compared to historical disease progression prior to treatment. Our method estimates the longitudinal trajectory of the outcome variable and adds an interaction term between an intervention indicator variable and the time since initiation of the intervention. This method is appropriate for a situation in which the intervention slows or arrests the effect of the disease on the outcome, as is the case in our motivating example. By simulation in small samples and restricted sets of treatment initiation times, we show that the generalized estimating equations (GEE) formulation with small sample adjustments can bound the Type I error rate better than GEE and linear mixed models without small sample adjustments. Permutation tests (permuting the time of treatment initiation) is another valid approach that can also be useful. We illustrate the methodology through an application to a prospective cohort of NOMID patients enrolled at the NIH clinical center. [ABSTRACT FROM AUTHOR]

Autonomous intelligent systems have been widely implemented in a broad range of applications. These applications often involve data-dense tasks where an efficient feature selection process is required to eliminate redundant features and improve model performance. However, the feature selection tasks with high dimensionality still remain challenging to deal with. In order to address high-dimensional feature selection problems with greater effectiveness and efficiency, this paper proposes a particle swarm optimizer variant named PSO-BEE that allows the swarm to take full advantage of exploration and exploitation at due evolutionary stages. At the early stage, a large size of candidate exemplar group is formed for diversity enhancement, attempting to find as many new combinations of features as possible. At the later stage, a tiny size of candidate exemplar group is constructed, allowing updated particles to learn from the few best elite exemplars to continuously refine feature selection solutions with a lower classification error rate. Three PSO-BEE variants with distinct preferences for exploration and exploitation are proposed based on different parameter adjustment strategies. Experiments are executed on 500, 1000, and 2000-dimensional benchmark suites presented by CEC and real-world feature selection datasets from the Machine Learning Repository. Experimental results demonstrate the competitive performance of PSO-BEE in high-dimensional global optimization and feature selection when compared with several state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Sea turtles exhibit high migratory rates and occupy a broad range of habitats, which in turn makes monitoring these taxa challenging. Applying deep learning (DL) models to vast image datasets collected from citizen science programs can offer promising solutions to overcome the challenge of monitoring the wide habitats of wildlife, particularly sea turtles. Among DL models, object detection models, such as the You Only Look Once (YOLO) series, have been extensively employed for wildlife classification. Despite their successful application in this domain, detecting objects in images with complex backgrounds, including underwater environments, remains a significant challenge. Recently, instance segmentation models have been developed to address this issue by providing more accurate classification of complex images compared to traditional object detection models. This study compared the performance of two state-of-the-art DL methods namely; the object detection model (YOLOv5) and instance segmentation model (YOLOv5-seg), to detect and classify sea turtles. The images were collected from iNaturalist and Google and then divided into 64% for training, 16% for validation, and 20% for test sets. Model performance during and after finishing training was evaluated by loss functions and various indexes, respectively. Based on loss functions, YOLOv5-seg demonstrated a lower error rate in detecting rather than classifying sea turtles than the YOLOv5. According to mean Average Precision (mAP) values, which reflect precision and recall, the YOLOv5-seg model showed superior performance than YOLOv5. The mAP0.5 and mAP0.5:0.95 for the YOLOv5 model were 0.885 and 0.795, respectively, whereas for the YOLOv5-seg, these values were 0.918 and 0.831, respectively. In particular, based on the loss functions and classification results, the YOLOv5-seg showed improved performance for detecting rather than classifying sea turtles compared to the YOLOv5. The results of this study may help improve sea turtle monitoring in the future. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we propose two downlink multiple access architectures for networks where human‐type communication users (HTCUs) and machine‐type communication devices (MTCDs) coexist. The proposed schemes combine non‐orthogonal multiple access (NOMA), orthogonal frequency division multiplexing (OFDM), and index modulation (OFDM‐IM) concepts. In the first scheme, the base station (BS) transmits bits of HTCUs using modulated symbols and bits of MTCDs by the subcarrier activation pattern (SAP). This approach called IM‐NOMA with null subcarriers (IM‐NOMA‐NS) ensures that inactive subcarriers are always null, which improves the system bit error rate (BER) performance. To improve the spectral efficiency (SE), we propose a second approach, termed IM‐NOMA with dual‐mode modulation (IM‐NOMA‐DM), in which the HTCUs' bits are transmitted using two‐dimensional modulation and the MTCDs' bits are transmitted using one‐dimensional modulation and the SAP. For each proposed system, a near‐optimal low‐complexity detector, based on the energy‐detection (ED) and the log‐likelihood ratio (LLR) criterion, is provided to mitigate the detection burden of the optimal maximum‐likelihood (ML) detector. The BER performances and SE of the proposed schemes are investigated. The average BERs of IM‐NOMA‐NS and IM‐NOMA‐DM are derived in closed‐form expressions corroborated by the simulation results. We have proved numerically that the proposed schemes achieve a good trade‐off between BER performance, SE, and the number of supported users, making them more suitable for Internet of Things (IoT) applications. [ABSTRACT FROM AUTHOR]

Huber regression plays a critical role in robust learning owing to its robustness to outliers and heavy-tailed noises. In this paper, we investigate the theoretical foundation of an Ivanov regularized Huber regression within a Reproducing Kernel Hilbert Space (RKHS) framework. By imposing a weak moment condition on the conditional distribution, we show that Huber regression estimator with the scale parameter adapted to sample size can learn the underlying regression function very well. Specifically, we establish the explicit convergence rates of the prediction error even when the regression function is outside the associated RKHS. Our statistical analysis could greatly improve the understanding of the learning behavior of Huber regression in the absence of light-tailed assumption on the conditional distribution. [ABSTRACT FROM AUTHOR]

The two‐trials rule for drug approval requires "at least two adequate and well‐controlled studies, each convincing on its own, to establish effectiveness." This is usually implemented by requiring two significant pivotal trials and is the standard regulatory requirement to provide evidence for a new drug's efficacy. However, there is need to develop suitable alternatives to this rule for a number of reasons, including the possible availability of data from more than two trials. I consider the case of up to three studies and stress the importance to control the partial Type‐I error rate, where only some studies have a true null effect, while maintaining the overall Type‐I error rate of the two‐trials rule, where all studies have a null effect. Some less‐known P$$ P $$‐value combination methods are useful to achieve this: Pearson's method, Edgington's method and the recently proposed harmonic mean χ2$$ {\chi}^2 $$‐test. I study their properties and discuss how they can be extended to a sequential assessment of success while still ensuring overall Type‐I error control. I compare the different methods in terms of partial Type‐I error rate, project power and the expected number of studies required. Edgington's method is eventually recommended as it is easy to implement and communicate, has only moderate partial Type‐I error rate inflation but substantially increased project power. [ABSTRACT FROM AUTHOR]

Eye movements (EM) during naming alphabetic versus logographic stimuli in children with and without developmental dyslexia (DD) were examined for each stimulus separately to identify conspicuous characteristics that influence naming performance. 40 children (group DD = 18; control group C = 22) were taught Chinese characters. EM were recorded during naming alphabetic words, pictures and Chinese characters. Main variables were articulation latencies, numbers and durations of fixations, secondary variables were fixation locations and error rates. Group DD showed significantly longer latencies and more fixations while reading words, but only insignificantly more fixations while naming pictures and Chinese characters. However, their error rate was significantly higher during naming Chinese characters but correlated neither with severity of phonological deficit nor with visual complexity. Their first fixation was significantly more often on the center of characters, in group C on the left. In both groups, EM variables were influenced by conspicuous features of characters, such as visual complexity, composition and structure. EM variables and scanning behavior while naming Chinese characters indicate holistic processing in the visuo-spatial pathway and were affected by conspicuous features of characters. The higher error rate in group DD could be determined by several factors, without a major role of the phonological deficit. [ABSTRACT FROM AUTHOR]

Semi-supervised learning techniques utilize both labeled and unlabeled images to enhance classification performance in scenarios where labeled images are limited. However, challenges such as integrating unlabeled images with incorrect pseudo-labels, determining appropriate thresholds for the pseudo-labels, and label prediction fluctuations on low-confidence unlabeled images, hinder the effectiveness of existing methods. This research introduces a novel framework named Interpolation Consistency for Bad Generative Adversarial Networks (IC-BGAN) that utilizes a new loss function. The proposed model combines bad adversarial training, fusion techniques, and regularization to address the limitations of semi-supervised learning. IC-BGAN creates three types of image augmentations and label consistency regularization in interpolation of bad fake images, real and bad fake images, and unlabeled images. It demonstrates linear interpolation behavior, reducing fluctuations in predictions, improving stability, and facilitating the identification of decision boundaries in low-density areas. The regularization techniques boost the discriminative capability of the classifier and discriminator, and send a better signal to the bad generator. This improves the generalization and the generation of diverse inter-class fake images as support vectors with information near the true decision boundary, which helps to correct the pseudo-labeling of unlabeled images. The proposed approach achieves notable improvements in error rate from 2.87 to 1.47 on the Modified National Institute of Standards and Technology (MNIST) dataset, 3.59 to 3.13 on the Street View House Numbers (SVHN) dataset, and 12.13 to 9.59 on the Canadian Institute for Advanced Research, 10 classes (CIFAR-10) dataset using 1000 labeled training images. Additionally, it reduces the error rate from 22.11 to 18.40 on the CINIC-10 dataset when using 700 labeled images per class. The experiments demonstrate the IC-BGAN framework outperforms existing semi-supervised methods, providing a more accurate classification solution with smoother class label estimates, especially for low-confidence unlabeled images. [ABSTRACT FROM AUTHOR]

Most people are tending to download or purchase apps, as a result of globally spreading technologies and smartphone usability. The Google Play Store app market is one of the most famous and rapidly increasing app markets and it captured the users' thoughts, feelings, and, opinions about the appropriate apps they used. It is helpful for new users, app developers, and app creators to gain insights into the existing audience's opinion about relevant apps. Therefore, this study mainly aims to perform sentiment analysis on Google Play Store app users' reviews based on the 15 latest apps. We collected 33,000 user reviews and implemented a machine-learning algorithms after initially pre-processing the data and extracting features through Term Frequency—Inverse Document Frequency (TF-IDF) vectorizer tool. The Artificial Neutral Network (ANN), Long Short-Term Memory (LSTM), and Support Vector Machine (SVM) algorithms were used for the comparison of results. By applying these algorithms, users' reviews are mainly categorized into neutral, positive, and negative in each app separately. The overall results show that LSTM outperformed both ANN, and SVM and had a greater accuracy, recall, f-measure, and lowest error rates across all apps for providing valuable insights for sentiment classification. According to the outcomes, LSTM produces the best sentiment analysis outcomes for keeping track of users' app reviews. Affirming 80–90% accuracy in each Apps, the outcomes reinforce the all model's validity in understanding user attitudes, consolidating the method's effectiveness. The results useful for new app users and app developers to taking their decisions. [ABSTRACT FROM AUTHOR]

Objectives Methods Results Conclusion Hair density estimation is crucial in dermatology and trichology; however, manual counting is time‐consuming and error‐prone. Although automated approaches have been developed using image processing, neural networks, and deep learning, creating a robust and widely applicable method remains challenging. This study explored the use of XGBoost to estimate hair density with the aim of developing a more accurate and versatile approach.The study utilized 895 scalp images to extract features and developed an XGBoost model for hair density estimation using 745 images to train the model and testing its performance on 150 images to evaluate the accuracy, error rate, and scatter plot.The XGBoost model outperformed previous methods, achieving 89.5% accuracy on the training set and 95.3% accuracy on the test set. This surpassed the results of Kim et al. (52.4%), Urban et al. (79.6%), and Sacha et al. (88.2%) for the test set.The XGBoost algorithm proved to be effective for automated hair density estimation, achieving an accuracy of 95.3% on the test set. This approach, which focusses on scalp coverage and erosion features, can streamline and improve the objectivity of clinical hair analysis. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, a passive reflective intelligent surface (RIS) assisted bidirectional full‐duplex (FD) wireless system in a realistic Nakagami‐m$$ m $$ fading with N$$ N $$ reflective elements and two source nodes is proposed. The source nodes are equipped with dual antennas for FD mode transmission and reception. The primary cause of degradation in the performance of the FD system is self‐interference, which can be reduced by increasing the number of reflective elements on the meta‐surface. Analytical expressions have been derived for the proposed RIS‐aided bidirectional FD wireless systems in terms of outage probability and bit error rate (BER). Numerical results show that doubling the number of reflecting elements considerably improves the outage performance of the proposed system in terms of signal‐to‐noise ratio (SNR) compared with RIS‐assisted half‐duplex (HD) mode. Additionally, different modulation schemes have been employed to evaluate the BER performance of the proposed system. The accuracy of analytical expressions is validated by matching Monte Carlo with analytical simulations. [ABSTRACT FROM AUTHOR]

Inconsistent results are observed in the effects of transcranial direct current stimulation (tDCS) with different montages on motor learning. This study aimed to compare the effects of anodal and cathodal tDCS (c‐tDCS) over primary motor cortex (M1) at different intensities on motor learning in healthy young adults. The participants were randomly divided into: (1) 1 mA M1 c‐tDCS, (2) 1 mA M1 anodal tDCS (a‐tDCS), (3) 2 mA M1 c‐tDCS, (4) 2 mA M1 a‐tDCS and (5) M1 sham tDCS groups. The groups received 20‐min stimulation with serial reaction time task (SRTT) incidentally, while the tDCS was turned off after 30 s in the sham tDCS group. Response time (RT) and error rate (ER) during SRTT were assessed prior, during and 72 h after the intervention. The results of the paired t‐test indicated that online learning occurred in all groups (p < 0.05), except in M1 c‐tDCS (1 mA) (p > 0.05). One‐way ANOVA analysis also indicated that there were differences in offline learning (RT (F(DF) = 5.19(4); p < 0.001; and ER (F(DF) = 9(4), p < 0.0001) among groups, with more offline learning in 1 mA M1 a‐tDCS, 2 mA M1 c‐tDCS and 2 mA M1 a‐tDCS groups (p < 0.05). On the other hand, the 1 mA M1 c‐tDCS group did not indicate any consolidation effect or even a trend toward negative offline learning. M1 a‐tDCS with different intensities and also 2 mA M1 c‐tDCS may be helpful for the enhancement of motor learning in young healthy adults. This study enhances our understanding of tDCS intensity and polarity effects on motor learning, with potential for optimizing therapeutic protocols. [ABSTRACT FROM AUTHOR]

Ceramic matrix composites (CMCs) have been widely used in aerospace thermal-structures due to their excellent high-temperature performance. It is essential to understand the damage evolution of CMCs. However, in previous research work, the effect of thermal stress induced damage during CMCs fabrication on tensile response was often ignored. A damage evolution model that considers axial and radial thermal stresses to predict tensile response of unidirectional ceramic matrix composites was proposed in this study. The average relative errors between the current prediction and experimental data in the literature were calculated as 1.64 % and 1.91 %, validating this approach. Meantime, the damage evolution laws of interface debonding and the critical stress of matrix cracking in the Budiansky–Hutchinson–Evans (BHE) model were corrected to satisfy the discontinuous interfacial shear stress. According to the experimental data, the current model predicted critical stresses of matrix cracking better than the BHE model, with error rate reductions of 6.52 % and 15.38 %. [ABSTRACT FROM AUTHOR]

Intelligent algorithms have shown promise in supporting marketing strategy decisions through data mining. However, existing methods have primarily relied on expertise, lacking autonomous decision-making abilities. Consequently, a marketing strategy decision model based on particle swarm optimization and multi-objective programming is proposed. This study first explores the potential for integrating particle optimization and multi-objective programming models partially, and then assesses the overall effectiveness of each marketing strategy by defining a fitness function. Subsequently, the particle swarm optimization algorithm is employed to search for and optimize decision variables to identify the optimal combination of marketing strategies. Finally, several simulation experiments are conducted using external real data. The research findings indicate that the algorithm's error rate in this study was initially 0.23. However, after 500 training sessions, it decreased to 0.08 and maintained a relatively low level. The proportion of marketing strategy revenue increased by 15.2 percentage points between 0 and 100 training sessions, then remained relatively stable at over 30%. Its revenue proportion continued to rise during the training process, significantly surpassing that of other algorithms. [ABSTRACT FROM AUTHOR]

Due to their customizability, touchscreens continue to advance as a device of choice when designing aircraft cockpits. Previous studies investigated the effect of turbulence on human performance when using touchscreens, but few have evaluated its performance for realistic aviation-specific tasks. In this study, we compared four touchscreen widgets and three mechanical devices during realistic data input on a primary flight display (PFD). Twenty participants took part in the experiment at a constant level of vibration, while simultaneously completing a secondary tracking task. Results indicated that virtual keypads lead to faster completion time for medium to large changes while keeping error rates low. Rotary knobs were fastest for small changes. Virtual keypads also had lower workload and discomfort compared to rotary knobs and drag-based widgets. We found the completion time to be the most important factor in tracking task performance, which translated in higher precision for keypads. These findings suggest that virtual keypads represent an efficient and secure option for numerical data input at low-to-medium vibration. [ABSTRACT FROM AUTHOR]

Sweat is a biomarker-rich fluid with potential for continuous patient monitoring via wearable devices. However, biomarker concentrations vary with the sweat rate per gland, posing a challenge for sweat sensing. To address this, we propose an algorithm to compute both the number of active sweat glands and their individual sweat rates. We developed models of sweat glands and a discrete sweat-sensing device to sense sweat volume. Our algorithm estimates the number of active glands by decomposing the signal into patterns generated by the individual sweat glands, allowing for the calculation of individual sweat rates. We assessed the algorithm's accuracy using synthetic datasets for varying physiological parameters (sweat rate and number of active sweat glands) and device layouts. The results show that device layout significantly affects accuracy, with error rates below 0.2% for low and medium sweat rates (below 0.2 nL min−1 per gland). However, the method is not suitable for high sweat rates. The suitable sweat rate range can be adapted to different needs through the choice of device. Based on our findings, we provide recommendations for optimal device layouts to improve accuracy in estimating active sweat glands. This is the first study to focus on estimating the sweat rate per gland, which essential for accurate biomarker concentration estimation and advancing sweat sensing towards clinical applications. [ABSTRACT FROM AUTHOR]

In this paper, a differential chaotic shift keying communication system based on noise reduction with orthogonal double bit rate (NR-ODBR-DCSK) is proposed. The system incorporates Walsh orthogonalization at the transmitter side to orthogonalize the information signals so that two mutually orthogonal signals can be superimposed. At the receiving end, because the principle of orthogonal signals is used, it achieves the characteristic of double information transmission rate for information signal transmission while avoiding the problem of chaotic synchronization. In addition, the system employs a noise reduction transmission mechanism, which reduces the noise variance in the received signal, further reducing the BER of the system and thus improving the performance of the communication system. By analyzing the signal format of the system, the transmitter and receiver structures of the communication system are designed. Subsequently, theoretical analyses and simulations in an additive white Gaussian noise (AWGN) channel demonstrate the good performance of the system, including a low bit error rate (BER) and a good data-energy to bit-energy ratio (DBR). Finally, a simulation test of the NR-ODBR-DCSK system for a semi-physical communication system was carried out using two USRP devices to verify the experimental feasibility of the system. The simulation analysis results show that comparative analyses with conventional DCSK and SR-DCSK systems highlight the superior performance of the NR-ODBR-DCSK system. [ABSTRACT FROM AUTHOR]

Without a GNSS (global navigation satellite system) signal, the integrated navigation system in vehicles with a fixed trajectory (e.g., railcars) is limited to the use of micro-electromechanical system-inertial navigation system (MEMS-INS) and odometer (ODO). Due to the significant measurement error of the MEMS inertial device and the inability of ODO to output attitude, the positioning error is generally large. To address this problem, this paper presents a new integrated navigation algorithm based on a dynamically constrained Kalman model. By analyzing the dynamics of a railcar, several new observations have been investigated, including errors of up and lateral velocity, centripetal acceleration, centripetal D-value (difference value), and an up-gyro bias. The state transition matrix and observation matrix for the error state model are represented. To improve navigation accuracy, virtual noise technology is applied to correct errors of up and lateral velocity. The vehicle-running experiment conducted within 240 s demonstrates that the positioning error rate of the dead-reckoning method based on MEMS-INS is 83.5%, whereas the proposed method exhibits a rate of 4.9%. Therefore, the accuracy of positioning can be significantly enhanced. [ABSTRACT FROM AUTHOR]

Recently, a new set of biometric traits, called medical biometrics, have been explored for human identity verification. This study introduces a novel framework for recognizing human identity through heart sound signals, commonly referred to as phonocardiograms (PCGs). The framework is built on extracting and suitably processing Mel-Frequency Cepstral Coefficients (MFCCs) from PCGs and on a classifier based on a Multilayer Perceptron (MLP) network. A large dataset containing heart sounds acquired from 206 people has been used to perform the experiments. The classifier was tuned to obtain the same false positive and false negative misclassification rates (equal error rate: EER = FPR = FNR) on chunks of audio lasting 2 s. This target has been reached, splitting the dataset into 70% and 30% training and testing non-overlapped subsets, respectively. A recurrence filter has been applied to also improve the performance of the system in the presence of noisy recordings. After the application of the filter on chunks of audio signal lasting from 2 to 22 s, the performance of the system has been evaluated in terms of recall, specificity, precision, negative predictive value, accuracy, and F1-score. All the performance metrics are higher than 97.86% with the recurrence filter applied on a window lasting 22 s and in different noise conditions. [ABSTRACT FROM AUTHOR]

Inter-Satellite Optical Wireless Communication (Is-OWC) is a pivotal technology for advancing global connectivity and the effectiveness of space-based operations. It serves as the linchpin for various applications such as global internet coverage, Earth observation, and remote sensing, bolstering our capacity to monitor the planet and deliver essential services. This paper presents the design and performance evaluation of a Polarization Division Multiplexing (PDM) and Multiple Input Multiple Output (MIMO)-based Is-OWC system operating at a data rate of 60 Gbps. The system was tested under varying transmission distances, atmospheric turbulence, and pointing error conditions. At a transmission distance of 10,000 km, the system achieved a Bit Error Rate (BER) of 6.76 × 10⁻3 for Channel 1 (X polarization) and 7.1 × 10⁻3 for Channel 4 (Y polarization), both within Forward Error Correction (FEC) limits. The introduction of a 4 × 4 MIMO configuration extended the transmission range to 14,000 km, with a corresponding BER of 9.1 × 10⁻3 for Channel 1 and 8.7 × 10⁻⁵ for Channel 4. Eye diagrams confirm successful signal reception, demonstrating that the system can maintain high data rates and low error rates over long distances. The proposed PDM-MIMO system showcases high-capacity, robust performance under challenging conditions, such as turbulence and pointing errors, validating its suitability for space-based communication applications. These findings highlight the potential of the system for future deployments in satellite networks, offering reliable, high-throughput, and low-latency data transmission over extended distances. [ABSTRACT FROM AUTHOR]

Decision‐making of project‐level road maintenance is the process of mapping road information into a maintenance plan. Even though benefitting from deep learning, the decision‐making still faces the problem of maintenance data uncertainty. The data uncertainty derives from imperfect road information collection and arbitrary selection of maintenance plans. Such uncertainty always leads to unreasonable maintenance decision‐making. This study proposes an evidential approach using information entropy (IE) and Dempster–Shafer theory (DST) to capture and handle uncertainty in the decision‐making of project‐level road maintenance. The approach first uses an IE‐based judgment method (IE‐based method) to capture and observe quantitative data uncertainty. The DST‐based method is then developed to handle maintenance data uncertainty through utilizing evidential neural network and set‐valued decision‐making. A numerical experiment is performed on the maintenance data with 280 km of semi‐rigid base highways in China. The results indicate that the IE‐based method can measure the data uncertainty in the information of road sections. The DST‐based method captures the cautious intuition on the selection of maintenance plans, thereby reducing the decision error rate by over 14% under specific conditions when facing data uncertainty. [ABSTRACT FROM AUTHOR]

Many individually randomized group treatment (IRGT) trials randomly assign individuals to study arms but deliver treatments via shared agents, such as therapists, surgeons, or trainers. Post‐randomization interactions induce correlations in outcome measures between participants sharing the same agent. Agents can be nested in or crossed with trial arm, and participants may interact with a single agent or with multiple agents. These complications have led to ambiguity in choice of models but there have been no systematic efforts to identify appropriate analytic models for these study designs. To address this gap, we undertook a simulation study to examine the performance of candidate analytic models in the presence of complex clustering arising from multiple membership, single membership, and single agent settings, in both nested and crossed designs and for a continuous outcome. With nested designs, substantial type I error rate inflation was observed when analytic models did not account for multiple membership and when analytic model weights characterizing the association with multiple agents did not match the data generating mechanism. Conversely, analytic models for crossed designs generally maintained nominal type I error rates unless there was notable imbalance in the number of participants that interact with each agent. [ABSTRACT FROM AUTHOR]

One‐dimensional thrust vectoring turbojet vertical takeoff and landing (VTOL) aircraft have attracted research attention due to their high thrust‐to‐weight ratios and their solution for the slow speed response of turbojet engines. However, there are some uncertainties and physical limitations in their systems, such as external disturbances, unknown parameters and input saturation. To improve the accuracy and convergence speed of trajectory tracking, a fixed‐time control method that is robust to saturation is proposed. The system dynamics are established, and an auxiliary system is built within a fixed time control framework to address the influence of input saturation and increase the error convergence rate. Nonsingular fast terminal sliding mode technology is combined with a few adaptive laws to ensure the robustness of the closed‐loop system against dynamic uncertainties and improve the precision of steady‐state control. The stability of the control system is proven based on Lyapunov, and the controller parameters are optimized based on particle swarm optimization (PSO). The proposed method based on fixed‐time stability guarantees that the states of the closed‐loop system can reach the residual set around zero within the designed time, and an expression for the upper bound on the convergence time is given. Finally, numerical simulations demonstrate the superiority of the proposed method based on the error integral criterion. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel keyframe extraction extraction method based on the gray wolf optimization (GWO) algorithm, addressing the challenge of information loss in traditional methods due to redundant and similar frames. The proposed method GWOKConvLSTM prioritizes speed, accuracy, and compression efficiency while preserving semantic information. Inspired by wolf behavior, we construct a fitness function that minimizes reconstruction error and achieves optimal compression ratios below 8%. Compared to traditional methods, our GWO method achieves the lowest reconstruction error for a given compression rate, providing a concise and visually coherent summary of keyframes while maintaining consistency across similar motions. Additionally, we propose a template-based method for video classification tasks, achieving the highest accuracy when combined with pre-trained CNNs and ConvLSTM. Our method effectively prevents dynamic background noise from affecting keyframe selection, leading to significantly improve video classification performance using deep neural networks. [ABSTRACT FROM AUTHOR]

Summary: Underwater wireless sensor networks (UWSNs) based on autonomous underwater vehicles (AUVs) have become the standard technology for underwater search tasks with the advent of new underwater information and communication technologies (ICTs). It is also vulnerable to threats and malicious attacks because of its inherent characteristics, open acoustic channel and hostile underwater environment. The purpose of this research is to develop a secure data transfer (SDF) system based on software‐defined networking (SDN) architecture with cooperative searching scheme (CSS) (SDN‐SDF‐CSS) for AUV‐based UWSN. This part employs data, local control and the primary control layer to provide a scalable SDN‐based architecture for the AUV‐based UWSN. To accomplish the underwater search operation, the data layer is primarily deployed to gather AUVs (G‐AUVs) and store AUVs (S‐AUVs). In order to schedule the AUV, the clustering process takes place based on priority ranking with respect to the average power of each cluster. Then, the CSS is developed, and it is performed in the data layer. The hierarchical localization framework (HLF) can be used to locate each AUV's location within the data layer, which is a necessary step in deploying the cooperative searching model. Finally, for an efficient data transfer, the communication model is deployed in the data layer. UWSNs are vulnerable to various malicious attacks (such as bad‐mouthing attacks, on–off attacks, blackhole attacks and wormhole attacks) because of the high bit error rate and unstable optical/acoustic channels in the underwater environment. To overcome this, an SDF technique is used, which involves isolating the attacker node from the data layer. The suggested SDN‐SDF‐CSS model is implemented on the MATLAB platform, and its performance is evaluated using various evaluation metrics, both with and without attacks. As a result, the proposed SDN‐SDF‐CSS model has achieved better performance and proved its superiority in the UWSN environment. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions Eye movement tasks, especially anti‐saccade tasks, have been used to assess cognitive function in patients with neuropsychiatric disorders. Although it has been shown that individuals with cognitive impairment perform worse on anti‐saccades tasks, there is a lack of systematic evaluation of the sensitivity of parameters of anti‐saccades to assess different subtypes of cognitive impairment.A total of 158 participants were enrolled in this study, consisting of 66 men and 92 women, with an average age of 50.2 ± 10 years. The comparison of pro‐saccade reaction time, anti‐saccade reaction time, and error rates in the saccade task between individuals with cognitive impairments and a normal group was conducted. Furthermore, we systematically analyzed the correlations between the performance in neurological function tests (Mini‐Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Stroop) and these anti‐saccade parameters. Especially, the correlation between these parameters and cognitive function in different domains of the MoCA task were also evaluated.The pro‐saccade reaction time, anti‐saccade reaction time and error rate were negatively correlated with the MMSE and MoCA scores (P < 0.001), and positively correlated with the time used in Stroop tasks. Among them, the error rate had the strongest correlation with the performance of MMSE, MoCA and Stroop tasks (MoCA: P < 0.0001, r2 = −0.608; MMSE: P < 0.0001, r2 = −0.344; Stroop: P < 0.0001, r2 = 0.455). Among the seven cognitive domains examined by the MoCA task, error rates had relatively high correlations with visuospatial/executive (P < 0.0001, r2 = −0.4660) and delayed recall (P < 0.0001, r2 = −0.4228) compared to naming, language (P = 0.0004, r2 = −0.0788), attention (P = 0.0004, r2 = −0.0780), abstraction (P < 0.0001, r2 = −0.1515), orientation (P < 0.0001, r2 = −0.1075). Moreover, pro‐saccade reaction time, anti‐saccade reaction time and error rate of people with high MoCA scores were significantly higher than those of people with low MoCA scores, which can be used to identify people with mild cognitive impairment.Our study's results provide valuable clinical evidence supporting the effectiveness of anti‐saccades in assessing cognitive impairment, which is beneficial for screening and timely clinical intervention in individuals with specific cognitive impairment. [ABSTRACT FROM AUTHOR]