Miracle-dz/UniFAR-FTUs huggingface dataset:
Taking Locomotion Mode as Prior: One Algorithm-Enabled Gait Events and Kinematics Prediction on Various Terrains

Both the discrete motion states and continuous joint kinematics are essential for controlling assistive robots under changeable environmental conditions. However, few studies investigate both the discrete and continuous motion intents. This article is the first work to propose an end-to-end motion intent decoding method that integrates recognition of discrete locomotion modes and prediction of continuous joint kinematics and gait events. First, we propose a data-driven approach to segment the transitional periods of the adjacent locomotion modes and determine their boundaries. Second, we build a convolutional neural network (CNN)-long short-term memory (LSTM) network that utilizes locomotion mode classification as the prior of joint kinematics and gait event prediction and thus decodes discrete and continuous motion intents. Finally, we evaluate our method through extensive experiments and comparisons. The proposed method for locomotion mode recognition achieves an accuracy of 98.73% for steady state and 97.53% for transitional periods. For knee angle prediction, the method achieves the ahead-of-time prediction with normalized root-mean-square error (NRMSE) lower than 8.41%, ${R}$ -value higher than 0.93, and ${R}$ -square higher than 0.88 for various prediction times. For gait event prediction, it predicts the future timing of events with an error of 8.46 ms for initial contact, 32.76 ms for heel off, and 8.91 ms for toe off. This study reveals the feasibility of predicting locomotion-based kinematics and gait events on various terrains. The rich motion intents studied in this work highlight the potential of a more detailed lower limb rehabilitation system.
Fuzzy min-max neural networks for function approximation

The fuzzy min-max function approximation neural network is introduced, and results of its performance on a sample problem are presented. The function approximation network is realized by modifying the previously developed fuzzy min-max clustering network to include an output layer that sums and thresholds the hidden layer membership functions. The approximation of a test function to a small tolerance and robustness when trained on sparse data is demonstrated. <<ETX>>
Synthesis of Qualitative Evidence on Malaria in Pregnancy, 2005–2022: A Systematic Review

Qualitative research on malaria in pregnancy (MiP) is incipient, therefore its contextual, experiential and symbolic associated factors are unknown. This study systematizes the qualitative research on MiP, describes knowledge, perceptions and behaviors about MiP, and compiles individual, socioeconomic, cultural and health system determinants of MiP through a meta-synthesis in 10 databases. A total of 48 studies were included with 2600 pregnant women, 1300 healthcare workers, and 2200 relatives or community members. Extensive knowledge was demonstrated on ITN and case management, but it was lacking on SP-IPTp, risks and consequences of MiP. Attitudes were negative towards ANC and MiP prevention. There were high trustfulness scores and preference for traditional medicine and distrust in the safety of drugs. The main determinants of the Health System were rationing, copayments, delay in payment to clinics, high out-of-pocket expenses, shortage, low workforce and work overload, shortcomings in care quality, low knowledges of healthcare workers on MiP and negative attitude in care. The socioeconomic and cultural determinants were poverty and low educational level of pregnant women, distance to the hospital, patriarchal–sexist gender roles, and predominance of local conceptions on maternal–fetal–neonatal health. The meta-synthesis demonstrates the difficulty to detect MiP determinants and the importance of performed qualitative research before implementing MiP strategies to understand the multidimensionality of the disease.
Facilitating Group Learning Using an Apprenticeship Model: Which Master is More Effective in Programming Instruction?

Computer programming is a difficult course for many students. Prior works advocated for group learning pedagogies in pursuit of higher-level reasoning and conceptual understanding. However, the methodological gaps in existing implementations warrant further research. This study conducted a three-armed cluster-randomized controlled trial to comparatively evaluate the social and cognitive effects of group learning pedagogies in computer programming. Following an apprenticeship model, each group has a designated master: drivers in pair programming (PP), peer leaders in peer-led team learning (PLTL), and practitioners in practitioner-assisted group learning (PAGL). In all course deliverables, the PP group received the lowest mean scores. Meanwhile, no significant difference was found between the PLTL and PAGL groups. Except for psychological safety, social factors such as task cohesion, interdependence, and group potency were significantly different between the groups. Both PLTL and PAGL groups reported a significant increase in social factors after 14 weeks of intervention. These findings provide a rationale for educational leaders and teachers to formulate curricular plans that integrate PLTL and PAGL in computer programming education. Overall, this study contributes to the literature on group learning, expands the pedagogies in computer programming, and serves as additional empirical evidence on cognitive apprenticeship and sociocultural perspectives of learning.
Expanding, Complementing, or Substituting Multilateralism? EU Preferential Trade Agreements in the Migration Regime Complex

Intense pressure for international solutions and weak support for multilateral cooperation have led the EU to increasingly rely on its strongest foreign policy tool in the pursuit of migration policy goals: preferential trade agreements (PTAs). Starting from the fragmentary architecture of the migration regime complex we examine how the relevant content of the EU PTAs relates to multilateral institutions. Depending on the constellation of policy objectives, EU competence, and international interdependence, we propose a set of hypotheses regarding the conditions under which EU bilateral outreach via PTAs expands, complements, or substitutes international norms. Based on an original dataset of migration provisions in all EU PTAs signed between 1960 and 2020, we find that the migration policy content in EU PTAs expands or complements the objectives of multilateral institutions only to a very limited extent. Instead, the predominant constellation is one of substitution in which the EU uses its PTAs to promote migration policy objectives that depart from those of existing multilateral institutions.
A novel intelligent chromo capsule endoscope for the diagnosis of neoplastic lesions in the gastrointestinal tract

Abstract Background Chromoendoscopy has not been fully integrated into capsule endoscopy. This study aimded to develop and validate a novel intelligent chromo capsule endoscope (ICCE). Methods The ICCE has two modes: a white-light imaging (WLI) mode and an intelligent chromo imaging (ICI) mode. The performance of the ICCE in observing colors, animal tissues, and early gastrointestinal (GI) neoplastic lesions in humans was evaluated. Images captured by the ICCE were analysed using variance of Laplacian (VoL) values or image contrast evaluation. Results For color observation, conventional narrow-band imaging endoscopes and the ICI mode of the ICCE have similar spectral distributions. Compared with the WLI mode, the ICI mode had significantly higher VoL values for animal tissues (2.154 ± 1.044 vs 3.800 ± 1.491, P = 0.003), gastric precancerous lesions and early gastric cancers (2.242 ± 0.162 vs 6.642 ± 0.919, P < 0.001), and colon tumors (3.896 ± 1.430 vs 11.882 ± 7.663, P < 0.001), and significantly higher contrast for differentiating tumor and non-tumor areas (0.069 ± 0.046 vs 0.144 ± 0.076, P = 0.005). More importantly, the sensitivity, specificity, and accuracy of the ICI mode for early GI tumors were 95.83%, 91.67%, and 94.64%, respectively, which were significantly higher than the values of the WLI mode (78.33% [P < 0.001], 77.08% [P = 0.01], and 77.98% [P < 0.001], respectively). Conclusions We successfully integrated ICI into the capsule endoscope. The ICCE is an innovative and useful tool for differential diagnosis based on contrast-enhanced images and thus has great potential as a superior diagnostic tool for early GI tumor detection.
Exit from Mitosis in Budding Yeast: Protein Phosphatase 1 is Required Downstream from Cdk1 Inactivation

We show that inactivation of the protein kinase Cdk1/Cyclin B (Cdc28/Clb 2 in the budding yeast Saccharomyces cerevisiae) is not only necessary for cells to leave mitosis, as is well known, but also sufficient to trigger mitotic exit. Cells carrying the mutation cdc28-as1, which makes Cdc28 (Cdk1) uniquely sensitive to the ATP analog 1NM-PP1, were arrested with spindle poisons and then treated with 1NM-PP1 to inhibit Cdk1. This treatment caused the cells to exit mitosis and enter G1-phase as shown by initiation of rebudding (without cytokinesis), production of “shmoos” (when α-factor was present), stabilization of Sic1, and degradation of Clb2. This result provides a system in which to test whether particular gene products are required downstream from Cdk1 inactivation in exit from mitosis. In this system, the mutation cdc28-as1 is combined with a conditional mutation in the gene of interest. Using this approach, we demonstrate that Protein Phosphatase 1 (PPase1; Glc7 in S. cerevisiae) is required for reestablishment of G1-phase following Cdk1 inactivation. This system could be used to test whether other protein phosphatases are also needed downstream from Cdk1 inactivation, and it could be combined with phosphoproteomics to gain information about the substrates those phosphatases act on during mitotic exit.
All-Electron Scalar Relativistic Basis Sets for the Actinides

Increasing interest in the computational modeling of actinide compounds creates the need for alternative choices when in comes to fine tuning the computational methodology in order to best fit the problem at hand. All-electron scalar relativistic density functional theory can be a useful approach for a variety of actinide systems and would benefit from atomic basis sets geared to that level of theory. In this paper we present segmented all-electron relativistically contracted (SARC) basis sets for the complete actinide series 89Ac−103Lr, optimized for use with the popular Douglas−Kroll−Hess to the second order and zeroth-order regular approximation scalar relativistic Hamiltonians. The quality of the SARC basis sets is assessed in terms of their intrinsic incompleteness and contraction errors, with respect to total energies, orbital properties, and ionization energies. Calculations on diatomic Ac and Lr molecules confirm that the valence-space construction results in negligible basis set superposition err.
Direct Numerical Simulation of Transient Growth on a Flat Plate at Mach 3

The transient growth phenomenon in three dimensional shear flows has been shown to account for large disturbance growth in cases where traditional stability analyses show limited or no growth. For this reason, transient growth has been put forward as a potential cause of bypass transition. The purpose of the current study is to investigate the presence of non-linear eects as the energy of disturbances increases and their potential role in boundary layer transition on a flat plate in supersonic, compressible flow. An optimal disturbance solver based on linear theory was created and is validated against previously published data. The disturbances generated by this solver are input into a computational fluid dynamics (CFD) solver to model the disturbance evolution in three dimensions. The results from the CFD solver are compared with linear theory to ensure accuracy in the full three-dimensional simulations. Finally, the input energies are increased to investigate the eects of non-linear terms in the governing equations. Results show that a non-dimensional input energy of Ein = 3:024 10 3 is required for a departure from linear behavior for the mean flow conditions considered.
Exploring the effects of caffeine on Aedes albopictus (Diptera: Culicidae) survival and fecundity

Investigating new avenues of mosquito control is an important area of entomological research. Examining the effects of various compounds on mosquito biology contributes to the foundation of knowledge from which novel control methods can be built. Caffeine, in particular, is a commonly consumed compound that has not been thoroughly studied for its potential in disrupting the natural life cycle of mosquitoes. In this exploratory study, we analyzed caffeine's effect on the blood-feeding behavior, survival, and fecundity of Aedes albopictus Skuse (Diptera: Culicidae) mosquitoes.Two outcomes, blood-feeding behavior and fecundity, were analyzed in the first experiment in which mosquitoes were exposed to caffeine doses ranging from 0.2 to 2.4 mg/ml. We found a negative linear relationship between dose and fecundity, but no significant impact on blood-feeding behavior. Adjustments were made to the experimental design in which mosquitoes were exposed to doses ranging from 2.5 to 20 mg/ml. From this experiment, we found that caffeine negatively affected blood-feeding behavior, survival, and fecundity especially at higher concentrations.These results suggest that caffeine could be a potential target for future mosquito control research.
Learning Algorithms for RBF Functions and Subspace Based Functions

The renaissance of neural network and then machine learning since the 1980’s is featured by two streams of extensive studies, one on multilayer perceptron and the other on radial basis function networks or shortly RBF net. While a multilayer perceptron partitions data space by hyperplanes and then making subsequent processing via nonlinear transform, a RBF net partitions data space into modular regions via local structures, called radial or non-radial basis functions. After extensive studies on multilayer perceptron, studies have been turned to RBF net since the late 1980’s and the early 1990’s, with a wide range applications (Kardirkamanathan, Niranjan, & Fallside, 1991; Chang & Yang, 1997; Lee, 1999; ABSTRACT
Systematic Review and Meta-Analyses: Safety and Efficacy of Complementary and Alternative Treatments for Pediatric Attention-Deficit/Hyperactivity Disorder

This article has supplementary material on the web site: www.jdbp. org. Abstract Objective: Complementary and alternative treatments (CATs) for ADHD have proliferated over the past decade; however, their safety and efficacy remain uncertain. We completed a systematic review and meta-analyses across CAT domains. Methods: Systematic search and data extraction identified randomized controlled trials for pediatric ADHD (ages 3–19 years) that included probably blind ADHD symptom outcome measures. We evaluated basic (RCT of a CAT compared with sham/placebo, attention/active control, treatment as usual, and waitlist control), complementary (RCTs comparing an evidence-based treatment with a CAT and the same evidence-based treatment), and alternative (evidence-based treatment to CAT) efficacy. Random-effect meta-analyses were conducted when at least 3 blinded studies were identified for a specific CAT domain. Results: Eighty-seven of 2253 nonduplicate screened manuscripts met inclusion criteria. No study reported significantly greater adverse effects for CATs than controls; naturopathy reported fewer adverse effects than evidence-based treatments but did not demonstrate basic efficacy. In the systematic review of basic efficacy, evidence of effectiveness was mixed but replicated previous evidence for the possible efficacy of cognitive training, neurofeedback, and essential fatty acid supplementation for certain patients. With respect to alternative and complementary efficacy, no CAT outperformed or enhanced evidence-based treatments (stimulant medications and behavioral therapy) when replication was required. Individual meta-analyses indicated that cognitive training was the only CAT that demonstrated overall basic efficacy (SMD = 0.216; p = 0.032). Conclusion: Clinicians may cautiously recommend (but monitor) cognitive training when evidence-based treatments are not feasible or effective for a patient. Additional studies are needed to further understand the potential of CAT domains.
Following a moving target—Monte Carlo inference for dynamic Bayesian models

Markov chain Monte Carlo (MCMC) sampling is a numerically intensive simulation technique which has greatly improved the practicality of Bayesian inference and prediction. However, MCMC sampling is too slow to be of practical use in problems involving a large number of posterior (target) distributions, as in dynamic modelling and predictive model selection. Alternative simulation techniques for tracking moving target distributions, known as particle filters, which combine importance sampling, importance resampling and MCMC sampling, tend to suffer from a progressive degeneration as the target sequence evolves. We propose a new technique, based on these same simulation methodologies, which does not suffer from this progressive degeneration.
Discovering a pregnancy after 30 weeks: a qualitative study on explanations for unperceived pregnancy

Abstract “Unperceived pregnancy” names the phenomenon when a person becomes pregnant unintentionally and is not aware of being pregnant. Scientific explanations are roughly based on two hypotheses: psychological and physiological. We aim to gain a better understanding of unperceived pregnancy by studying the perspectives of people who experienced an unperceived pregnancy and obstetric professionals. Seventeen semi-structured interviews were conducted: eight with women who had experienced an unperceived pregnancy (≥30 weeks’ gestation), six with midwives, and three with gynecologists. Our findings show that women’s explanations for not noticing their pregnancy center around the absence of pregnancy symptoms. The failure to recognize more subtle signs of pregnancy was enforced by inattention, physical distractions, and psychological factors. In contrast, psychological explanations are dominant among obstetric professionals. Our study demonstrates a discrepancy in the explanations provided by women who had experienced an unperceived pregnancy and obstetric professionals. Potentially, this could result in people being unheard and misunderstood. We recommend that future research moves beyond a focus on “denial of pregnancy” to consider both psychological and physiological factors, and how these could potentially interrelate. This broadened approach will enhance our understanding of unperceived pregnancy and can contribute to improved counseling by obstetric professionals.
Insecticide application timing effects on alfalfa insect communities

Timing of insecticide application can impact efficacy, given variation in both weather and development of the crop and its insect pests. Both target and nontarget insects may vary in life stage and abundance at the time of application. In alfalfa Medicago sativa L. cropping systems, producers have interest in early-season insecticide applications to eliminate last-minute decisions about preharvest applications for alfalfa weevil Hypera postica (Gyllenhal) (Coleoptera: Curculionidae). The standard recommendation is based on scouting larvae close to the first harvest time. We compared early and standard timing of application of a lambda-cyhalothrin pyrethroid on alfalfa pest and beneficial insects. Field trials at a university research farm were conducted in 2020 and 2021. In 2020, early application was as effective as the standard timing against alfalfa weevil, as compared to the untreated control, but less effective than the standard timing in 2021. Effects of timing against Lygus bugs (Hemiptera: Miridae), grasshoppers (Orthoptera: Acrididae), and aphids (Hemiptera: Aphididae) were inconsistent between years. We observed the potential for early application to reduce negative impacts on ladybird beetles (Coleoptera: Coccinellidae) and spiders (Araneae), however, damsel bugs (Hemiptera: Nabidae) were similarly reduced by insecticide application regardless of timing. Overall arthropod community composition differed by both year and treatment. Future research should explore potential trade-offs of spray timing at larger spatial scales.
Cylindrical tank of fluid oscillating about a state of steady rotation

A cylindrical tank, full of fluid, is oscillating with frequency ω and rotating with angular velocity Ω about its axis of symmetry. It is assumed that the amplitude of oscillation, δ, is small and the viscosity is low such that boundary layers exist. Analysis shows that the unsteady boundary layer is of thickness [ε/(1 − 2Ω/ω)]½ on the top and bottom plates and of thickness ε½ on the side walls, where ε = ν/2ω. The interior unsteady flow shows source-like behaviour at the corners. The steady flow field is caused by the steady component of the non-linear centrifugal forces coupled with an induced steady rotation of the interior. This rotation, of order δ2ω, is prograde when Ω/ω < 0·118 and retrograde otherwise. Maximum retrograde rotation occurs at Ω/ω = 0·5. A steady boundary layer of thickness [ε/(1 − 2Ω/ω)]½ exists on the top and bottom plates, and of thicknesses \[ \epsilon^{\frac{1}{2}},\quad (\nu/L^2\Omega)^{\frac{1}{3}},\quad (\nu/L^2\Omega)^{\frac{1}{4}} \] on the side walls. Experimental measurements of the interior induced steady rotation compare well with theory.
Predicting Survivorship Appointment Nonattendance in a Community Cancer Center: A Machine-Learning Approach

Understanding and predicting cancer survivors’ health care utilization is critical to promote quality care. The consultative system of survivorship care uses a onetime consultative appointment to transition patients from active treatment into survivorship follow-up care. Knowledge of attributes associated with nonattendance at this essential appointment is needed. An ability to predict patients with a likelihood of nonattendance would be of value to practitioners. Unfortunately, traditional data modeling techniques may not be useful in working with large numbers of variables from electronic medical record platforms. A variety of machine-learning algorithms were used to develop a model for predicting 843 survivors’ nonattendance at a comprehensive community cancer center in the southeastern United States. A parsimonious model resulted in a k-fold classification accuracy of 67.3% and included three variables. Practitioners may be able to increase utilization of follow-up care among survivors by knowing which patient groups are more likely to be survivorship appointment nonattenders.
Face illumination compensation based on wavelet transform

A novel illumination compensation algorithm for face recognition under nonuniform illumination conditions is presented.The face images are transformed from spatial domain to logarithm domain and 2-dimension wavelet transform is calculated by the algorithm.Then coefficients of low-frequency subband image are discarded for face illumination compensation in logarithm domain. Because the purpose of illumination compensation is to improve the performance of face recognition,the effect of face illumination compensation is defined by face recognition rate.The proposed algorithm is compared with the Discrete Cosine Transformation(DCT) method in logarithm domain in Yale B face database,the experimental results show that the average error rate of the proposed algorithm can reach 0.18%,which is lower than that of the DCT method.In addition,the experimental results in CAS_PEAL face database indicate that the performance of the proposed algorithm is equivalent to that of the DCT method.