Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining the Minimally Clinically Important Difference for the Disability Rating Scale in Persons With Chronic Traumatic Brain Injury

The Extended Glasgow Outcome Scale (GOSE) is accepted as the primary outcome measure in registrational studies for traumatic brain injury (TBI). The Disability Rating Scale (DRS) is used to assess functional progress from initial acute injury, through rehabilitation and reintegration into the community and life. For these reasons, the DRS is an alternative measure for assessing meaningful global outcomes in chronic TBI. The objective of this study was to determine the minimally clinically important difference (MCID) for the DRS in chronic TBI, by determining the magnitude of DRS change associated with the MCID for the GOSE of 1 point. This study is a retrospective analysis of the multi-center, prospective, longitudinal, Traumatic Brain Injury Model Systems National Database of persons with outcomes at 1, 2, and 5 years and every 5 years thereafter post-injury. Spearman's correlations for dynamic and static relationships between the DRS and GOSE were significant. For the 1-point MCID for the GOSE, the dynamic MCID estimate for the DRS of a −0.68-point change was calculated as the mean DRS change associated with the difference of the GOSE score between year 1 and year 2 (score range, 3–8), using all persons in the study (n = 11,102), whereas the exploratory static MCID estimate for the DRS of −1.28 points was calculated from the slope of the best-fit line between the DRS and GOSE at year 1 follow-up (score range, 3–8; n = 13,415). The final MCID for the DRS was calculated by using the triangulation method (i.e., the arithmetic mean of the dynamic and exploratory static MCID estimates), which resulted in a −1.0-point change. The significant correlation between the DRS and GOSE has allowed for the establishment of a −1.0-point MCID for the DRS, which supports the use of the DRS as an alternative primary outcome measure for chronic TBI research studies, including clinical trials.
Nonlinear Effects and Higher-Order Aberrations on Beam Optics of Energy Selection System for Proton Therapy

The energy selection system (ESS) with achromatic capability is essential for a cyclotron-based proton therapy facility. Its purpose is to match the degraded beam to the downstream beam delivery system. In contrast to beam optics calculations using hard-edge and linear transport, the actual fringe field of the magnet and higher-order aberrations of the particle motion lead to a mismatch between the original design and the real situation. In this study, we perform a 3-D magnet simulation with ANSYS Maxwell to evaluate the field distribution of the ESS. The multipole components are realistically included. The transfer matrices are determined from the particle equations of motion using the fourth-order Runge–Kutta (RK4) method. The beam properties at the exit of the ESS are then calculated by transfer mapping. The results show that the beam spot size of the third-order optics approximately doubles in the $x$ -plane compared to the hard-edge model. The fringe fields contribute most to the beam spot deformation, while the variation caused by higher-order aberrations accounts for about 4%. Imaged optics are represented to reduce distortion by refitting the magnetic fields using the COZY INFINITY code with the Enge function. The analysis and results provide a more accurate representation of the fringe field and its effect on the beam, which can be used as a reference for field optimization during beam commissioning. Realistic field distributions should be considered when designing lattices to obtain reasonable beam transport predictions.
Midway reassignment of Runge–Kutta step length

At the end of each Runge–Kutta (RK) step, a new step‐size is predicted, and later, during the evaluations of the next step, if we feel that the prediction was not correct, nothing can be done. Here, we suggest an intermediate error estimation of low order, at the beginning of the integration, that can be used for the reconsideration of the step length before this is completed. This requires the evaluation of a variable coefficient RK. These coefficients depend on the value ϵ$$ \epsilon $$ , where ϵh$$ \epsilon h $$ is the new prediction of the step‐size. In addition, a new control for the step length at the end of each integration is designed taking advantage from the extra estimator. The basic concept of this idea is to produce general purpose RK methods that have the capability to reduce the number of step rejections for difficult problems (such as orbits with high eccentricities and Van der Pole equation).
Realizing the government performance evaluation index additivity in China: A probe into the aggregate degree and additivity of evaluation index

Studies and practices in China unanimously ignored the additivity of government performance evaluation index. In the present evaluation systems, the total score of government performance is added by simply putting the indexes values (numbers) together. Neither the researchers nor the practitioners pay any attention to the reality that the government performance evaluation indexes belong to high attribute dimensions, and they cannot be added directly. To process these high attribute indexes of government performance evaluation, we have to follow their clustering features and reduce dimensions to convert high attribute dimensions to low attribute dimensions. In this study, binary state variable was adopted to reduce dimensions. We reduce the dimension of the performance evaluation index by 4 steps: (1) separating the hazy description of into measurable sub-indexes; (2) treating each sub-index as a binary variable by judging it false or true; true and false are respectively indicated as 1 and 0 in the statistical software or mathematical language; (3) using the methods of aggregate degree, aggregate vector, and set theory to make the sub-indexes aggregate in a same class; (4) nondimensionalising the values of sub-indexes and realizing the additivity of all the sub-indexes.
Yield stability of soybean promising lines across environments

Soybean is grown in the wide range of environments in Indonesia, hence the genotype and environment interaction is become one of major complication in the breeding programs. The objective of this study was to evaluate the stability performance of soybean promising lines in eight locations during the dry season 2015. A completely randomized block design with four replicates was used to arrange the experiment. An additive main effects and multiplicative interaction (AMMI) model was used to examine the performance of yield stability. Soybean seed yield was affected by genotype (9.23%), environment (45.23%) and their interaction (45.62%). The first two principal component axes (PCA 1 and PCA 2) were significant (p < 0.01) and cumulatively contributed to 77.41% of the total variation. Based on AMMI multivariate method, two genotypes (G511H/Anj-1-6 and G511H/Anj-1-4) with seed yield of 2.67 and 2.74 t/ha, respectively, were identified as high yielding and stable promising lines across environments.
Self-Adaptive Gradient Quantization for Geo-Distributed Machine Learning Over Heterogeneous and Dynamic Networks

Geo-Distributed Machine Learning (Geo-DML) has been proposed to collaborate geographically dispersed data centers (DCs) and train large scale machine learning (ML) models for various applications. While Geo-DML can achieve excellent performance, it also injects massive data traffic into the Wide Area Networks (WANs) in order to exchange gradients during model training process. Such a huge amount of traffic will not only incur network congestion and prolong the training procedure, but also result in straggler problem when DCs are working in heterogeneous network environments. To alleviate these problems, we propose Self-Adaptive Gradient Quantization (SAGQ) for Geo-DML in this work. In SAGQ, each worker DC adopts specific quantization method based on the heterogeneous and dynamic link bandwidth in order to reduce the communication overhead and balance the communication time among worker DCs. By doing so, SAGQ will speed up the Geo-DML training process without sacrificing the ML model performance. Extensive experiments show that compared with the state-of-the-art techniques, SAGQ reduces the Wall-clock time spent to train an ML model by 1.13×–21.31×. In addition, SAGQ can also improve the model accuracy by 0.11%–2.27% over baselines.
The genome of okra (Abelmoschus esculentus) provides insights into its genome evolution and high nutrient content

Abstract Okra (Abelmoschus esculentus) is an important vegetable crop with high nutritional value. However, the mechanism underlying its high nutrient content remains poorly understood. Here, we present a chromosome-scale genome of okra with a size of 1.19 Gb. Comparative genomics analysis revealed the phylogenetic status of A. esculentus, as well as whole-genome duplication (WGD) events that have occurred widely across the Malvaceae species. We found that okra has experienced three additional WGDs compared with the diploid cotton Gossypium raimondii, resulting in a large chromosome number (2n = 130). After three WGDs, okra has undergone extensive genomic deletions and retained substantial numbers of genes related to secondary metabolite biosynthesis and environmental adaptation, resulting in significant differences between okra and G. raimondii in the gene families related to cellulose synthesis. Combining transcriptomic and metabolomic analysis, we revealed the relationship between gene expression and metabolite content change across different okra developmental stages. Furthermore, the sinapic acid/S-lignin biosynthesis-related gene families have experienced remarkable expansion in okra, and the expression of key enzymes involved in the sinapic acid/S-lignin biosynthesis pathway vary greatly across developmental periods, which partially explains the differences in metabolite content across the different stages. Our study gains insights into the comprehensive evolutionary history of Malvaceae species and the genetic basis that underlies the nutrient content changes in okra, which will facilitate the functional study and genetic improvement of okra varieties.
Robust compressed sensing with side information based on Laplace mixtures models

—In this paper, we propose a new method for the recovery of a sparse signal from few linear measurements using a reference signal as side information. Modeling the signal coefﬁcients with a double Laplace mixture model, and assuming that the distribution of the components of the prior information differs slightly from the unknown signal, the problem is formulated as a weighted (cid:96) 1 minimization problem. We derive sufﬁcient conditions for perfect recovery and we show that our method is able to reduce signiﬁcantly the number of measurements required for reconstruction. Numerical experiments demonstrate that the proposed approach outperforms the best algorithms for compressed sensing with prior information and is robust in imperfect scenarios
Cluster state generation in one-dimensional Kitaev model via shortcut to adiabaticity

We propose a scheme to obtain computationally useful resource states called cluster states used in measurement-based quantum computation via transitionless quantum driving algorithm. We show that the original and the auxiliary Hamiltonians needed to realize our proposal are composed of only two-body interactions, which are well within the reach of the current state-of-the-art quantum technologies.
FUSION INDUCED BY ENERGY-INTENSIVE MULTIFUNCTION CAVITATION IN CONJUNCTION WITH POSITRON IRRADIATION

This review summarizes the development of multifunction cavitation technology and assesses future applications for this process. The focus is on attempts to increase the surface strength and functionality of various metals and plastics and to elucidate the conditions that can induce nuclear fusion by cavitation. An experimental setup capable of producing cavitation fusion based on positron-irradiated laser-assisted high-field energy-intensive functional cavitation is described, having the same basic structure as a prior device without positron irradiation. A combination of water jet, ultrasonic and magnetic field energy sources has been found to increase the sonoluminescence intensity and to theoretically exceed the threshold required for the D-T fusion reaction. This paper describes the incorporation of positron and laser energy sources to this system as a means of further increasing the internal temperature and pressure values of bubbles. Specifically, a Na-22 positron beam source was placed in the upper part of the reaction vessel in the direction of the magnetic field such that positrons were imparted to cavitation bubbles floating on the surface of degassed heavy acetone. These positrons were partially annihilated by interactions with electrons via the Compton effect to generate gamma-rays and energy via the reaction e+ + e- → 2γ + l.02 MeV. This energy promoted the D-T chain reaction D + T → 4He + n + 14 MeV to increase the probability of cavitation fusion.
Semi-supervised Review-Aware Rating Regression (Student Abstract)

Semi-supervised learning is a promising solution to mitigate data sparsity in review-aware rating regression (RaRR), but it bears the risk of learning with noisy pseudo-labelled data. In this paper, we propose a paradigm called co-training-teaching (CoT2), which integrates the merits of both co-training and co-teaching towards the robust semi-supervised RaRR. Concretely, CoT2 employs two predictors and each of them alternately plays the roles of "labeler" and "validator" to generate and validate pseudo-labelled instances. Extensive experiments show that CoT2 considerably outperforms state-of-the-art RaRR techniques, especially when training data is severely insufficient.
Three-dimensional kinematics of shoulder laxity examination and the relationship to clinical interpretation*

Abstract Understanding clinical test kinematics improves utility of exam techniques. The purposes of this study were as follows: (1) determine inter-examiner repeatability of translation magnitude for the Anterior/Posterior Drawer and Sulcus shoulder laxity tests; (2) describe the relationships between glenohumeral joint translations and subjective grades for each laxity test; and (3) describe the relationship of overall glenohumeral joint laxity to a composite subjective score from the three laxity tests. Eleven subjects with shoulder symptomology were examined with three laxity tests. Motion was tracked with electromagnetic sensors affixed to the humerus and scapula via transcortical pins. ICCs were calculated to determine repeatability of translation magnitudes between two examiners for each test. Descriptive statistics and regression analyses were performed for comparisons of single laxity test grades with translation magnitudes and for composite subjective laxity scores and overall translation across all three tests. Inter-examiner ICCs regarding kinematic repeatability were 0.87 for Anterior Drawer, 0.84 for the Sulcus test, and not calculable for the Posterior Drawer. No linear relationships between subjective grades of individual tests and translation magnitudes were found. The relationship of overall translation with the composite subjective score from all laxity tests was r2 = 0.75 (r = 0.86). Clinicians from different disciplines are capable of imparting similar translations during laxity tests. Single-test subjective laxity grades demonstrate large ranges of translation between subjects for the same grade. By combining results of three laxity tests, clinicians are capable of identifying the level of overall shoulder joint laxity in patients.
A linear model for simultaneous estimation of 3D motion and depth

A novel model for simultaneous estimation of full 3D motion and 3D position in world coordinates from multi camera sequences is presented. To this end scene flow (see Vedula, S. et al., ICCV 1999, p.722-9, 1999) and disparity are estimated in a single estimation step. The key idea is to interpret sequences of 2 or more cameras as one 4D data set. In this 4D space, a given motion model, a camera model and a brightness change model are combined into a brightness change constraint equation (BCCE) combining changes due to object motion and different camera positions. The estimation of parameters in this constraint equation is demonstrated using a weighted total least squares estimator, called structure tensor. An evaluation of systematic errors and noise stability for a 5 camera setup is shown as well as results on synthetic sequences with ground truth and data acquired under controlled laboratory conditions.
On polynomials construction

The solutions of many physical problems are obtained as expansions in series of orthogonal functions. In this paper, construction of new class of orthogonal polynomials is proposed. This class of polynomials is valid in the interval [-1, 1] with respect to constant fractional weight function, 2 1 ) (  x w . The recurrence relation of the class of polynomials is presented as we hope to further discuss additional properties and full application of the class of polynomials in the future paper.
Wagering with an incomplete deck—refining conservation plans for the New Mexico Meadow Jumping Mouse (Zapus luteus luteus)

Limited sampling for imperiled taxa inhibits effective management by obscuring windows into ecological and evolutionary processes and ultimately thwarting thoughtful conservation efforts. We report eight new locations for the endangered New Mexico Meadow Jumping Mouse (Zapus luteus luteus) detected across three states that expand their known distribution. When combined with existing curated museum records, we develop an ensemble species distribution model to evaluate persistence of populations over the next 50 years. Predicted distributions indicate complex future changes, including regional expansion and the likelihood that half of the designated critical habitat areas will be unsuitable by 2070. Three of the newly discovered populations occur where predicted climate conditions suggest extirpation is likely. Importantly, indices of historical sampling efforts show that recognized and potential distributions are mismatched, highlighting vast areas that have been insufficiently surveyed. Ongoing habitat degradation and climate disruption are projected to synergistically erode genetic diversity across four of the five divergent phylogroups. Considering these combined results, a holistic sampling strategy is needed to more completely document the distribution of jumping mice and facilitate genomic analyses aimed at establishing a roadmap for improving our understanding of geographic variation and adaptive potential. Current management efforts are not only costly, but they are lacking key biological insights, essentially wagering actions with an incomplete deck of cards.
Artificial Intelligence Technique-Based EV Powertrain Condition Monitoring and Fault Diagnosis: A Review

Electric powertrain used in electric vehicles (EVs), which is constituted of a motor, transmission unit, inverter, battery packs, and so on, is a highly integrated system. Its reliability and safety are not only related to industrial costs but more importantly to the safety of human life. This review contributes to comprehensively summarizing artificial intelligence (AI)-based/AI-supported approaches in EV powertrain condition monitoring and fault diagnosis that can be used in EV applications. The application of AI on PE in EV is a new attempt, which can solve many issues with better performance than traditional methods and even achieve functions that the conventional methods cannot achieve. This article thoroughly discusses the motivation, advantages, limitations, and challenges associated with AI-supported methods through case summaries, classification, comparisons, and quantitative analyses between conventional and AI-based approaches. Furthermore, the review concludes by proposing forward-looking future trends in this field.
Joint Energy and Information Precoding for NOMA-Based WPCNs Aided by Reconfigurable Intelligent Surface

In a wireless powered communication network (WPCN), wireless energy acquisition is weak due to the fading characteristic of wireless link, which makes it hard to supply the energy. Reconfigurable intelligent surface (RIS) is a promising performance enhancement technology for WPCNs, which can improve the energy and spectral efficiency, expand the network coverage, and thus improve the throughput. This article proposes a new framework for energy harvesting and information transmission of non-orthogonal multiple access (NOMA)-based WPCNs aided by RIS. The total transmission capacity is maximized by joint optimization of energy precoding, information precoding, and the phase shift of RIS, which turns out to be a non-convex problem. Under the multi user scenario, we propose a joint optimization algorithm combing alternating optimization (AO), semidefinite relaxation (SDR), and successive convex approximation (SCA) methods, which is called ASS algorithm for short. Numerical results prove that compared with the non-RIS system, the ASS can increase the throughput of the system under multiuser scenario by 502.49%. For the system under single scenario, the energy precoding and information precoding are determined respectively by using the beamforming and the traditional maximal ratio combining (MRC) algorithm. The optimization problem is transformed into a complex higher order form optimization problem. Finally, a closed-form expression of the channel capacity is derived.
Novel scene understanding, from gist to elaboration

ABSTRACT We examined the cognitive experience of novel pictorial scenes, using observers’ words. On each of two critical trials, a single novel scene (a photo) was presented briefly, after which observers described what they saw. The reports were highly valid, assessed against details of the stimuli. The most frequent concepts used by the observers defined scene gist empirically – there was wide agreement on the basic scene interpretation. The average extent of the reports, or cognitive bandwidth, was fairly high overall: a median of 14.4 correct meaningful words per scene. However, there was a wide range of report lengths across observers. With length, the reports became increasingly elaborative and unique (and still correct). The longer reports document the richer and more unique side of a gist-to-elaboration continuum. In addition, a manipulation of immediately prior experience (priming with other scenes) caused an increase in the reporting of prime-related information, conveyed by a wide range of concepts, including relational concepts. The results are consistent with cognitive theories of perception and the claim of substantial cognitive bandwidth. Furthermore, the highly elaborative reports document a measured diversity of powerful scene understanding.
HOW A SUPPLY CHAIN PROCESS MATTERS IN FIRMS’ PERFORMANCE-AN EMPIRICAL EVIDENCE OF PAKISTAN

In recent years, supply chain processes (i.e. demand management, customer relationship management, and new product development) have gained a great importance from academicians and practitioners. Yet, research into the subject of supply chain processes’ effects on performance is nascent. The purpose of this study is to examine the process dimensions’ effects on supply chain (SC) partners’ trust and firms’ performance as supply chain processes are essential for firms’ competitiveness. The research framework consists of seven hypotheses. An empirical study was conducted in Pakistan, and the data were collected from 164 supply chain firms. The reliability and validity of the model were examined through the exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA). The results delineate that supply chain processes have a significant relationship with SC partners’ trust and performance. Thus, all hypotheses were supported. This study presents interesting theoretical contributions and managerial implications. At last, limitations regarding future research directions are discussed.