Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Control in Discrete-Time Nonlinear Coupling Dynamics of a Soft Exo-Digit and a Human Finger Using Input–Output Feedback Linearization

This paper presents a quasi-static model-based control algorithm for controlling the motion of a soft robotic exo-digit with three independent actuation joints physically interacting with the human finger. A quasi-static analytical model of physical interaction between the soft exo-digit and a human finger model was developed. Then, the model was presented as a nonlinear discrete-time multiple-input multiple-output (MIMO) state-space representation for the control system design. Input–output feedback linearization was utilized and a control input was designed to linearize the input–output, where the input is the actuation pressure of an individual soft actuator, and the output is the pose of the human fingertip. The asymptotic stability of the nonlinear discrete-time system for trajectory tracking control is discussed. A soft robotic exoskeleton digit (exo-digit) and a 3D-printed human-finger model integrated with IMU sensors were used for the experimental test setup. An Arduino-based electro-pneumatic control hardware was developed to control the actuation pressure of the soft exo-digit. The effectiveness of the controller was examined through simulation studies and experimental testing for following different pose trajectories corresponding to the human finger pose during the activities of daily living. The model-based controller was able to follow the desired trajectories with a very low average root-mean-square error of 2.27 mm in the x-direction, 2.75 mm in the y-direction, and 3.90 degrees in the orientation of the human finger distal link about the z-axis.
Carbon allotropes with triple bond predicted by first-principle calculation: Triple bond modified diamond andT-carbon

We have designed new structures of carbon allotropes combining mixture of sp (triple bond) and sp 3 (single bond) hybridization through first-principle calculation. These two structures named Yne-carbon (Y-carbon) and Tetrayne-carbon (TY-carbon), respectively, not only maintained the cubic structure and space group ( Fd3m ) of diamond but were also energetically more stable than recently proposed carbon allotrope, T-carbon. A phonon calculation revealed these structures to be stable, and the nature of the triple bond was illustrated by the unique phonon spectrum with an eigen-frequency of 2200 cm -1 . The band gap of Y-carbon was found to be larger than that of diamond, whereas the band gap of TY-carbon was smaller than that of T-carbon, which is closely related to the properties of a carbon tetrahedron. The existence of triple bonding of carbon is associated with a dimerization phenomenon due to Peierls instability.
An Extended Mumford-Shah Model Integrated with Fuzzy Clustering

Fuzzy clustering and Mumford-Shah model are two fundamental methods for image segmentation.In this paper,an extended Mumford-Shah model integrated with fuzzy clustering,called FCMS,is presented.FCMS has some remarkable characteristics and better performance.The framework of FCMS consists of three strategies,which are also available for integrating various popular fuzzy clustering algorithms into many Mumford-Shah's variations.In particular,an example of FCMS using the FCM and original Mumford-Shah model is completely studied in this paper.The results on theoretical and experimental analysis demonstrate the rationality and effectiveness of the proposed model.
Comparison of algorithms for decoding monotone functions by the statistical simulation method

Algorithms for decoding monotone functions find practical application in problems of designing expert knowledge bases. In these problems, it is important not only to obtain worst-case estimates for the optimality of an algorithm, but also to estimate its efficiency for arbitrary boundaries between the classes of solutions. In this paper, the quality of four algorithms is estimated by the statistical simulation method.
Theoretical Analysis and Application of Boosting Algorithm

Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, overviews some of the recent work on boosting including analyses of AdaBoost's training error and generalization error; boosting's connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems.
The effects of feeding α-amylase enhanced corn silage with different dietary starch concentrations to lactating dairy cows on milk production, nutrient digestibility, and blood metabolites.

Corn silage is one of the most common ingredients fed to dairy cattle. Advancement of corn silage genetics has improved nutrient digestibility and dairy cow lactation performance in the past. A corn silage hybrid with enhanced endogenous α-amylase activity (Enogen, Syngenta Seeds LLC) may improve milk production efficiency and nutrient digestibility when fed to lactating dairy cows. Furthermore, evaluating how Enogen silage interacts with different dietary starch content is important because the ruminal environment is influenced by the amount of rumen fermentable organic matter consumed. To evaluate the effects of Enogen corn silage and dietary starch content, we conducted an 8-wk randomized complete block experiment (2-wk covariate period, 6-wk experimental period) with a 2 × 2 factorial treatment arrangement using 44 cows (n = 11/treatment; 28 multiparous, 16 primiparous; 151 ± 42 d in milk; 668 ± 63.6 kg of body weight). Treatment factors were Enogen corn silage (ENO) or control (CON) corn silage included at 40% of diet dry matter and 25% (LO) or 30% (HI) dietary starch. Corn silage used in CON treatment was a similar hybrid as in ENO but without enhanced α-amylase activity. The experimental period began 41 d after silage harvest. Feed intake and milk yield data were collected daily, plasma metabolites and fecal pH were measured weekly, and digestibility was measured during the first and final weeks of the experimental period. Data were analyzed using a linear mixed model approach with repeated measures for all variables except for body condition score change and body weight change. Corn silage, starch, week, and their interactions were included as fixed effects; baseline covariates and their interactions with corn silage and starch were also tested. Block and cow served as the random effects. Plasma glucose, insulin, haptoglobin, and serum amyloid A concentrations were unaffected by treatment. Fecal pH was greater for cows fed ENO versus CON. Dry matter, crude protein, neutral detergent fiber, and starch digestibility were all greater for ENO than CON during wk 1, but differences were less by wk 6. The HI treatments depressed neutral detergent fiber digestibility compared with LO. Dry matter intake (DMI) was not affected by corn silage but was affected by the interaction of starch and week; in wk 1, DMI was similar but by wk 6, cows fed HI had 1.8 ± 0.93 kg/d less DMI than LO cows. Milk, energy-corrected milk, and milk protein yields were 1.7 ± 0.94 kg/d, 1.3 ± 0.70 kg/d, and 65 ± 27 g/d greater for HI than LO, respectively. In conclusion, ENO increased digestibility but it did not affect milk yield, component yields, or DMI. Increasing dietary starch content improved milk production and feed efficiency without affecting markers of inflammation or metabolism.
Phenotypic Diversity Analysis in Elaeagnus angustifolia Populations in Gansu Province, China

As a highly resistant urban ornamental plant, Elaeagnus angustifolia L. is often used in dry land, saline-alkali land shelter forest, and landscape horticulture. It is the main windbreak and sand-fixing tree species in Gansu Province, China. The special geographical and climatic environment makes the distribution and growth of E. angustifolia in Gansu Province show different degrees of difference. In order to evaluate the phenotypic diversity of E. angustifolia in different populations and its variation patterns under different geographical and climatic conditions, 35 phenotypic traits (trunk, branch, leaf, and flower related traits) of 90 plants from 10 populations in Gansu Province were measured and analyzed. The results showed the following: (1) E. angustifolia has rich phenotypic variation. The variation is greater among populations. The traits with the largest and smallest coefficients of variation were “under-branch height” and “flower diameter”, respectively. The variation in the Qilihe population was the largest, and the variation in the Ganzhou population was the smallest. The diversity of flowers and leaves is relatively higher. (2) Correlation analysis showed that most of traits were closely related. Leaf traits showed a gradient variation law dominated by altitude and precipitation. Flower traits were affected by the synergistic effects of various geographical and climatic factors. (3) The results of the principal component analysis (PCA) showed that the primary traits affecting the phenotypic diversity of E. angustifolia were leaf size and branch length among the related traits of trunk, branch, leaf, and flower. (4) Cluster analysis showed that 90 E. angustifolia plants were clustered into four clusters that were not completely clustered according to geographical distance and may be randomly affected by genotypic or environmental factors. These results will lay a foundation for further analysis of the genetic mechanism of phenotypic traits of E. angustifolia and also provide a reference for the collection, preservation, and variety improvement of E. angustifolia germplasm resources.
Planned Trajectory Classification for Wheeled Mobile Robots to Prevent Rollover and Slip

In this letter, a novel planned trajectory classification method (PTCM) is proposed to evaluate the safety of the car-like four-wheeled mobile robots (4-WMRs) with Ackermann steering and high-stiffness suspensions. To classify a planned trajectory to be safe or unsafe before the 4-WMR actually follows it, the conditions of the wheel forces (WFs: longitudinal, lateral, and normal forces for each wheel) necessary to execute the planned trajectory without rollover and slip are calculated using the passive decomposition of the WMR dynamics with the Pfaffian constraints of the no-rollover and no-slip conditions. Similar to the case of Navier's table problem, only nine-dimensional WFs projected onto the constrained space are identifiable among the twelve-dimensional WFs. This indeterminacy turns out not to affect the rollover prediction, yet does so for the slip prediction. For this, we propose novel optimistic and pessimistic methods, together upper and lower bounding the exact slip prediction. The proposed PTCM classifies the planned trajectory as safe if rollover and slip are not predicted and unsafe otherwise. The proposed PTCM is demonstrated and validated by simulations and outdoor experiments.
Biological Interfacial Materials for Organic Light-Emitting Diodes

Organic optoelectronic devices have received appreciable attention due to their low cost, mechanical flexibility, band-gap engineering, lightness, and solution processability over a broad area. Specifically, realizing sustainability in organic optoelectronics, especially in solar cells and light-emitting devices, is a crucial milestone in the evolution of green electronics. Recently, the utilization of biological materials has appeared as an efficient means to alter the interfacial properties, and hence improve the performance, lifetime and stability of organic light-emitting diodes (OLEDs). Biological materials can be known as essential renewable bio-resources obtained from plants, animals and microorganisms. The application of biological interfacial materials (BIMs) in OLEDs is still in its early phase compared to the conventional synthetic interfacial materials; however, their fascinating features (such as their eco-friendly nature, biodegradability, easy modification, sustainability, biocompatibility, versatile structures, proton conductivity and rich functional groups) are compelling researchers around the world to construct innovative devices with enhanced efficiency. In this regard, we provide an extensive review of BIMs and their significance in the evolution of next-generation OLED devices. We highlight the electrical and physical properties of different BIMs, and address how such characteristics have been recently exploited to make efficient OLED devices. Biological materials such as ampicillin, deoxyribonucleic acid (DNA), nucleobases (NBs) and lignin derivatives have demonstrated significant potential as hole/electron transport layers as well as hole/electron blocking layers for OLED devices. Biological materials capable of generating a strong interfacial dipole can be considered as a promising prospect for alternative interlayer materials for OLED applications.
Direct torque control of PMSM using sliding mode backstepping control with extended state observer

Based on the nonlinear characteristics of permanent magnet synchronous motor (PMSM), a nonlinear speed and direct torque control (DTC) using sliding mode backstepping method for PMSM is presented in this paper. The sliding mode speed controller is implemented with exponential reaching law to improve the robustness of the system, and further a step-by-step recursive design for backstepping torque and flux controllers is presented. The system stability with proposed scheme is mathematically proved using Lyapunov stability criteria. At the same time, the load torque is observed with the extended state observer (ESO), and is fed-forward to the controller for rejecting the load disturbance and to mitigate the chattering affect due to the sliding mode controller. Finally, simulation test results are demonstrated to support the effectiveness and feasibility of the proposed strategy.
Improved Ant Colony Algorithm Base on Normal Distribution for Knapsack Problem

Ant Colony Algorithm exhibitions excellent performance in solving knapsack problem.To improve the robustness,a novel improved ant colony algorithm based on the normal distribution was proposed.The sorted items' selective probabilities were updated to standard normal distribution density function.By keeping the probability in a certain range,the diversity of solutions was ensured.On the other hand,the convergence of the algorithm was improved by using strategies of "elite-update" and "winner-heredity". Experimental results show that the method is superior to existing methods in performance of robust and convergence.
Von Neumann algebras of sofic groups with β(2)1=0 are strongly 1-bounded

We show that if Γ is a finitely generated finitely presented sofic group with zero first L2-Betti number, then the von Neumann algebra L(Γ) is strongly 1-bounded in the sense of Jung. In particular, L(Γ)≆L(Λ) if Λ is any group with free entropy dimension >1, for example a free group. The key technical result is a short proof of an estimate of Jung
The Application of Duffing Oscillator in Weak Signal Detection

In this paper, a method for identifying the chaotic state of du±ng oscillator is proposed where the duffing oscillator is used for weak signal detection. This method is based on frequency spectrum analysis and filtering. Some relative aspects of this method for practical using are studied in details too. The proposed method has three properties; reasonable calculation complexity, robustness to moderate noise amount, and capability of detection with short signal sequence. The proposed approach has good robustness, which is successfully shown in this paper.
Driving Digital Transformation: Addressing the Barriers to Engagement in University-Industry Collaboration

Collaboration between university and industry can improve knowledge exchange and innovation, which is required for driving digital business transformation in organisations. This paper discusses the results of a case study that was conducted to investigate the personal (‘lived’) experiences of both industry practitioners and academics from the STEM academic unit at the University of Enterprise in Australia, regarding the barriers to building trusting knowledge-related collaborative relationships. Semi-structured face-to-face interviews were conducted with sixteen academics and industry representatives to elicit their views. We found that university and business leaders must overcome barriers to establish higher numbers of University-Industry Collaboration (UIC) agreements, including limited resources, lack of capabilities and the absence of university support. However, they must also address the challenges facing individual academics and industry professionals, such as conflicting interests and expectations, and limitations to academics’ career progression. The paper discusses how the recently established Enterprise Hub at the University supports collaboration and fostering healthy relationships between academic staff and industry partners. The findings will provide guidance to other universities and businesses to establish such supportive entities at their institutions to drive innovation and digital business transformation initiatives.
Effects of L-Carnitine Intake on Exercise-Induced Muscle Damage and Oxidative Stress: A Narrative Scoping Review

Exercise-induced muscle damage results in decreased physical performance that is accompanied by an inflammatory response in muscle tissue. The inflammation process occurs with the infiltration of phagocytes (neutrophils and macrophages) that play a key role in the repair and regeneration of muscle tissue. In this context, high intensity or long-lasting exercise results in the breakdown of cell structures. The removal of cellular debris is performed by infiltrated phagocytes, but with the release of free radicals as collateral products. L-carnitine is a key metabolite in cellular energy metabolism, but at the same time, it exerts antioxidant actions in the neuromuscular system. L-carnitine eliminates reactive oxygen and nitrogen species that, in excess, alter DNA, lipids and proteins, disturbing cell function. Supplementation using L-carnitine results in an increase in serum L-carnitine levels that correlates positively with the decrease in cell alterations induced by oxidative stress situations, such as hypoxia. The present narrative scoping review focuses on the critical evaluation of the efficacy of L-carnitine supplementation on exercise-induced muscle damage, particularly in postexercise inflammatory and oxidative damage. Although both concepts appear associated, only in two studies were evaluated together. In addition, other studies explored the effect of L-carnitine in perception of fatigue and delayed onset of muscle soreness. In view of the studies analyzed and considering the role of L-carnitine in muscle bioenergetics and its antioxidant potential, this supplement could help in postexercise recovery. However, further studies are needed to conclusively clarify the mechanisms underlying these protective effects.
A Microwell Device for the Efficient Generation of Arrays of Microtissues and Humanized Bone Marrow Micro-Ossicles

(1) Background: There are no high-throughput microtissue platforms for generating bone marrow micro-ossicles. Herein, we describe a method for the assembly of arrays of microtissues from bone marrow stromal cells (BMSC) in vitro and their maturation into bone marrow micro-ossicles in vivo. (2) Methods: Discs with arrays of 50 microwells were used to assemble microtissues from 3 × 105 BMSCs each on a nylon mesh carrier. Microtissues were cultured in chondrogenic induction medium followed by hypertrophic medium to drive endochondral ossification, and then they were implanted in NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ (NSG) mice, where they were remodeled into bone marrow micro-ossicles. Mice were transplanted with 105 human umbilical cord blood CD34+ cells. (3) Results: Micro-ossicles contained more human CD45+ cells, but fewer human CD34+ progenitor cells than mouse marrow. Human hematopoietic progenitor cells cycle rapidly at non-physiological rates in mouse marrow, and reduced CD34+ cell content in micro-ossicles is consistent with the notion that the humanized niche better controls progenitor cell cycling. (4) Conclusions: Assembling microtissues in microwells, linked by a nylon membrane carrier, provides an elegant method to manufacture and handle arrays of microtissues with bone organ-like properties. More generally, this approach and platform could aid bridging the gap between in vitro microtissue manipulation and in vivo microtissue implantation.
Developing nonlinear k-nearest neighbors classification algorithms to identify patients at high risk of increased length of hospital stay following spine surgery.

OBJECTIVE Spondylolisthesis is a common operative disease in the United States, but robust predictive models for patient outcomes remain limited. The development of models that accurately predict postoperative outcomes would be useful to help identify patients at risk of complicated postoperative courses and determine appropriate healthcare and resource utilization for patients. As such, the purpose of this study was to develop k-nearest neighbors (KNN) classification algorithms to identify patients at increased risk for extended hospital length of stay (LOS) following neurosurgical intervention for spondylolisthesis. METHODS The Quality Outcomes Database (QOD) spondylolisthesis data set was queried for patients receiving either decompression alone or decompression plus fusion for degenerative spondylolisthesis. Preoperative and perioperative variables were queried, and Mann-Whitney U-tests were performed to identify which variables would be included in the machine learning models. Two KNN models were implemented (k = 25) with a standard training set of 60%, validation set of 20%, and testing set of 20%, one with arthrodesis status (model 1) and the other without (model 2). Feature scaling was implemented during the preprocessing stage to standardize the independent features. RESULTS Of 608 enrolled patients, 544 met prespecified inclusion criteria. The mean age of all patients was 61.9 ± 12.1 years (± SD), and 309 (56.8%) patients were female. The model 1 KNN had an overall accuracy of 98.1%, sensitivity of 100%, specificity of 84.6%, positive predictive value (PPV) of 97.9%, and negative predictive value (NPV) of 100%. Additionally, a receiver operating characteristic (ROC) curve was plotted for model 1, showing an overall area under the curve (AUC) of 0.998. Model 2 had an overall accuracy of 99.1%, sensitivity of 100%, specificity of 92.3%, PPV of 99.0%, and NPV of 100%, with the same ROC AUC of 0.998. CONCLUSIONS Overall, these findings demonstrate that nonlinear KNN machine learning models have incredibly high predictive value for LOS. Important predictor variables include diabetes, osteoporosis, socioeconomic quartile, duration of surgery, estimated blood loss during surgery, patient educational status, American Society of Anesthesiologists grade, BMI, insurance status, smoking status, sex, and age. These models may be considered for external validation by spine surgeons to aid in patient selection and management, resource utilization, and preoperative surgical planning.