Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Variable Impedance Control of a Powered Knee-Ankle Prosthesis for Sit, Stand, and Walk with Minimal Tuning

Although the average healthy adult transitions from sit to stand over 60 times per day, most research on powered prosthesis control has only focused on walking. In this paper, we present a data-driven controller that enables sitting, standing, and walking with minimal tuning. Our controller comprises two high level modes of sit/stand and walking, and we develop heuristic biomechanical rules to control transitions. We use a phase variable based on the user's thigh angle to parameterize both walking and sit/stand motions, and use variable impedance control during ground contact and position control during swing. We extend previous work on data-driven optimization of continuous impedance parameter functions to design the sit/stand control mode using able-bodied data. Experiments with a powered knee-ankle prosthesis used by a participant with above-knee amputation demonstrate promise in clinical outcomes, as well as trade-offs between our minimal-tuning approach and accommodation of user preferences. Specifically, our controller enabled the participant to complete the sit/stand task 20% faster and reduced average asymmetry by half compared to his everyday passive prosthesis. The controller also facilitated a timed up and go test involving sitting, standing, walking, and turning, with only a mild (10%) decrease in speed compared to the everyday prosthesis. Our sit/stand/walk controller enables multiple activities of daily life with minimal tuning and mode switching.
The impact of pre-existing venous pathology on lower limb free flap reconstruction and the role of preoperative screening: a systematic review

Lower limb reconstruction is performed to replace like with like and achieve tissue durability. Free flaps are a method of reconstruction commonly used to manage cases of lower limb deficits. However, the failure rate is 8.5%, with venous thrombosis and congestion playing a significant role. Despite this, preoperative venous mapping of recipient site before free flap reconstruction is not routinely practiced, often resulting in pathologies remaining unidentified until the intraoperative stage. The aim of the review was to evaluate the existing literature on the topic of lower limb preoperative venous assessment, screening, and its effect on lower limb free flap survival. Five different databases were searched from their inception to August 2021. The search terms and included studies were independently reviewed by two investigators for their eligibility. Eleven articles were eligible for inclusion, with a combined patient population of 99, and 107 flaps were identified to have lower limb pathology at the donor or recipient vein. Venous pathology was detected preoperatively in 69 veins using ultrasound duplex scanning and computed tomography angiography; of them, 3 (4.34%) resulted in failure. In comparison, 38 veins were diagnosed with venous pathology intraoperatively; of them, 5 (13.85%) failed. The studies evaluated in this review demonstrated that preoperative screening for venous pathology showed a higher flap survival rate. It can therefore be inferred that developing a standardized preoperative process for identifying venous issues in lower limb free flap reconstruction may improve outcomes. This can be explored in future research, with a focus on assessing the validity and efficacy of such screening tools, and their role in the management of patients identified with venous pathology.
Therapeutic Effect of Kidney Tubular Cells-Derived Conditioned Medium on the Expression of MicroRNA-377, MicroRNA-29a, Aquapurin-1, Biochemical, and Histopathological Parameters Following Diabetic Nephropathy Injury in Rats

Background: Diabetic nephropathy (DN) is a critical complication of diabetes mellitus. This study evaluates whether administration of conditioned medium from kidney tubular cells (KTCs-CM) has the ability to be efficacious as an alternative to cell-based therapy for DN. Materials and Methods: CM of rabbit kidney tubular cells (RK13; KTCs) has been collected and after centrifugation, filtered with 0.2 filters. Four groups of rats have been utilized, including control, DN, DN treated with CM, and sham group. After diabetes induction by streptozotocin (50 mg/kg body weight) in rats, 0.8 ml of the CM was injected to each rat three times per day for 3 consecutive days. Then, 24-h urine protein, blood urea nitrogen (BUN), and serum creatinine (Scr) have been measured through detection kits. The histopathological effects of CM on kidneys were evaluated by periodic acid–Schiff staining and the expression of microRNAs (miRNAs) 29a and 377 by using the real-time polymerase chain reaction. The expression of aquapurin-1 (AQP1) protein was also examined by Western blotting. Results: Intravenous injections of KTCs-CM significantly reduced the urine volume, protein 24-h, BUN, and Scr, decreased the miRNA-377, and increased miRNA-29a and AQP1 in DN treated with CM rats. Conclusion: KTCs-CM may have the potential to prevent kidney injury from diabetes by regulating the microRNAs related to DN and improving the expression of AQP1.
Robot Skill Learning with Identification of Preconditions and Postconditions via Level Set Estimation

Hierarchical algorithms have often been used to plan and execute complicated robotic sequential manipulation tasks, where an abstract planner searches for a skill sequence in an abstract space, and each skill generates actual motions on the basis of the planned skill sequences. To generate executable plans, the abstract planner should know the pre-/postconditions of each skill and appropriately choose skills so that the generated plan satisfies their pre-/postconditions. For such hierarchical planning, this paper presents a novel method for robot skill learning that learns not only a control policy but also the learned skill's pre-/postconditions to complete a given task. Our method combines an optimal control method and an active learning approach called level set estimation (LSE) to effectively collect training data for learning control policies and pre-/postconditions. Although there exists a LSE-based policy learning algorithm that identifies preconditions, its performance is limited to cases where the dimension of the search space for pre-/postconditions is low. The main contribution of this paper is the proposal of a new learning method that can handle tasks having a high-dimensional search space for pre-/postconditions. We demonstrate our proposed method in two robotic tasks. The results show that our method can more effectively learn a control policy and its pre-/postconditions compared with the existing LSE-based method.
Study of Electromagnetic Vibration Suppression for Permanent Magnet Synchronous Motors

The vibration of permanent magnet synchronous motors (PMSM) can be divided into three frequency bands, mechanical vibration at low frequency, mid-frequency electromagnetic vibration caused by electromagnetic force at even multiples of electrical frequency, and high-frequency vibration near the switching frequency. This paper studies the influence of different rotor structures on the torque ripple and electromagnetic vibration of the PMSM. Taking a PMSM with 9 slots and 6 poles (9s6p) as example, simulation results show that its electromagnetic vibration is mainly caused by the electromagnetic force with the 3rd spatial order and the 2nd time order. The vibration can be reduced, without compromising the torque, by optimizing the motor geometric parameters. Finally, vibration test of the PMSM is carried out, and the experiment results verify the simulation.
The Relationship Between Emotional Maturity and Aggressiveness in the Football Supporters Group in Sidoarjo

Recently, cases about the aggressiveness of football fans are increasingly being reported in the mass media. Emotional maturity is one of the factors that triggers the emergence of aggressiveness in football fans. The purpose of this study was to determine the relationship between emotional maturity and aggressiveness in the football supporters group X in Gedangan sub-district. This study uses a correlational quantitative approach. In this study used a sample of 108 people obtained from sampling using purposive sampling technique. The data collection technique was carried out using the aggressiveness scale and emotional maturity scale, which were compiled using a Likert scale. The data were analyzed by testing the hypothesis using the Pearson Correlation Product Moment with the help of the SPSS v.19 for windows program. The results showed a correlation coefficient of -. 961 (significant. 0.000 <0.05). Thus, the research hypothesis is accepted, that there is a significant negative relationship between emotional maturity and aggressiveness in the football supporters group X in Gedangan sub-district. A person's high emotional maturity will have an impact on low aggressiveness. Conversely, a person's low emotional maturity will result in an increase in aggressiveness. In addition, it is known that half of all X football supporters in Gedangan sub-district (52.8%) have low emotional maturity. Meanwhile, in terms of aggressiveness, half of all X football supporters in Gedangan sub-district (54.6%) show high aggressiveness. Meanwhile, the results of the coefficient of determination test show that the emotional maturity variable has an influence of 77.5% on the aggressive behavior variable.
A Training-Evaluation Method for Nursing Telerobot Operator with Unsupervised Trajectory Segmentation

To cope with the difficulty of training and eval-uation for nursing telerobot operator. This paper proposes a training-evaluation method for operator with unsupervised trajectory segmentation. To evaluate the dexterity and proce-dural knowledge of the operators objectively, we propose a new unsupervised model TSC-CRP that can automatically segment trajectory from nursing robotic training sessions. By comparing the segmented sub-trajectories and the standard sub-trajectory process, the method can provide objective evaluation and meaningful feedback without the intervention from experts. Experiments show that TSC-CRP has higher segmentation accuracy than other unsupervised methods, and it can identify the operators with different skill levels. In practical, the proposed training-evaluation system allows to provide an in-depth analysis of operator action to assess their skills precisely.
Isotropic Turbulent Mixing under a Second‐Order Chemical Reaction

The decay of a dynamically passive reactant in a stationary isotropic turbulent velocity field is investigated using the direct‐interaction and (modified) quasi‐normal closures. The reactant undergoes an isothermal second‐order chemical reaction of single species. The dynamical behavior can be characterized approximately by the ratio of the Damkohler to the Peclet numbers. For slow reactions, only the convective mixing action is important and the closures can be applied without restrictions just as in the turbulent mixing case. For the closures to yield consistent results with fast reactions, the initial mean concentration must be at least twice the initial rms fluctuations. Then the effect of reactive nonlinear interaction is so meager that the two modes of closure make little difference. Otherwise, application of the closure schemes which evoke initial Gaussian conditions is unwarranted and thus presents a convergence difficulty. Simple decay expressions of the mean concentration and rms fluctuations ar.
Efficient horn antennas for next-generation terahertz and millimeter-wave space telescopes

Astronomical observations in the far-infrared are critical for investigation of cosmic microwave background (CMB) radiation and the formation and evolution of planets, stars and galaxies. In the case of space telescope receivers a strong heritage exists for corrugated horn antenna feeds to couple the far-infrared signals to the detectors mounted in a waveguide or cavity structure. Such antenna feeds have been utilized, for example, in the Planck satellite in both single-mode channels for the observation of the CMB and the multi-mode channels optimized for the detection of foreground sources. Looking to the demands of the future space missions, it is clear that the development of new technology solutions for the optimization and simplification of horn antenna structures will be required for large arrays. Horn antennas will continue to offer excellent control of beam and polarization properties for CMB polarisation experiments satisfying stringent requirements on low sidelobe levels, symmetry and low cross polarization in large arrays. Similarly for mid infrared systems multi-mode waveguide structures will give high throughput to reach the required sensitivities. In this paper we present a computationally efficient approach for modelling and optimising horn characteristics. We investigate smooth-walled profiled horns that have a performance equivalent to that of the corrugated horns traditionally used for CMB measurements. We discuss the horn optimisation process and the algorithms available to maximise performance of a merit parameter such as low cross polarisation or high Gaussicity.
The Impact of Different Drying Temperatures on Black Ginger Slices in Relation to Different Applications of Growing Media

Black ginger (Kaempferia parviflora Wall. Ex. Baker) or “halia hitam” in Malay has been traditionally used as health-promoting herbs in relieving body pains, allergies, gastrointestinal disorders, and fungal infections. Recently, black ginger has increased attention from many researchers to identify its medicinal value to treat diseases. In line with that, the objective of the study is to determine the impact of drying temperatures of black ginger rhizomes slices on the different applications of growing media. The experiment was arranged in a complete randomized design with two factors viz. i) different growing media [cocopeat, cocopeat+rice husk biochar, cocopeat+palm kernel shell biochar, cocopeat+sugarcane bagasse biochar, and cocopeat+coconut shell biochar] and ii) two different drying temperature (50 °C & 75 °C) with three replications. The postharvest parameters were total phenolic compound (TPC), total antioxidant (TA), water activity (Aw), and mineral nutrients content. In conclusion, rhizomes planted in Cocopeat+600g biochar substrates such as sugarcane bagasse, rice husk, palm kernel shell, and coconut shell could be suggested as effective growing media for black ginger cultivation under fertigation system and continued with 75 °C drying temperatures without adversely affecting the postharvest quality of black ginger.
Regional vs global physical therapy interventions to treat chronic pain in survivors of trauma: a randomized controlled trial

ABSTRACT Background A history of traumatic life events is associated with chronic pain in later life. Physical therapists utilize a variety of methods to treat pain, however, they have struggled to find effective interventions to improve patient outcomes. Objective To compare impairment-based, regional (REGION-PT) physical therapy (PT) to a global (GLOBAL-PT) model consisting of pain neuroscience education, graded motor imagery, and exercise for adults with chronic pain and history of trauma. Design Randomized Controlled Trial. Methods Adults ≥ 18 years of age with chronic pain and a history of ≥1 trauma identified through the Life Events Checklist received the allocated intervention once a week for six weeks. Treatment effects were assessed using linear mixed models. Results Ninety-eight participants completed the trial. There were no difference in outcomes between groups. There were significant interactions between race and intervention. Both interventions were associated with improvements in pain interference for white participants, but non-white participants experienced improvement only with GLOBAL-PT. Regardless of allocation, participants improved in physical function, six of the PROMIS-29 domains, and in pain interference measures. Conclusion Both interventions are reasonable strategies for individuals with chronic pain and a history of trauma.
Meeting the confused patient with confidence: perceived benefits of communication partner training in subacute TBI

ABSTRACT Objective This study aims to explore health professionals’ perceived benefits of implementing Communication Partner Training (CPT) using Supported Conversation for Adults with Aphasia (SCA™) in a subacute rehabilitation setting with patients in post-traumatic confusional state (PTCS) after TBI. Method The study was conducted in a clinical setting using a pre-post questionnaire design to explore change. One hundred and four interdisciplinary clinicians attended CPT in the SCA™ method and subsequent implementation support. Participants completed a questionnaire with both quantitative and qualitative questions before and after the training and implementation period. Data were analyzed using descriptive and inferential statistics and qualitative content analysis. Results Participants’ perceived confidence and self-assessed ability to communicate with patients in PTCS significantly increased after CPT (p = 0.006). While participants still experienced communication challenges, they reported using CPT-related tools and strategies in their interactions. Participants found they could apply strategies to improve patients’ comprehension of information and to confirm their understanding of patients’ communication. However, using strategies to enhance patients’ expressive abilities was perceived as more challenging. Conclusions Training health professionals in CPT increase their confidence in managing communication with patients in PTCS. Further research is needed to evaluate the efficacy of CPT within a more rigorous research design.
Slip Anticipation for Grasping Deformable Objects Using a Soft Force Sensor

Robots using classical control have revolutionised assembly lines where the environment and manipulated objects are restricted and predictable. However, they have proven less effective when the manipulated objects are deformable due to their complex and unpredictable behaviour. The use of tactile sensors and continuous monitoring of tactile feedback is there-fore particularly important for pick-and-place tasks using these materials. This is in part due to the need to use multiple points of contact for the manipulation of deformable objects which can result in slippage with inadequate coordination between manipulators. In this paper, continuous monitoring of tactile feedback, using a liquid metal soft force sensor, for grasping deformable objects is presented. The trained data-driven model distinguishes between successful grasps, slippage and failure during a manipulation task for multiple deformable objects. Slippage could be anticipated before failure occurred using data acquired over a 30 ms period with a greater than 95% accuracy using a random forest classifier. The results were achieved using a single sensor that can be mounted on the fingertips of existing grippers and contributes to the development of an automated pick-and-place process for deformable objects.
Fraud Detection in Banking Data by Machine Learning Techniques

As technology advanced and e-commerce services expanded, credit cards became one of the most popular payment methods, resulting in an increase in the volume of banking transactions. Furthermore, the significant increase in fraud requires high banking transaction costs. As a result, detecting fraudulent activities has become a fascinating topic. In this study, we consider the use of class weight-tuning hyperparameters to control the weight of fraudulent and legitimate transactions. We use Bayesian optimization in particular to optimize the hyperparameters while preserving practical issues such as unbalanced data. We propose weight-tuning as a pre-process for unbalanced data, as well as CatBoost and XGBoost to improve the performance of the LightGBM method by accounting for the voting mechanism. Finally, in order to improve performance even further, we use deep learning to fine-tune the hyperparameters, particularly our proposed weight-tuning one. We perform some experiments on real-world data to test the proposed methods. To better cover unbalanced datasets, we use recall-precision metrics in addition to the standard ROC-AUC. CatBoost, LightGBM, and XGBoost are evaluated separately using a 5-fold cross-validation method. Furthermore, the majority voting ensemble learning method is used to assess the performance of the combined algorithms. LightGBM and XGBoost achieve the best level criteria of ROC-AUC = 0.95, precision 0.79, recall 0.80, F1 score 0.79, and MCC 0.79, according to the results. By using deep learning and the Bayesian optimization method to tune the hyperparameters, we also meet the ROC-AUC = 0.94, precision = 0.80, recall = 0.82, F1 score = 0.81, and MCC = 0.81. This is a significant improvement over the cutting-edge methods we compared it to.
PowerDP: De-Obfuscating and Profiling Malicious PowerShell Commands With Multi-Label Classifiers

In recent years, PowerShell has become the common tool that helps attackers launch targeted attacks using living-off-the-land tactics and fileless attack techniques. Unfortunately, malware-derived PowerShell Commands (PSCmds) have typically been obfuscated to hide the malicious intent from detection and analysis. Also, malicious PSCmds’ expansive use of multiple obfuscation strategies and encryption methods makes them difficult to be revealed. Despite the advances in malicious PSCmds detection incorporating new approaches such as machine learning and deep learning, there is still no consensus on the solution to de-obfuscating malicious PSCmds and profiling their behavior. To address this challenge, we propose a hybrid framework that combines deep learning and program analysis for automatic PowerShell De-obfuscation and behavioral Profiling (PowerDP) through multi-label classification in a static manner. First, we use character distribution features to forecast obfuscation types of malicious PSCmds. Second, we developed an extensive de-obfuscator utilizing static regular expression replacement to recover the original content of obfuscated PSCmds based on the predicted obfuscation types. Finally, we profile the behavior of PSCmds by features extracted from the abstract syntax tree of PSCmds after de-obfuscation. Our results show that PowerDP achieves a promising 99.82% accuracy and 0.18% hamming loss in obfuscation multi-label classification using deep learning. Furthermore, the successful recovery rate of the de-obfuscator against 15 obfuscation types is 98.11% on average with semantic similarity comparison, and the accuracy of the behavior multi-label classification for identifying 5 behaviors in malicious PSCmds averages 98.53%. The evaluation indicates that PowerDP is able to classify and profile complicated PSCmds.
Random polytopes and the Efron--Stein jackknife inequality

Let K be a smooth convex body. The convex hull of independent random points in K is a random polytope. Estimates for the variance of the volume and the variance of the number of vertices of a random polytope are obtained. The essential step is the use of the Efron-Stein jackknife inequality for the variance of symmetric statistics. Consequences are strong laws of large numbers for the volume and the number of vertices of the random polytope. A conjecture of Barany concerning random and best-approximation of convex bodies is confirmed. Analogous results for random polytopes with vertices on the boundary of the convex body are given.