Miracle-dz/UniFAR-FTUs huggingface dataset:
SIMULATION LAB #4: Dynamic Modeling and Simulation of Muscle-Tendon Actuators

The force producing properties of muscle are complex, highly nonlinear and can have substantialeffects on movement (See McMahon 1984 for review). For simplicity, lumped-parameterdimensionless muscle models, capable of representing a range of muscles with differentarchitectures, are commonly used in the dynamic simulation of movement (Zajac, 1989). In thistutorial, we will review the differential equations that describe muscle activation and muscle-tendon contraction dynamics when using a Hill-type muscle model. You will useSIMM/Pipeline routines to implement muscle-tendon models and conduct some simulations toinvestigate how various model parameters can affect the dynamic response of the actuators. Thelab will conclude with a Virtual Muscle Tug-of-War in which you will design an optimal muscleand compete directly against others in the class– may the best muscle win.
Activation of WNT/beta-Catenin Signaling Enhances Pancreatic Activation of WNT/beta-Catenin Signaling Enhances Pancreatic Cancer Development and the Malignant Potential Via Up- Cancer Development and the Malignant Potential Via Up-regulation of Cyr61 regulation of Cyr61

Pancreatic ductal adenocarcinoma (PDAC), a poor prognostic cancer, commonly develops following activating mutations in the KRAS oncogene. Activation of WNT signaling is also commonly observed in PDAC. To ascertain the impact of postnatal activation of WNT-stimulated signaling pathways in PDAC development, we combined the Elastase-tva -based RCAS-TVA pancreatic cancer model with the established LSL-Kras G12D , Ptf1a-cre model. Delivery of RCAS viruses encoding β -catenin S37A and WNT1 stimulated the progression of premalignant pancreatic intraepithelial neoplasias (PanIN) and PDAC development. Moreover, mice injected with RCAS- β -catenin S37A or RCAS- Wnt1 had reduced survival relative to RCAS- GFP- injected controls ( P b .05). Ectopic expression of active β -catenin, or its DNA-binding partner TCF4, enhanced transformation associated phenotypes in PDAC cells. In contrast, these phenotypes were significantly impaired by the introduction of ICAT, an inhibitor of the β -catenin/TCF4 interaction. By gene expression profiling, we identified Cyr61 as a target molecule of the WNT/ β -catenin signaling pathway in pancreatic cancer cells. Nuclear β -catenin and CYR61 expression were predominantly detected in moderately to poorly differentiated murine and human PDAC. Indeed, nuclear β -catenin- and CYR61-positive PDAC patients demonstrated poor prognosis ( P b .01). Knockdown of CYR61 in a β -catenin-activatedpancreaticcancer celllinereduced softagar, migrationand invasionactivity. Together,thesedatasuggestthatthe WNT/ β -catenin signaling pathway enhances pancreatic cancer development and malignancy in part via up-regulation of CYR61.
The CREB Coactivator CRTC2 Is a Lymphoma Tumor Suppressor The CREB Coactivator CRTC2 Is a Lymphoma Tumor Suppressor that Preserves Genome Integrity through Transcription of DNA that Preserves Genome Integrity through Transcription of DNA Mismatch Repair Genes Mismatch Repair Genes

of that and its target MMR genes are in speciﬁc T cell lymphoma subtypes, which are microsatellite unstable. The levels of acetylated histone H3 on the CRTC2 promoter are signiﬁcantly reduced in lymphoma in comparison to normal tissue, explaining the decreased CRTC2 expression. Our results establish a role for CRTC2 as a lymphoma tumor suppressorgene that preserves genome integrity by stimulating transcription of MMR
Intelligent tutoring systems: how well can they guide students in problem-based learning scenarios

Problem-based learning (PBL) has a strong focus on skills and requires students to use self-regulated learning (SRL). However, SRL puts considerable demands on learners and often leads to students making frequent requests for guidance. This paper reports the outline design, construction and evaluation of a simple prototype web-based Intelligent Tutoring System (ITS) which was constructed to provide guidance for self-regulated learning activities in a PBL scenario. It was constructed using Chatbot technology which provides extended dialogue with students in order to guide them through the initial stages of developing learning objectives. User testing with undergraduates suggests that the system prompted them to analyse the scenario in more detail (one of the foundational skills needed in a PBL task) and that the technology can be both usable and provide the adaptability required.
TBM Excavation in Difficult Ground Conditions: Case Studies from Turkey

The book relates experience of TBM drives in difficult geology, making use of case studies from Turkey to demonstrate the influence of the local geotechnical conditions on the selection of a tunnel boring machine and the selection of tools. There is an extensive description how various geological phenomena, such as for example transition zones, dikes, rock discontinuities, blocky ground, squeezing ground, swelling clays and high strength and abrasive rocks, can reduce the advance rate and what countermeasures can be introduced. There is also a discussion of necessary advance probing and safety measures. Since the presented practical experience from Turkey can also be applicable for other tunnel projects in difficult geology, the book represents a valuable source of knowledge for every tunneler.
Faceting of twin interfaces in rolled pure magnesium

ABSTRACT Recent studies proved that the faceting of twin boundaries is a common feature in hcp materials. It was demonstrated that the asymmetrical facets in the most frequent and twin interfaces are the consequence of the twin boundary migration mechanism. We demonstrate that a similar mechanism is also applicable to less common twins, observed in pure magnesium rolled at room temperature. The migration of twin boundary is mediated by disconnections, which have Burgers vector and step height equal to four interplanar distances. The mechanism of asymmetrical facets nucleation is also demonstrated by using atomistic simulations. The simulations are supported by transmission electron microscopy (TEM) of asymmetrical basal-pyramidal interfaces of twins.
An efficient method for speed control of induction wind turbine generator with dual AC-DC-AC converter

One of the key issues in the efficient conversion of wind kinetic energy into electricity is the regulation of turbine speed to achieve maximum electrical power generation. The asynchronous generator with full load double AC-DC-AC power converter has not been widely used due to its poor performance in low wind speed. In this paper a method for turbine speed control of induction generator with full-scale double AC-DC-AC power converter to maximize absorbed wind power in the wide wind speed range, using the calculated maximum turbine power as a reference, is proposed. The configuration of an AC-DC-AC converter for connecting an asynchronous generator to the grid, as well as modeling of Pulse Width Modulation converter is presented in detail. Performance of the proposed control concept to maximize the absorbed wind power is verified through the simulation in MATLAB®. Finally, the advantages and disadvantages of the proposed control concept are discussed.
Middleware Infrastructure for Monitoring Bed Activity

This work describes a service-oriented middleware platform for ambient-assisted living and its use in two different bed-activity services: preventing bedsore and monitoring sleep. It includes detailed description of the middleware platform, its elements and interfaces, as well as a service that can classify typical user bed positions. The key idea behind our work is to leverage wireless sensor networks by collecting the received signal strength (RSS) measured among fixed general-purpose wireless devices, deployed in the environment, and a wearable one. The use of middleware infrastructure that can provide data from any kind of sensor installed at the assisted person's home is essential for ambient assisted living (AAL) applications where context information can be shared among different services. The objective of this work is to provide a middleware infrastructure for the rapid prototyping of applications of ambient intelligence (AMI) for healthcare and AAL, with a certain degree of dependability. In particular, we propose a bed position detection service that is the input for other two important services, namely bedsore prevention and sleep monitoring services. These are the core services for bed activity monitoring service (1) providing inputs for further analysis by other AAL services. Wireless sensor networks (WSNs) are supposed to be widely deployed in indoor settings and on people's bodies in tomorrow's pervasive computing environments. The proposed system leverages the presence of WSNs by collecting the received signal strength (RSS) measured among fixed general purpose wireless devices, deployed in the environment, and a wearable one. The RSS measurements are used to classify a set of user's positions in the bed, monitoring the activities of the user, and thus supporting the bedsores and the sleep monitoring issues. The proposed component-based architecture lets developers use services in a modular way. Future services will be able to use the data produced by the bed position detection service without rewriting a new ad hoc component. Bedsore prevention service: Nursing homes require caregivers that ideally observe the elderly around the clock to prevent bedsores. The caregivers have to provide a high degree of surveillance and attendance to the elderly all the time. Moreover, the knowledge and personality of caregivers affect the quality of nursing care. The most widely accepted ways of preventing bedsores is to actively turn the
Reduced temporal activation during a verbal fluency test in clinical high risk of psychosis: a functional near-infrared spectroscopy-based study

Background Clinical high risk (CHR) of psychosis is a state in which positive symptoms cause the subjects distress but do not approach a severity level that fulfils the criteria for a psychotic episode. CHR exhibits cognitive deficits; however, the underlying neurobiological mechanisms remain unclear. This study aimed to investigate whether brain activation measured by the levels of oxygenated hemoglobin (oxy-Hb) in CHR subjects could be correlated with cognitive deficits. Methods Fifty-eight CHR individuals who fulfilled the criteria for attenuated positive syndrome as specified in the Structured Interview for Prodromal Syndrome (SIPS) and the Scale of Prodromal Syndrome (SOPS) and 58 age- and sex-matched healthy participants were included in the study. All subjects completed the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) that includes tests measuring attention, verbal memory, verbal fluency, executive function, and general intelligence. Functional near-infrared spectroscopy (fNIRS) was used to measure the level of oxy-Hb in the dorsolateral prefrontal and frontotemporal cortices. Results We observed significantly decreased oxy-Hb levels in channel 32 (located in the right superior temporal gyrus, rSTG)) within the CHR individuals compared with that in the healthy controls (HCs) (t=−3.44, Bonferroni-corrected p=0.002), indicating lower brain activity. A significant positive correlation was observed between task-related β values and working memory in the CHR group (r=0.35, p=0.008). Conclusions The brain activation of rSTG is abnormal among subjects at clinicial high risk for psychosis. This abnormality is probably associated with the neural mechanisms of deficits in the working memory during the early stage of psychosis.
A Simulation-Based Toolbox to Expedite the Digital Design of Bellow Soft Pneumatic Actuators

Task-oriented applications of soft pneumatic actuators (SPAs) are growing, as digital design and rapid prototyping allow the actuator to be customised. The bellow SPA is promising for current soft material 3D printing due to small material stretch during actuation, yet the complex geometry causes challenges in physical modelling and design. This article provides the users with an open-source toolbox of simulation for a standardised bellow soft pneumatic actuator that can be 3D printed to expedite the design process. Based on MATLAB and COMSOL Multiphysics, the toolbox allows users to customise design features, automatically generate the CAD and then simulate the actuators' performance. It provides two methods to analyse the design: 1) a theoretical modelling method to quickly generate the actuator's workspace with interactive results, 2) a finite element method to evaluate in detail the actuator's deformation and force performance as well as the mechanical properties. The modelling is introduced and validated by comparing the experimental results of 3D printed bellow actuator prototypes with simulation results for angular deflection and blocked force. The maximum root-mean-square error for the theoretical modelling of deformation, finite element method of deformation and force are $9.00^{\circ}, 7.40^{\circ}$ and 1.66 g, respectively.
ReRobot: Recycled Materials for Trustworthy Soft Robots

The ethical sourcing and disposal of materials has become ubiquitous and necessary across society. However, this trend towards increased sustainability has yet to find parallels in robotics research. Within the field of soft robotics, large quantities of silicones, rubbers and other elastomers are used to construct the various soft bodies and actuators. As the field grows, this will have a large negative impact on the environment. In this work we propose the effective recycling of elastomeric materials, thereby reducing the amount of new material needed, lowering costs and reducing the amount of harmful waste. We present a non-chemical process of elastomer recycle-and-reuse, where elastomeric material from old and broken soft actuators is ground into granules ranging from $\leq \boldsymbol{1}$ mm in diameter to 3 mm in diameter and used to create new soft actuators without loss of function. Characterisation tests show that although ultimate yield strain and stress reduce with the percentage of recycled material, the silicone composites exhibit very comparable elastic properties to the pristine silicone. This suggests an effective recycling pipeline where materials are recycled into different, lower-risk, applications at each iteration. We demonstrate the effectiveness of this process in a high strain recycled bladder actuator and a soft gripper.
Electro-Ribbon Muscles for Biomimetic Wing Flapping

Birds and bats flap their wings using muscles attached directly to the wing. Bumblebees and other insects, however, drive wing movement by two pairs of muscles in the thorax, the contraction of which squeezes the whole body and causes the wings to flap. Here, we present a bumblebee-inspired compliant actuation mechanism for future autonomous and aerial robotics. The mechanism is powered by a novel thin, lightweight, inexpensive, efficient and powerful solenoid electro-ribbon actuator (SERA) that exploits the dielectrophoretic liquid zipping phenomenon. The actuator can be easily fabricated using a combination of flexible conducting and insulating materials. A SERA, made of a circular steel strip and PVC dielectric, was characterised in terms of stroke, load and work. Its behaviour with different stiffnesses was studied and demonstrated in a flapping-wing architecture to quantify wing shaft movement at various frequencies.
Impact of adverse childhood experiences on the symptom severity of different mental disorders: a cross-diagnostic study

Background Adverse childhood experiences have a significant impact on different mental disorders. Objective To compare differences in adverse childhood experiences among those with different mental disorders and their relationships in a cross-disorder manner. Methods The study included 1513 individuals aged ≥18 years : 339 patients with substance use disorders, 125 patients with schizophrenia, 342 patients with depression, 136 patients with bipolar disorder, 431 patients with obsessive-compulsive disorder (OCD), and 140 healthy controls. The Early Trauma Inventory Self Report-Short Form was used to investigate childhood traumatic experiences, and the Addiction Severity Index, Positive and Negative Syndrome Scale, Hamilton Depression Scale, Young Mania Rating Scale, and Yale-Brown Obsessive-Compulsive Scale were used to assess mental disorder severity. Correlation and multivariate logistic regression were analysed between adverse childhood experiences and clinical features. Results Levels of adverse childhood experiences were significantly different among different mental disorders. Moreover, 25.8% of patients with substance use disorders reported childhood trauma, which was significantly higher than found in the other four psychiatric disorder groups. Emotional abuse scores were positively correlated with disease severity: the higher the total trauma score, the more severe the mental disorder. Conclusions Adverse childhood experiences are a common phenomenon in those with mental disorders, and the level of trauma affects mental disorder severity. Emotional abuse is closely related to many mental disorders. The incidence or severity of mental disorders can be reduced in the future by reducing the incidence of adverse childhood experiences or by timely intervention in childhood trauma.
Modified Asymmetrical Doherty Power Amplifier Based on Symmetrical Devices for 5G Applications

This paper presents a high efficiency Doherty power amplifier (DPA) for sub-6GHz 5G base station applications. A modified asymmetrical approach based on augmentation of asymmetrical drain bias and uneven power divider is proposed for enhancing output power back-off efficiency and compensating for the gain reduction issue associated with asymmetrical drain bias DPA. Simulation results show a drain efficiency of 43.2–52.4% at 10 dB output power back-off over the frequency band of 3.4–3.6 GHz.
Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network

The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.
Psychometric Properties and Factor Structure of the Patient Health Questionnaire-9 as a Screening Tool for Depression Among Ecuadorian College Students

Background The Patient Health Questionnaire-9 (PHQ-9) is the most well-known self-report measure to screen for depressive symptomatology, although discerning which is the factor structure that represents the best fit remains a challenge. Objective The aim of this study was to analyze the psychometric properties and factorial structure of the Spanish version of the PHQ-9 in a large sample of Ecuadorian college students. Methods A total of 5,394 students (M = 22.03 years; SD = 3.05; 54.8% female) from three Ecuadorian universities were surveyed using a computerized questionnaire within a 4-week assessment period. The PHQ-9 was tested for factorial structure, reliability, and correlations with other mental health-related measures. Results The hierarchical model that included a general factor and three nested factors had a better fit, and such a hierarchical structure remained invariant across sex and age. Total omega value (ω) was 0.90 for the total scale and hierarchical omega values (ωh) were 0.68 for the general factor, as well as 0.34, 0.38, and 0.44 for the respective nested factors (somatic, cognitive/affective, concentration/motor). Moreover, PHQ-9 scores were significantly higher among women, although effect sizes of comparison of those sex differences were very small. Finally, the PHQ-9 scores correlated with multiple mental health indicators (psychological inflexibility, loneliness, stress, and satisfaction with life). Discussion The Spanish version of the PHQ-9 showed good psychometric properties among Ecuadorian college students. The hierarchical structure showed the best fit for the data, including not only a dominant general factor, but also the somatic, cognitive/affective and concentration/motor subcomponents. In conclusion, the PHQ-9 tool seems to represent an easy-to-implement screening questionnaire to design preventive programs for those Ecuadorian college students at risk of developing depressive disorders.
Distribution of Different Scrippsiella acuminata (Dinophyta) Cyst Morphotypes in Surface Sediments of the Black Sea: A Basin Scale Approach

Plankton cyst abundance and distribution is controlled by multiple factors. The stress linked to the fluctuations and variations of the environmental conditions in the water column is a major vector of encystment and intraspecific variability is an important adaptive strategy. The present study aims to disclose a link between the spatial distribution and abundance of different cyst morphotypes of Scrippsiella acuminata complex in surface sediments collected in the Black Sea at 34 sites and selected environmental variables. With this purpose, a basin scale data set was analyzed for patterns of intraspecific spatial heterogeneity. Redundancy analysis (RDA) was implemented to identify explanatory environmental variables associated with the cyst morphotypes abundance. Environmental multiyear data were used to ensure better approximation of a model that links environmental gradients with cyst abundance. Our results show that all S. acuminata cysts morphotypes are significantly correlated to one or a combination of the environmental variables, i.e., salinity, temperature and nutrients (nitrates and phosphates). The geographical distribution of Scrippsiella blooms in the Black Sea indicates that the interplay between the planktonic and benthic habitat of the dinoflagellate gives to S. acuminata the advantage to dominate in the plankton communities.