Miracle-dz/UniFAR-FTUs huggingface dataset:
Passivity-based Dynamic Bipedal walking with Terrain Adaptability - Dynamics, Control and Robotic Applications

This paper presents an approach for passivity-based bipedal robots to achieve stable walking on uneven terrain. A powered two-dimensional seven-link walking model with flat feet and compliant ankles has been proposed to analyze and simulate the walking dynamics. We further describe an optimization based control method, which uses optimized hip actuation and ankle compliance as control parameters of bipedal walking. Satisfactory results of simulations and real robot experiments show that the passivity-based walker can achieve stable bipedal walking with larger ground disturbance by the proposed method in view of stability and efficiency.
Generate‐map‐reduce: An extension to map‐reduce to support shared data and recursive computations

It is difficult to express the parallelism present in complex computations by using existing higher level abstractions such as MapReduce and Dryad. These computations include applications from wide variety of domains, like Artificial Intelligence, Decision Tree Algorithms, Association Rule Mining, Recommender Systems, Graph Algorithms, Clustering Algorithms, Compute Intensive Scientific Workflows, Optimization Algorithms, and so forth. Their execution graphs introduce new challenges in terms of programmer expressibility and runtime performance such as iterative and recursive computations, shared communication model, and so forth. We propose an extension to MapReduce, called Generate‐Map‐Reduce (GMR), targeted towards modeling these applications. GMR introduces a new Generate abstraction into the MapReduce framework that captures recursive computations. The runtime also supports iterative jobs and a distributed communication model by using shared data structures. We illustrate recursive computations with GMR by modeling complex applications such as simulated annealing, A* search, and adaptive quadrature computation that require recursive spawning of new tasks to handle variable degree of parallelism. GMR runtime supports caching of common data across iterations in memory and local disks. We illustrate how this caching helps in achieving significant speedup for iterative computations by modeling k‐means clustering. Copyright © 2013 John Wiley & Sons, Ltd.
A dual Iterative Learning Control loops for cascade systems

Iterative Learning Control (ILC) is a practical control methodology which can “learn” from the experience gained from past iterations. ILC has been widely used in many industrial repetitive processes and has shown its potential in achieving perfect tracking performance. This paper focuses on time-varying cascade systems with two sub-systems. An ILC algorithm is available for each sub-system to ensure the convergence. By connecting two ILC loops with a proper time-scale separation, the main result shows that the cascade system semi-globally practically uniformly converges to the desired trajectory. Simulation result supports the main result.
The Effects of Running 1,200 km Within 17 Days on Body Composition in a Female Ultrarunner—Deutschlandlauf 2007

We describe the changes of body composition in the female overall winner of the Deutschlandlauf 2007 over 17 stages from the northeast to the southwest of Germany with average daily running stages of 70.9 km to cover the total distance of 1,200 km. Determined by bioelectrical impedance analysis, body mass (BM) increased, percent body fat (% BF) decreased, and percent body water as well as lean body mass (LBM) increased. Skeletal muscle mass and % BF as determined by an anthropometric method showed no changes. This data show, that this female runner achieved an excellent performance and that it is possible for a woman to beat all the men. This type of analysis provides a unique opportunity to gain insight into the physiological changes during multiday running in ultraendurance athletes.
Sketch Recognition with Deep Visual-Sequential Fusion Model

In this paper, a deep end-to-end network for sketch recognition, named Deep Visual-Sequential Fusion model (DVSF) is proposed to model the visual and sequential patterns of the strokes. To capture the intermediate states of sketches, a three-way representation learner is first utilized to extract the visual features. These deep features are simultaneously fed into the visual and sequential networks to capture spatial and temporal properties, respectively. More specifically, visual networks are novelly proposed to learn the stroke patterns by stacking the Residual Fully-Connected (R-FC) layers, which integrate ReLU and Tanh activation functions to achieve the sparsity and generalization ability. To learn the patterns of stroke order, sequential networks are constructed by Residual Long Short-Term Memory (R-LSTM) units, which optimize the network architecture by skip connection. Finally, the visual and sequential representations of the sketches are seamlessly integrated with a fusion layer to obtain the final results. Experiments conducted on the benchmark sketch dataset TU-Berlin demonstrate the effectiveness of the proposed method, which outperforms the state-of-the-art approaches.
Impact of a Short-Term, Moderate Intensity, Lower Volume Circuit Resistance Training Programme on Metabolic Risk Factors in Overweight/Obese Type 2 Diabetics

The purpose of this study was to evaluate the effects of an 8-week, low frequency, hospital-based resistance training programme on metabolic risk factors in type 2 diabetic patients. Participants were self-selected into either an 8-week resistance training programme or a control group. Anthropometric indices, fasting glucose, HbA1c, total cholesterol, HDL and LDL lipoproteins, triglycerides, fasting insulin, and insulin sensitivity were assessed at baseline and 8 weeks later. Six participants were recruited (age 53 ± 9 years; BMI 32 ± 3 kg·m−2), and a further six participants acted as controls (age 55 ± 9 years; BMI 31 ± 3 kg·m−2). After training, waist circumference and waist-to-hip ratio were significantly reduced, with no associated changes in the control group. Metabolic risk factors remained unchanged following training (P > 0.05). We concluded that an 8-week, low frequency, resistance training programme reduced abdominal fat content but had little impact on metabolic risk factor modification in type 2 diabetics.
Hepatopulmonary syndrome in Egyptian patients with HCVRelated chronic liver disease

Background and Aim: Hepatopulmonary syndrome (HPS) is defined as the presence of liver disease in association with intrapulmonary vascular dilatation and arterial hypoxemia. This work aimed at evaluation of hepatopulmonary syndrome in Egyptian patients with HCV-related chronic liver disease. Patients and Methods: This cross-sectional study included sixty patients with HCV-related chronic liver disease who underwent complete clinical evaluation, laboratory investigations, abdominal ultrasonography, plain chest x-ray, arterial blood gas analysis to assess partial pressure of arterial oxygen (PaO2), partial pressure of arterial carbon dioxide (PaCO2) and alveolar-arterial oxygen gradient (A-aDO2) in addition to pulmonary function tests. Results: The prevalence of HPS was 55% (33 out of the 60 patients). Thirteen cases of them were in Child B and 20 cases in Child C group. There was a highly significant difference between Child A & C and between Child B & C classes as regards PaO2.There was highly significant difference between the three Child classes as regards spirometry pattern, where 40% of Child C patients showed mild restrictive pattern. There was highly significant difference between positive HPS and negative HPS patients as regards PaO2, A-aDO2, VC (L), FVC (L), FEV-1(L/sec), and FEF25-75% (L/sec). There was highly significant positive correlation between A-aDO2 and each of age, INR and portal vein diameter. There was highly significant negative correlation between A-aDO2 and each of serum albumin, PaO2, VC, FVC, FEV-1 and FEF 25-75%. A cutoff level ≥ 18.4 mmHg for A-aDO2 could detect positive HPS with a sensitivity of 100%, specificity of 96.3%, PPV of 97.1% and NPV of 100%. Conclusion: A cutoff level ≥ 18.4 mmHg for A-aDO2 could detect HPS. There is significant correlation between the severity of chronic liver disease and the presence of HPS.
Slippage control in hand prostheses by sensing grasping forces and sliding motion

Controlling grasp and avoiding object slippage is very important in hand prosthetic systems. At present the amputee must use vision to monitor grasp, and adjust grasping force based on this estimate. We investigate the problem of measuring normal and tangential forces at the gripper-object interface, and design a slip tactile sensing system for this purpose. In this paper we discuss the design, fabrication and testing of a tactile sensing system intended for application to a myoelectrically controlled hand prosthesis, and outline a control strategy for preventing object slippage using sensory feedback. <<ETX>>
DFT‐based QSAR Models to Predict the Antimycobacterial Activity of Chalcones

In this study, antimycobacterial activity of a set of synthesized chalcone derivatives against Mycobacterium tuberculosis H37Rv was investigated by quantitative structure–activity relationship (QSAR) analysis using density functional theory (DFT) and molecular mechanics (MM+)‐based descriptors in both gas and solvent phases. The best molecular descriptors identified were hardness, EHOMO, MRA‐4 and MRB‐4′ that contributed to the antimycobacterial activity of the chalcones as independent factors. The correlation of these four descriptors with their antimycobacterial activity increases with the inclusion of solvent medium, indicating their importance in studying biological activity. QSAR models revealed that in gas phase, lower values of EHOMO, MRA‐4 and MRB‐4′ increase the antimycobacterial activity of the chalcone molecules. However, in solvent phase, lower values of EHOMO and MRB‐4′ and higher values of MRA‐4 increase their activity.
Right kidney Left kidney Liver Spleen Nucleus Cytoplasm Nucleus Stroma

Histology is the study of the microscopic anatomy of cells and tissues of organisms. Histological analysis is performed by examining a thin slice (section) of tissue under a light (optical) or electron microscope (Jungueira & Carneiro, 2005, Kiernan, 2008, Ross et al., 2002, Mills, 2006). After a sequence of technical procedures for tissue preparation (i.e., fixation, dehydration, clearing, infiltration, embedding, sectioning, and staining), histology images can be produced by different imaging techniques (Murphy, 2001), based on which manual or automated analysis can be conducted to detect diseased tissues. In present research, the study of histology images is commonly regarded as the gold standard for the clinical diagnosis of cancers and identification of prognostic and therapeutic targets. In practice, histopathologists visually examine the regularities of cell shapes and tissue distributions and determine cancerous regions and malignancy degree. In addition, histology analysis may be applied to connect the tissue study to gene function analysis at molecular level. For example, in the research of pleiotropy (the association of multiple phenotypes with a single gene), histology analysis provides a potentially powerful tool to study functional genomics by exploring various phenotypic traits. Histopathological study has been conducted for numerous cancer detection and grading applications, including cervix (Keenan et al., 2000, Guillauda et al., 2004, 2005, Price et al., 2003), prostate (Monaco et al., 2009, Naik et al., 2007, Doyle et al., 2007), breast (Doyle et al., 2008, Naik et al., 2008), and lung (Kayser et al., 2002, Schmid et al., 2006, Jütting et al., 1999) cancer grading. A complete review of all these applications is beyond the scope of this chapter. In this chapter we use cervical intraepithelial neoplasia (CIN) classification as an example to illustrate common histopathology image analysis functions. For other cancers, interested readers are referred to the references. Overall, these applications share similar features in the computer techniques that are applied to support clinicians by automatic histology image feature extraction and categorization. Medical image processing and analysis in radiology (e.g. X-ray, ultrasound, Computed Tomography (CT), and Magnetic Resonance image (MRI)) and cytology domains have been major research fields for several decades, and numerous systems (Bankman, 2008, He, 2009, Greenberg, 1984) and software platforms1 2, 3 (Ibanez & Schroeder, 2005) have been developed for these domains. The application of these systems to histology analysis is not straightforward due to the significantly different
An integrated metamodel for knowledge representation in geolibraries

Geolibraries containing large volumes of complex spatiotemporal data occur in a variety of application domains. Considerable effort has gone into the design of metadata standards to enable exchange and sharing of georeferenced data sets. Strategies for federating information stores have to deal with integration at several levels, syntax, structure as well as semantics. This paper describes a generic metamodel for geolibraries. It encompasses a conceptually partitioned view of the data-space including metadata at various levels of a geolibrary. The model places particular emphasis on ontologies representing domain-dependent knowledge that exists within a research community, to facilitate resource discovery and retrieval of information from heterogeneous data stores.
The Effect of Pituitary Adenylate Cyclase Activating Polypeptide (PACAP) on Exocrine Pancreatic Secretion in Dogs

The effect of new hypothalamic peptides, PACAP38 and PACAP27, on exocrine pancreatic secretion was studied in five conscious dogs each with a chronic gastric fistula and a Thomas duodenal fistula. The intravenous injections of PACAP38 and PACAP27 (2.5–100 pmol/kg) induced dose-related increases in pancreatic fluid, bicarbonate, and protein secretion. The potency of PACAP27 was equal to that of VIP but was only 1/100 that of secretin; PACAP38 was less active (<%o) than PACAP27. PACAP38 and PACAP27, in contrast to secretin and VIP, stimulated protein secretion; the potency of the former was about Vw that of the latter. The peak response to PACAP27 was about 44% of the maximal response to CCKS. Pancreatic protein secretion, but not fluid and bicarbonate secretion, in response to PACAP38 and PACAP27 was significantly (p <0.05) reduced by atropine. It is concluded that PACAP has a VIP-like action on pancreatic fluid and bicarbonate secretion in conscious dogs, but differs from VIP in stimulating protein secretion via a cholinergic mechanism.
Laser‐Assisted Hair Transplantation: Histologic Comparison Between CO2 and Ho:YAG Lasers

BACKGROUND Various laser wavelengths and devices have been advocated for use in the creation of recipient channels during hair transplant surgery, including flash-scanned CO2, Ho:YAG (λ = 2.12 μm), and Er:YAG (λ = 2.94 μm). OBJECTIVE To determine the tissue injury caused by flash-scanned CO2 and pulsed Ho:YAG lasers during the creation of hair transplant recipient channels and to assess the efficacy of the Ho:YAG laser. METHODS Recipient channels were created in vivo in human scalp tissue using both lasers, and were excised and prepared for histologic examination. Optical micrometry of tissue sections was used to assess thermal injury. RESULTS The Ho:YAG laser created jagged, irregular-shaped channels with larger zones of thermal injury (superficial deepithelialization, thermal necrosis, and thermal damage). In contrast, the CO2 laser produced well-defined cylindrically shaped channels free of cellular debris with minimal epithelial disruption and significantly less lateral thermal injury. CONCLUSION Given that the Ho:YAG produced larger regions of thermal injury and recipient channels that were unacceptable for graft, the CO2 laser remains the better choice for the creation of recipient channels during hair transplant surgery. However, ongoing research will be necessary to determine the optimal laser wavelength and/or devices for this procedure.
Lumbar Segmental Nerve Blocks with Local Anesthetics, Pain Relief, and Motor Function: A Prospective Double-Blind Study Between Lidocaine and Ropivacaine

Selective segmental nerve blocks with local anesthetics are applied for diagnostic purposes in patients with chronic back pain to determine the segmental level of the pain. We performed this study to establish myotomal motor effects after L4 spinal nerve blocks by lidocaine and ropivacaine and to evaluate the relationship with pain. Therefore, 20 patients, of which 19 finished the complete protocol, with chronic lumbosacral radicular pain without neurological deficits underwent segmental nerve blocks at L4 with both lidocaine and ropivacaine. Pain intensity scores (verbal numeric rating scale; VNRS) and the maximum voluntary muscle force (MVMF; using a dynamometer expressed in newtons) of the tibialis anterior and quadriceps femoris muscles were measured on the painful side and on the control side. The median VNRS decrease was 4.0 (P < 0.00001; Wilcoxon’s signed rank test), without significant differences between ropivacaine and lidocaine (Mann-Whitney U-test). A difference in effect on MVMF was found for affected versus control side (P = 0.016; Tukey test). Multiple regression revealed a significant negative correlation for change in VNRS score versus change in median MVMF (Spearman R = −0.48:P = 0.00001). This study demonstrates that in patients with unilateral chronic low back pain radiating to the leg, pain reduction induced by local anesthetic segmental nerve (L4) block is associated with increased quadriceps femoris and tibialis anterior MVMF, without differences for lidocaine and ropivacaine.
The c-Jun δ-Domain Inhibits Neuroendocrine Promoter Activity in a DNA Sequence- and Pituitary-specific Manner*

The transcription and transformation activity of c-Jun is governed by a 27-amino acid regulatory motif, labeled the δ-domain, which is deleted in v-Jun. We have previously shown that c-Jun is a potent inhibitor of the rat prolactin (rPRL) promoter activity induced by either oncogenic Ras or phorbol esters. Here, we have characterized the structural and cell-specific requirements for this c-Jun inhibitory response, and we show that this c-Jun inhibitory response mapped to the rPRL footprint II repressor site, was pituitary-specific and required the c-Jun δ-domain. Moreover, alteration of any one of these features (e.g., cis-element, trans-factor, or cell-specific background) switched c-Jun to a transcriptional activator of the rPRL promoter. In HeLa nonpituitary cells, c-Jun alone activated the rPRL promoter via the most proximal GHF-1/Pit-1 binding site, footprint I, and synergized with GHF-1. Finally, recombinant GHF-1 interacted directly with c-Jun but not c-Fos proteins. These data provide important fundamental insights into the molecular mechanisms by which the c-Jun δ-domain functions as a modulatory switch and further imply that the functional role of c-Jun is dictated by cell-specific influences and the δ-domain motif.
Robust fault detection for Markovian jump systems with unreliable communication links

This article addresses the problem of robust fault detection for Markovian jump linear systems with unreliable communication links. In the network communication channel, the effects of signal quantisation and measurement missing, which appears typically in a network environment, are taken into consideration simultaneously. A stochastic variable satisfying the Bernoulli random binary distribution is utilised to model the phenomenon of the measurements missing. The aim is to design a fault detection filter such that, for all unknown input and incomplete measurements, the error between the residual and weighted faults is made as small as possible. A sufficient condition for the existence of the desired fault detection filter is established in terms of a set of linear matrix inequalities. A simulation example is provided to illustrate the effectiveness and applicability of the proposed techniques.
Efficient characterization of tennis shots and game analysis using wearable sensors data

Recent trends show that wearable devices with high-range inertial sensors are actively being used for outdoor activities. The paper describes our developed sports analytics engine used for self-learning and/or coach-assisted training for swing-based games like tennis, golf, etc. by utilizing rich set of data collected from these wearable sensors. The sports analytics engine for tennis uses techniques based on modified Pan-Tompkins algorithm for detecting the shot and then uses time-warping based hierarchical shot classifier which uses Dynamic Time Warping (DTW) at first level (forehand, backhand and serve) and Quaternion Dynamic Time Warping (QDTW) at second level (slice and non-slice). Major challenges included distinguishing shots from noise in sensor data, classifying the shots based on information only from wrist of player and capturing the various playing styles across different players. Based on efficacy of the developed engine, we foresee wider usages of the proposed techniques in developing learning applications for swing-based sports.
Learning of Tool Affordances for autonomous tool manipulation

We present the concept of Tool Affordances to plan a strategy for target object manipulation by a tool via understanding of bi-directional association between Actions, Tools and Effects. Tool Affordances include the awareness within robot about the different kind of effects it can create in the environment using an action and a tool. Robot learns tool affordances by exploring the environment through its motor actions using different tools and learning their association with observed effects. The strength of our model is the robots ability of prediction and inference given some evidence. To deal with uncertainty, redundancy and irrelevant information Bayesian Network as the probabilistic model is chosen for implementation of our Tool Affordance model. We demonstrate a preliminary experiment where robot uses learnt Tool Affordances to correctly infer the most appropriate novel Action and Tool given the observed effects.
Improved model predictive control for high voltage quality in microgrid applications

This paper proposes an improvement of the finite control set model predictive control (FCS-MPC) strategy for enhancing the voltage regulation performance of a voltage source converter (VSC) used for standalone microgrid and uninterrupted power supply (UPS) applications. The modification is based on tracking the voltage reference trajectory derivative using a single step prediction horizon. The main results are: 1) reduction of the total harmonic distortion (THD) and better tracking of fundamental voltage component compared with the conventional FCS-MPC, 2) lower computational burden compared to multi-step prediction horizon strategies, and 3) preserved fast dynamic response to abrupt load changes. These findings have been validated for both linear and nonlinear loads through experimental verification on a 15kW two-level VSC prototype.