Miracle-dz/UniFAR-FTUs huggingface dataset:
DeNOC Based Dynamic Modelling of a Tree Type Humanoid Upper-body Robot with Fixed Base

Better dynamic model of humanoid robots helps to study the dynamic interaction of coupled systems, stable locomotion, and the model-based controller design. The dynamic analysis of the tree-type robotic mechanism is complex due to the presence of higher degrees of freedom. This paper presents the forward and inverse dynamic analysis of a tree-type upper body humanoid robot with fixed base. The Newton-Euler (NE) formulation is used for determining the dynamic model. Decoupled Natural Orthogonal Complement (DeNOC) methodology is included in the dynamic modeling to couple the various branches of the tree-type upper body. Torques at various joints are analyzed. This paper also presents a method for deriving the inverse of the Generalized Inertia Matrix (GIM) in forward dynamics.
Drag Coefficient in Solid–Fluid System Modeling with the Eulerian Multiphase Model

This article reviews the formulas determining the drag coefficient of spherical particles moving in a fluid system in the context of numerical modeling of spouted bed grain dryers. The study describes several formulas that have been implemented in code Fluent 6.3, in a simulation model of a spouted bed based on the Eulerian multiphase model. The article also analyzes the voidage function that accounts for the effect of particle density on the value of the drag coefficient. Simulation results were compared with the basic model, which has been discussed in detail in a different study carried out by the author. This study investigates the computer model's response to changes in the manner of computing the drag coefficient and the possibility of applying various computational formulas in spouted bed modeling. The experiment was conducted by the author in view of the scarcity of publications on the subject and the need to organize the existing knowledge on drag coefficient modeling, which is the main contribution of the article.
A Lizard-Inspired Quadruped Robot Based on Pressure Sensitive Adhesion Mechanism for Wall Climbing

This paper reports the design, fabrication, and control of a quadruped wall-climbing robot employing pressure sensitive adhesion (PSA) mechanism for climbing vertical walls. The developed robot’s kinematics is similar to a lizard’s motion. The robot’s gait is designed in line with the lizard’s bionic gait. The key feature of the developed robot is the compliant mechanism for adhering and peeling of feet and the locomotion mechanism capable of straight as well as turning motion. We demonstrate the robot’s climbing experiment on an acrylic plexiglass surfaced vertical wall, which act as an evidence that the robot can perform vertical climbing. We obtained an average robot speed of 1.35 cm/s without payload and 1.25 cm/s with a payload of 20 gm while performing a vertical climbing. Entire robot structure has been fabricated using a combination of 3D printing and 2D laser cutting operations which allows an easy replication of the platform into swarm of climbing robots. We envisage that the possible applications of this robot can be in reconnaissance, inspection, cleaning, and repairing as the lizard-inspired robot offers a higher degree of stealth compared to commonly used drones.
No Evidence of Low Genetic Diversity Despite High Levels of Inbreeding and Poor Genetic Connectivity among Tetrastigma loheri (Vitaceae) Populations in Remaining Forest Areas in Cebu, Philippines

Abstract Little is known about the effects of habitat fragmentation on the patterns of genetic diversity and genetic connectivity of species in the remaining tropical forests of Southeast Asia. This is particularly evident in Cebu, a Philippine island that has a long history of deforestation and has lost nearly all of its forest cover. To begin filling this gap, data from 13 microsatellite loci developed for Tetrastigma loheri (Vitaceae), a common vine species in Philippine forests, were used to study patterns of genetic diversity and genetic connectivity for the four largest of the remaining forest areas in Cebu. Evidence of relatively high levels of inbreeding was found in all four areas, despite no evidence of low genetic diversity. The four areas are genetically differentiated, suggesting low genetic connectivity. The presence of inbreeding and low genetic connectivity in a commonly encountered species such as T. loheri in Cebu suggests that the impact of habitat fragmentation is likely greater on rare plant species with more restricted distributions in Cebu. Conservation recommendations for the remaining forest areas in Cebu include the establishment of steppingstone corridors between nearby areas to improve the movement of pollinators and seed dispersers among them.
What Determines Ion Content of Lumbricid Casts: Soil Type, Species, or Ecological Group?

ABSTRACT Most soils possess a large supply of nutrients, but these reserves cannot be mobilized by plants, as they exist in a bound form. Therefore, it is need to understand mechanisms that naturally increase the acquisition of nutrients by plants. Earthworms are crucial soil engineers that play a key role in soil functioning. In this study we have estimated the impact of three factors, earthworm species, ecological group, and soil type, on the ion content of casts of Eisenia nordenskioldi, E. ventripapillata, E. nana, Lumbricus rubellus, and Aporrectodea caliginosa. Laboratory experiments were performed in microcosms on three soil types. We analyzed five water-soluble cations and six anions in the earthworm casts by capillary zone electrophoresis to determine their content in casts compared to soil. For three of the five species studied this is the first information obtained on the matter. The obtained data demonstrated that soil type has the biggest impact on the observed changes in the cast ion content, probably due to the differences in the initial concentrations and soil properties. Within each soil type, the observed effects were mostly species-specific, and the species belonging to the same ecological group (endogeic and epi-endogeic) did not have similar effects. The exposure to different earthworm species resulted in pronounced differences in the concentrations of magnesium, calcium, ammonium, and chloride, while the soil type had the highest impact on sulfate, fluoride, acetate, and sodium ions. Therefore, the terms ‘ecological group’ and ‘functional group’ are not equivalent for earthworm casts, and replacing an earthworm species with another belonging to the same ecological group may result in changes of the soil ion composition.
Myrcia longipetiolata (Myrtaceae), a New Species from the Atlantic Forest of Bahia, Brazil

Abstract Myrcia longipetiolata, a new species of Myrcia, is here described and illustrated. With a phylogenetic hypothesis, we show that this species belongs to Myrcia clade 10, a morphologically recognized and narrowly distributed group of species, which was recently discovered. Myrcia longipetiolata resembles M. unana due to its long leaves, acute or acuminate leaf apex, cuneate leaf base, long petioles, and they share the same area of occurrence. Myrcia longipetiolata differs from M. unana by its larger leaves, larger number of leaf secondary veins, marginal vein more distant from the leaf margin, lanceolate bracteoles, smaller and truncate, rounded, or apiculate calyx lobes with whitish to yellowish trichomes, and smaller floral disc. Besides the phylogenetic placement of M. longipetiolata within the genus, morphological comparisons with related species and comments on its distribution, habitat, and conservation status are also provided.
Prioritized Path Planning of Multiple Autonomous Vehicles in Urban Environments

Autonomous Vehicles (AVs) are poised to bring in the next transformation in urban mobility. However, for efficient performance these vehicles need to be able to leverage the information gathered from the environment to plan optimal paths to their destination. Simultaneous planning the paths of multiple AVs is a challenging task because of the stringent safety and efficiency constraints. Prioritized approaches provide a practical solution to this problem by decoupling the simultaneous path planning problem into a series of individual path planning tasks based on a priority order. In this paper we present a new prioritized approach based on the PD* algorithm for the path planning of a system of AVs in a given environment. The PD* algorithm has been extended to incorporate multiple destinations, delayed starts and a new prioritization metric based on a combination of freedom-index and distance to target; to make the algorithm more suitable to the unique challenges of AV path planning. Further, the suitability of the algorithm has been demonstrated through simulations on the Moving AI Lab’s database of city maps in multiple scenarios.
Espeletia ocetana (Millerieae, Asteraceae), a New Tall Caulirosula from Colombia

Abstract A new species, Espeletia ocetana (Asteraceae), from about 3500 m a. s. l. in Páramo de Ocetá, Mongua, Boyacá department, Colombia, is described and illustrated. The new species exhibits a caulescent rosette habit (0.7–1.8 m tall), sessile leaves, elliptic leaf laminae with greyish indumentum, robust bracteate thyrsoid capitulescences spreading laterally from rosette, each holding 16–37(–69) capitula, external phyllaries 14.2–31.1 mm long, and ray florets 13.9–21.3 mm long arranged in 2.0–2.9 series. It is markedly different from the majority of other members of the genus, and only slightly similar to E. jaramilloi, from which it can be easily distinguished by its taller stems, wider leaves with a smaller length/width ratio, and longer sheaths. Furthermore, E. ocetana has capitula with ray florets arranged in fewer series, and with longer phyllaries, ray corollas, ray corolla limbs, ray styles, ray style branches, disc florets, and disc styles. In addition, E. ocetana is distributed allopatrically in regard to E. jaramilloi, and differs as well in its ecological preference for humid to very humid shrubby páramos. Espeletia ocetana is rather abundant in its type locality, which is a relatively well-conserved páramo located within the limits of Parque Natural Regional Siscunsí-Ocetá. However, it is absent from road margins, abandoned agriculture fields, and other areas impacted by human activities. Further studies will be necessary to know appropriately the extent of the geographic distribution of E. ocetana, its ecological requirements and its phylogenetic affinities with other species.
Relationships of Oxylobus, an Alpine Genus of Eupatorieae (Asteraceae)

Abstract A molecular phylogenetic investigation was carried out to clarify aspects of the systematics of Oxylobus, a primarily Mexican alpine genus of Eupatorieae. Analysis of sequence data from two nuclear (nrDNA ITS, ETS) and three plastid markers (rbcL, ndhF, matK) confirmed the monophyly of Oxylobus and placed species of Ageratina as its sister group. A survey of 56 samples of Oxylobus using nrDNA ITS and ETS provided support for the currently accepted species, and showed the recently described O. coyulensis to be distinct and the sister group to the rest of the genus. The results also confirmed the placement of O. juarezensis in synonymy with O. subglabrus. The results of a broad survey of Ageratina for ITS data showed that it is likely not monophyletic as currently circumscribed. The phylogenetic results also highlighted the distinctiveness of Piqueria and Piqueriopsis as a distinct clade at the base of Eupatorieae.
LIFECON DELIVERABLE D 3.2 Service Life Models Instructions on methodology and application of models for the prediction of the residual service life for classified environmental loads and types of structures in Europe

This report comprises the mathematical modeling of corrosion induction due to carbonation and chloride ingress, corrosion propagation, frost (internal damage and surface scaling) and alkaliaggregate reaction. Models are presented on a semi-probabilistic and a full-probabilistic level. Semi-probabilistic models only include parameters obtainable throughout structure investigations, without making use of default material and environmental data. Full-probabilistic models are applicable for service life design purposes and for existing objects, including the effect of environmental parameters. For each full-probabilistic model a parameter study was performed in order to classify environmental data. The application of the models for real structures is outlined. The objects of the case studies have been assessed in order to obtain input data for calculations on residual service life. Each degradation mechanism will be treated separately hereby demonstrating: possible methods to assess concrete structures the sources for necessary input data approach used in durability design application of models for existing structures the precision of the applied models necessary assumptions due to lack of available data possible method to update data gained from investigations throughout condition assessment default values for input data output of the calculations The use of full-probabilistic models for the calibration of the Markov Chain approach is described. European Community. Fifth Framework Program: GROWTH RDT Project: Life Cycle Management of Concrete Infrastructures for Improved Sustainability: LIFECON 7(169) List of
A modeling assistant for cognifying MBSE tools

The steep learning curve of tools for Model-Based Software Engineering still slows down its widespread adoption. Users indeed encounter a variety of technical difficulties when modeling, including using modeling tools that are often considered too complex. Because they usually require too much advanced or diverse skills, modeling tools push users to focus on how to model with the tool instead of on what to model with the tool, and hence curb users’ creativity and productivity. Our goal is to address this issue by refocusing the modeling activity on the content of the model, and by facilitating the creation of new model elements. In this tool demonstration, we present our modeling assistant, which gathers knowledge from model repositories and provides in-editor modeling recommendations for users to complete their models. Our system cognifies the Papyrus modeling tool by augmenting its users with a recommender system fed with more than 90,000 models to assist UML class diagrams authoring. It features an architecture scalable to multiple recommendation clients, and can be deployed locally to ensure the confidentiality of the source models. We showcase our modeling assistant with a use case and discuss preliminary measures.
Neighborhood racial composition, income, and distance to grocery retailers in Seattle

This article examines whether racial disparities exist in travel distances and travel times to grocery stores in Seattle, WA. In contrast to the findings of studies conducted in other metropolitan cities, Seattle, WA, has few food deserts. We find that disparities in travel distance and travel time to supermarkets vary with the percentage of specific racial and ethnic populations. Greater Asian populations in a census tract are associated with shorter travel distance to the grocery, low income, and low vehicle access. For the Hispanic population, a greater percentage in a census tract increases the predicted travel distances and travel times to grocery stores. Greater income in tracts with more Hispanic population is associated with a shorter distance, and those tracts with more Asian population are associated with a longer distance, possibly due to cultural differences in diet.
Physics based capacitance modeling of short-channel double-gate MOSFETs

A precise compact 2D capacitance model for nanoscale DG MOSFETs in all operating regions is presented. The modeling of the sub-threshold capacitances is based on the conformal mapping technique, where the corners of the device are treated specifically. In moderate to strong inversion, we obtain self-consistent results based on the 2D Poisson's equation. The results are in excellent agreement with numerical simulations. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Model-based reasoning for fault diagnosis of twin-spool turbofans

A simplified rule-based expert system can be of great use to maintenance engineers involved in on-line health monitoring of aero-engines. The development of a rule base has been done on the following lines: 1 A design and off-design mathematical model of the turbofan has been developed. 2 Healthy and fault-implanted engines have been run at various off-design conditions to see the effect of faults on certain aero-thermodynamic performance parameters, with appropriate selection of independent parameter(s). 3 From these calculated values, fault matrices have been developed for sea-level static conditions taking the net thrust as an independent parameter. 4 From these fault matrices, rules have been developed which form the knowledge core of the expert shell. These rules have been developed for Garrett TFE 731-2, a moderate bypass and overall pressure ratio, generic twin-spool turbofan and so the package (off-design code plus expert system) can serve as a pedagogical tool for training of engineers in the aero-engine industry and academic institutions.
Design of currents to reduce torque ripple in brushless permanent magnet motors

A method to design an improved motor excitation for three-phase brushless permanent magnet motors is presented. The unique motor excitation reduces ripple in the developed torque, reduces the effects of cogging or detent torque, and is also a minimum average power excitation. Practical benefits are reduced vibration and acoustic noise in speed control applications, and improved accuracy in position control applications. First, an analysis of torque ripple is presented using the exponential Fourier series in the torque model. The analysis is simple, yet extends some well known results by predicting the presence of additional harmonic components. Next, the design of an optimal weighting of stator current harmonics is cast as a type of constrained minimisation problem. In contrast to iterative approaches that have been reported in the past, the new design method determines the current harmonic weights in closed form. Steps in the design procedure are demonstrated using measured back EMF data from a 2 hp brushless DC motor. < >
Investigating Logic Tensor Networks for Neural-Symbolic Argument Mining

We present an application of neural-symbolic learning to the task of argument mining, where argument components and their relations are extracted from unstructured textual corpora. We use the framework of Logic Tensor Networks to train neural models to jointly ﬁt the data and satisfy speciﬁc domain rules. Our experiments on a corpus of scientiﬁc abstracts indicate that including symbolic rules during the training process improves classiﬁcation performance, compliance to the rules, and robustness of the results. As in the case of other neural-symbolic applications, we further discuss how scalability remains a crucial issue.
In Silico Prediction of Indonesian Herbs Compounds as Covid-19 Supportive Therapy using Support Vector Machine

Many kinds of research on drug discovery using computational or in silico methods have been carried out. In this era of the Covid-19 pandemic, this research was also carried out by utilizing a commonly used technique, namely using machine learning to predict the interaction of compounds and proteins. This technique is known as Drug Target Interaction (DTI). The compounds used are herbal originating from Indonesia, and the protein used is a potential Covid-19 protein, one of which is SARS-CoV-2. The prediction process with machine learning can only be done on structured data. The data on herbal and protein were processed in this research using the Fingerprint as a descriptor compound and Pseudo Amino Acid Composition (PseAAC) as a protein descriptor technique. The result is structured data processed with the Support Vector Machine algorithm to create an interaction prediction model. The result is that the prediction accuracy is 95.96%. Furthermore, this model can predict Indonesian herbal compounds as drug candidates for Covid-19 supportive therapy.
Hippocampal Sclerosis in Pilocarpine Epilepsy: Survival of Peptide-Containing Neurons and Learning and Memory Disturbances in the Adult NMRI Strain Mouse

The present experiments reveal the alterations of the hippocampal neuronal populations in chronic epilepsy. The mice were injected with a single dose of pilocarpine. They had status epilepticus and spontaneously recurrent motor seizures. Three months after pilocarpine treatment, the animals were investigated with the Barnes maze to determine their learning and memory capabilities. Their hippocampi were analyzed 2 weeks later (at 3.5 months) with standard immunohistochemical methods and cell counting. Every animal displayed hippocampal sclerosis. The neuronal loss was evaluated with neuronal-N immunostaining, and the activation of the microglia was measured with Iba1 immunohistochemistry. The neuropeptide Y, parvalbumin, and calretinin immunoreactive structures were qualitatively and quantitatively analyzed in the hippocampal formation. The results were compared statistically to the results of the control mice. We detected neuronal loss and strongly activated microglia populations. Neuropeptide Y was significantly upregulated in the sprouting axons. The number of parvalbumin- and calretinin-containing interneurons decreased significantly in the Ammon’s horn and dentate gyrus. The epileptic animals displayed significantly worse learning and memory functions. We concluded that degeneration of the principal neurons, a numerical decrease of PV-containing GABAergic neurons, and strong peptidergic axonal sprouting were responsible for the loss of the hippocampal learning and memory functions.
Prediction Of Chronic Kidney Disease Using Different Classification Algorithms: A Comparative Study.

- Kidney disease (KD) is a serious public health concern is defined as a disorder that disrupts the kidneys' normal function. As the rate of people influenced by CKD continues to rise, efficient prediction techniques should be considered. Therefore, the goal of this work is to help in the prevention of Chronic Kidney Disease (CKD) applying machine learning approaches to detect CKD early on and keep chronic kidney disease from getting worse. In this paper, we focus on applying various classification algorithms perform on a dataset of 400 patients with 24 variables linked to chronic kidney disease diagnosis in aims to compare the performance of these algorithms in making predictions and justify the algorithm that achieves more correct prediction. The classification techniques used in this study include Random Forest (RF), Decision Tree (DT), and Naive Bayes (NB). To perform experiments, all missing and outlier values in the dataset were replaced by the mean of the corresponding attributes. Next, in the feature selection step, we optimizing the prediction model and obtaining the optimal subset of features from the dataset. In the proposed approach, correlation coefficient, and recursive feature elimination have been used as feature selection methods to decrease the number of features by eliminating irrelevant and useless features to maintain a good analytical result. Finally, different assessment parameters are used to evaluate the classifiers' performance such as Accuracy, Precision, Recall, and F1-score. The empirical results from the experiments serve that RF performed better than DT, and NB with accuracies of 100% in CKD predicting.