Miracle-dz/UniFAR-FTUs huggingface dataset:
Finding the Location of Axonal Activation by a Miniature Magnetic Coil

Magnetic stimulation for neural activation is widely used in clinical and lab research. In comparison to electric stimulation using an implanted electrode, stimulation with a large magnetic coil is associated with poor spatial specificity and incapability to stimulate deep brain structures. Recent developments in micromagnetic stimulation (μMS) technology mitigates some of these shortcomings. The sub-millimeter coils can be covered with soft, biocompatible material, and chronically implanted. They can provide highly specific neural stimulation in the deep neural structure. Although the μMS technology is expected to provide a precise location of neural stimulation, the exact site of neural activation is difficult to determine. Furthermore, factors that could cause the shifting of the activation site during μMS have not been fully investigated. To estimate the location of axon activation in μMS, we first derived an analytical expression of the activating function, which predicts the location of membrane depolarization in an unmyelinated axon. Then, we developed a multi-compartment, Hodgkin-Huxley (H-H) type of NEURON model of an unmyelinated axon to test the impact of several important coil parameters on the location of axonal activation. The location of axonal activation was dependent on both the parameters of the stimulus and the biophysics properties of the targeted axon during μMS. The activating function analysis predicted that the location of membrane depolarization and activation could shift due to the reversal of the coil current and the change in the coil-axon distance. The NEURON modeling confirmed these predictions. Interestingly, the NEURON simulation further revealed that the intensity of stimulation played a significant role in the activation location. Moderate or strong coil currents activated the axon at different locations, mediated by two distinct ion channel mechanisms. This study reports several experimental factors that could cause a potential shift in the location of neural activation during μMS, which is essential for further development of this novel technology.
miR-29c Increases Protein Synthesis in Skeletal Muscle Independently of AKT/mTOR

microRNAs negatively regulate gene expression by blocking translation or increasing mRNA degradation. In skeletal muscle, these molecules play important roles in adaptive responses, and ongoing investigations are necessary to understand the fine-tune regulation of skeletal muscle mass. Herein we showed that skeletal muscle overexpression of miR-29c increased fiber size and force at 7 and 30 days after electrotransfer. At both time points, AKT/mTOR pathway components were downregulated, and, surprisingly, overall protein synthesis was strongly elevated at day 7, which normalized by day 30 after pCMVmiR-29c electrotransfer. These results indicate that miR-29c expression induces skeletal muscle hypertrophy and gain of function, which involves increased overall protein synthesis in spite of the deactivation of the AKT/mTOR pathway.
Algorithmic Differentiation for Sensitivity Analysis in Cloud Microphysics

The role of clouds for radiative transfer, precipitation formation, and their interaction with atmospheric dynamics depends strongly on cloud microphysics. The parameterization of cloud microphysical processes in weather and climate models is a well‐known source of uncertainties. Hence, robust quantification of this uncertainty is mandatory. Sensitivity analysis to date has typically investigated only a few model parameters. We propose algorithmic differentiation (AD) as a tool to detect the magnitude and timing at which a model state variable is sensitive to any of the hundreds of uncertain model parameters in the cloud microphysics parameterization. AD increases the computational cost by roughly a third in our simulations. We explore this methodology as the example of warm conveyor belt trajectories, that is, air parcels rising rapidly from the planetary boundary layer to the upper troposphere in the vicinity of an extratropical cyclone. Based on the information of derivatives with respect to the uncertain parameters, the ten parameters contributing most to uncertainty are selected. These uncertain parameters are mostly related to the representation of hydrometeor diameter and fall velocity, the activation of cloud condensation nuclei, and heterogeneous freezing. We demonstrate the meaningfulness of the AD‐estimated sensitivities by comparing the AD results with ensemble simulations spawned at different points along the trajectories, where different parameter settings are used in the various ensemble members. The ranking of the most important parameters from these ensemble simulations is consistent with the results from AD. Thus, AD is a helpful tool for selecting parameters contributing most to cloud microphysics uncertainty.
Polishing Orientation Affecting Surface Charging and Flashover Characteristics of GIL/GIS Epoxy Spacer

In this article, the influence of polishing orientation on surface charge accumulation and flashover characteristics of the basin spacer in a downsized dc-gas insulated transmission line (GIL) are explored by experiments. Results show that the surface charge distribution of the polished spacers is different from the untreated one. Heteropolar charges can be observed in the polished area and outward expansion area under prestressing condition. The flashover voltages of all polished spacers are decreased. The radial polishing (RP) has the greatest impact on the flashover voltage, and the flashover voltage of the radially polished spacer is decreased by 11% under positive prestressing condition. The metal dust adsorptions of the spacers are also measured, which shows that the radially polished spacer is easier to absorb metal dust and the metal dust diffuses in the radial direction. SEM and energy spectrum scanning of different spacers show that there are more exposed alumina particles on the surface of the polished area. The microdischarge induced by alumina particles is the source of heteropolar charges. When the polished area of the spacers is repaired with epoxy paint, heteropolar charges disappear on the surface of the repaired spacer. It is confirmed that charge accumulation and flashover along the spacer surface are mainly affected by the change of surface morphology on the polished area.
Space Life Science of China in 2015

In the past two years, space life science research in China is characterized by a wide area of basic researches for providing foundation for the future China Space Station. The effect of microgravity and radiation was further studied from physiology phenomena to the level of bio-molecule mechanisms. Chinese space life science is maturing in a new era of comprehensive development.Here, we review and summarize researches on space life sciences which were contributed by Chinese scientists.
Urban Cold Chain Logistics B2B/B2C Integration Process Reengineering

Aiming at the problem of high cost and low efficiency of individual distribution of B2C in the current cold chain distribution in cities, this paper analyzes and reengineers urban cold-chain logistics distribution process, on the basis of the existing distribution process, rebuilt process by Petri and BPR theory. Finally, analyzing a case study with Arena simulation technology, which indicates that B2B/B2C integration can improve logistics efficiency and reduce logistics cost.
1 Mapping agent beliefs to Dempster-Shafer sets

Existing cognitive agent programming languages that are based on the BDI model employ logical representation and reasoning for implementing the beliefs of agents. In these programming languages, the beliefs are assumed to be certain, i.e. an implemented agent can believe a proposition or not. These programming languages fail to capture the underlying uncertainty of the agent’s beliefs which is essential for many real world agent applications. We introduce Dempster-Shafer theory as a convenient method to model uncertainty in agent’s beliefs. 1 Mapping agent beliefs to Dempster-Shafer sets In Dempster-Shafer theory[4], a frame of discernment Ω is defined as the set of all hypotheses in a certain domain. On the power set 2, a mass function m(X) is defined for every X ⊆ Ω, with m(X) ≥ 0 and ∑ X⊆Ω m(X) = 1. If there is no information available with respect to Ω, m(Ω) = 1, and m(X) = 0 for every subset of Ω. A simple support function is a special case of a mass function, where the evidence supports one set of hypotheses A, and zero mass value is assigned to any subset of Ω other than A. Mass functions can be combined using Dempster’s Rule of Combination. This combination rule for mass function m1 and m2 is denoted as m1 ⊕ m2 and is defined, for X, Y, Z ⊆ Ω, as:
VARIATION OF COTTON PROPERTIES DUE TO THE APPLICATION OF SEA FRAGANCE MICROCAPSULES

Cotton fabrics are used in multiple applications consequently, are susceptible to treatments for its functionalization. Microcapsules interest is increasing among the industry field as they allow to incorporate multiple functions to textiles. The finishing treatments that incorporate microcapsules contain in their formulations binding agents in order to increase their permanence on the textile. In this work, different concentrations of resin are tested in order to evaluate weather its application on industrial formulations with microcapsules influences the flexural rigidity on the cotton fabric. Considering that the application of the microcapsules is carried out in the final phase of the textile proces, it can modify the fabric appearenace. The possible color modification that is generated on three colors, red, yellow and blue at 2% on fiber weight has also been evaluated. The study shows that not only the resin influences the flexural rigidity of the tissue, but the presence of microcapsules also modifies this behavior. In addition, it is proven that the color variation is not significant to detect a perceptible change in tissue coloration. Flexural rigidity modification can be obsereved previusly to observe changes in color. Keywords: Fabric, cotton, microcapsules, rigidity, colour
Áudio imersivo em narrativas jornalísticas de Realidade Virtual, Aumentada e Estendida

A última década consolidou o conceito de jornalismo imersivo com as narrativas em Realidade Virtual, Aumentada, Estendida e em 360 Graus, que tiveram seu ápice com a popularização dos óculos de RV e as produções nas organizações jornalísticas como Globo, Folha de S. Paulo, Vice, The Guardian, The New York Times, El País. A análise dessas produções tem se concentrado no aspecto imagético e esférico das narrativas como característica proeminente. Entretanto, o áudio imersivo é um elemento central na percepção do storytelling do jornalismo imersivo e pretendemos explorar essa dimensão no artigo. A metodologia inclui discussão teórico-conceitual e pesquisa exploratória de narrativas em áudio imersivo em casos ilustrativos. Os resultados indicam a incipiência do uso do recurso nas produções e a escassez de abordagem na literatura nos 12 anos de pesquisa sobre jornalismo imersivo.
Synthesis of silver nanoparticles from pericarp of Ziziphus jujuba Mill. and their in vitro antimicrobial activity

Silver nanoparticles (AgNPs) used in this study were prepared from Ziziphus jujuba Mill. aqueous pericarp extract. Potent antibacterial activity was recorded from AgNPs–Z. jujuba pericarp against P. Pseudomonas aeruginosa > K. Klebsiella pneumoniae > M. Micrococcus luteus > S. Staphylococcus aureus > P. Proteus mirabilis > S. Shigella flexneri. Less antifungal activity was found against phytopathogenic fungi with inhibition efficacy of P. Penicillium expansum > M. Macrophomina phaseolina > A. Alternaria alternata > D. Drechslera halodes, F. Fusarium oxysporum f. sp. lycopersici. UV-Vis spectral analysis of formed AgNPs– Z. jujuba was recorded at 450 nm and three characteristic diffraction peaks observed at 2Φ =38.235, 43.95 and 64.42. FTIR patterns were located in the region of 4000–400 cm−1 and AgNPs particle size were noticed in between 20 and 71 nm with spherical shape embedded in bio-materials. These findings indicate that AgNPs–Z. jujuba pericarp aqueous extract could be utilized to control various bacterial infections to human and as well as against phytopathogenic fungi. Bangladesh J. Bot. 51(2): 305-312, 2022 (June)
Towards the Detection of a Semantic Gap in the Chain of Commonsense Knowledge Triples

A commonsense knowledge resource organizes common sense that is not necessarily correct all the time, but most people are expected to know or believe. Such knowledge resources have recently been actively built and utilized in artificial intelligence, particularly natural language processing. In this paper, we discuss an important but not significantly discussed the issue of semantic gaps potentially existing in a commonsense knowledge graph and propose a machine learning-based approach to detect a semantic gap that may inhibit the proper chaining of knowledge triples. In order to establish this line of research, we created a pilot dataset from ConceptNet, in which chains consisting of two adjacent triples are sampled, and the validity of each chain is human-annotated. We also devised a few baseline methods for detecting the semantic gaps and compared them in small-scale experiments. Although the experimental results suggest that the detection of semantic gaps may not be a trivial task, we achieved several insights to further push this research direction, including the potential efficacy of sense embeddings and contextualized word representations enabled by a pre-trained language model.
SansTib, a Sanskrit - Tibetan Parallel Corpus and Bilingual Sentence Embedding Model

This paper presents the development of SansTib, a Sanskrit - Classical Tibetan parallel corpus automatically aligned on sentence-level, and a bilingual sentence embedding model. The corpus has a size of about 317,289 sentence pairs and 14,420,771 tokens and thereby is a considerable improvement over previous resources for these two languages. The data is incorporated into the BuddhaNexus database to make it accessible to a larger audience. It also presents a gold evaluation dataset and assesses the quality of the automatic alignment.
DCPT: Distributed Critical Path Tracing for Latency Profiling

Latency profiling in distributed systems is critical for site reliability, service robustness, and fairness. However, traditional latency profiling techniques have high overhead and collect data aggregates that usually monitor a specific service at a single node. Critical path tracing can be applied to diagnose performance issues faster with minimal disruptions in large scale distributed services foregoing complex and invasive infrastructure. We implement Distributed Critical Path Tracing (DCPT) as a low latency and request-conscious form of latency tracing. Our implementation is based in XDR [2] and exposes a minimally-invasive API for performance tracing. We test our implementation with simulated workloads and analyze time and space overheads in extreme circumstances.
Increasing CMDI’s Semantic Interoperability with schema.org

The CLARIN Concept Registry (CCR) is the common semantic ground for most CMDI-based profiles to describe language-related resources in the CLARIN universe. While the CCR supports semantic interoperability within this universe, it does not extend beyond it. The flexibility of CMDI, however, allows users to use other term or concept registries when defining their metadata components. In this paper, we describe our use of schema.org, a light ontology used by many parties across disciplines.
Prediction Model of Compressive Strength Development in Concrete Containing Four Kinds of Gelled Materials with the Artificial Intelligence Method

Green concrete has been widely used in recent years because its production compliments environmental conservation. The prediction of the compressive strength of concrete using non-destructive techniques is of interest to engineers worldwide. Such methods are easy to carry out because they require little or no sample preparation. Conventional models and artificial intelligence models are two main types of models to predict the compressive strength of concrete. Artificial intelligence models main include the artificial neural network (ANN) model, back propagation (BP) neural network model, fuzzy model etc. Since both conventional models and artificial intelligence models are flawed. This study proposes to build a concrete compressive strength development over time (CCSDOT) model by using conventional method combined with the artificial intelligence method. The CCSDOT model performed well in predicting and fitting the compressive strength development in green concrete containing cement, slag, fly ash, and limestone flour. It is concluded that the CCSDOT model is stable through the use of sensitivity analysis. To evaluate the precision of this model, the prediction results of the proposed model were compared to that of the model based on the BP neural network. The results verify that the recommended model enjoys better flexibility, capability, and accuracy in predicting the compressive strength development in concrete than the other models.
Expressivity of Planning with Horn Description Logic Ontologies (Extended Abstract)

AI planning is a well-investigated framework for describing the evolution of system states through actions [2]. Usually, a planning task describes an initial state of the agent’s environment, a set of actions that can affect that environment, and a goal formula that is to be satisfied. In order to reach the goal, actions can be applied whenever their preconditions are satisfied in the current state expressed as a finite set of facts. A plan is a sequence of (ground) actions that transform the initial state into a state satisfying the goal. First-order (FO) formulas in the preconditions and the goal are evaluated using a closed-domain, closed-world semantics, i.e. the domain is fixed a priori and facts that are not contained in a state are assumed to be false. In contrast to preconditions which need to hold locally, the system can require to have global state constraints , constraints that should hold at every state. Knowledge representation formalisms are a natural way to introduce global constraints on permissible states; however, they usually interpret FO formulas over arbitrary models, instead of just one model with a fixed domain. Moreover, early work on open-world, open-domain formalisms for constraining possible states quickly noticed the so-called ramification problem [3], i.e. an action that makes a fact true also has to take care of satisfying the state constraints, possibly requiring to add or remove other facts, which may also involve new objects. One approach to deal with this problem is to not view states as finite interpretations, but as sets of formulas interpreted in an open-world fashion. In this way, implicit knowledge about the world added by the state constraints does not have to be made explicit in the states themselves. This means that the semantics of actions must
ACT2: A multi-disciplinary semi-structured dataset for importance and purpose classification of citations

Classifying citations according to their purpose and importance is a challenging task that has gained considerable interest in recent years. This interest has been primarily driven by the need to create more transparent, efficient, merit-based reward systems in academia; a system that goes beyond simple bibliometric measures and considers the semantics of citations. Such systems that quantify and classify the influence of citations can act as edges that link knowledge nodes to a graph and enable efficient knowledge discovery. While a number of researchers have experimented with a variety of models, these experiments are typically limited to single-domain applications and the resulting models are hardly comparable. Recently, two Citation Context Classification (3C) shared tasks (at WOSP2020 and SDP2021) created the first benchmark enabling direct comparison of citation classification approaches, revealing the crucial impact of supplementary data on the performance of models. Reflecting from the findings of these shared tasks, we are releasing a new multi-disciplinary dataset, ACT2, an extended SDP 3C shared task dataset. This modified corpus has annotations for both citation function and importance classes newly enriched with supplementary contextual and non-contextual feature sets the selection of which follows from the lists of features used by the more successful teams in these shared tasks. Additionally, we include contextual features for cited papers (e.g. Abstract of the cited paper), which most existing datasets lack, but which have a lot of potential to improve results. We describe the methodology used for feature extraction and the challenges involved in the process. The feature enriched ACT2 dataset is available at https://github.com/oacore/ACT2.
A Corpus of German Citizen Contributions in Mobility Planning: Supporting Evaluation Through Multidimensional Classification

Political authorities in democratic countries regularly consult the public in order to allow citizens to voice their ideas and concerns on specific issues. When trying to evaluate the (often large number of) contributions by the public in order to inform decision-making, authorities regularly face challenges due to restricted resources. We identify several tasks whose automated support can help in the evaluation of public participation. These are i) the recognition of arguments, more precisely premises and their conclusions, ii) the assessment of the concreteness of arguments, iii) the detection of textual descriptions of locations in order to assign citizens’ ideas to a spatial location, and iv) the thematic categorization of contributions. To enable future research efforts to develop techniques addressing these four tasks, we introduce the CIMT PartEval Corpus, a new publicly-available German-language corpus that includes several thousand citizen contributions from six mobility-related planning processes in five German municipalities. The corpus provides annotations for each of these tasks which have not been available in German for the domain of public participation before either at all or in this scope and variety.