gebhart/patrepeval huggingface dataset:
Classifying cooking object's state using a tuned VGG convolutional neural network (2018)

In robotics, knowing the object states and recognizing the desired states are very important. Objects at different states would require different grasping. To achieve different states, different manipulations would be required, as well as different grasping. To analyze the objects at different states, a dataset of cooking objects was created. Cooking consists of various cutting techniques needed for different dishes (e.g. diced, julienne etc.). Identifying each of this state of cooking objects by the human can be difficult sometimes too. In this paper, we have analyzed seven different cooking object states by tuning a convolutional neural network (CNN). For this task, images were downloaded and annotated by students and they are divided into training and a completely different test set. By tuning the vgg-16 CNN 77% accuracy was obtained. The work presented in this paper focuses on classification between various object states rather than task recognition or recipe prediction. This framework can be easily adapted in any other object state classification activity.
Comparison of wide-band vibrotactile and friction modulation surface\n gratings (2021)

This study seeks to understand conditions under which virtual gratings\nproduced via vibrotaction and friction modulation are perceived as similar and\nto find physical origins in the results. To accomplish this, we developed two\nsingle-axis devices, one based on electroadhesion and one based on out-of-plane\nvibration. The two devices had identical touch surfaces, and the vibrotactile\ndevice used a novel closed-loop controller to achieve precise control of\nout-of-plane plate displacement under varying load conditions across a wide\nrange of frequencies. A first study measured the perceptual intensity\nequivalence curve of gratings generated under electroadhesion and vibrotaction\nacross the 20-400Hz frequency range. A second study assessed the perceptual\nsimilarity between two forms of skin excitation given the same driving\nfrequency and same perceived intensity. Our results indicate that it is largely\nthe out-of-plane velocity that predicts vibrotactile intensity relative to\nshear forces generated by friction modulation. A high degree of perceptual\nsimilarity between gratings generated through friction modulation and through\nvibrotaction is apparent and tends to scale with actuation frequency suggesting\nperceptual indifference to the manner of fingerpad actuation in the upper\nfrequency range.\n
Secrecy Performance Analysis of Distributed Asynchronous Cyclic Delay Diversity-Based Cooperative Single Carrier Systems (2020)

A joint data and interference transmission scheme based on a new distributed asynchronous cyclic delay diversity (dACDD) technique is proposed for cooperative communication systems. Without any perfect channel state information from a legitimate user (LU) and an eavesdropping user (EU), joint remote radio head (RRH) selection for the data and jamming signal transmissions is proposed for dACDD to achieve the maximum diversity gain at the LU, while degrading the receive signal-to-interference-plus-noise ratio at the EU. The proposed dACDD is the extension of distributed cyclic delay diversity, which requires a tight synchronization among the central control unit and RRHs. Thus, processing at each RRH causing no intersymbol interference at the LU is developed. Then, the selection scheme for a data RRH is proposed, which selects a single RRH connected with the channel having the greatest channel magnitude as the data RRH to transmit a desired confidential message and controls the remaining RRHs to transmit an artificial interference sequence to the LU and EU. For the proposed distributed system, the marginal secrecy outage probability and marginal probability of non-zero achievable secrecy rate are analyzed by deriving closed-form expressions, whose correctness is verified via link-level simulations over non-identically distributed frequency selective fading channels.
Structure-Engineered Core–Shell Ni–Co–O/NiCo-LDH Nanospheres as High-Performance Supercapacitor Electrodes (2023)

The development of a novel electrode material for energy storage devices is a grand challenge. Here, through a rational design of the structure, the electrochemical performance of the prepared sample could be improved while enhancing the conductivity and the synergistic effect of its components. Herein, we constructed a core–shell composite named Ni–Co–O/NiCo-LDH as the electrode material by a self-template method, which comprised hydrothermal and annealing steps. The as-prepared material exhibited a Chinese chestnut-like structure, and the core–shell structure was based on nanoneedles. The pseudocapacitance characteristics of the Ni–Co–O/NiCo-LDH electrode were significantly improved due to the good electrical conductivity of the core material and the unique core–shell structure, which led to a high electrochemical performance, reaching a high specific capacitance of 1434 F g−1 at the current density of 1 A g−1. When assembled in a device with activated carbon (AC) as the negative electrode, the supercapacitor showed an energy density of 26 Wh kg−1 at a power density of 807 W kg−1. Simultaneously, the device showed an excellent cycle stability, with 95% capacity retention after 3600 cycles at a current density of 6 A g−1, which could largely widen the application of the supercapacitor.
The evolving philosophers problem: dynamic change management (1990)

A model for dynamic change management which separates structural concerns from component application concerns is presented. This separation of concerns permits the formulation of general structural rules for change at the configuration level without the need to consider application state, and the specification of application component actions without prior knowledge of the actual structural changes which may be introduced. In addition, the changes can be applied in such a way so as to leave the modified system in a consistent state, and cause no disturbance to the unaffected part of the operational system. The model is applied to an example problem, 'evolving philosophers'. The principles of this model have been implemented and tested in the Conic environment for distributed systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>
Advanced Mitigating Measures for the Cell Internal Short Risk (Presentation) (2010)

This presentation describes mitigation measures for internal short circuits in lithium-ion battery cells.
CCDC 972315: Experimental Crystal Structure Determination (2014)

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
A Generic Business Artifacts Based Authorization Framework for Cross-Enterprise Collaboration (2012)

Business artifacts provide an approach to Business Process Management that combines data and process in a holistic way. Previous research introduced artifact-centric Interoperation Hubs (I-Hubs) as a data-centric alternative to conventional service orchestration for enabling the cooperative interaction of multiple organizations with shared business objectives. The current paper extends this vision by describing an approach for implementing I-Hubs that supports rich access control mechanisms. This reflects the data-process duality of business artifacts, the approach borrows from access control disciplines for both data and process. The paper describes how the approach is being applied in connection with two models for artifact lifecycles, a procedural one based on finite state machines and a declarative one based on guards, stages, and milestones.
Self-tuning multirate control of binary distillation columns (1988)

Two multivariable self-tuning control algorithms based on predictive control techniques are presented and applied to some realistic simulation models of binary distillation columns. The nonlinear model of binary distillation columns subsequently used in simulation is summarized. The good agreement between the behaviour of an industrial plant and the model is highlighted by analyzing the effects on the composition products of open loop step variations in the vapour and distillate flow rates. The multirate generalized minimum variance (MGMV) algorithm is briefly presented. The algorithm is then applied to the control of an 11 plates binary distillation column model. The obtained results are presented and show the improvements obtained by means of a multirate scheme. However, the simulated plant does not exhibit the 'nonminimum-phase' behaviour typical of real columns. In these circumstances, MGMV can still ensure closed loop stability at the cost of oscillatory and sluggish transients. In order to overcome these frequently encountered difficulties, attention is drawn towards the development of a multistep-multirate algorithm obtained by suitably rearranging the generalized predictive control method. The derived algorithm multirate generalised predictive control is then used for control of a highly 'nonminimum-phase' column. The corresponding results are reported. The advantages of resorting to multirate control algorithms are clearly pointed out in this case too. Finally the paper ends with some concluding remarks and hints for future work.
Abstract 1050: Characterization of human cancer cell line xenografts in humanized mice (2019)

Studying immune-oncology (IO) agents that target the human immune system in mice have posed challenges for oncology researchers. Since the commercial introduction of humanized mice, antitumor efficacy studies can now be performed with human cancer cells within mice bearing the components of a human immune system. However, development and characterization of these models is necessary to understand which model may be best for different agents. To this end, we characterized A375, A549, Caki-1, H1299, H1975, HCC827, HCT116, KU19-19, MDA-MB-231, and RKO human cancer cell lines in CD34+ humanized NSG mice for tumor growth rate and immune cell profiling in tumor and spleen compartments. We found that CD4+ T cell composition in both the spleen and tumor varied among the models, with A375, Caki-1, and RKO expressing higher levels of CD4+ T cells compared to the other models. Many of the models had moderate levels of CD8+ T cells in the tumor. Spleen analysis revealed higher NK cell populations in mice bearing Caki-1 tumors, while A549 tumor samples had a higher percentage of NK cells relative to the other models. Surprisingly, many of the tumor models showed an abundance of myeloid cells in the spleen and tumor compartments, although these were mostly comprised of B cells and dendritic cells and lower levels of macrophages and MDSCs. The expression level and percentage of cells expressing PD-1, and PD-L1 in the models were also determined. Furthermore, the antitumor response of select models to PD-1 and PD-L1 antibodies will be presented. In summary, these data demonstrate that there are tumor-intrinsic factors that influence the immune cell repertoire within tumors and spleen. These data may also aid in selection of tumor models to test antitumor activity of novel IO agents.Citation Format: Christina Stevens, Christopher Maddage, Kerri Lasky, Jonathan Rios-Doria, Holly Koblish. Characterization of human cancer cell line xenografts in humanized mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1050.
A clothes-peg-shaped binucleartrans-bis(2-aminotroponato)palladium(II) complex bearing pentamethylene spacers (2013)

rac-Bis{μ-trans-2,2'-[pentane-1,5-diylbis(azanediyl)]ditroponato}dipalladium(II), [Pd2(C19H20N2O2)2], has been synthesized and fully characterized using single-crystal X-ray diffraction, (1)H NMR, FT-IR and mass spectroscopy. The trans coordination, vaulted structure and anti conformation have been unequivocally established from the X-ray diffraction studies. This is the first example of a bis(aminotroponato)palladium complex. In the crystalline state, the molecule has twofold symmetry and each molecular unit undergoes intermolecular offset π-stacking of the tropone rings to afford heterochiral interpenetrating dimers that are aligned in a lamellar manner with a herringbone packing motif.
Microtubule‐based nanomaterials: Exploiting nature's dynamic biopolymers (2015)

For more than a decade now, biomolecular systems have served as an inspiration for the development of synthetic nanomaterials and systems that are capable of reproducing many of unique and emergent behaviors of living systems. One intriguing element of such systems may be found in a specialized class of proteins known as biomolecular motors that are capable of performing useful work across multiple length scales through the efficient conversion of chemical energy. Microtubule (MT) filaments may be considered within this context as their dynamic assembly and disassembly dissipate energy, and perform work within the cell. MTs are one of three cytoskeletal filaments in eukaryotic cells, and play critical roles in a range of cellular processes including mitosis and vesicular trafficking. Based on their function, physical attributes, and unique dynamics, MTs also serve as a powerful archetype of a supramolecular filament that underlies and drives multiscale emergent behaviors. In this review, we briefly summarize recent efforts to generate hybrid and composite nanomaterials using MTs as biomolecular scaffolds, as well as computational and synthetic approaches to develop synthetic one-dimensional nanostructures that display the enviable attributes of the natural filaments.
Band Relevance Factor (BRF): a novel automatic frequency band selection method based on vibration analysis for rotating machinery (2022)

The monitoring of rotating machinery has now become a fundamental activity in the industry, given the high criticality in production processes. Extracting useful information from relevant signals is a key factor for effective monitoring: studies in the areas of Informative Frequency Band selection (IFB) and Feature Extraction/Selection have demonstrated to be effective approaches. However, in general, typical methods in such areas focuses on identifying bands where impulsive excitations are present or on analyzing the relevance of the features after its signal extraction: both approaches lack in terms of procedure automation and efficiency. Typically, the approaches presented in the literature fail to identify frequencies relevant for the vibration analysis of a rotating machinery; moreover, with such approaches features can be extracted from irrelevant bands, leading to additional complexity in the analysis. To overcome such problems, the present study proposes a new approach called Band Relevance Factor (BRF). BRF aims to perform an automatic selection of all relevant frequency bands for a vibration analysis of a rotating machine based on spectral entropy. The results are presented through a relevance ranking and can be visually analyzed through a heatmap. The effectiveness of the approach is validated in a synthetically created dataset and two real dataset, showing that the BRF is able to identify the bands that present relevant information for the analysis of rotating machinery.
Introduction of an agent-based multi-scale modular architecture for dynamic knowledge representation of acute inflammation (2008)

A series of ABMs are presented extending from the level of intracellular mechanism to clinically observed behavior in the intensive care setting. The ABMs all utilize cell-level agents that encapsulate specific mechanistic knowledge extracted from in vitro experiments. The execution of the ABMs results in a dynamic representation of the multi-scale conceptual models derived from those experiments. These models represent a qualitative means of integrating basic scientific information on acute inflammation in a multi-scale, modular architecture as a means of conceptual model verification that can potentially be used to concatenate, communicate and advance community-wide knowledge.
Rough Sets and Few-Objects-Many-Attributes Problem: The Case Study of Analysis of Gene Expression Data Sets (2007)

We discuss two methodologies developed within the theory of rough sets, which may be used in the analysis of data with relatively low number of objects (rows, experiments) and large number of attributes (columns, features). We illustrate their applicability by referring to the gene expression data sets, which are widely known to suffer from such a ”few-objects-many-attributes” problem. The first methodology is based on the notion of a reduct, aiming at very clear and simple representation of (possibly inexact) functional dependencies between groups of attributes in a data table. In particular, we talk about collections of decision reducts and association reducts, useful, respectively, in the new case classification and the data-based knowledge representation tasks, where genes are interpreted as attributes and where reducts represent (inexact) functional dependencies between expressions of groups of genes. The second methodology is referred to as to rough discretization (sometimes called also dynamic discretization or roughfication, in contrast to fuzzification known from fuzzy logic.) Given a data set with numeric attributes, rough discretization produces a new data set with the same number of attributes, where, however, the number of records is squared and the values become symbolic. Basically, it uses each of original records to discretize the others by mutual comparisons between values over all numeric attributes, without any additional, externally tuned discretization parameters needed. Such roughfied data sets are a perfect source for calculations of decision and association reducts, which result in rules interpreted in terms of inequalities involving original attributes and their respective value domains. As a summary, the two presented rough-set-based methods applied together provide a simple, easily interpretable, and noninvasive framework for gene expression data analysis.
Unexpected ring-opening of 2,3-dihydropyridines (2021)

Abstract The reaction of 2,3-dihydropyridines with sulfonyl halides surprisingly yielded open chain dienes with sulfonylimine structure. The products were specific out of several possible isomers and, therefore, a separation of isomers was not necessary. All new compounds were characterized using FT-IR spectroscopy, HRMS, and NMR spectroscopy. A bicyclic by-product from the reaction of a 2,3-dihydropyridine with mesyl chloride was isolated and its structure elucidated using a single X-ray crystal analysis. Some biological activities, like antimicrobial and cytotoxic properties were investigated. Graphic abstract
ChemInform Abstract: SYNTHESIS AND LUMINESCENT PROPERTIES OF 4‐SUBSTITUTED DERIVATIVES OF NAPHTHALIC ANHYDRIDE AND NAPHTHALIMIDE (1983)

Abstract Durch Kondensation des Anhydrids (I) mit den sek. Aminen (II) werden die 4‐Aminonaphthalanhydride (III) erhalten, die im gelbgrünen Teil des Spektrums mit unterschiedlicher Quantenausb.
Reverse Engineering of Experimental Tests Results of Ballistic Impact for the Validation of Finite Element Simulations (2010)

In this paper the set up and the carrying out of experimental ballistic tests on a tail rotor transmission shafts for helicopter, which are impacted by a 7.62 NATO projectile, are presented. After the tests, a 3D acquisition of the impacted area on each shaft has been performed in order to acquire exactly the shape of the damage. The acquisition has been carried out with a 3D range camera. The experimental activities have been compared with the results of a numerical simulation of the impact, which has been computed with an explicit finite element code. The direct comparison has been done by superimposing the two meshes (from FE analysis and from 3D acquisition). This method has proved to be effective for identifying analogies and differences and for giving the possibility to promote a “quantitative” discussion with the aim of improving the accuracy of the numerical models and simulation conditions. The adoption of the Reverse Engineering practice has proved to be a powerful method for integrating and comparing the simulation data with real data, and give suggestions to further analysis.
Botulinum toxin treatment of muscle cramps: A clinical and neurophysiological study (1997)

Botulinum toxin is now widely used in the treatment of several hyperkinetic movement disorders. To evaluate its efficacy in treating muscle cramping syndromes, we studied clinical and neurophysiological variables before and after botulinum toxin injections into calf muscles and small flexor muscles of the foot in patients with an inherited benign cramp-fasciculation syndrome. At each assessment the clinical severity of cramp was scored and the cramp threshold frequency was measured with repetitive electrical peripheral nerve stimulation. Botulinum toxin injection significantly lowered our patients' clinical cramp severity scores (mean +/- SD: before, 3.80 +/- 0.44; after, 1.40 +/- 0.54), left muscle strength unchanged and significantly increased their cramp threshold frequencies (before, 4.22 +/- 2.26 Hz; after, 10.0 +/- 3.74 Hz). The clinical benefit induced by botulinum toxin lasted about 3 months. Botulinum toxin injections also significantly reduced fasciculation potentials in relaxed muscles (before, 0.86 +/- 0.19 fasciculations/sec; after, 0.45 +/- 0.11 fasciculations/sec). These findings show that local intramuscular injections of botulinum toxin provide effective, safe, and long-lasting relief of cramps possibly by reducing presynaptic cholinergic stimulation of motor nerve terminals and by impairing the input/output function of intrafusal and extrafusal motor end plates.