Miracle-dz/UniFAR-FTUs huggingface dataset:
An Interface-Oriented Resource Capability Model to Support Reconfiguration of Manufacturing Systems

Global trends in manufacturing force companies to deal with a production environment with increasing dynamics. In such an environment, companies have to cope with frequent changes of production requirements and conditions. In order to cope with these frequent changes, manufacturing machines have been designed to be reconfigurable. Although machines of this type have the potential to deal with a dynamic production environment, their full potential cannot be exploited, yet. One crucial reason for this is the long reaction time for the identification and design of a required system configuration. A first step towards problem solving is to have a capability model of manufacturing resources. Based on such a capability model, the manufacturability of production requests could be quickly analyzed. In addition, these models can be utilized to generate new configurations, which match changed requirements. However, state of the art approaches have several drawbacks regarding the efficient analysis of manufacturability and feasible resource reconfigurations. Accordingly, this contribution presents an interface-oriented approach for the modeling of manufacturing resource capabilities. The approach aims at the facilitation of the manufacturability check and reconfiguration analysis. Furthermore, the implementation of the modeling concept in AutomationML is presented, which allows a machine-interpretable representation of the model in a neutral data format. The applicability of the approach is illustrated by an example of an automated riveting system at Airbus and a discussion regarding the advantages and drawbacks of the developed approach is given.
Not All Lies Are Equal. A Study Into the Engineering of Political Misinformation in the 2016 US Presidential Election

We investigated whether and how political misinformation is engineered using a dataset of four months worth of tweets related to the 2016 presidential election in the United States. The data contained tweets that achieved a significant level of exposure and was manually labelled into misinformation and regular information. We found that misinformation was produced by accounts that exhibit different characteristics and behaviour from regular accounts. Moreover, the content of misinformation is more novel, polarised and appears to change through coordination. Our findings suggest that engineering of political misinformation seems to exploit human traits such as reciprocity and confirmation bias. We argue that investigating how misinformation is created is essential to understand human biases, diffusion and ultimately better produce public policy.
Study on grid feed forward strategy under Unbalanced Grid Condition

In the case of unbalanced power grid, the disturbance of the power grid is introduced into the feed forward accurately, thus, the output feed-forward part of the inverter is in perfect agreement with the unbalanced power grid, in order to achieve good and effective control of the current of the converter in the dynamic change of the power grid. Due to the filtering delay of voltage sampling circuit, in the case of unbalanced power grid, phase compensation is also required for the delay of the negative sequence component, to ensure that the feedforward part of the grid is correct. The feed-forward control strategy of power grid under unbalanced and balanced power grid is simulated. The results show that the scheme can optimize the feed-forward component of the power grid and control the current of the converter effectively.
Phase-Only Radar Waveform Design for Spectrally Dense Environments

This paper deals with the synthesis of waveforms that optimize radar performance while satisfying multiple spectral compatibility constraints. For each shared band, precise control is imposed on the injected interference energy. Furthermore, compliance with amplifiers operating in saturation is ensured at the design stage where phase-only waveforms are considered. To tackle the resulting NP-hard optimization problem, an iterative procedure based on the coordinate descent method is introduced. Hence, some case studies are reported to highlight the effectiveness of the technique.
Development of combination therapies to maximize the impact of KRAS-G12C inhibitors in lung cancer

Combined targeting of KRAS-G12C, mTOR, and IGF1R enhances and extends the response to recently developed KRAS-G12C inhibitors in lung cancer models. Cutting off tumors’ escape routes Cancer-driving mutations in the KRAS oncogene are common in many cancer types, including lung cancer. The combination of inhibitors for the kinases MEK and IGF1R is effective in KRAS-mutant lung cancer, but some cancer cells can still survive this treatment. Molina-Arcas et al. used an shRNA screening approach to identify another category of drugs that can be added to the therapeutic regimen to enhance its effectiveness. The authors then substituted an inhibitor of mutant KRAS to replace the MEK inhibitor in the combination to decrease treatment toxicity. The resulting triple-drug combination showed promising results in mouse models, with improved efficacy and tolerability. KRAS represents an excellent therapeutic target in lung cancer, the most commonly mutated form of which can now be blocked using KRAS-G12C mutant-specific inhibitory trial drugs. Lung adenocarcinoma cells harboring KRAS mutations have been shown previously to be selectively sensitive to inhibition of mitogen-activated protein kinase kinase (MEK) and insulin-like growth factor 1 receptor (IGF1R) signaling. Here, we show that this effect is markedly enhanced by simultaneous inhibition of mammalian target of rapamycin (mTOR) while maintaining selectivity for the KRAS-mutant genotype. Combined mTOR, IGF1R, and MEK inhibition inhibits the principal signaling pathways required for the survival of KRAS-mutant cells and produces marked tumor regression in three different KRAS-driven lung cancer mouse models. Replacing the MEK inhibitor with the mutant-specific KRAS-G12C inhibitor ARS-1620 in these combinations is associated with greater efficacy, specificity, and tolerability. Adding mTOR and IGF1R inhibitors to ARS-1620 greatly improves its effectiveness on KRAS-G12C mutant lung cancer cells in vitro and in mouse models. This provides a rationale for the design of combination treatments to enhance the impact of the KRAS-G12C inhibitors, which are now entering clinical trials.
Assessing the depletion of lincomycin in feathers from treated broiler chickens: a comparison with the concentration of its residues in edible tissues

ABSTRACT Lincomycin is the first antimicrobial agent described for the lincosamide class and it is commonly used for the treatment of infectious enteric and respiratory diseases in poultry. Maximum residue limits (MRLs) in edible tissues have been established for this antimicrobial, however, no regulation has been proposed yet for by-products that are not intended for direct human consumption. Feathers are a by-product from poultry farming that might be used as an ingredient for diets fed to other farm animal species. The presence of antimicrobial residues in them is not monitored in spite of the fact that several studies have proved that they can persist in feathers. Currently though, no evidence has been presented regarding the behaviour of lincomycin in this matrix. Hence, this work intended to assess the depletion of lincomycin residues in feathers of birds treated with therapeutic doses and compare them with those detected in muscle and liver samples. Samples were collected for several days after ceasing treatment from a group of broiler chickens treated with a 25% lincomycin formulation. Methanol and Florisil® columns were used to extract and retain the analyte, and samples were analysed using a triple quadrupole mass spectrometer (API 5500, AB SCIEX™). On day 1 after ceasing treatment, average concentrations of lincomycin detected in feather samples reached up to 8582 μg kg−1 and by day 16, these had only declined by 63%, to an average of 3138 μg kg−1. Lincomycin residues were detected in feathers at every sampling point, even after they were not detectable in edible tissues. Depletion time was 98 days for feathers, considering the LOQ established for the methodology as cut-off value for the calculations. Data showed that lincomycin is highly persistent in feathers, which may result in this matrix becoming a re-entry route for its residues into the food chain. Graphical abstract
Neutron spin echo monitoring of segmental-like diffusion of water confined in the cores of carbon nanotubes.

Following the stream of increasing scientific interest in condensed-matter systems under ultra-hydrophobic confinement, the present work reports the first incoherent neutron spin echo assessment of the dynamics of water axially confined inside single-wall carbon nanotubes of diameter d ∼ 1.4 nm. At the time scale of nanoseconds, two water populations are retrieved, whose relative proportion matches the one expected for a concentric shell + chain arrangement with cylindrical symmetry. The time dependence of the mean square displacement related to the external component is found to be subdiffusive, with peculiar resemblance to segmental diffusion typical of entangled polymeric systems.
Effect of PPARGC1A on the development and metabolism of early rabbit embryos in vitro

Peroxisome proliferator‐activated receptor gamma coactivator 1‐alpha (PPARGC1A) is a central regulator of mitochondrial biogenesis and metabolism, and its expression is closely related to embryo development. To gain insights into the possible mechanisms of PPARGC1A during early embryogenesis, the development potential, mitochondrial biogenesis, and the culture medium metabolomics of embryos were evaluated when PPARGC1A overexpressed or suppressed in rabbit zygotes. Results showed that different PPARGC1A levels in rabbit zygotes could affect blastocyst percentage, and the expressions of mitochondrial biogenesis and metabolic‐related genes, as well as the glutathione and adenosine triphosphate levels during early embryo development. In addition, compared with the controls, 12 and 10 different metabolites involved in carbohydrate, amino acid, and fatty acid metabolism were screened in the 5 day's spent culture medium of PPARGC1A overexpressed and suppressed embryos by gas chromatography–mass spectrometer, respectively. Consistent with these metabolite changes, the transcriptions of genes encoding glucose transporters and fatty acid biosynthetic proteins in the embryos from different groups were regulated by PPARGC1A during rabbit embryo development. Taken together, these data provide evidence that PPARGC1A may regulate early rabbit embryo development through mitochondrial biogenesis and metabolism.
A distinct transition from cell growth to physiological homeostasis in the tendon

Changes in cell proliferation define transitions from tissue growth to physiological homeostasis. In tendons, a highly organized extracellular matrix undergoes significant postnatal expansion to drive growth, but once formed, it appears to undergo little turnover. However, tendon cell activity during growth and homeostatic maintenance is less well defined. Using complementary methods of genetic H2B-GFP pulse-chase labeling and BrdU incorporation in mice, we show significant postnatal tendon cell proliferation, correlating with longitudinal Achilles tendon growth. Around day 21, there is a transition in cell turnover with a significant decline in proliferation. After this time, we find low amounts of homeostatic tendon cell proliferation from 3 to 20 months. These results demonstrate that tendons harbor significant postnatal mitotic activity, and limited, but detectable activity in adult and aged stages. It also points towards the possibility that the adult tendon harbors resident tendon progenitor populations, which would have important therapeutic implications.
Fungal metabolite and mycotoxins profile of cashew nut from selected locations in two African countries

ABSTRACT Infestation of food crops by fungi has led to serious economic losses in the international market. There is a paucity of information on the multi-mycotoxin profile of cashew nuts from different geographical locations in Nigeria and South Africa. This work aims at comparing the fungal metabolite profile of cashew nut from two African countries using HPLC-FLD and LC-MS/MS. The differences in distribution of the fungal metabolites across the two countries were also examined. A total of 75 (39; Nigeria, 36; South Africa) roasted cashew nuts were purchased from 14 different locations in Nigeria and 10 major supermarkets in South Africa. The samples were subjected to HPLC analysis for aflatoxin and zearalenone and LC-MS/MS analysis for multiple metabolite profiling. Total aflatoxins (0.03-0.77 µg/kg and 0.01-0.28 µg/kg) and zearalenone (123.2 and 788.6 µg/kg) were quantified in nut samples from South Africa and Nigeria, respectively. In contrast, LC-MS/MS analysis showed that none of the major fungi metabolites except for one (citrinin) was present in detectable amount in Nigerian cashew nuts (31.3 μg/kg). Other microbial metabolites present in the nuts were; Aspergillus metabolites (< LOD to 398 × 104 μg/kg and < LOD to 1760 μg/kg); flavoglaucin (5.4-177.4 μg/kg and 45.5-18,368 μg/kg), 7-hydroxykaurenolide (5.5-45.8 μg/kg and 3.5-33.7 μg/kg), chloramphenicol (0.3-4.5 μg/kg) for South African and Nigerian cashew nuts, respectively. The incidence rate of the fusarium toxins beauvericin and bikaverin were higher in Nigerian cashew nut (100% and 17%) than the South African nuts (8% and 8%), 7-Hydroxykaurenolide and chloramphenicol were found in all the cashew nut samples. A total of 15 unspecific metabolites (17-100%) were recorded in the nuts from both countries. Information on the metabolite profile of cashew nuts will provide useful information for policy makers, border agencies and other health-related Institutes. Graphical abstract Fungi metabolites profile of Cashew nut from selected locations in Two African Countries. *Roasted cashew nut image-www.gettyimages. com
System characteristics of a micro UAV defense system

This paper is part of a broader research effort to systematically design a defense system against micro UAS. An interceptor UAV is used to prevent an intruder UAV from entering a defined airspace. The design needs to meet two main requirements, a dynamic requirement and a control requirement. The dynamic requirement describes the required agility of the interceptor UAV to allow it to intercept a commercial off-the-shelf UAV. The agility is analyzed through dynamic modeling of the UAV and the powertrain combined with suitable path planning and control design. The control requirement describes the need to intercept the intruder UAV without an uncontrolled crash of either the interceptor or the intruder. An analysis of the flight stability during the intercept is conducted in this paper.
R-MetaboList 2: A Flexible Tool for Metabolite Annotation from High-Resolution Data-Independent Acquisition Mass Spectrometry Analysis

Technological advancements have permitted the development of innovative multiplexing strategies for data independent acquisition (DIA) mass spectrometry (MS). Software solutions and extensive compound libraries facilitate the efficient analysis of MS1 data, regardless of the analytical platform. However, the development of comparable tools for DIA data analysis has significantly lagged. This research introduces an update to the former MetaboList R package and a workflow for full-scan MS1 and MS/MS DIA processing of metabolomic data from multiplexed liquid chromatography high-resolution mass spectrometry (LC-HRMS) experiments. When compared to the former version, new functions have been added to address isolated MS1 and MS/MS workflows, processing of MS/MS data from stepped collision energies, performance scoring of metabolite annotations, and batch job analysis were incorporated into the update. The flexibility and efficiency of this strategy were assessed through the study of the metabolite profiles of human urine, leukemia cell culture, and medium samples analyzed by either liquid chromatography quadrupole time-of-flight (q-TOF) or quadrupole orbital (q-Orbitrap) instruments. This open-source alternative was designed to promote global metabolomic strategies based on recursive retrospective research of multiplexed DIA analysis.
Brain Tumor Segmentation with Generative Adversarial Nets

Accurate brain tumor segmentation is the key of clinical diagnostics and treatment planning. However, a large quantity of data produced by Magnetic resonance imaging (MRI) prevents manual segmentation in a reasonable time. So, automatic approaches are required for quick and effective segmentation. Nevertheless, the spatial and structural variability among brain tumors bring a challenge to automatically segment MRI images. In this paper, an automatic end-to-end method based on Generative Adversarial Nets (GAN) is proposed for brain tumor segmentation. This method combines the generating model with the discriminant model and takes GAN instead of conditional random fields (CRF) as high-order smoothing method. The proposed method was validated in the BRATS 2015 database, it can be proven that the proposed method achieves a competitive result and the use of GAN improve the performance of networks. Furthermore, comparing with other recent CNN-based methods, the approach only takes about 10.8s to segment a patient case.
NLOS Target Localization with a UWB Radar

This paper addresses a localization problem for a single non-line-of-sight (NLOS) target shielded by a rectangular building corner in an urban environment exploiting an ultra-wideband (UWB) radar. A novel electromagnetic waves propagation model referring to such building corner is established. Specifically, we firstly analyze the characteristics of the radar echoes propagating along the direct diffraction path and the side wall reflection path. Then, we extract the lengths of these two paths after the target detecting via CFAR technique. Next, based on the priori-known building layout, we obtain the position of the hidden target accurately. Finally, numerical simulations and experimental data validate the proposed approach.
STAP based on Clean technology and Conjugate Gradient method

We propose a space-time adaptive processing(STAP) algorithm which consists of a clean technology and the conjugate gradient(CG) method. Herein, our algorithm is refer to as the CCG algorithm. The conjugate gradient method plays a special role in the CCG algorithm because of its computational advantage in the matrix inversion, especially, when the matrix has a low-rank correction structure. The CCG algorithm can reduce the need of the data from the secondary range cells in conventional STAP approaches and degrade the mismatch problem in the iterative adaptive approach (IAA) to some extent. In addition, the CCG algorithm has a lower computational cost than IAA. We also analysis the output signal-to-interference-and-noise ratio(SINR) of CCG with separated interferences, which verifies the excellent output SINR performance of CCG. At last, the performance of the CCG algorithm is validated by numerical results.
Ball Trajectory Tracking and Prediction for a Ping-Pong Robot

Robot and vision systems designed for table tennis games have been a challenging and popular research topic. In this paper, we provide three indispensable procedures of a vision system for a ping-pong robot. To begin with, object recognition and tracking are preliminary in the system through image processing. In track the ball position in a three-dimensional workspace, we use cameras and adopt the binocular triangulation algorithm. In order to hit the ball precisely, the prediction of the ball position is necessary. Thus, we present a scheme for the ball trajectory prediction through Back Propagation Neural Network (BPN) without measuring the ball states of current velocity, acceleration, and self-rotational velocity. Because a ping-pong trajectory has two flight parabolas, we use two neural networks to learn the complete ping-pong flight trajectory. Namely, the first and second networks are trained for two parabolas, respectively. By doing this, the robot can only use the first ten points of the first parabola and then predict the striking point through the trained network. In our experiment, we provide an extensive validation of the proposed approach showing about 88% rate of success with flight trajectory prediction. Through refining the learning scheme, the rate of success could be up to 97%.
Robust Predictive Current Control for Photovoltaic Energy Storage System Based on Second-order Tracking Differentiator

Aiming at the problem of DC bus voltage fluctuation due to the change of photovoltaic (PV) output power, load mutations and nonlinear loads connection in DC microgrid system including PV power generation and energy storage, the robust predictive current control (PCC) for photovoltaic energy storage system based on second-order tracking differentiator (STD) is proposed in this paper. The traditional current loop controller based on PI is replaced by the controller based on the PCC. And the second-order tracking differentiator for the DC bus voltage control system of bidirectional DC-DC converters is designed. The second-order tracking differentiator can track the equivalent disturbance current quickly in the system without adding additional current sensors. Feed forward method based on the STD is proposed to improve the disturbance rejection capability of the system. The simulation results show that the DC bus voltage fluctuations can be suppressed effectively by using the predictive current control based on second-order tracking differentiator, and the dynamic performance and robustness of the system are improved.
Use of non-verbal vocalizations for continuous emotion recognition from speech and head motion

Dyadic interactions are reflective of mutual engagement between their participants through different verbal and non-verbal voicing cues. This study aims to investigate the effect of these cues on continuous emotion recognition (CER) using speech and head motion data. We exploit the non-verbal vocalizations that are extracted from speech as a complementary source of information and investigate their effect for the CER problem using gaussian mixture and convolutional neural network based regression frameworks. Our methods are evaluated on the CreativeIT database, which consists of speech and full-body motion capture under dyadic interaction settings. Head motion, acoustic features of speech and histograms of non-verbal vocalizations are employed to estimate activation, valence and dominance attributes for the CER problem. Our experimental evaluations indicate a strong improvement of CER performance, especially of the activation attribute, with the use of non-verbal vocalization cues of speech.
Computer Shogi Tournaments and Techniques

Shogi is a major chess variant in Japan. This article presents two shogi competitions: the World Computer Shogi Championship, the largest annual competition held by the Computer Shogi Association, and The Floodgate, which is a complementary internet competition held voluntarily by researchers. Notable technical contributions made by participants and useful resources for prospective developers are also presented. The techniques and therefore playing strength are still improving, partly because both competitions have attracted active participants, even after the playing strength of computer programs has apparently surpassed that of human players.