Miracle-dz/UniFAR-FTUs huggingface dataset:
Computationally Efficient Trajectory Generation for Fully Actuated Multirotor Vehicles

This paper presents a computationally efficient method of generating state-to-state trajectories for fully actuated multirotor vehicles. The approach consists of computing translational and rotational motion primitives that guide the vehicle from any initial state, defined by position, velocity, and attitude, to any end state in a given time, and subsequently verifying the motion primitives’ feasibility. Computationally lightweight motion primitives for which closed-form solutions exist are presented and an efficient method to test their feasibility is derived. The algorithm is shown to be able to generate trajectories and verify their feasibility within a few microseconds and can thus be used as an implicit feedback law or in high-level path planners that involve evaluating a large number of possible trajectories to achieve some high-level goal. The algorithm's performance is analyzed by comparing it with time-optimal trajectories. An experimental demonstration that requires the computation of trajectories for a large set of end states in real time is used to evaluate the approach.
BCL2 and BCL(X)L selective inhibitors decrease mitochondrial ATP production in breast cancer cells and are synthetically lethal when combined with 2-deoxy-D-glucose

Cancer cells display differences regarding their engagement of glycolytic vs. mitochondrial oxidative phosphorylation (OXPHOS) pathway. Triple negative breast cancer, an aggressive form of breast cancer, is characterized by elevated glycolysis, while estrogen receptor positive breast cancer cells rely predominantly on OXPHOS. BCL2 proteins control the process of mitochondrial outer membrane permeabilization during apoptosis, but also regulate cellular bioenergetics. Because BCL2 proteins are overexpressed in breast cancer and targetable by selective antagonists, we here analysed the effect of BCL2 and BCL(X)L selective inhibitors, Venetoclax and WEHI-539, on mitochondrial bioenergetics and cell death. Employing single cell imaging using a FRET-based mitochondrial ATP sensor, we found that MCF7 breast cancer cells supplied with mitochondrial substrates reduced their mitochondrial ATP production when treated with Venetoclax or WEHI-539 at concentrations that per se did not induce cell death. Treatments with lower concentrations of both inhibitors also reduced the length of the mitochondrial network and the dynamics, as evaluated by quantitative confocal microscopy. We next tested the hypothesis that mitochondrial ATP production inhibition with BCL2 or BCL(X)L antagonists was synthetically lethal when combined with glycolysis inhibition. Treatment with 2-deoxy-D-glucose in combination with Venetoclax or WEHI-539 synergistically reduced the cellular bioenergetics of ER+ and TNBC breast cancer cells and abolished their clonogenic potential. Synthetic lethality was also observed when cultures were grown in 3D spheres. Our findings demonstrate that BCL2 antagonists exert potent effects on cancer metabolism independent of cell death-inducing effects, and demonstrate a synthetic lethality when these are applied in combination with glycolysis inhibitors.
Mathematical Models for Predicting and Mitigating the Spread of Chlamydia Sexually Transmitted Infection

Chlamydia trachomatis (Ct) is the most common bacterial sexually transmitted infection (STI) in the United States and is major cause of infertility, pelvic inflammatory disease, and ectopic pregnancy among women. Despite decades of screening women for Ct, rates continue to increase among them in high prevalent areas such as New Orleans. A pilot study in New Orleans found approximately 11% of 14-24 year old of African Americans (AAs) were infected with Ct. Our goal is to mathematically model the impact of different interventions for AA men resident in New Orleans on the general rate of Ct among women resident at the same region. We create and analyze mathematical models such as multi-risk and continuous-risk compartmental models and agent-based network model to first help understand the spread of Ct and second evaluate and estimate behavioral and biomedical interventions including condom-use, screening, partner notification, social friend notification, and rescreening. Our compartmental models predict the Ct prevalence is a function of the number of partners for a person, and quantify how this distribution changes as a function of condom-use. We also observe that although increased Ct screening and rescreening, and treating partners of infected people will reduce the prevalence, these mitigations alone are not sufficient to control the epidemic. A combination of both sexual partner and social friend notification is needed to mitigate Ct.
History attention for source-target alignment in neural machine translation

Attention mechanism has enhanced state-of-the-art Neural Machine Translation (NMT) by focusing on parts of the source sentence when predicting each target word. However we find that most of the attention context vector calculation is directly dependent on the current decoder hidden state. It tends to ignore past translated information, which often leads to over-translation and under-translation. When target sentence is very long, or the words relation inside the sentence are not tight, for example, there are some separators in the sentence, the model can get wrong translation. Aiming to solve these problems, in this paper, we propose a history attention structure that takes advantage of translated information. This architecture easily captures history information, helps model alleviate the memory vanishing problem introduced by long sentences and avoid focusing on one local part. In experiments, we show our history attention with gate improves translation quality.
Combined approach based on ACO with MTSP for optimal internal electrical system design of large offshore wind farm

The wind turbine (WT) layout and electrical system layout plays a vital role in offshore wind farm (OWF) design. The wake effect has a significant impact on the power production of the OWF. WTs in wake region will not experience healthy wind; hence it affects the power production. The proper placement of WTs can reduce the wake effect in OWF. The optimal design of large OWF is based on combined approach of Ant colony optimization (ACO) with multiple travelling salesmen problem (MTSP) is presented. The objectives of the approach are to improve power production, minimize the length of cable and cable cost. By considering (a) placement of WT with consideration of wake effect, (b) placement of substation, (c) selection of submarine cables with higher reliability and minimal power loss, and (d) minimum length of WT cable routing with zero cross connection. ACO-MTSP approach is applied on large OWF connected with 280 WTs and results are compared with the outcome of reference OWF.
Power losses assessments of LCC-based HVDC converter stations using datasheet parameters and IEC 61803 STD

In distribution and transmission, particularly when the power is transported over a transmission line of distance 500km and above, a considerable amount of power is lost during power systems operation, which consist of all the components that are used in generation, transmission and distribution of power. Therefore, it is imperative to estimate the power losses due to some power equipment on the electrical network during transmission and distribution systems. The overall power consumption in transmission of bulk electric power is reasonably reduced by the power loss. The transmission power is growing with the increase of HVDC transmission voltage. It is important to study and critically analyze the losses including HVDC system to actually improve the economics of HVDC system. It is the purpose of this research work to explicitly discuss the procedures for verifying, determining and calculating the overall losses of various equipment of LCC-based high-voltage direct current (HVDC) converter station under operating and standby mode using datasheet parameters (Phase Control Thyristor Type DCR3030V42), since loss calculations are most times based on datasheet values and experimental measurements. The loss calculations in this research work are precisely applicable to all parts of the converter station and cover standby, partial-load, and full-load losses and methods of calculation. According to IEC 61803 of Determination of power losses in high-voltage direct current (HVDC) converter stations, the loss calculation models of converter station equipment under operation and standby mode are elaborately established.
Quantifying Cognitive Workload in Simulated Flight Using Passive, Dry EEG Measurements

A reliable method for quantifying cognitive workload in pilots could find uses in flight training and scheduling, cockpit design, and improving flight safety. Many proposed methods for monitoring cognitive workload in this population rely on measuring physiological responses to externally delivered probe stimuli and/or use traditional gel-based electroencephalography (EEG) sensors. Here we develop passive, probe-independent algorithms for classifying three levels of flight task complexity based on 4-channel, gel-free EEG during simulated flight. Using a library of 168 input features drawn from different data science application domains, we evaluated 13 different classifiers, using a nested tenfold cross-validation procedure to estimate generalization performance. The best subsets of features yielded a median classification accuracy of 90.17% across subjects, with perfect accuracy in one subject and greater than 75% in 16 of 21 subjects. Though EEG line length and linear discriminant analysis were generally among the most effective features and classifiers, respectively, we find that to maximize prediction accuracy, feature set-classifier combinations should be individualized. No single channel proved more valuable than another in predicting flight task complexity, but combining EEG features across channels maintained or improved performance in 81% of subjects.
Inferring implicit social ties in mobile social networks

Mobile social networks provide a platform to collect users' mobility information via mobile devices, and foster many location-aware services, which enable us to infer mobile users' implicit social ties from their mobility information. Some researches have focused on identifying social ties from users' co-occurrences. However, they fail to consider that users meet at different times and places indicates different social tie strength. Furthermore, statistics show that most users with social ties meet with each other few times and even never meet in geography, making social tie inference more challenging. In this paper, we propose a unified framework to infer implicit social ties. Specifically, we explore different aspects of social ties on causing users' co-occurrences, such as location popularity, co-occurrence diversity, and users' mobility behaviors, and further incorporate them to capture the spatial-temporal dynamics for accurately distinguishing social ties from coincidences. For the case of few or no co-occurrences, we build three types of networks: location network, user-location cross network and incomplete user network. Then, a mutual impact factor graph model is proposed to infer missing social ties in the incomplete user network by transferring knowledge extracted from given location and user-location cross network. Experiments conducted on datasets from a real mobile social network show not only the superiority of distinguishing social ties from coincidences but also validate the predictability of social ties although mobile users have few interactions in the physical world.
Adsorption probability of CH4, H2O and H2 in two-dimensional zinc oxide matrix: A prediction by DFT analysis

The aim of the paper is to investigate the physisorption probability of CH4, H2O and H2 in graphene like two-dimensional (2D) Zinc oxide monolayer (g-ZnO) using density functional theory (DFT) incorporated with Quantumwise Atomistix Toolkit (ATK) (v.2016.4). For all the species, the adsorption distance, adsorption energy and charge transfer were calculated for three different adsorption sites on g-ZnO viz., atop Zn atom, atop oxygen atom and atop hollow position. It was found that H2O and CH4 show considerable adsorption probability in g-ZnO whereas H2 shows weak physisorption. Considerably high adsorption energy and charge transfer of H2O indicate that g-ZnO is suitable for designing humidity sensor.
Sensorless position control for a single DOF system

This paper proposes a new method for sensorless position control for a mass which is connected with a servo motor through an elastic element, the system was assumed to have unknown parameter, so recursive least square (RLS) method was used to estimate these parameters depending on the reflected torque on the motor's shaft. Kalman filter (KF) is also used as an observer to estimate the position of the mass, depending on system dynamics and the measured states (angular displacement for motor's shaft and the armature current). Finally, PID controller was used to control the position of the mass depending on the estimated state (mass position). This method is very useful in applications that contain unmeasured states with unknown parameters. The system was implemented experimentally, and driven using Arduino Uno and L298, the current through the motor was measured using ACS712 module.
Dynamic model averaging for daily forex prediction: A comparative study

There are primary concerns in forecasting of time series especially in financial and economical data; model uncertainty in selecting predictors, manipulating the interested parameters in time-varying state vector, and to achieve the improvement on predictive performance in terms of Mean Square Error (MSE) and Mean Absolute Deviation (MAD). Theoretically, the algorithm in dynamic model averaging (DMA) is considered to be handling all the concerns simultaneously. In this work, we apply DMA and other models that are transformed from general DMA to predict daily percentage change in log-return of three most traded forex, i.e., JPY-USD, EUR-USD, and GBP-USD. According to empirical results, structural break model like AR(4) and TVP-AR(4) perform the best in forecasting USD-JPY. Moreover, in order to get the best performance to forecast EUR-USD, it is better to use parsimonious model. In other words, models which allow coefficients to evolve via random walk is preferable. Finally, we found that predictive performance of much complicated methods in DMA and DMS is extremely consistent over time in both forecasting exercises h = 1 and h = 4.
Machine learning based false data injection in smart grid

Smart grids have two-way power flow, two-way communication system, automated and distributed Energy Network. Communication is the main feature that makes a grid smart but that is the feature, which makes it vulnerable to cyber-attacks. Smart meters are installed to measure the real time data and after measurement this data is sent to control room. In the control room, all the control decisions are based on this received data. In communication lines, this data can be tampered or attacked to mislead the decision-making done in the control room. Load shading, power theft, and delay or blocking of data can be the purpose of an attack. State estimation, support vector machine, and observation of previous patterns are the techniques that can be used to detect the false data injected into the power system. In an effort to devise robust strategies against communication line. we put forth a novel attack strategy, which has not been dealt in the literature earlier. We inject false data into the power system by using Linear regression. We also show that none of the existing defence technique are able to detect the false data.
Large-Area Low-Cost Plasmonic Perfect Absorber Chemical Sensor Fabricated by Laser Interference Lithography

We employ laser interference lithography as a reliable and low-cost fabrication method to create nanowire and nanosquare arrays in photopolymers for manufacturing plasmonic perfect absorber sensors over homogeneous areas as large as 5.7 cm2. Subsequently, we transfer the fabricated patterns into a palladium layer by using argon ion beam etching. Geometry and periodicity of our large-area metallic nanostructures are precisely controlled by adjusting the interference conditions during single- and double-exposure processes, resulting in active nanostructures over large areas with spectrally selective perfect absorption of light from the visible to the near-infrared wavelength range. In addition, we demonstrate the method’s applicability for hydrogen detection schemes by measuring the hydrogen sensing performance of our polarization independent palladium-based perfect absorbers. Since palladium changes its optical and structural properties reversibly upon hydrogenation, exposure of the sample to hydrogen caus.
The Evolutionary Consequences of Transposon-Related Pericentromer Expansion in Melon

Abstract Transposable elements (TEs) are a major driver of plant genome evolution. A part from being a rich source of new genes and regulatory sequences, TEs can also affect plant genome evolution by modifying genome size and shaping chromosome structure. TEs tend to concentrate in heterochromatic pericentromeric regions and their proliferation may expand these regions. Here, we show that after the split of melon and cucumber, TEs have expanded the pericentromeric regions of melon chromosomes that, probably as a consequence, show a very low recombination frequency. In contrast, TEs have not proliferated to a high extent in cucumber, which has small TE-dense pericentromeric regions and shows a relatively constant recombination rate along chromosomes. These differences in chromosome structure also translate in differences in gene nucleotide diversity. Although gene nucleotide diversity is essentially constant along cucumber chromosomes, melon chromosomes show a bimodal pattern of genetic variability, with a gene-poor region where variability is negatively correlated with gene density. Interestingly, genes are not homogeneously distributed in melon, and the high variable low-recombining pericentromeric regions show a higher concentration of melon-specific genes whereas genes shared with cucumber and other plants are essentially found in gene-rich chromosomal arms. The results presented here suggest that melon pericentromeric regions may allow gene sequences to evolve more freely than in other chromosomal compartments which may allow new ORFs to arise and eventually be selected. These results show that TEs can drastically change the structure of chromosomes creating different chromosomal compartments imposing different constraints for gene evolution.
High-Glucose or -Fructose Diet Cause Changes of the Gut Microbiota and Metabolic Disorders in Mice without Body Weight Change

High fat diet-induced changes in gut microbiota have been linked to intestinal permeability and metabolic endotoxemia, which is related to metabolic disorders. However, the influence of a high-glucose (HGD) or high-fructose (HFrD) diet on gut microbiota is largely unknown. We performed changes of gut microbiota in HGD- or HFrD-fed C57BL/6J mice by 16S rRNA analysis. Gut microbiota-derived endotoxin-induced metabolic disorders were evaluated by glucose and insulin tolerance test, gut permeability, Western blot and histological analysis. We found that the HGD and HFrD groups had comparatively higher blood glucose and endotoxin levels, fat mass, dyslipidemia, and glucose intolerance without changes in bodyweight. The HGD- and HFrD-fed mice lost gut microbial diversity, characterized by a lower proportion of Bacteroidetes and a markedly increased proportion of Proteobacteria. Moreover, the HGD and HFrD groups had increased gut permeability due to alterations to the tight junction proteins caused by gut inflammation. Hepatic inflammation and lipid accumulation were also markedly increased in the HGD and HFrD groups. High levels of glucose or fructose in the diet regulate the gut microbiota and increase intestinal permeability, which precedes the development of metabolic endotoxemia, inflammation, and lipid accumulation, ultimately leading to hepatic steatosis and normal-weight obesity.
Statins enhance efficacy of venetoclax in blood cancers

Statins cooperate with proapoptotic drugs to enhance tumor killing in blood cancers. Yet another health benefit of statins Statins, common drugs used for the treatment of high cholesterol, may have found another potential application. In addition to their effects on the cholesterol pathway, statins can also inhibit the activity of BCL2 and related proteins, which interfere with apoptosis in many cancer types. In light of this connection, Lee et al. examined the effects of statins on several types of leukemias and lymphomas. The authors determined that statins prime cancer cells for apoptosis and work in synergy with drugs such as venetoclax, an inhibitor of BCL2. In addition to demonstrating this synergy in mouse models, the authors provided data from three studies of patients with chronic lymphocytic leukemia, showing that patients receiving statins had better clinical responses to venetoclax. Statins have shown promise as anticancer agents in experimental and epidemiologic research. However, any benefit that they provide is likely context-dependent, for example, applicable only to certain cancers or in combination with specific anticancer drugs. We report that inhibition of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) using statins enhances the proapoptotic activity of the B cell lymphoma-2 (BCL2) inhibitor venetoclax (ABT-199) in primary leukemia and lymphoma cells but not in normal human peripheral blood mononuclear cells. By blocking mevalonate production, HMGCR inhibition suppressed protein geranylgeranylation, resulting in up-regulation of proapoptotic protein p53 up-regulated modulator of apoptosis (PUMA). In support of these findings, dynamic BH3 profiling confirmed that statins primed cells for apoptosis. Furthermore, in retrospective analyses of three clinical studies of chronic lymphocytic leukemia, background statin use was associated with enhanced response to venetoclax, as demonstrated by more frequent complete responses. Together, this work provides mechanistic justification and clinical evidence to warrant prospective clinical investigation of this combination in hematologic malignancies.
Hypoxia-targeted gold nanorods for cancer photothermal therapy

Tumor hypoxia is a well-recognized driver of resistance to traditional cancer therapies such as chemotherapy and radiation therapy. We describe development of a new nanoconstruct composed of gold nanorods (GNRs) conjugated to carbonic anhydrase IX (CAIX) antibody that specifically binds to CAIX, a biomarker of hypoxia, to facilitate targeting tumor hypoxic areas for focused photothermal ablation. Physicochemical characterization studies confirmed the size, shape, monodispersity, surface charge, and serum stability of the GNRs. Enzyme-linked immunosorbent assays and cellular binding and uptake studies confirmed successful conjugation of antibody to the GNRs and specificity for CAIX. Near-infrared irradiation of CAIX-overexpressing cells treated with GNR/anti-CAIX resulted in significantly higher cell death than cells treated with control GNRs. In vivo biodistribution studies using hyperspectral imaging and inductively coupled plasma mass spectrometry confirmed intravenous administration results not only in greater accumulation of GNR/anti-CAIX in tumors than control GNRs but also greater penetration into hypoxic areas of tumors. Near-infrared ablation of these tumors showed no tumor regression in the sham-treated group, regression but recurrence in the non-targeted-GNR group, and complete tumor regression in the targeted-GNR group. GNR/anti-CAIX nanoconstructs show promise as hypoxia targeting and photothermal ablation agents for cancer treatment.