Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromagnetic Field Due to Lightning Strike to a Tall Tower Sitting on a Mountainous Terrain

This paper presents the results of a radiated electromagnetic field due to lightning strike to a tall tower sitting on a mountainous terrain when considering both the first and subsequent return strokes. A cone-shaped ground with finite conductivity represents the mountainous terrain; and a cylindrical two-dimensional finite-difference time-domain (FDTD) scheme is used to determine the lightning-generated electromagnetic fields. The return stroke channel is modeled using the antenna theory model with fixed inductive loading which is appropriately incorporated into the FDTD algorithm. Simulation results are presented for different values of the cone angle and height, and for different values of the tower height. It is shown that when the first and subsequent return strokes hit a tower on a cone-shaped ground, the radiated electric and magnetic fields experience an enhancement in their amplitudes as compared to the case where the tower sits on a flat ground. Also, the current reflections from the tower base, influencing the current distribution along the tower and radiated electromagnetic field waveforms, depend on the cone angle and height, as well as the tower height.
Crowd-Sourced Data Collection for Urban Monitoring via Mobile Sensors

A considerable amount of research has addressed Internet of Things and connected communities. It is possible to exploit the sensing capabilities of connected communities, by leveraging the continuously growing use of cloud computing solutions and mobile devices. The pervasiveness of mobile sensors also enables the Mobile Crowd Sensing (MCS) paradigm, which aims at using mobile-embedded sensors to extend monitoring of multiple (environmental) phenomena in expansive urban areas. In this article, we discuss our approach with a cloud-based platform to pave the way for applying crowd sensing in urban scenarios. We have implemented a complete solution for environmental monitoring of several pollutants, like noise, air, electromagnetic fields, and so on in an urban area based on this paradigm. Through extensive experimentation, specifically on noise pollution, we show how the proposed infrastructure exhibits the ability to collect data from connected communities, and enables a seamless support of services needed for improving citizens’ quality of life and eventually helps city decision makers in urban planning.
Aerosol hygroscopicity parameter derived from the light scattering enhancement factor measurements in the North China Plain

Abstract. The relative humidity (RH) dependence of aerosol light scattering is an essential parameter for accurate estimation of the direct radiative forcing induced by aerosol particles. Because of insufficient information on aerosol hygroscopicity in climate models, a more detailed parameterization of hygroscopic growth factors and resulting optical properties with respect to location, time, sources, aerosol chemistry and meteorology are urgently required. In this paper, a retrieval method to calculate the aerosol hygroscopicity parameter, κ, is proposed based on the in situ measured aerosol light scattering enhancement factor, namely f(RH), and particle number size distribution (PNSD) obtained from the HaChi (Haze in China) campaign. Measurements show that f(RH) increases sharply with increasing RH, and that the time variance of f(RH) is much greater at higher RH. A sensitivity analysis reveals that the f(RH) is more sensitive to the aerosol hygroscopicity than PNSD. f(RH) for polluted cases is distinctly higher than that for clean periods at a specific RH. The derived equivalent κ, combined with the PNSD measurements, is applied in the prediction of the cloud condensation nuclei (CCN) number concentration. The predicted CCN number concentration with the derived equivalent κ agrees well with the measured ones, especially at high supersaturations. The proposed calculation algorithm of κ with the f(RH) measurements is demonstrated to be reasonable and can be widely applied.
Improved quantum query algorithms for triangle finding and associativity testing

We show that the quantum query complexity of detecting if an n-vertex graph contains a triangle is O(n9/7). This improves the previous best algorithm of Belovs [2] making O(n35/27) queries. For the problem of determining if an operation o: S X S → S is associative, we give an algorithm making O(|S|10/7) queries, the first improvement to the trivial O(|S|3/2) application of Grover search. Our algorithms are designed using the learning graph framework of Belovs. We give a family of algorithms for detecting constant-sized subgraphs, which can possibly be directed and colored. These algorithms are designed in a simple high-level language; our main theorem shows how this high-level language can be compiled as a learning graph and gives the resulting complexity. The key idea to our improvements is to allow more freedom in the parameters of the database kept by the algorithm. As in our previous work [9], the edge slots maintained in the database are specified by a graph whose edges are the union of regular bipartite graphs, the overall structure of which mimics that of the graph of the certificate. By allowing these bipartite graphs to be unbalanced and of variable degree we obtain better algorithms.
DNA recovery from soils of diverse composition

A simple, rapid method for bacterial lysis and direct extraction of DNA from soils with minimal shearing was developed to address the risk of chimera formation from small template DNA during subsequent PCR. The method was based on lysis with a high-salt extraction buffer (1.5 M NaCl) and extended heating (2 to 3 h) of the soil suspension in the presence of sodium dodecyl sulfate (SDS), hexadecyltrimethylammonium bromide, and proteinase K. The extraction method required 6 h and was tested on eight soils differing in organic carbon, clay content, and pH, including ones from which DNA extraction is difficult. The DNA fragment size in crude extracts from all soils was > 23 kb. Preliminary trials indicated that DNA recovery from two soils seeded with gram-negative bacteria was 92 to 99%. When the method was tested on all eight unseeded soils, microscopic examination of indigenous bacteria in soil pellets before and after extraction showed variable cell lysis efficiency (26 to 92%). Crude DNA yields from the eight soils ranged from 2.5 to 26.9 micrograms of DNA g-1, and these were positively correlated with the organic carbon content in the soil (r = 0.73). DNA yields from gram-positive bacteria from pure cultures were two to six times higher when the high-salt-SDS-heat method was combined with mortar-and-pestle grinding and freeze-thawing, and most DNA recovered was of high molecular weight. Four methods for purifying crude DNA were also evaluated for percent recovery, fragment size, speed, enzyme restriction, PCR amplification, and DNA-DNA hybridization. In general, all methods produced DNA pure enough for PCR amplification. Since soil type and microbial community characteristics will influence DNA recovery, this study provides guidance for choosing appropriate extraction and purification methods on the basis of experimental goals.
Micelle-based nanoparticles with stimuli-responsive properties for drug delivery

Cancer treatment often causes adverse effects and toxicity, as chemotherapy drugs affect both cancerous and healthy cells. Scientists seek to target tumor cells specifically and minimize harm to normal cells. Smart nanoparticles (NPs) are a modern technique that can release drugs when triggered by internal or external stimuli, such as temperature, pH, ultrasound, etc. This review covers stimuli-responsive micelle-based nanoparticles (SRM-NPs), a promising drug delivery platform that can enhance drug efficacy and reduce toxicity. It discusses the recent developments and applications of SRM-NPs, their responsiveness to different stimuli, and their potential to overcome drug resistance and adaptive responses. It also addresses the challenges and issues related to their stability, reproducibility, biocompatibility, safety, and optimization. The study concludes that SRM-NPs have great potential for drug delivery, but more research and development are needed to improve their clinical utility.
Omnidirectional Cylindrical Dielectric Resonator Antenna With Dual Polarization

This letter proposes an annular cylindrical dielectric resonator antenna (DRA) with two-port diversity realizing simultaneously omnidirectional horizontally and vertically polarized radiation patterns with low cross coupling. The horizontally and vertically polarized radiation patterns are achieved by exciting the orthogonal TE01δ and TM01δ modes in a single cylindrical dielectric resonator (DR). Due to the high Q-factor of the TE01δ mode, an air gap and multiple feeding lines are introduced to increase the impedance bandwidth. The proposed antenna provides an impedance bandwidth of 19.1% in the vertically polarized mode and an overlapping bandwidth of 7.4% in the horizontally polarized mode, with the overlapping band ranging from 3.78 to 4.07 GHz. Antenna diversity and multiple-input-multiple-output (MIMO) performance are examined in terms of envelope correlation coefficient and mean effective gain.
Homeostatic control of slow vacuolar channels by luminal cations and evaluation of the channel-mediated tonoplast Ca2+ fluxes in situ

Ca2+, Mg2+, and K+ activities in red beet (Beta vulgaris L.) vacuoles were evaluated using conventional ion-selective microelectrodes and, in the case of Ca2+, by non-invasive ion flux measurements (MIFE) as well. The mean vacuolar Ca2+ activity was ∼0.2 mM. Modulation of the slow vacuolar (SV) channel voltage dependence by Ca2+ in the absence and presence of other cations at their physiological concentrations was studied by patch-clamp in excised tonoplast patches. Lowering pH at the vacuolar side from 7.5 to 5.5 (at zero vacuolar Ca2+) did not affect the channel voltage dependence, but abolished sensitivity to luminal Ca2+ within a physiological range of concentrations (0.1–1.0 mM). Aggregation of the physiological vacuolar Na+ (60 mM) and Mg2+ (8 mM) concentrations also results in the SV channel becoming almost insensitive to vacuolar Ca2+ variation in a range from nanomoles to 0.1 mM. At physiological cation concentrations at the vacuolar side, cytosolic Ca2+ activates the SV channel in a voltage-independent manner with Kd=0.7–1.5 μM. Comparison of the vacuolar Ca2+ fluxes measured by both the MIFE technique and from estimating the SV channel activity in attached patches, suggests that, at resting membrane potentials, even at elevated (20 μM) cytosolic Ca2+, only 0.5% of SV channels are open. This mediates a Ca2+ release of only a few pA per vacuole (∼0.1 pA per single SV channel). Overall, our data suggest that the release of Ca2+ through SV channels makes little contribution to a global cytosolic Ca2+ signal.
Mobility prediction for handover management in cellular networks with control/data separation

In research community, a new radio access network architecture with a logical separation between control plane (CP) and data plane (DP) has been proposed for future cellular systems. It aims to overcome limitations of the conventional architecture by providing high data rate services under the umbrella of a coverage layer in a dual connection mode. This configuration could provide significant savings in signalling overhead. In particular, mobility robustness with minimal handover (HO) signalling is considered as one of the most promising benefits of this architecture. However, the DP mobility remains an issue that needs to be investigated. We consider predictive DP HO management as a solution that could minimise the out-of-band signalling related to the HO procedure. Thus we propose a mobility prediction scheme based on Markov Chains. The developed model predicts the user's trajectory in terms of a HO sequence in order to minimise the interruption time and the associated signalling when the HO is triggered. Depending on the prediction accuracy, numerical results show that the predictive HO management strategy could significantly reduce the signalling cost as compared with the conventional non-predictive mechanism.
Interference Optimization for Transmit Power Reduction in Tomlinson-Harashima Precoded MIMO Downlinks

A novel strategy for reducing the power loss in Tomlinson-Harashima precoding (THP) is explored in this paper, based on optimizing the interference to be canceled. A multiple input multiple output (MIMO) downlink transmission is considered and the proposed strategy is motivated by the fact that both the desired and interfering signals originate from the base station (BS) of the downlink system itself. The resulting interference can therefore be influenced to reduce the transmission power required to cancel it, without altering the information content of the downlink message. This optimization aims at bringing the interference closer to the replicas of the desired symbols for all users in the THP modulo-extended constellation. By doing so, the quantized distance between the useful signal and interference is reduced and therefore the power required to presubtract interference is decreased. Based on this concept, a new practical THP transmitter for multiple input multiple output systems (MIMO-THP) is designed. The effect of the interference optimization is studied by means of mathematical analysis and simulation and towards this end, the Gaussian-Modulo distribution is derived and used to describe the distribution of the modulo-precoded transmitted symbols. This is proven to provide a closer approximation of the power loss for both conventional and proposed MIMO-THP techniques, compared to the approximation based on the uniform distribution used in the literature. Theoretical and simulation results both confirm that, by optimizing the interference to be canceled, the proposed technique offers a considerable transmit power reduction compared to conventional THP while securing a slightly improved error rate performance.
Data-Driven Dependency Parsing across Languages and Domains: Perspectives from the CoNLL-2007 Shared task

The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in 2006, the shared task has been devoted to dependency parsing, this year with both a multilingual track and a domain adaptation track. In this paper, I summarize the main findings from the 2007 shared task and try to identify major challenges for the parsing community based on these findings.
Cycloolivil Isolated from Nardostachys jatamansi Inhibits TNF-α/IFN-γ-Induced Chemokine Production by Blocking NF-κB and JAK/STAT Activation in HaCaT Keratinocytes

Nardostachys jatamansi is widely used as a traditional medicine in Asian countries. Numerous recent studies have reported the biological activities of its secondary metabolites and extracts. In this study, a total of 14 components were isolated, including cycloolivil and 2-(3′-hydroxy-5′-ethoxyphenyl)-3-hydroxylmethyl-7-methoxy-2,3-dihydrobenzofuran-5-carboxylic acid, which were first discovered in N. jatamansi. The isolated compounds were investigated for their anti-inflammatory effects on HaCaT keratinocytes and their potential to alleviate skin inflammation. The results of the screening revealed that cycloolivil and 4β-hydroxy-8β-methoxy-10-methylene-2,9-dioxatricyclo[4.3. 1.03,7]decane reduced the production of inflammatory cytokines induced by TNF-α/IFN-γ, such as IL-6, IL-8, and RANTES, in keratinocytes. This study focused on exploring the biological effects of cycloolivil, and the results suggested that cycloolivil inhibits the expression of COX-2 proteins. Further mechanistic evaluations confirmed that the anti-inflammatory effects of cycloolivil were mediated by blockage of the NF-κB and JAK/STAT signaling pathways. These results suggest that cycloolivil isolated from N. jatamansi could be used to treat skin inflammatory diseases.
Resting-State Whole-Brain Functional Connectivity Networks for MCI Classification Using L2-Regularized Logistic Regression

Mild cognitive impairment (MCI) has been considered as a transition phase to Alzheimer's disease (AD), and the diagnosis of MCI may help patients to carry out appropriate treatments to delay or even prevent AD. Recent advanced network analysis techniques utilizing resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been widely used to get more comprehensive understanding of neurological disorders at a whole-brain connectivity level. However, how to explore effective brain functional connectivity from fMRI data is still a challenge especially when the ultimate goal is to train classifiers for discriminating patients effectively. In our research, we studied the functional connectivity of the whole brain by calculating Pearson's correlation coefficients based on rs-fMRI data, and proposed a set of novel features by applying Two Sample T-Test on the correlation coefficients matrix to identify the most discriminative correlation coefficients. We trained a L2-regularized Logistic Regression classifier based on the five novel features for the first time and evaluated the classification performance via leave-one-out cross validation. We also iterated 10-fold cross validation ten times in order to evaluate the statistical significance of our method. The experiment result demonstrates that classification accuracy and the area under receiver operating characteristic (ROC) curve in our method are 87.5% and 0.929 respectively, and the statistical results prove that our method is statistically significant better than other three algorithms, which means our method could be meaningful to assist physicians efficiently in “real-world” diagnostic situations.
A Comparison of Usability Factors of Four Mobile Devices for Accessing Healthcare Information by Adolescents

Summary Background: Mobile health (mHealth) is a growing field aimed at developing mobile information and communication technologies for healthcare. Adolescents are known for their ubiquitous use of mobile technologies in everyday life. However, the use of mHealth tools among adolescents is not well described. Objective: We examined the usability of four commonly used mobile devices (an iPhone, an Android with touchscreen keyboard, an Android with built-in keyboard, and an iPad) for accessing healthcare information among a group of urban-dwelling adolescents. Methods: Guided by the FITT (Fit between Individuals, Task, and Technology) framework, a think-aloud protocol was combined with a questionnaire to describe usability on three dimensions: 1) task-technology fit; 2) individual-technology fit; and 3) individual-task fit. Results: For task-technology fit, we compared the efficiency, and effectiveness of each of the devices tested and found that the iPhone was the most usable had the fewest errors and prompts and had the lowest mean overall task time For individual-task fit, we compared efficiency and learnability measures by website tasks and found no statistically significant effect on tasks steps, task time and number of errors. Following our comparison of success rates by website tasks, we compared the difference between two mobile applications which were used for diet tracking and found statistically significant effect on tasks steps, task time and number of errors. For individual-technology fit, interface quality was significantly different across devices indicating that this is an important factor to be considered in developing future mobile devices. Conclusions: All of our users were able to complete all of the tasks, however the time needed to complete the tasks was significantly different by mobile device and mHealth application. Future design of mobile technology and mHealth applications should place particular importance on interface quality. Citation: Sheehan B, Lee Y, Rodriguez M, Tiase V, Schnall R. A Comparison of Usability Factors of Four Mobile Devices for Accessing Healthcare Information by Adolescents. Appl Clin Inf 2012; 3: 356–366 http://dx.doi. org/10.4338/ACI-2012-06-RA-0021
The Effect of Water Stress on Bioactive Compounds in Australian-Grown Black Sesame

Sesame is an emerging crop of interest in Australia and has attracted widespread interest due to the health-benefitting properties of its bioactive compounds, including fatty acids, lignans, and polyphenols. This study aimed to investigate the impact of drought stress on these bioactive compounds, using eleven cultivars of black sesame seeds grown in Australia. Specific varieties responded positively to water deficit (WD) conditions, showing increased levels of TPC, FRAP, CUPRAC, and lignans. Varieties 1, 4, 7, and 12 showed significantly increased FRAP values ranging from 158.02 ± 10.43 to 195.22 ± 9.63 mg TE/100 g DW in the WD treatment compared to the well-watered (WW) treatment, whereas varieties 7, 10, 12, 13, and 18 demonstrated the highest CUPRAC values of all varieties (2584.86 ± 99.68–2969.56 ± 159.72 mg TE/100 g) across both WW and WD conditions, with no significant variations between irrigation regimes. Moreover, lignan contents (sesamin and sesamolin) were higher in varieties 1, 2, 5, and 8 grown in WD conditions. Compared to the optimal unsaturated to saturated fatty acid ratio (Σ UFA/Σ SFA ratio) of 0.45, all sesame genotypes showed superior ratios (ranging between 1.86 and 2.34). Moreover, the ω-6/ω-3 PUFA ratio varied from 33.7–65.5, with lower ratios in varieties 2, 4, 5, 8, and 18 under WD conditions. The high levels of phenolic compounds and healthy fats suggest the potential of black sesame to be incorporated into diets as a functional food. Furthermore, the enhanced phytochemistry of these cultivars in WD conditions is promising for widespread adoption. However, larger trial studies to confirm these findings across different geographic locations and seasons are warranted.
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction

In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.
Evaluation of the BD GeneOhm Methicillin-Resistant Staphylococcus aureus (MRSA) Assay as a Method for Detection of MRSA Isolates, Using a Large Collection of European and North African Isolates

ABSTRACT Using a large collection of European and North African methicillin-resistant Staphylococcus aureus (MRSA) isolates with a variety of genetic backgrounds and staphylococcal cassette chromosome mec (SCCmec) types, we evaluated the reliability of the BD GeneOhm MRSA assay. Results revealed high performance of this test for detecting MRSA strains provided from Europe and North Africa (98.3%).