Miracle-dz/UniFAR-FTUs huggingface dataset:
Antibacterial activity of Silver Nanoparticles Synthesized from Fusarium semitectum and Green Extracts

Biological synthesis of silver nanomaterials is an important aspect of nanotechnology today. The present study deals with the synthesis of silver nanoparticles employing Fusarium semitectum and leaf extracts of papaya, neem and parthenium. The formation of nanoparticles was monitored by UV-Vis spectral analysis. The synthesized silver nanoparticles were tested for antibacterial activity against selected pathogenic bacterial strains like Pseudomonas aeruginosa, Salmonella typhi, Staphylococcus aureus and Streptococcus pyogenes. The antibacterial activity of silver nanoparticles from F. semitectum and parthenium extract was highest against S. pyogenes followed by S. aureus, P. aeruginosa and S. typhi when compared to the reference drug. The antibacterial activity against Gram positive bacteria was comparatively better than against Gram negative bacteria. Application of silver nanoparticles in sewage treatment showed a significant decrease in the bacterial load. This effect was found to be concentration and time dependent.
Design and Realization of a Multiple Access Wireless Power Transfer System for Optimal Power Line Communication Data Transfer

In this contribution, the authors evaluate the possibility of using separated access points for power and data transfer in a coupled Wireless Power Transfer-Powerline Communication system. Such a system has been previously proposed by the authors for specific applications, in which Wireless Power Transfer (WPT) should take place in a system where data are transmitted over the power grid. In previous works the authors have performed lab tests on a two coils WPT system equipped with a set of filters to also allow an efficient data transfer. When a multiple coil WPT system is chosen, additional possibilities arise: the access point for power and data can be differentiated, with the aim of maintaining the designed power efficiency and increase data transfer capacity. In this study a four coils WPT system is thoroughly analyzed, modelled, implemented and measured, and a set of guidelines for the correct design (in terms of performance optimization) of the data transfer is given.
Evaluating Mobility Performance of Unmanned Ground Vehicles

As the penetration levels of unmanned ground vehicles (UGV) in military applications increase, there is a growing need to evaluate their mobility across different latencies and various modes of operation ranging from pure teleoperation to full autonomy. State-of-the-art tools to evaluate mobility of ground vehicles do not address this need due to their not accounting for UGV technologies and the associated latencies. Although the trade-off between latency and performance has been thoroughly studied in the telerobotics literature and the results may qualitatively shed light onto the UGV domain, as well, a quantitative generalization is not possible due to the differences in context. Recognizing this gap, this paper presents a functional relationship between mobility and latency in high-speed, teleoperated UGVs under the context of path following. Specifically, data from human-in-the-loop simulations performed in this paper are combined with data from prior studies to span three vehicle types, three courses, and teleoperation latencies ranging from 0 s to 1 s. This combination yields for the first time a diverse data set for the context of path following in high speed, teleoperated UGVs. Based on this data set, empirical relationships are derived to quantify the trade-off between latency versus average speed and lane keeping error. This relationship can be used to establish a benchmark to evaluate the performance of autonomy-enabled UGV systems.
Adaptive neural network sliding mode control of a nonlinear two‐degrees‐of‐freedom helicopter system

The helicopter can play an important role in military and civil applications owing to its super maneuvering ability, which is closely related to its control system. To improve control performance, this study presents an adaptive sliding mode control strategy merging an adaptive neural network for a nonlinear two‐degrees‐of‐freedom (2‐DOF) helicopter system. By setting up the Lyapunov function, the asymptotic stability of the closed‐loop system is guaranteed, the astringency of the neural network weight renewal course is pledged, and the asymptotic attitude adjustment and trajectory tracking for the desired set point are realized. The availability of the adaptive radial basis function sliding mode control is finally verified via the simulation and real implementation on a nonlinear 2‐DOF helicopter platform.
Systematic differences of two similar approaches to the determination of the AOD from Brewer direct sun UV measurements

The determination of the aerosol optical depth (AOD) in the ultraviolet spectral region has become interesting due to the important role that the particles play in the Earth climate and its relation to the photochemical effects. The aim of this work is to determine the systematic differences of two similar approaches which use the same methodology to retrieve AOD from Brewer direct sun measurements. Those proposed by Sellitto et al. (2006), and by Stanek (http://www.o3soft.eu/o3baod.html). The tests have been performed using one-year Brewer measurements at Lampedusa Station. The Stanek AOD values are smaller than those provided by Sellitto et al., showing absolute differences between them from 0.02 to 0.7. No dependence on wavelength and the increase with AOD was observed. Linear regression between both approaches has very good agreement for all the wavelengths (R > 0.997). We have reprocessed the Stanek values using the linear regression coefficients yielding absolute deviations less than 0.04 which falls inside of the error assigned to the AOD.
Single-nucleosome imaging reveals steady-state motion of interphase chromatin in living human cells

Dynamic chromatin behavior plays a critical role in various genome functions. However, it remains unclear how chromatin behavior changes during interphase, where the nucleus enlarges and genomic DNA doubles. While the previously reported chromatin movements varied during interphase when measured using a minute or longer time scale, we unveil that local chromatin motion captured by single-nucleosome imaging/tracking on a second time scale remained steady throughout G1, S, and G2 phases in live human cells. This motion mode appeared to change beyond this time scale. A defined genomic region also behaved similarly. Combined with Brownian dynamics modeling, our results suggest that this steady-state chromatin motion was mainly driven by thermal fluctuations. Steady-state motion temporarily increased following a DNA damage response. Our findings support the viscoelastic properties of chromatin. We propose that the observed steady-state chromatin motion allows cells to conduct housekeeping functions, such as transcription and DNA replication, under similar environments during interphase.
Neural Correlates of Smooth Pursuit Eye Movements in Schizotypy and Recent Onset Psychosis: A Multivariate Pattern Classification Approach

Schizotypy refers to a set of personality traits that bear resemblance, at subclinical level, to psychosis. Despite evidence of similarity at multiple levels of analysis, direct comparisons of schizotypy and clinical psychotic disorders are rare. Therefore, we used functional magnetic resonance imaging (fMRI) to examine the neural correlates and task-based functional connectivity (psychophysiological interactions; PPI) of smooth pursuit eye movements (SPEM) in patients with recent onset psychosis (ROP; n=34), participants with high levels of negative (HNS; n=46) or positive (HPS; n=41) schizotypal traits, and low-schizotypy control participants (LS; n=61) using machine learning. Despite strong previous evidence that SPEM is a highly reliable marker of psychosis, patients and controls could not be significantly distinguished based on SPEM performance or blood oxygen level dependent (BOLD) signal during SPEM. Classification was, however, significant for the right frontal eye field (FEF) seed region in the PPI analyses but not for seed regions in other key areas of the SPEM network. Applying the right FEF classifier to the schizotypal samples yielded decision scores between the LS and ROP groups, suggesting similarities and dissimilarities of the HNS and HPS samples with the LS and ROP groups. The very small difference between groups is inconsistent with previous studies that showed significant differences between patients with recent onset psychosis and controls in both SPEM performance and underlying neural mechanisms with large effect sizes. As the current study had sufficient power to detect such differences, other reasons are discussed.
Agrobacterium Mediated Genetic Transformation of Rice for Salinity Tolerance for Combating Climate Change

Rice based coastal livelihoods of Bangladesh are now vulnerable for sea-level rising and salinity stress. Transgenic salt tolerant rice were developed by inoculating mature embryos with Agrobacterium tumefaciens strain LBA4404 carrying a binary vector p with GUS and nptll gene. Transformation was performed with different infection times and co-cultivation periods. Infection was most effective when explants were inoculated for 25 minutes (69.444 % GUS positive) and co-cultivated for 4 days (72.22% GUS positive). Among the varieties, Binadhan-6 showed highest response to GUS assay (65.185% GUS positive). Putative transformed plantlets were also the highest in Binadhan-6 (7.222%). Infection time 25 minutes and 4 days cocultivation period were effective for regeneration of shoots (9.444 % and 12.222 %, respectively). Binadhan-6 produced the greatest number of rooted shoots (60%). This protocol should help to develop salinity tolerance rice line(s) for combat climate change.
Enabling Reversible Addition-Fragmentation Chain-Transfer Polymerization for Brush Copolymers with a Poly(2-oxazoline) Backbone

: The combination of di ﬀ erent monomer classes has been sought after to access a wider range of brush copolymers owing to their unique properties derived from their dense macromolecular structures. Herein, we report the synthesis of a 2-oxazoline monomer (RAFTOx) containing a chain transfer agent at the 2-position and its subsequent utilization in reversible addition-fragmentation chain-transfer (RAFT) polymerization. With the aim of tuning the brush density in these polymers, homopolymers, block copolymers, and gradient copolymers of various ratios with 2-ethyl-2-oxazoline and RAFTOx have been prepared. Selected brush macroCTAs were then used for the RAFT polymerization of N , N -dimethylacrylamide and 2-ethyhexyl acrylate to prepare brush copolymers of di ﬀ erent architectures.
An Integrated Process for Recycling of ThO 2 Based Mixed Oxide Rejected Nuclear Fuel Pellets

This paper presents a study on the process engineering aspects of relevance to the industrial implementation of ThO2 and (Th, U)O2 mixed oxide (MOX) pellet type fuel manufacturing. The paper in particular focuses on the recycling of thoria based fuel production scrap which is an economically important component in the fuel manufacturing process. The thoria based fuels are envisaged for Advanced Heavy Water Reactor (AHWR) and other reactors important to the Indian Nuclear Power Programme. A process was developed for recycling the chemically clean, off-specification and defective sintered ThO2 and (Th, U)O2 MOX nuclear fuel pellets. ThO2 doesn’t undergo oxidation or reduction and thus, more traditional methods of recycling are impractical. The integrated process was developed by combining three basic approaches of recycling namely mechanical micronisation, air oxidation (for MOX) and microwave dissolution-denitration. A thorough investigation of the influence of several variables as heating method, UO2 content, fluoride and polyvinyl alcohol (PVA) addition during microwave dissolution-denitration was recorded on the product characteristics. The suitability evaluation of the recycled powder for re-fabrication of the fuel was carried out by analyzing the particle size, BET specific surface area, phase using XRD, bulk density and impurities. The physical and chemical properties of recycled powder obtained from the sintered (Th1-y, Uy)O2 (y; 0 - 30 wt%) pellets advocate 100% utilisation for fuel re-fabrication. Recycled ThO2 by integrated process showed distinctly high sinterability compared to standard powder evaluated in terms of surface area and particle size.
Stratify or Inject: Two Simple Training Strategies to Improve Brain Tumor Segmentation.

Deep learning methods for brain tumor segmentation are typically trained in an ad hoc fashion on all available data. Brain tumors are tremendously heterogeneous in image appearance and labeled training data is limited. We argue that incorporation of additional prior information, specifically tumor grade, associated with tumor imaging phenotypes during model training can significantly improve segmentation performance. Two strategies for incorporation of tumor grade during model training are proposed and their impact on segmentation performance is demonstrated on the BRATS 2018 dataset.
Open habitats in a tropical biodiversity hotspot support pollinator diversity in both protected and unprotected areas

Protected areas (PAs) are vital in the global effort to preserve biodiversity, particularly for disturbance‐intolerant pollinator species in the tropics. As there is little information on the potential of PAs for pollinator conservation in sensitive tropical ecosystems, we assessed here insect pollinator diversity in protected vs. unprotected areas in two vegetation zones in Nigeria, within the West African Guinea Biodiversity Hotspot. We selected two land‐use types based on predominant canopy cover type (open and tree‐shaded habitats) and these were sampled in both protected and unprotected areas of tropical rain forest and savanna vegetation zones. Pollinator composition varied significantly between protected vs. unprotected areas in each vegetation zone, signifying both areas support unique assemblages of insect pollinators. However, pollinator diversity varied according to land‐use type (open vs. shaded habitats) rather than protected area per se, such that pollinators were more abundant and species‐rich in open habitat than shaded habitat. These findings emphasize that beyond the protection of ecosystems, fine scale habitat management promoting the availability of adequate floral resources in natural areas is critical for ensuring conservation of these pollinators in tropical ecosystems.
#Nomask on Instagram: Exploring Visual Representations of the Antisocial Norm on Social Media

Social media (SM) functions such as hashtags and photo uploading can enrich and expedite user interactions, but can also facilitate the online spread of antisocial norms. Mask aversion is one such antisocial norm shared on SM in the current COVID-19 pandemic circumstances. This study utilized the social representation theory (SRT) to explore how mask aversion is visually represented in the Instagram photos tagged with #NoMask. It examined the overall content of the photos, the characteristics of the faces portrayed in the photos, and the presented words in the photos. Additionally, the study grouped the photos through k-means clustering and compared the resulting clusters in terms of content, characteristics of the faces, presented words, pixel-level characteristics, and the public’s responses to the photos. The results indicate that people, especially human faces, were visually represented the most in the Instagram photos tagged with #NoMask. Two clusters were generated by k-means clustering—Text-centered and people-centered. The visual representations of the two clusters differed in terms of content characteristics and pixel-level attributes. The texts presented in the photos manifested a unique way of delivering key messages. The photos of the people-centered cluster received more positive comments than the text-centered one; however, the two clusters were not significantly different in eliciting engagement. This study can contribute to expanding the scope of SRT to visual representations and hashtag movements.
Newton-Euler equations in general coordinates

For the computation of rigid body dynamics, the Newton–Euler equations represent a crucial relation unifying the laws of motion by Newton and Euler using the language of instantaneous screws. Typically, Newton–Euler equations are stated in spatial or in body coordinates, respectively. In this paper, a general formulation of Newton–Euler equations is provided for arbitrary reference systems. In particular, the general form unifies the known equations in spatial and body coordinates. To the best of the authors’ knowledge, this relation between the spatial and the body form has not been reported in literature. The novel formulation is based on the concept of time differentiation with respect to moving reference systems.
Augmenting data with GANs for firearms detection in cargo x-ray images

We propose a framework and impact of applying Machine Learning-based generated imagery to augment data variations for firearm detection in cargo x-ray images. Deep learning-based approaches for object detection have rapidly become the state-of-art and crucial technology for non-intrusive inspection (NII) based on x-ray radiography. The technology is widely employed to reduce or replace tedious labor-intensive inspection to verify cargo content and intercept potential threats at border crossings, ports, and other critical infrastructure facilities. However, the need for variations in the threat cargo content makes accumulating training data for such a system an increasing development cost. Even though threat image projection (TIP) is widely employed to simplify the process into artificially projecting the known threat, a considerable amount of threat object appearances is still needed. To further reduce the cost, we explore the use of GenerativeAdversarial-Network (GAN) to aid dataset creation. GAN is a successful deep learning technique for generating photo-real imagery in many domains. We propose a three-stage training framework dedicated to firearm detection. First, GAN is trained to generate variations of X-ray firearm appearance from binary masks for better image quality compared to the commonly used random noise. Second, the detection training dataset is created in combinations of generated images and actual firearms using TIP. Finally, the dataset is used to train RetinaNet for the detection. Our evaluations reveal that GAN can reduce the training cost in increase detection performance as using the combination of the real and generated firearms increase performance for unseen firearms detection.
Turbulence structures in the near‐neutral surface layer on the Tibetan Plateau

We investigated the turbulence structures in the surface layer over a flat and homogeneous prairie on the Tibetan plateau. In general, turbulence statistics show similar results to those reported in the literature from other normal sites. However, further scrutiny of turbulence data from different heights and (co)spectra analysis revealed that 1) height‐dependent turbulence statistics of vertical wind velocity; 2) a spectral gap with the absence of k−1 power law in mid frequencies in the spectra of lateral wind velocity; and 3) the deviation of heat flux cospectra in low frequencies from Monin‐Obukhov theory. These results not only provide the evidence of interactions between active and inactive turbulence but also shed light on different views on the fundamental mechanism of such interactions in the atmospheric surface layer.
AN APPLICATION OF DIGITAL IMAGE PROCESSING FOR UAV ’ S AUTOMATIC LANDING SYSTEM AID

This work proposes a cheap and lightweight system based on lasers pointers and digital image processing to estimate height and attitude relative to the ground. The main expected application of the module is small Unmanned Aerial Vehicles (UAVs), where simple and small attitude estimation systems are required by the characteristics of this kind of aircraft. The module shall be capable of providing data with sufficient precision so that an automatic landing system implementation is possible.
Iterative algorithms for distributed leader-follower model predictive control

Two distributed algorithms to estimate the optimal control input sequence that solves a finite horizon quadratic optimization are proposed. The first algorithm utilizes information from 2-hop neighbors, whereas the second only considers 1-hop neighbors. The estimates obtained from both algorithms converge asymptotically, under appropriate assumptions, for any initialization of the algorithm. For the 2-hop algorithm, we show that the converged estimate is the optimal solution to the original optimization problem, while for the 1-hop algorithm the result is generally a suboptimal solution. We evaluate the methods with simulations for a leader-follower model predictive control problem with unstable linear agents dynamics.
Financial Literacy of Students: University Case Studies in Indonesia

The purpose of this research is to examine student financial literacy in Indonesia. A case study is used as a design method to analyze and explore the financial lives of students at Swadaya Gunung Jati University. By using descriptive statistics, this paper has demonstrated that more than half of students who participate in financial literacy can manage their finances wisely. The present findings have important implications for developing the curriculum in financial management subject in Indonesia, precisely in managing finances for students’ daily life, which ignoring by the majority of financial management courses in Indonesian universities.
Estimation Error Based Disturbance Observer Design for Flexible Loop Shaping

This note presents an estimation error based disturbance observer (EEDOB) to reduce the effects of external disturbances. In the proposed control structure, a difference between an estimator output and a plant output is considered as an equivalent disturbance. Therefore, when a disturbance appears, the proposed disturbance observer (DOB) is activated. Unlike conventional DOB, this method does not require the plant inverse model or additional stabilizing filters. In addition, the proposed method always satisfies closed loop systems stability, which is definitely different from conventional DOB. To verify the effectiveness, this method was applied to commercial storage systems. From the experimental results, it is confirmed that tracking performance is improved by 23.5%.