Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Experimental Study on Dynamic Behavior of Stepper-Actuated Dual-Axis Data Transmission Antenna

The satellite-borne data transmission antenna is the main disturbing source of low-frequency microvibration of spacecraft, which immensely affects the image quality of remote sensing satellite. In this paper, the dynamic characteristics of flexible load driven by stepping motor on flexible boundary are studied. The dynamic equation of the stepping motor driven by current subdivision is simplified by linearization method. The dynamic model of flexible load driven by stepping motors on flexible boundary is established by using the Dynamic Substructure Method, and the analytical expression of microvibration of the data transmission antenna is given. The coupling relationship between the stepping motors and the flexible structure is analyzed by modal coordinate transformation. The microvibration model is verified by simulation and experiment, and the main causes and coupling factors of microvibration are explained. The results show that the model can accurately predict the microvibration of the satellite antenna and can be applied to the microvibration prediction in orbit. The reasonable selection of the working velocity of the stepping motors can effectively reduce the microvibration, which provides the basis for the design of the antenna control system.
Anisotropic pseudospin tunneling in two-dimensional black phosphorus junctions

We investigate the role of pseudospin structure of few-layer black phosphorus (BP) in interband tunneling properties in lateral BP junctions. We find that interband tunneling is critically dependent on junction directions because of the anisotropic pseudospin structure of BP. When the armchair direction of BP is normal to the interface, pseudospins of incident and transmitted carriers are nearly aligned so that interband tunneling is highly effective, analogous to the Klein tunneling in graphene. However, when the zigzag direction is normal to the interface, interband tunneling is suppressed by misaligned pseudospins. We also study junctions of band-gap inverted BP where the electronic structure is characterized by two Dirac cones. In this case, intervalley tunneling is prohibited either by momentum conservation or by pseudospin mismatch while intravalley tunneling is Klein-like irrespective of the junction direction. These results provide a foundation for developing high-performance devices from BP and other pseudospin materials.
Survey On The application of Deep Learning in Internet of Things (IoT)

The Internet of Things (IoT) is a network of physical instruments, software, sensors that all are connected to the Internet. The IoT produces massive data, where, this enormous volume of data allows the use of deep learning algorithms (DLAs). Recently, the increase of the large body of data and their availability has been one of the main reasons for paying attention to this issue. Further, the recent upgrade of the hardware boosting the computational power has resulted in the utilize of deep learning alongside the IoT. Therefore, the purpose of the present research is to review the relevant conference and journal articles in IoT and deep learning from 2012 to July 2019. To review the publications, a composition of Systematic Mapping and systematic literature review has been employed for creating a survey paper. Accordingly, some questions have been raised; to answer which, 32 articles have been investigated. The papers have been categorized into four sections including a focus on data, network, computing environment, application with each being examined, and analyzed. This article would be beneficial for researchers who want to investigate the field of deep learning and IoT.
GENES CONFERRING ANTIMICROBIAL-RESISTANCE AMONG KLEBSIELLA PNEUMONIAE IN THE ARABIAN GULF COUNTRIES: A SYSTEMATIC REVIEW AND META-ANALYSIS

Objective: The objective of the study was to look on the prevalence of six AMR genes (CTX-M, TEM, SHV, NDM-1, OXA-48, and VIM genes) in the province of the Arabian Gulf. We performed a systematic review and meta-analysis of the published studies from the Arabian Gulf countries and analyzed the antimicrobial resistance (AMR) genes pattern present in Klebsiella pneumoniae. Methods: The present study used the Meta-analysis Of Observational Studies in Epidemiology as a guideline for reporting findings. An electronic search was conducted in online databases such as PubMed/MEDLINE, EMBASE, Scopus, Google Scholar, Science Direct, and Web of Science from January 2014 to June 2020 following the inclusion and exclusion criteria. Articles published were included in the study resistance pattern among 2036 isolates were analyzed. These isolates conferred the AMR genes including OXA-48 (n=500), CTX-M (n= 1796), SHV (n=1637), TEM (n=1492), NDM-1 (n=500), and VIM (n=302). Results: Of 160 initially searched studies, 28 entries met the inclusion criteria and were subjected to meta-analysis. Critical appraisal of studies or quality assessment revealed a mean quality score was 4.2, with an SD of 1.6. The analysis revealed predominant AMR genes wereOXA-48 followed by CTX-M, SHV, TEM, NDM-1, and VIM in the Arabian Gulf region. Conclusion: The Arabian Gulf countries share a high prevalence of OXA-48, CTX-M followed by SHV, TEM, NDM-1, and VIM genes. Antimicrobial-resistant in K. pneumoniae is a threat to public health and this needs strong surveillance to curb this threat.
Use of Raman spectroscopy for analyzing edible vegetable oils

ABSTRACT Vegetable oils provide high nutritional value in the human diet. Specifically, extra virgin olive oil is one of the main ingredients of the Mediterranean diet, which is among the healthiest of eating practices. This article reviews the use of Raman spectroscopy for analyzing edible vegetable oils including olive oil. Although the spectra for edible vegetable oils are similar, they exhibit some differences which, however small, enable their discrimination. Thus, Raman spectra allow one to determine the degree of unsaturation of oils. This property is correlated with the iodine value but much faster and simpler to obtain. The degree of unsaturation can be used to classify and authenticate oils, which is especially useful with high-quality oils. In fact, adulteration with mixtures of more inexpensive oils can be easily detected by Raman spectroscopy. This technique additionally allows some minor components present in unsaponifiable matter to be identified. Fats in general and vegetable oils in particular, are prone to oxidation. Thus, double bonds in them are oxidized to form triglycerides. Vegetable oils are widely used for frying and Raman spectroscopy allows for their oxidative stability against heating at the usual frying temperatures to be assessed.
Opuntia ficus-indica Pruning Waste Recycling: Recovery and Characterization of Mucilage from Cladodes

The present work focuses on the proximate physical and chemical profile of Opuntia ficus-indica mucilage, mechanically extracted from cladodes, a waste of pruning in traditional organic cactus pear orchards in Italy (San Cono, Sicily). The mechanical extraction increased the mucilage yield to 30% dry weight. Physical characterization concerns pH, viscosity, free acidity and density, useful for emulsifying capacity. Spectrophotometric analysis was applied to assess total carbohydrates, proteins, uronic acids, total polyphenols content and antioxidant capacity. DART-MS and SEM-EDX were performed to evaluate functional mucilage components and relative amounts of minerals, respectively. From the main results Opuntia ficus-indica by-product, in addition to the preponderant total carbohydrates content, shows the high concentration in calcium and potassium and a fair amount of health-promoting phytochemicals, which make it a good candidate for the different type of industrial applications.
HyperFork: Improving Serverless Latency Through Virtual Machine Flash-Cloning

Serverless computing provides a convenient infrastructure for performing computations in response to certain triggers. In most common sev-erless deployments, computations are executed in virtual machines. As a result, the boot time of a virtual machine is a primary component of these services’ latency. We propose an alternative paradigm, in which incoming requests trigger a ﬂash-clone of an existing, pre-booted virtual machine. Our implementation HyperFork builds on top of KVM and utilizes the copy-on-write functionality of the fork system call to improve performance. We demonstrate that Hy-perFork improves virtual machine start times by nearly two orders of magnitude while maintaining throughput. We ﬁnd that copy-on-write performance penalties are minimal for real-world workloads and suggest methods for further improving throughput.
The Politics of Maladaptation

An emerging component of the adaptation discourse, embracing theory, practice and review, is that of the negative assessment of adaptation, namely, maladaptation. Political theories and concepts have been applied as one of these assessment tools, giving rise to a political critique of maladaptation. Such a critique contrasts with the more conventional scientific and technical assessments of adaptation policies, programs and practices. Key political themes in studies of maladaptation include resource management and allocations, decision making processes, equity and fairness, gender, power and influence, and Nature and ecology. Within the scholarship on the politics of maladaptation, overlapping frameworks can be identified. Critiques of adaptation have been applied to the preconditions of adaptation, adaptation decision making processes and institutions, and to adaptation outcomes. There are a number of conceptual challenges in undertaking political analyses of adaptation. In this article, we outline the origins of the adaptation and maladaptation concepts, we describe the key political issues, we identify the application of politics in the maladaptation discourse and identify the major political perspectives. Finally, we draw conclusions on the state of the maladaptation discourse.
Substance use characteristics, treatment completion rates and related factors of patients in Gaziantep AMATEM in 2019; a retrospective study

ABSTRACT Objective Substance use and addiction are an increasing problem worldwide. Knowledge of the frequency and features of substance use contribute to ascertaining the addiction trends in a country, region or city, taking precautions for drug use, establishing policies on treatment and rehabilitation, and determining patient needs. Methods The files of individuals who were hospitalized in the Gaziantep Alcohol and Substance Addictions Treatment Center (AMATEM) Clinic between 01.01. 2019 and 31.12. 2019 were examined retrospectively. The SPSS 22.0 program was used in statistical analyses of patient data. Results This study included 694 patients, where 670 of whom were male (96.5%) and 24 were female (3.5%). Considering the diagnoses of the patients, opioid use disorder and amphetamine/methamphetamine use disorder were the most common diagnoses at 47.1% and 13%, respectively. A statistically significant difference was found between the diagnostic groups in terms of age, educational status, occupation, whether there was any suicide attempts, and treatment outcomes (p value for all <0.05). Conclusions Our study is the first study conducted in the Gaziantep AMATEM Clinic, which is the only inpatient drug and alcohol center in the Southeastern Anatolia region. Our findings showed that alcohol/substance use is a dynamic problem that negatively affects many areas of life including work and education life, and family and social relationships.
Assessing diversity, abundance, and mass of microplastics (~ 1–300 μm) in aquatic systems

Recent improvements in survey methods for microplastic (MP) pollution in natural waters have enabled quantification of MP particles < 300 μm in diameter. Historically, this putatively important size fraction of MP particles has not been sampled adequately, possibly leading to significant underestimations of plastic inventories in aquatic systems. Furthermore, reported MP particle numbers are poorly constrained proxies for MP mass loadings as sizes vary widely and densities differ within and among polymers, consequently compromising mass flux estimations. However, determination of MP number, identity, and mass can resolve these issues and enable more accurate estimations of MP loadings. Using Raman microspectroscopy coupled with three‐dimensional (3D) spectral imaging, we have developed an automatable methodology to detect, identify, and quantify MPs retained on filters from discrete water samples. Raman spectral information enabled identification of plastic polymers in particles between 0.5 and 300 μm in diameter and generation of 3D chemical maps. To accelerate volume determinations of each identified MP particle, we developed an algorithm to derive volumes from image analysis of two‐dimensional micrographs that are statistically indistinguishable from those directly measured by 3D Raman chemical maps. Masses of each Raman‐identified MP particle are determined from their volumes and known plastic specific weights. This methodology provides accurate estimations of the smallest MP particles' contributions to total MP mass in discrete water samples, providing vital information for mass flux calculations of MPs in natural waters.
On the selection of forecasting accuracy measures

Abstract A lot of controversy exists around the choice of the most appropriate error measure for assessing the performance of forecasting methods. While statisticians argue for the use of measures with good statistical properties, practitioners prefer measures that are easy to communicate and understand. Moreover, researchers argue that the loss-function for parameterizing a model should be aligned with how the post-performance measurement is made. In this paper we ask: Does it matter? Will the relative ranking of the forecasting methods change significantly if we choose one measure over another? Will a mismatch of the in-sample loss-function and the out-of-sample performance measure decrease the performance of the forecasting models? Focusing on the average ranked point forecast accuracy, we review the most commonly-used measures in both the academia and practice and perform a large-scale empirical study to understand the importance of the choice between measures. Our results suggest that there are only small discrepancies between the different error measures, especially within each measure category (percentage, relative, or scaled).
The Algorithms Cruz Rodriguez (CR) are Proposing A Novel Vaccine RNA-Peptide against Breast, Ovarian, and Lung Cancers Disease: Exosomes as Carrier in Cancer Progression and Metastasis

Exosomes are a novel horizon in modern therapy and open exciting new opportunities for advanced drug transport and targeted release. Exosomes are biological nanoparticles with dimensions ranging from 30 to 100 nm and are generated by all cell types in the human body. Exosomes-which are extracellular vesicles that function in intercellular communication-may play a key role in the dissemination of vaccine as well as host-derived molecules during cancer disease. We highlight the composition and function of exosomes produced during cancer disease in our work, how these vesicles could function as carriers of the RNA-peptide molecule. Finally, according to the algorithms CRUZ RODRIGUEZ (CR) we are proposing various methods to develop a novel vaccine against breast, ovarian, and lung cancers. Therefore, six RNA-peptides from our previous study were chosen based on the potential of inducing strong fusion stability (FS=58.50 cruz) and exosome affinity (EA=59.80 ro), Biological Action (BA= 1.0223 ro/cruz). This result may lead to the development of promising new therapeutic approaches in cancer management by using exosome-mediated miRNA-peptide vaccine therapy. Due to, according to the algorithm CR the Optimal Biological Action (OBA) value for antitumoral RNA-peptide with Exosome as carrier are: 0.8 < OBA < 1.3. These results suggest that the designed vaccine can elicit specific immune responses against three types of cancer; however, these results need experimental studies to confirm the efficacy and safety profile of the proposed vaccine structure. Review Article
Fast target recognition and location based on graph model and point cloud model

When using the point cloud model to identify and locate the target, there are some problems, such as low recognition accuracy and slow positioning speed. In order to solve these problems, this paper proposes a fast target recognition and location method based on point cloud model and graph model, which combines the characteristics of graph model processing speed, algorithm efficiency and recognition accuracy. Through the analysis of the characteristics of the target graph model, the pixel coordinates and rotation angle of the target grabbing points are calculated. After the transformation matrix and mapping relationship, the spatial coordinates of the point cloud model are obtained. The efficiency and accuracy of the method are verified by the recognition and positioning experiments on the robot target grabbing platform.
M-AR: A Visual Representation of Manual Operation Precision in AR Assembly

ABSTRACT The research of augmented reality physical tasks for human-computer collaboration is still an attractive dynamic research field. In recent years, AR Instruction supporting collaborative assembly has been developed, which can transfer graphic annotation between man and machine. However, there is little research on high precision manual assembly. This paper proposes a high-precision cognitive AR representation (M-AR) for manual operation guidance, which aims to transform a high-precision assembly relationship into a series of visual information to meet the user’s cognition of high-precision task intention. Using this representation, we conducted a within-subject case study to compare it with original AR representation (original AR) and low-precision cognitive AR representation. The results showed that there were significant differences in execution time and operation errors. The user’s subjective feedback also showed that M-AR supporting cognitive feedback can significantly improve a user’s cognitive experience in a physical task.
Downregulation of FOXO4 promotes neuronal survival by mediating oxidative-stress–induced apoptosis after cerebral ischemia/reperfusion injury

Purpose: To investigate the effect of FOXO4 on cerebral ischemia/reperfusion (CIR) injury and the underlying mechanism.Methods: An in vitro ischemia/reperfusion (IR) model was achieved using oxygen-glucose deprivation/reoxygenation (OGD/R). Expression of RNA and protein was determined using quantitative real time polymerase chain reaction (qRT-PCR) and western blotting, respectively. Cell viability and apoptosis were determined using MTT assay and flow cytometry, respectively. Commercial kits were used to measure lactate dehydrogenase (LDH), reactive oxygen species (ROS), chloramphenicol acetyltransferase (CAT), malondialdehyde (MDA), and superoxide dismutase (SOD). Results: Following OGD/R, FOXO4 mRNA and protein expressions were upregulated in SH-SY5H human neuroblastoma cells. ODG/R reduced cell proliferation and increased the proportion of apoptotic cells, and these effects were inhibited by knockdown of FOXO4 (p < 0.05). Levels of cleaved caspase 3 and cleaved poly(ADP-ribose) polymerases (PARPs) were increased after ODG/R and these increaseswere inhibited by FOXO4 knockdown. ROS content and levels of LDH and MDA were increased after ODG/R and decreased by knockdown of FOXO4 (p < 0.05). Levels of CAT and SOD were reduced after ODG/R, and this reduction was reversed by knockdown of FOXO4 (p < 0.05). Conclusion: The results demonstrate that knockdown of FOXO4 promotes cell proliferation and inhibits cellular apoptosis via reduction of oxidative stress after CIR injury, indicating a new therapeutic target for the treatment of CIR injury. Keywords: FOXO4, Neuronal survival, Oxidative stress, Cerebral ischemia/reperfusion injury
Chemical Structures of Premixed iso-Butanol Flames.

The combustion chemistry of iso-butanol was investigated by determining the chemical composition of premixed, laminar low-pressure flames and by an automatically generated combustion chemistry model. The flames, which were stabilized on a flatflame burner under a reduced pressure of 15 to 30 Torr, were quantitatively analyzed by flame-sampling molecular-beam mass spectrometry. The new set of data consists of isomer-resolved mole fraction profiles for more than 40 species in each of the four flames and provides a comprehensive benchmark for testing of high-temperature oxidation chemistry model for iso-butanol. Temperature profiles were measured using OH laser-induced fluorescence. The kinetic model, which has been extensively tested against other experimental data (ignition delay times, profiles in jet-stirred reactor and flow reactor) shows impressive capabilities for predicting the new flame data presented here. Predictions of the C2H4O, C3H6O, and C4H8O enol-aldehyde-ketone isomers were significantly improved compared to the earlier work by Hansen et al. [Phys. Chem. Chem. Phys. 13 (2011) 20262-20274] on similar nbutanol flames. A reaction path analysis identified prominent fuel-consumption oxidation sequences, and some significant differences with previous published models are highlighted.
Mobility Trace Analysis for Intelligent Vehicular Networks

Intelligent vehicular networks emerge as a promising technology to provide efficient data communication in transportation systems and smart cities. At the same time, the popularization of devices with attached sensors has allowed the obtaining of a large volume of data with spatiotemporal information from different entities. In this sense, we are faced with a large volume of vehicular mobility traces being recorded. Those traces provide unprecedented opportunities to understand the dynamics of vehicular mobility and provide data-driven solutions. In this article, we give an overview of the main publicly available vehicular mobility traces; then, we present the main issues for preprocessing these traces. Also, we present the methods used to characterize and model mobility data. Finally, we review existing proposals that apply the hidden knowledge extracted from the mobility trace for vehicular networks. This article provides a survey on studies that use vehicular mobility traces and provides a guideline for the proposition of data-driven solutions in the domain of vehicular networks. Moreover, we discuss open research problems and give some directions to undertake them.
Investigation of γ-ray variability and glitches of PSR J1420−6048

PSR J1420−6048 is a young γ-ray pulsar with recurrent glitches. Utilizing long-term monitoring data obtained from the Fermi Gamma-ray Space Telescope, we found that PSR J1420−6048 has shown γ-ray flux variation and we also detected four glitches between 2008 and 2019. Two of the glitches are previously unknown, and their γ-ray spectrum also shows variability between each glitch. Because the results might be contaminated by background sources, we discuss whether the observed changes in flux and spectra were caused by artificial misallocations of photons from a nearby pulsar wind nebula (HESS J1420−607) and a pulsar (PSR J1418−6058), or by a change of the emission geometry from the target pulsar itself. We examine the correlation of the flux changes and the alternating pulse structure to investigate whether the emission geometry in the outer magnetosphere was changing. By assuming that the observational features did not totally result from the background environment, we compare our results with similar phenomena observed in other γ-ray pulsars. We propose that a strong crust crack can cause timing anomaly of a neutron star, which can affect the particle accelerations or pair creation regions, resulting in the changes of emission behaviours.