Miracle-dz/UniFAR-FTUs huggingface dataset:
One-Step Synthesis of Nanosized and Stable Amino-Functionalized Calcium Phosphate Particles for DNA Transfection

Calcium phosphate (CaP) is used for in vitro transfection because of low toxicity and simple and low cost synthesis. The transfection results however vary because the precipitation lacks reproducibility and results in polydispersed, agglomerated particles. Here a reproducible, one-step procedure for the preparation of amino-modified CaP nanoparticles (NPs) is described using N-(2-aminoethyl)-3-aminopropyltrimethoxysilane as modifying and dispersing agent. The aim was to produce homogeneous, stable CaP NPs, which are loaded with DNA after particle formation. The refined wet-precipitation method yielded NPs with a narrow size distribution (∼140 nm) and positive zeta potential at physiological pH. FTIR and Raman spectroscopy analysis verified the aminosilane modification. Interestingly two types of CaP crystalline structures, Brushite and Hydroxyapatite, can be produced depending on the pH and without hydrothermal treatment. Both CaP crystalline phases were characterized using X-ray diffraction (XRD), transm.
DVCC+ Based Immittance Function Simulators Including Grounded Passive Elements Only

Eight new immittance function simulators (IFSs) with only grounded passive elements are proposed in this paper. All of the IFSs consist of only two DVCC+s and a minimum number of passive components without needing any passive element matching constraints. Each of the proposed IFSs can provide one of [Formula: see text]L with series [Formula: see text]R and [Formula: see text]L with parallel [Formula: see text]R. As an application example, a second-order mixed-mode (MM) multifunction filter is developed from the proposed +L with series +R and +L with parallel +R. Furthermore, a proportional integral derivative (PID) controller is derived from the proposed +L with series +R. Many simulation results through the SPICE program and several experimental ones are included to verify the theory.
Rotation periods for cool stars in the open cluster NGC 3532

Context. A very rich cluster intermediate in age between the Pleiades (150 Myr) and the Hyades (600 Myr) is needed to probe the rotational evolution, especially the transition between fast and slow rotation that occurs between the two ages. Aims. We study the rich 300 Myr-old open cluster NGC 3532 to probe this important transition and to provide constraints on angular momentum loss. Measuring the rotation periods builds on our prior work of providing spectroscopic membership information for the cluster, and it supports the chromospheric activity measurements of cluster stars that we provide in a companion paper. Methods. Using 42 d-long photometric time series observations obtained with the Yale 1 m telescope at CTIO, we measured rotation periods for members of NGC 3532 and compared them with the predictions of angular momentum evolution models. Results. We directly measured 176 photometric rotation periods for the cluster members. An additional 113 photometric rotation periods were identified using activity information, described fully in the companion paper, resulting in a total sample containing 279 rotation periods for FGKM stars in NGC 3532. The colour-period diagram constructed from this rich data set shows a well-populated and structured slow rotator sequence, and a fast rotator sequence evolved beyond zero-age main sequence age whose stars are in transition from fast to slow rotation. The slow rotator sequence itself is split into slightly slower and faster rotators, a feature we trace to photometric binary status. We also identify an extended slow rotator sequence extending to P ∼ 32 d, apparently the analogue of the one we previously identified in NGC 2516. We compare our period distribution to rotational isochrones in colour-period space and find that all considered models have certain shortcomings. Using more detailed spin-down models, we evolve the rotation periods of the younger NGC 2516 forward in time and find that the spindown of the models is too aggressive with respect to the slow rotators. In contrast, stars on the evolved fast rotator sequence are not spun down strongly enough by these models. Our observations suggest a shorter crossing time for the rotational gap, one we estimate to be ∼80 Myr for early-K dwarfs.
Methane mitigating options with forages fed to ruminants

Nutritional strategies, including feed management measures, are promising methods for CH 4 and overall GHG reduction. Evidence from literature is reviewed in this article in relation to the effects of forage quality (digestible organic matter, DOM) and forage type (grasses vs. legumes, and maize). The major determinants of forage quality are botanical composition and phenological stage, i.e., at advanced growth stages of plants, the fibre content increases while DOM decreases. Methane yield (g/kg DMI) decreases with increased digestibility of forages in both dairy cattle and sheep, and also CH 4 intensity (g/kg milk) decreases with increased digestibility of forages for dairy cattle. Using forage legumes in ruminant feeding systems can reduce overall GHG emissions due to decreased N fertilizer use and related emissions. Recommended dietary mitigation measures are often related to a reduction in N excretion such as better matching of dietary protein to animal needs, shifting N excretion from urine to faeces (by tannin inclusion at low levels) and reducing the amount of excreted fermentable organic matter. Methane decreases with increasing intake of forage legumes rich in tannins and there is a shift of N partition from urinary N towards faecal N. Reduced CH 4 emissions from ruminants fed on forage- based diets will decrease the carbon footprint of livestock and agriculture and improve the efficiency of productive ruminants in both developing and developed countries. Likewise, esti-mations of net CH 4 output should account for enteric CH 4 emissions and soil carbon (C) sequestration of land used for feed production (i.e., grasslands and croplands).
3D‐printed ceramics with engineered anisotropy for dielectric resonator antenna applications

In this letter, we study the design of three-dimensional (3D)-printed ceramics exhibiting anisotropic dielectric permittivities at microwave frequencies for dielectric resonator antenna (DRA) applications. The anisotropy is engineered by using periodic structures made up of subwavelength asymmetric unit cells ﬁlled with zirconia and air. Ceramic samples with uniaxial anisotropy are designed, 3D-printed, and measured. Birefringence up to 8 is achieved by controlling the volume ﬁll rate of the unit cell. Besides, a single-fed circularly polarised (CP) DRA that relies on a 3-D-printed uniaxial anisotropic ceramic is proposed in the 2.45 GHz ISM band. Its simulated and measured reﬂection coef-ﬁcient, axial ratio, and realised gain patterns are in good agreement, thus demonstrating the possibility of exploiting 3-D-printed anisotropic ceramics for DRA applications.
Adaptive Resource Allocation in SWIPT-Enabled Cognitive IoT Networks

Integrating simultaneous wireless information and power transfer (SWIPT) and cognitive radio (CR) technologies into Internet-of-Things (IoT) networks, named SWIPT-enabled cognitive IoT networks, has become an effective approach to resolve the short lifetime of battery-constrained IoT Devices (IoDs) and spectrum scarcity. In this type of networks, IoDs are regarded as secondary users (SUs) being charged with wireless power. To improve the sum throughput of IoDs, we allow IoDs to switch among spectrum sensing, SWIPT and information transmission adaptively. Correspondingly, three-dimensional resources, i.e., time (for performing the three actions), power (including power transmitted from an IoT controller to each IoD and power for receiving information and charging at each IoD) and spectrum, are jointly and adaptively allocated to maximize the sum throughput of IoDs. Since the formulated problem is a mixed-integer nonlinear program (MINLP), we adopt an auxiliary variable to convert the original problem into a tractable problem, which is then solved by an efficient algorithm involving the Lagrangian dual method, the subgradient method and the multiple one-dimensional search algorithm. Simulation results show our adaptive design yields superior performance in terms of the sum throughput of IoDs.
Disapproval rating, VIX index, COVID-19 cases and Trump’s tweeting against China

ABSTRACT The primary goal of this paper is to provide empirical evidence for how non-trade factors attribute to the China-US trade war. Trump’s tweets provide us with a unique perspective. After analysing 31,166 Trump’s tweets, we have the following findings: (1) It’s non-trade factors rather than trade-related factors that can significantly predict whether Trump posts negative tweets involving China’s economic and trade issues. For every 1 increase in Trump’s disapproval rating and VIX index, the likelihood of Trump posting negative tweets involving China’s economic and trade issues increases by 0.61 and 0.16 respectively. (2) Tweeting against China sometimes becomes a tool to divert domestic criticism. After the COVID-19 outbreak, the higher growth rate of cumulative COVID-19 confirmed cases, the higher likelihood of Trump tweeting against China. (3) Tweeting against China can win public support and attention. Holding everything else constant, the number of likes and retweets of negative tweets about China are 10.2 and 14.6 more than those of positive tweets about China.
Hybrid apes in the Anthropocene: Burden or asset for conservation?

Abstract Conservationists often view hybrid animals as problematic, at least if anthropogenic influence caused the intermixing to occur. However, critics propose that humans should respect non‐human autonomy, reject and accept the creatures they have helped to create. Based on two case studies of our own ethological, genetic and ethnographic research about chimpanzee and orangutan subspecies hybrids, we assess what, if anything, should be done about such animals. We consider problems posed by cross‐bred apes relating to: (a) Breeding—Do hybrids really experience reduced reproductive success? How are population‐level concerns and welfare of individual animals balanced in conservation breeding? (b) Essentialism—Are anti‐hybrid arguments based on essentialist or purist thinking? Does essentialism vary by conservation context? (c) Pragmatism—How do socio‐economic circumstances influence whether hybrids are embraced or ignored? Does the erosion of ‘untouched nature’ render hybrids more important? We show that answers to these questions are complex and context‐specific, and that therefore decisions should be made on a case‐by‐case basis. For example, we find that anti‐hybrid arguments are essentialist in some cases (e.g. ape management in zoos) but not in others (e.g. ape reintroduction). Thus, rather than present recommendations, we conclude by posing nine questions that conservationists should ask themselves when making decisions about taxonomic hybrids. A free Plain Language Summary can be found within the Supporting Information of this article.
Modeling the mechanical properties of nanoparticles: a review

Nanoparticles are commonly used in various fields of applications such as electronics, catalysis or engineering where they can be subjected to a certain amount of stress leading to structural instabilities or irreversible damages. In contrast with bulk materials, nanoparticles can sustain extremely high stresses (in the GPa range) and ductility, even in the case of originally brittle materials. This review article focuses on the modeling of the mechanical properties of nanoparticles, with an emphasis on elementary deformation processes. Various simulation methods are described, from classical molecular dynamics calculations, the best suited method when applied to the modeling the mechanics of nanoparticles, to dislocation dynamics based hybrid methodologies. We detail the mechanical behaviour of nanoparticles for a large array of material classes (metals, semi-conductors, ceramics, etc.), as well as their deformation processes. Regular crystalline nanoparticles are addressed, as well as more complex systems such as nanoporous or core-shell particles. In addition to the exhaustive review on the recent works published on the topic, challenges and future trends are proposed, providing solid foundations for forthcoming investigations.
Convex vacancy creation costs and on‐the‐job search in a global economy

We combine convex vacancy creation costs and on-the-job search in a new trade theory model with heterogeneous firms to explain how trade liberalization affects trade patterns, wages, and wage inequality. To obtain a realistic trade pattern, we need convex vacancy creation costs; to obtain a realistic wage distribution, we need on-the-job search. To get an effect of trade liberalization on wage inequality, we need both because with linear vacancy creation costs trade liberalization affects all wages in equal proportion. Furthermore, with sufficiently high convex vacancy creation costs, not all firms export to all foreign markets even if trade is fully liberalized. This implies that wage inequality under free trade is always higher than under autarky.
Reduction in Anxiety and Depression Scores Associated with Improvement in Quality of Life in Patients with Inflammatory Bowel Disease.

Background The aim of this study was to examine the associations among depression, anxiety and health-related quality of life and predictors of improvement of quality of life in patients with inflammatory bowel disease. Methods This was a prospective cohort study conducted in the gastroenterology clinic at McMaster University Medical Center in Hamilton, Ontario, Canada from May 2014 to March 2015. We included 60 adult patients above the age of 18 years old with a diagnosis of inflammatory bowel disease. We assessed anxiety and depression using the Hospital Anxiety and Depression Scale (HADS) and Health Related Quality of Life (HRQoL) using the Short Inflammatory Bowel Disease questionnaire (SIBDQ) at baseline and after 6 months. Linear regression was performed to estimate the associations among depression, anxiety and predictors of improvement in health-related quality of life. Results The anxiety scores decreased over the span of 6 months (median HADS-A baseline 9.00 [interquartile range {IQR} 6 to 12], and median HADS-A 6 months 7.00 [IQR 3.75 to 7.00]). There was a moderate negative correlation between anxiety (baseline r = -0.510, and 6-month r = -0.620; P < 0.001), depression (baseline r = -0.630, and 6-month r = -0.670; P < 0.001) and HRQoL scores. Using a multivariate linear regression model, elevated HADS score were associated with lower SIBDQ scores at baseline (Beta coefficient -0.696 [95% confidence interval {CI} -1.51 to -0.842]; P < 0.001). Lower SIBDQ score at baseline predicted decreased SIBDQ at 6 months (Beta coefficient 0.712 [95% CI 0.486 to 1.02]; P < 0.001). Conclusion Anxiety and depression are frequently seen in inflammatory bowel disease patients and lead to poor HRQoL. Psychological comorbidities may contribute to maladaptive behaviours and difficult disease management.
Recommendation algorithm based on community structure and user trust

While contemporary community‐based recommendation algorithms based on a single community structure are more capable of processing large datasets than ever, they lack recommendation precision. This article proposes a collaborative filtering recommendation algorithm that integrates community structure and user implicit trust. The algorithm first applies a method based on the Gaussian function to fill the matrix of item ratings of users to alleviate data sparsity. It then uses the trust matrix to obtain the asymmetric trust relationship of the trustor and trustee, based on which the degree of users' implicit trust is calculated. The users are divided into communities based on the implicit trust degree to determine the influence among users more accurately. The algorithm then predicts the target user's rating using the ratings of users in the community to generate recommendations. To verify the performance of the proposed algorithm, we compared the proposed algorithm with three contemporary algorithms under the same conditions using FilmTrust datasets. The recommendation accuracy as well as the mean absolute error and root mean square error values of the proposed algorithm were better than those of the other four algorithms by approximately 14% and 4%, respectively. The experimental results demonstrate that the proposed algorithm can achieve better recommendation efficiency than existing algorithms.
Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression to Detect COVID-19 in Chest CT Images

The SARS-CoV-2 (COVID-19) has propagated rapidly around the world, and it became a global pandemic. It has generated a catastrophic effect on public health. Thus, it is crucial to discover positive cases as early as possible to treat touched patients fastly. Chest CT is one of the methods that play a significant role in diagnosing 2019-nCoV acute respiratory disease. The implementation of advanced deep learning techniques combined with radiological imaging can be helpful for the precise detection of the novel coronavirus. It can also be assistive to surmount the difficult situation of the lack of medical skills and specialized doctors in remote regions. This paper presented Deep Transfer Learning Pipelines with Apache Spark and KerasTensorFlow combined with the Logistic Regression algorithm for automatic COVID-19 detection in chest CT images, using Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception. Our model produced a classification accuracy of 85.64, 84.25, and 82.87 %, respectively, for VGG16, VGG19, and Xception. HIGHLIGHTS Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression using CT images to screen for Corona Virus Disease (COVID-19) Automatic detection of COVID-19 in chest CT images Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception to predict COVID-19 in Computed Tomography image GRAPHICAL ABSTRACT
Postsynthesis and Selective Oxidation Properties of Nanosized Sn-Beta Zeolite

Nanosized Beta zeolites were postsynthetically modi-fied through the solid−gas reaction of highly dealuminated Beta zeolite with SnCl4 vapor at elevated temperatures. The incorporation mechanism of Sn ions and the physicochemical properties of resultant Sn-Beta-PS were characterized by various techniques. Its catalytic performance in Baeyer−Villiger oxidation was compared with the micrometer-sized Sn-Beta-F hydrothermally synthesized by conventional fluoride method. The Sn species were inserted into the framework via the reaction of the SnCl4 molecules with the silanols in the hydroxyl nests that were created by dealumination and thus occupied predominately the tetrahedral coordination sites. The Sn content gained by postsynthesis reached up to 6.2 wt %, corresponding to a Si/Sn ratio of ca. 35. The isolated Sn species exhibited Lewis acidity useful for the Baeyer−Villiger oxidation of ketones. Containing higher Sn contents and more importantly proposing less diffusion limitations to the substrates with a.
Countering the “Sweep Effect”

One of the well-known properties of multimember plurality systems is their propensity toward producing the so-called “sweep effect,” manifesting itself in that the strongest party in a majority of districts sees its full slate of candidates elected even if the margin of plurality is small. Despite this property, and mostly for technical reasons, this system remains rather widely employed for conducting local elections both in democracies and in electoral authoritarian regimes. This article employs the evidence from the 2019 municipal elections in St. Petersburg to examine how increased strategic coordination of opposition voters became instrumental in countering the sweep effect and thus reducing the scope of political monopoly in an overtly authoritarian context. The analysis shows that this goal was achieved primarily by enabling opposition-minded voters to cast a greater number of votes than it otherwise would have been.
Connected Sensors, Innovative Sensor Deployment, and Intelligent Data Analysis for Online Water Quality Monitoring

The sensor technology for water quality monitoring (WQM) has improved during recent years. The cost-effective sensorised tools that can autonomously measure the essential physical–chemical–biological (PCB) variables are now readily available and are being deployed on buoys, boats, and ships. Yet, there is a disconnect between the data quality, data gathering, and data analysis due to the lack of standardized approaches for data collection and processing, spatiotemporal variation of key parameters in water bodies and new contaminants. Such gaps can be bridged with a network of multiparametric sensor systems deployed in water bodies using autonomous vehicles, such as marine robots and aerial vehicles to broaden the data coverage in space and time. Furthermore, intelligent algorithms [e.g., artificial intelligence (AI)] could be employed for standardized data analysis and forecasting. This article presents a comprehensive review of the sensors, deployment, and analysis technologies for WQM. A network of networked water bodies could enhance the global data intercomparability and enable WQM at a global scale to address global challenges related to food (e.g., aqua/agriculture), drinking water, and health (e.g., water-borne diseases).
Determination of amino acids of some plants from Gentianaceae family

Medicines from plants are widely used in the complex treatment of different diseases every day. Therefore, theoretical and practical interest is the in-depth study of the herb of perspective plants like Centaurium erythraea Rafn. and Gentiana cruciata L. These plants have a long history of usage and interest of people. The aim of the study was to determine the content of amino acids in these plants. The amino acids composition and content in the herb of study species of the family Gentianaceae determined by the HPLC method. The results of the research revealed that the raw material of Centaurium erythraea Rafn. and Gentiana cruciata L. contains free and bound amino acids. Sixteen free and seventeen bound amino acids were identified in the herb of Centaurium erythraea Rafn. The herb of Gentiana cruciata L. contained thirteen free and fifteen bound amino acids. L-glutamic acid, L-arginine, L-aspartic acid, and L-cystine were predominant of Centaurium erythraea Rafn. herb. Amino acids L-lysine, L-serine, L-aspartic acid, and L-phenylalanine were present in the herb of Gentiana cruciata L. in the greatest amount. The metabolic processes in which these amino acids are involved connected to the medicinal properties of the study plants according to their use in official or nontraditional medicine.
Prioritising COVID-19 Recovery Solutions for Tourism Small and Medium-sized Enterprises: A Rough Best-Worst method Approach

ABSTRACT Tourism was always one of the most lucrative industries in the service sector. However, with the emergence of the COVID-19 pandemic, the tourism industry faces serious challenges. Consequently, the small and medium-sized enterprises (SMEs) active in this industry experienced severe losses. To help the tourism SMEs, this paper aims to identify and prioritise recovery solutions. To this end, first, 21 solutions were identified using secondary data by reviewing the related literature and were classified into five main categories, namely operations, marketing and customer relations, human resources, financial, and communication solutions. The solutions then prioritised using rough Best-Worst method. Results showed that financial solutions are the most significant whereas communication solutions are the least important amongst the five main solution categories. Moreover, reducing costs, developing automation technologies and innovative business models, and applying for government support and loans are the most vital COVID-19 recovery solutions for tourism SMEs, respectively.