Miracle-dz/UniFAR-FTUs huggingface dataset:
MOBILE MARKETING AND ITS IMPACT ON CUSTOMER‟S BUYING BEHAVIOR: A STUDY OF GWALIOR REGION

Development makes promoting openings and difficulties. Mobile advertising, a zone of versatile business, is a type of promoting those objectives clients of handheld remote gadgets, for example, cell phones and PDAs. It can reach the target customers anywhere anytime. The primary objective of this study is to gain an insight into the perception of mobile users, towards mobile marketing/advertising and their utility value in terms of impact on the purchase decision. Hence we are attempting to explore consumers’ responsiveness to mobile marketing and their perception, taking into cognizance the impact of demographic factors. The study also aims to concretize some features enhancing the acceptability/ utility of mobile marketing/advertising and suggests an appropriate strategic initiative for the same. The major findings reveal that the perception of consumers towards mobile marketing is that they find it time consuming, always relayed at wrong time, too many ads creating a clutter and not suitable to their personal needs etc. Further results indicate that mobile marketing/ advertising (in its current format) does not have a significant impact on the purchase/brand decision of consumers. Mobile marketing is relatively at a nascent stage in India. Distinct
Total Heterotrophic Bacterial population in Achyranthes aspera L

Clinical microbiologists have great interest in screening of medicinal plants for antimicrobial activities and phytochemicals as potential new therapeutics. In vitro antibacterial assay to find the efficacy of Achyranthes aspera L. (stem) using various extracts like hexane, butanol, ethanol, chloroform and water was carried out and the result exhibited a broad spectrum of antibacterial potential. When compared the antibacterial efficiency of various extracts of A.aspera the gram negative bacteria like Pseudomonas aeruginosa and Klebsiella sp. , were found to inhibit more than the gram positive bacteria like Micrococcus sp. , and Staphylococcus aureus . Among the various extracts, the minimum inhibitory concentration (MIC) of A.aspera against bacterial pathogens, the ethanol extract showed highest activity than all other solvent extracts. The MIC value was found to be in the ranges from 25-30, 35-40, 45-50, 30-35 μg/ml against Staphylococcus aureus, Micrococcus sp. , Klebsiella sp. , and Pseudomonas aeruginosa respectively. In ethanol extract of A.aspera, the MBC value was highest in Klebsiella sp. , 80μg/ml and lowest in Staphylococcus aureus 50 μg/ml. In mice exposed to alloxan and ethanol extract of A.aspera significant reduction in their number compared to normal mice and the value was 34±2 CFU/g, 52±3 CFU/g and 144±10 CFU/g in stomach, intestine and rectum respectively. The present study clearly indicate that the test plant (A.aspera) has high level of antimicrobial activity and control the bacterial population in an said area.
Autonomous thermal operations

We consider a completely isolated quantum thermal machine, where an explicit clock is used to implement time-dependent transformations on a system, bath, and work storage device. Based on previously known results from the case without a clock system, we construct a new framework for this scenario in which the first and second laws of thermodynamics hold, and optimal work extraction and state transformations are possible. We thus show that including a clock explicitly need not entail an intrinsic thermodynamic cost, nor an ability to violate thermodynamic laws. Motivation. Recently there has been a great deal of interest in the application of thermodynamics to individual quantum systems, which may be composed of just a few atoms or qubits. Given that thermodynamics was invented before quantum theory was even envisaged, and typically applies to macroscopic objects, it is perhaps surprising how close an analogy can be drawn between the quantum and classical case. In [1], for instance, thermal machines are constructed out of quantum mechanical parts, with an explicit system and thermal bath in which the first and second laws of thermodynamics are obeyed, and optimal work extraction is achievable. In [2], the external work reservoir is replaced with a quantum work storage system represented by a weight. In other approaches [3, 4], the machine is a system with externally-controlled Hamiltonian and access to a thermal bath. So far, these frameworks all involve the external application of a unitary operation, or a sequence of discrete transformations, to the involved systems. An interesting open question which has been raised for instance in [2, 4] is whether this external control should carry a thermodynamic cost, and how to include this control explicitly in the framework. Here, we address this issue by extending the previous frameworks to include an explicit quantum clock by which the evolution of the system, bath and weight is controlled, and allowing the whole protocol to be carried out via a time-independent global Hamiltonian. Methods. Consider quantum system s on which a transformation is to be performed (see Fig. 1 a). We use a similar setting to the one used in [2] with an explicit thermal bath, b, at temperature T and a suspended weight, w, that extracts work from or supplies work to the system. This way we can keep track of all the thermodynamic resources, such as heat and work, that are used during the process. In addition we introduce a clock c. This is a quantum system with a continuous-spectrum Hamiltonian that may interact with system, bath and weight, and specifies time in terms of its position |x〉c. Initially the different subsystems are in a product state. Furthermore, the clock’s initial state must not be infinitely broad in position (Fig. 1 b).
Supplementary material to "A quantitative decoupling analysis (QDA v1.0) method for the assessment of meteorological, emission and chemical contributions to fine particulate pollution"

A comprehensive understanding of the effects of meteorology, emission and chemistry on severe haze is critical in the mitigation of air pollution. However, such understanding is largely hindered by the nonlinearity of atmospheric chemistry systems. Here, we developed a novel quantitative decoupling analysis (QDA) method to quantify the effects of emission, meteorology, chemical reaction, and their nonlinear interactions on the fine particulate matter (PM2.5) pollution based on the accompanying simulations for different atmospheric processes. Via embedding the QDA method into the Weather Research 15 and Forecasting-Nested Air Quality Prediction Modeling System (WRF-NAQPMS) model, we first employed this method into a typical heavy haze episode in Beijing. Different from the previously sensitive simulation method, which usually linked to a certain period, the QDA achieves the fully decomposing analysis of PM2.5 concentration during any pollution event into seven different parts, including meteorological contribution (M), emission contribution (E), chemical contribution (C), and interactions among these drivers (i.e., ME, MC, EC and MCE). The results show that the meteorology contribution varied 20 significantly at different stages of episode, from 0.21 μg∙m∙h during accumulation period to -11.82 μg∙m∙h during the removal period, dominating the hourly changes of PM2.5 concentrations. The chemical contributions were shown to increase with the level of haze, which become largest (0.37 μg∙m∙h) at the maintenance period, 25% higher than that during the clean period. The contribution of primary emission is relatively stable in all stages due to the use of fixed emission during the simulation. Besides, the QDA method highlights that there exist nonnegligible coupling effects of meteorology, emission and 25 chemistry on PM2.5 concentrations (-1.83 to 2.44 μg∙m∙h), which were commonly ignored in previous studies and the development of heavy-pollution control strategies. These results indicate that the QDA method can not only provide researchers and policy makers with valuable information for understanding of key factors to heavy pollution, but also help the modelers to find out the sources of uncertainties among numerical models. https://doi.org/10.5194/gmd-2021-259 Preprint. Discussion started: 1 September 2021 c © Author(s) 2021. CC BY 4.0 License.
Development of advanced electrode materials for alkali metal ions batteries

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Government Procurement and Wage Theft

Wage theft is the largest form of theft committed in the U.S. Typical violations related to wage theft include the lack of payment for overtime hours or forcing employees to underreport hours worked. We find that government contracting has an important effect on firm employees as it reduces wage theft. The reduction in wage theft is due to improvements in reporting and monitoring. Firms with government contracts have less restatements and the effect is stronger for contracts that are subject to increased government oversight. Finally, in order to mitigate endogeneity concerns we perform differences-in-differences identification tests based on first time contractors and the Obama Administration government contracting reform. This study contributes to extant literature examining the consequences of government contracting.
Susu kedelai merupakan salah satu olahan kedelai yang semakin banyak digemari masyarakat karena

Soymilk is an alternative healthy drink for all age groups. Currently, soymilk is made from the seeds. This study aimed to identify the physico-chemical and sensorial characteristics of soymilk prepared from sprouted and non-sprouted soybeans derived from five varieties namely Anjasmoro, Burangrang, Devon 1, Demas 1, and Gema. The sprouting was performed for 48 hours in humid and dark conditions. Soymilk was made using a wet method with the ratio of seeds or sprouted soybean to water was 1:8 (w/v). The trial was arraged using a randomized factorial design with two factors and three replicates. The results showed that protein and vitamin C contents of the sprouted soybean significantly increased compared to those of the seeds. Soymilk prepared from sprouted soybeans showed lighter color (L*), higher yield recovery, protein and vitamin C contents relative to those processed from soybean seeds. The protein contents of sprouted soybean milk that ranged from 2.99–3.31% had already met the national standard quality for soymilk. Among five varieties used, Gema, Demas 1, and Devon 1 varieties were promising to be used as ingredients of sprouted soybean milk with higher yield recovery, protein, and vitamin C contents. The taste of sprouted soybean milk prepared from Gema variety was fairly liked while Demas 1 and Devon 1 varieties were slightly liked.
PITCH CONTROL OF AN AIRCRAFT USING LEAD COMPENSATOR & FUZZY CONTROLLER

Abstract : The paper presents the control of an aircraft in the longitudinal plane using lead compensator and Fuzzy controller. A linearized longitudinal dynamics of mathematical model is necessary for the design of the flight system. Lead compensator has the characteristics of a lead network it improves the transient response of the system. For controlling the pitch angle Matlab – simulink model is used to tune the compensator and Mamdani type fuzzy logic controller (S.N.Deepa and Sudha G.2014) is used to tune the parameter by selecting appropriate fuzzy rules through simulations . The results of simulation is presented in time domain specification and the performance are analysed based on the step response . A comparison is made to identify which control strategy gives better response to the desired pitch angle.
Specific ion effects on protein fragments

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Effects of Different Intervention Time Points of Early Rehabilitation on Patients with Acute Ischemic Stroke: A Single-Center, Randomized Control Study

Objective To investigate effects of different intervention time points of early rehabilitation on patients with acute ischemic stroke. Methods We enrolled patients diagnosed with acute ischemic stroke in our hospital's rehabilitation ward from November 2013 to December 2015. Patients were randomly assigned to an ultraearly rehabilitation program (started within 72 hours of onset) or an early rehabilitation program (started from 72 hours to 7 days after onset). The efficacy was assessed by the NIH Stroke Scale (NIHSS) International, Barthel Index, and Fugl-Meyer Assessment at one and three months after rehabilitation. Data were analyzed by variance analysis of two-factor repeated measurement. Covariance analysis was used to adjust confounding factors for the determination of statistical differences. Results 41 patients were enrolled in the ultraearly rehabilitation group, while 45 patients were in the early rehabilitation group. There were no differences between the two groups at baseline data. Compared with the early rehabilitation group, patients in the ultraearly rehabilitation group have significantly improved NIHSS score, BMI score, and FMA score at one month and three months (P < 0.001). After adjusting for confounding factors (gender, age, severity of NIHSS score, location of stroke, hypertension, diabetes, atrial fibrillation, and coronary heart disease), the significant difference still existed between the two groups at one month and three months (P < 0.001). Conclusion Our study indicated a higher efficacy in the ultraearly rehabilitation group than the early rehabilitation group. The result suggests an important practical significance in favor of the clinical treatment of stroke.
Greenhouse gases emissions from riparian wetlands: an example from the Inner Mongolia grassland region in China

Abstract. Gradual riparian wetland drying is increasingly sensitive to global warming and contributes to climate change. Riparian wetlands play a significant role in regulating carbon and nitrogen cycles. In this study, we analyzed the emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) from riparian wetlands in the Xilin River basin to understand the role of these ecosystems in greenhouse gas (GHG) emissions. Moreover, the impact of the catchment hydrology and soil property variations on GHG emissions over time and space was evaluated. Our results demonstrate that riparian wetlands emit larger amounts of CO2 (335–2790 mgm-2h-1 in the wet season and 72–387 mgm-2h-1 in the dry season) than CH4 and N2O to the atmosphere due to high plant and soil respiration. The results also reveal clear seasonal variations and spatial patterns along the transects in the longitudinal direction. N2O emissions showed a spatiotemporal pattern similar to that of CO2 emissions. Near-stream sites were the only sources of CH4 emissions, while the other sites served as sinks for these emissions. Soil moisture content and soil temperature were the essential factors controlling GHG emissions, and abundant aboveground biomass promoted the CO2, CH4, and N2O emissions. Moreover, compared to different types of grasslands, riparian wetlands were the potential hotspots of GHG emissions in the Inner Mongolian region. Degradation of downstream wetlands has reduced the soil carbon pool by approximately 60 %, decreased CO2 emissions by approximately 35 %, and converted the wetland from a CH4 and N2O source to a sink. Our study showed that anthropogenic activities have extensively changed the hydrological characteristics of the riparian wetlands and might accelerate carbon loss, which could further affect GHG emissions.
Detecting Communities from Heterogeneous Graphs: A Context Path-based Graph Neural Network Model

Community detection, aiming to group the graph nodes into clusters with dense inner-connection, is a fundamental graph mining task. Recently, it has been studied on the heterogeneous graph, which contains multiple types of nodes and edges, posing great challenges for modeling the high-order relationship between nodes. With the surge of graph embedding mechanism, it has also been adopted to community detection. A remarkable group of works use the meta-path to capture the high-order relationship between nodes and embed them into nodes' embedding to facilitate community detection. However, defining meaningful meta-paths requires much domain knowledge, which largely limits their applications, especially on schema-rich heterogeneous graphs like knowledge graphs. To alleviate this issue, in this paper, we propose to exploit the context path to capture the high-order relationship between nodes, and build a Context Path-based Graph Neural Network (CP-GNN) model. It recursively embeds the high-order relationship between nodes into the node embedding with attention mechanisms to discriminate the importance of different relationships. By maximizing the expectation of the co-occurrence of nodes connected by context paths, the model can learn the nodes' embeddings that both well preserve the high-order relationship between nodes and are helpful for community detection. Extensive experimental results on four real-world datasets show that CP-GNN outperforms the state-of-the-art community detection methods1.
Nearest Neighbour Few-Shot Learning for Cross-lingual Classification

Even though large pre-trained multilingual models (e.g. mBERT, XLM-R) have led to significant performance gains on a wide range of cross-lingual NLP tasks, success on many downstream tasks still relies on the availability of sufficient annotated data. Traditional fine-tuning of pre-trained models using only a few target samples can cause over-fitting. This can be quite limiting as most languages in the world are under-resourced. In this work, we investigate cross-lingual adaptation using a simple nearest-neighbor few-shot (<15 samples) inference technique for classification tasks. We experiment using a total of 16 distinct languages across two NLP tasks- XNLI and PAWS-X. Our approach consistently improves traditional fine-tuning using only a handful of labeled samples in target locales. We also demonstrate its generalization capability across tasks.
James–Stein estimation of the first principal component

The Stein paradox has played an influential role in the field of high‐dimensional statistics. This result warns that the sample mean, classically regarded as the “usual estimator,” may be suboptimal in high dimensions. The development of the James–Stein estimator that addresses this paradox has by now inspired a large literature on the theme of “shrinkage” in statistics. In this direction, we develop a James–Stein‐type estimator for the first principal component of a high‐dimension and low‐sample size data set. This estimator shrinks the usual estimator, an eigenvector of a sample covariance matrix under a spiked covariance model, and yields superior asymptotic guarantees. Our derivation draws a close connection to the original James–Stein formula so that the motivation and recipe for shrinkage is intuited in a natural way.
Backdoor Attack and Defense for Deep Regression

We demonstrate a backdoor attack on a deep neural network used for regression. The backdoor attack is localized based on training-set data poisoning wherein the mislabeled samples are surrounded by correctly labeled ones. We demonstrate how such localization is necessary for attack success. We also study the performance of a backdoor defense using gradient-based discovery of local error maximizers. Local error maximizers which are associated with significant (interpolation) error, and are proximal to many training samples, are suspicious. This method is also used to accurately train for deep regression in the first place by active (deep) learning leveraging an"oracle"capable of providing real-valued supervision (a regression target) for samples. Such oracles, including traditional numerical solvers of PDEs or SDEs using finite difference or Monte Carlo approximations, are far more computationally costly compared to deep regression.
From Data Processes to Data Products: Knowledge Infrastructures in Astronomy

We explore how astronomers take observational data from telescopes, process them into usable scientific data products, curate them for later use, and reuse data for further inquiry. Astronomers have invested heavily in knowledge infrastructures - robust networks of people, artifacts, and institutions that generate, share, and maintain specific knowledge about the human and natural worlds. Drawing upon a decade of interviews and ethnography, this article compares how three astronomy groups capture, process, and archive data, and for whom. The Sloan Digital Sky Survey is a mission with a dedicated telescope and instruments, while the Black Hole Group and Integrative Astronomy Group (both pseudonyms) are university-based, investigator-led collaborations. Findings are organized into four themes: how these projects develop and maintain their workflows; how they capture and archive their data; how they maintain and repair knowledge infrastructures; and how they use and reuse data products over time. We found that astronomers encode their research methods in software known as pipelines. Algorithms help to point telescopes at targets, remove artifacts, calibrate instruments, and accomplish myriad validation tasks. Observations may be reprocessed many times to become new data products that serve new scientific purposes. Knowledge production in the form of scientific publications is the primary goal of these projects. They vary in incentives and resources to sustain access to their data products. We conclude that software pipelines are essential components of astronomical knowledge infrastructures, but are fragile, difficult to maintain and repair, and often invisible. Reusing data products is fundamental to the science of astronomy, whether or not those resources are made publicly available. We make recommendations for sustaining access to data products in scientific fields such as astronomy.