Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Media Marketing and Business Performance of MSMEs During the COVID-19 Pandemic

This study aims to examine the role of social media marketing on the performance of micro-, small and medium-sized enterprises (MSMEs) during the COVID-19 pandemic This research method uses a quantitative approach and the analytical tool used is Structural Equation Modeling (SEM) based on Partial Least Square The sampling technique used purposive sampling and the sample in this study was MSMEs who use social media for marketing The social media used are Facebook, Instagram, and Whatsapp The number of samples in this study was 254 MSMEs Data collection used online questionnaires The research findings show that the use of social media marketing is influenced by compatibility, perceived usefulness, and perceived ease-of-use The results of this study indicate that the use of social media for marketing has a positive effect on the performance of MSMEs, especially in increasing sales, customer relationships, productivity, and creativity This means that the better the management of social media for marketing, the better the performance of MSMEs will be The theoretical contribution to this research is built on the TAM and UTAUT theories and their impact on social media marketing on MSMEs in developing countries that are living through the COVID-19 pandemic © Copyright: The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons org/licenses/by-nc/4 0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited
Effect of fertility levels and boron on growth and yield of cauliflower

A field experiment consisting five levels of fertility and four levels of boron in RBD with three replications was conducted at Horticulture Farm, S.K. N. College of Agriculture, Jobner (Jaipur) during Rabi season 2016-17. Results revealed that different fertility levels influenced growth, yield and quality of cauliflower significantly, plant height at 30 and 60 DAT, number of leaves par plant at 30 and 60 DAT and leaf area were recorded highest with application of 75% RDF through inorganic fertilizers and 25% RDF through vermicompost but remained at par with application of 50% RDF through inorganic fertilizer and 50% RDF through vermicompost. It is also revealed that average weight of curd volume, curd yield per plant and per ha (190.89 q/ha), were found highest with application of 50% RDF through inorganic fertilizers and 50% RDF through vermicompost treatment. Similarly, the boron level with 2.5 kg per ha significantly increased the plant height at 30 and 60 DAT, number of leaves at 30 and 60 DAT, leaf area at 30 and 60 DAT, average weight of curd, Curd yield per plant and per ha (185.53 q/ha), as compared to control and 1.5 kg boron per ha but statistically at par with 2.0 kg boron per ha.
Nocturnal awakenings of Brazilian immigrants in Massachusetts

Objective The purpose of this study was to assess the relationship between the quantity of jobs and nocturnal awakenings of Brazilians living in Massachusetts. Material and Methods We sampled of 48 documented Brazilians around the age of 45.5 years old. 52.1% of them were women. Data gathering occurred for three weeks, using the Pittsburgh Sleep Quality Index. Participants also wore wrist actigraph and filled sleep/wake diary for a week. Results The sleep quality of immigrants with one job (mean=8.58, SD=4.16) is better when compared to immigrants with 2-3 jobs (mean=12.7, SD=3.57) according to the PSQI scores. Immigrants with 2-3 jobs reported dissatisfaction on three components of the scale: sleep duration, sleep efficiency and sleep quality. Discussion There is a positive relationship between the quantity of jobs and nocturnal awakenings and between nocturnal awakenings and complaints related to sleep apnea among Brazilians in Massachusetts. The assessment of systemic morbidities associated with sleep pattern changes should be considered in future research.
Underwater Source Localization Using an Artificial Lateral Line System With Pressure and Flow Velocity Sensor Fusion

Mimicking fish lateral line for hydrodynamic sensing, various artificial lateral line systems have been developed for underwater robotics. However, previous studies of artificial lateral line systems for underwater source localization were based on a single sensing modality of either pressure or flow velocity. Inspired by the functions of flow and pressure receptors of fish, in this article, we developed an artificial lateral line system integrated with pressure sensors and flow velocity sensors. A dual-sensor fusion modality was proposed to locate near-field dipole source by measuring hydrodynamic signals. A multilayer perceptron neural network was constructed to process the pressure and flow velocity signals and to predict near-field dipole coordinates in two and three dimensions. In a 2-D plane, compared with a single pressure- or velocity-sensing modality, the dual-sensor fusion modality reduced the mean localization error by 30%. In a 3-D space, the mean localization error based on the dual-sensor fusion modality was approximately 0.1 body lengths, when considering a source located within one body length from the artificial lateral line. In addition, we studied the influence of the number of sensors on the localization accuracy via analysis of variance. Our experimental results indicated that the bio-inspired dual-sensor fusion modality is beneficial for improving the performance of artificial lateral line systems for underwater source localization.
Nasal Polyps :An Etiological Analysis

Nasal polyps,the prolapsed linings of nasal mucosa are one of the commonly encountered conditions in theoutpatient department of otolaryngology, The aim of this study is to study the etiology of nasal polyps andalso to compare between the different methods used for diagnosis of nasal polyps.This study was submitted prospectively on 100 patients diagnosed with nasal polyps (diagnosed eitherclinically or radiologically) with evidence of nasal obstruction,rhinorrhae and headache. from April 2015till April 2017. All patients had been selected from the outpatient clinics of Al-Zahraa University Hospitaland Kobry El Koppa military hospital. The study was approved by the ethics committee of Al Azhar Facultyof Medicine.A total of 100 patients were analyzed, age range 18 to 56 yrs with a mean age of 38.5 yrs. There were 83 maleand 17 female. The main presenting symptoms are nasal obstruction and rhinorrhea. Most of the cases wasbilateral nasal polyps,. The commonest clinical diagnoses were non-neoplastic simple nasal polyps 84% andabout 13% were benign neoplastic, 3% were malignant . The commonest histological diagnosis among nonneoplastic nasal polyps was simple allergic nasal polyp, among benign neoplastic was inverted papilloma.most of neoplastic lesions were presented by unilateral nasal polyps,where epistaxis was noticed mostlywith vascular tumour and malignant ones. The results show that most of nasal polyps were simple nonneoplastic lesions especailly bilateral ones,also for proper evaluation of nasal polyps clinical, radiologicaland histopathological evaluation should be done in all patients.
Adapting to Unknown Conditions in Learning-Based Mobile Sensing

Many applications utilize sensors on mobile devices and apply deep learning for diverse applications. However, they have rarely enjoyed mainstream adoption due to many different individual conditions users encounter. Individual conditions are characterized by users’ unique behaviors and different devices they carry, which collectively make sensor inputs different. It is impractical to train countless individual conditions beforehand and we thus argue meta-learning is a great approach in solving this problem. We present MetaSense that leverages “seen” conditions in training data to adapt to an “unseen” condition (i.e., the target user). Specifically, we design a meta-learning framework that learns “how to adapt” to the target via iterative training sessions of adaptation. MetaSense requires very few training examples from the target (e.g., one or two) and thus requires minimal user effort. In addition, we propose a similar condition detector (SCD) that identifies when the unseen condition has similar characteristics to seen conditions and leverages this hint to further improve the accuracy. Our evaluation with 10 different datasets shows that MetaSense improves the accuracy of state-of-the-art transfer learning and meta learning methods by 15 and 11 percent, respectively. Furthermore, our SCD achieves additional accuracy improvement (e.g., 15 percent for human activity recognition).
Aspects of a Phase Transition in High-Dimensional Random Geometry

A phase transition in high-dimensional random geometry is analyzed as it arises in a variety of problems. A prominent example is the feasibility of a minimax problem that represents the extremal case of a class of financial risk measures, among them the current regulatory market risk measure Expected Shortfall. Others include portfolio optimization with a ban on short-selling, the storage capacity of the perceptron, the solvability of a set of linear equations with random coefficients, and competition for resources in an ecological system. These examples shed light on various aspects of the underlying geometric phase transition, create links between problems belonging to seemingly distant fields, and offer the possibility for further ramifications.
In vitro toxicity, apoptosis and antimicrobial effects of phyto-mediated copper oxide nanoparticles

Herein, copper oxide nanoparticles (CuONPs) are proposed for widespread use in emerging biomedical applications. The aim of this study is to synthesize and characterize CuONPs using the aqueous dried fruit extract of Tribulus terrestris and assess their potential in vitro cytotoxicity and antibacterial activity. The CuONPs bio-physical and morphological properties are characterized via different analytical techniques. The synthesized particles are highly stable and spherical with particle sizes in the range of 5–22 nm. Confocal microscopy reveals that ROS generation is the potent mechanism behind the role of CuONPs mediated antimicrobial toxicity. In addition, MTT and apoptotic analyses illustrate that the CuONPs are relatively cytofriendly toward human mesenchymal stem cells and also exhibit anti-cancer activity on human adenocarcinoma AGS cell lines. These results clearly indicate that the prepared copper oxide nanoparticles are safe and their application can be expanded as effectual antimicrobial and anticancer agents for pharmaceutical applications.
Effect of different weed management practices on growth and yield of okra (Abelmoschus esculentus (L.) Moench.)

The experiment was conducted during Kharif seasons of 2019-20 at AICRP (Vegetables) RHREC, Dharwad, Karnataka. The experiment was laid out in a randomized block design (RBD) with seven treatments with three replications. Significantly lowest dry weight of total weed biomass and Number of weeds per m2 area are registered under the treatment Pre-application of Pendimethalin @ 6 ml/L + one hand weeding of 7.43 g and 6.67 respectively at 60 DAS and also Higher gross return (Rs.175095), net return (Rs.108768) and B:C ration of 1.64.
Deep Neural Networks as Point Estimates for Deep Gaussian Processes

Neural networks and Gaussian processes are complementary in their strengths and weaknesses. Having a better understanding of their relationship comes with the promise to make each method benefit from the strengths of the other. In this work, we establish an equivalence between the forward passes of neural networks and (deep) sparse Gaussian process models. The theory we develop is based on interpreting activation functions as interdomain inducing features through a rigorous analysis of the interplay between activation functions and kernels. This results in models that can either be seen as neural networks with improved uncertainty prediction or deep Gaussian processes with increased prediction accuracy. These claims are supported by experimental results on regression and classification datasets.
Binarized Weight Error Networks With a Transition Regularization Term

This paper proposes a novel binarized weight network (BT) for a resource-efficient neural structure. The proposed model estimates a binary representation of weights by taking into account the approximation error with an additional term. This model increases representation capacity and stability, particularly for shallow networks, while the computation load is theoretically reduced. In addition, a novel regularization term is introduced that is suitable for all threshold-based binary precision networks. This term penalizes the trainable parameters that are far from the thresholds at which binary transitions occur. This step promotes a swift modification for binary-precision responses at train time. The experimental results are carried out for two sets of tasks: visual classification and visual inverse problems. Benchmarks for Cifar10, SVHN, Fashion, ImageNet2012, Set5, Set14, Urban and BSD100 datasets show that our method outperforms all counterparts with binary precision.
Investigation and Nanomechanical Behavior of the Microconstituents of Al-Si-Cu alloy After Solution and Ageing Heat Treatments

The effects of heat treatment on the microstructural evolution and mechanical behavior of Al-Si-Cu alloy were investigated. Solution heat treatment was performed at 500oC/6h and 540oC/6h, followed by quenching in warm water at 60oC. Lastly, artificial ageing treatment was performed at 160oC/6h, 190oC/4h and 210oC/2h. The microstructural evolution of the samples before and after heat treatments was analyzed by optical microscopy, scanning electronic microscopy (SEM) and Energy Dispersive Spectroscopy (EDS). Analyses revealed the presence Al5FeSi, Al15(FeMn)3Si2, Al2Cu and Si particles. The morphologies of the present phases were dependent on the heat treatment. Solution treatment at 540oC/6h resulted in significant softening of the alloy compared with the treated samples at 500oC/6h. Vickers hardness analysis indicated that the hardness peak of the alloy after heat treatment was about 125 HV. Mechanical properties of different phases have been investigated by nanoindentation technique, which indicated that the highest hardness and modulus of elasticity were found for the Si particle, especially those distributed in the microstructures of the samples submitted to higher solution and ageing temperatures.
DefSent: Sentence Embeddings using Definition Sentences

Sentence embedding methods using natural language inference (NLI) datasets have been successfully applied to various tasks. However, these methods are only available for limited languages due to relying heavily on the large NLI datasets. In this paper, we propose DefSent, a sentence embedding method that uses definition sentences from a word dictionary, which performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks than conventional methods. Since dictionaries are available for many languages, DefSent is more broadly applicable than methods using NLI datasets without constructing additional datasets. We demonstrate that DefSent performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks to the methods using large NLI datasets. Our code is publicly available at https://github.com/hpprc/defsent.
Coupled Finite Element and Extended-QD Circuit Induction Machine Model, Part I: Formulation

The design of squirrel-cage induction motors (IMs) entails the capability to predict the motor efficiency map with high accuracy over an operating range. In particular, modeling harmonic rotor bar currents becomes important for loss analysis. In theory, it is possible to analyze an induction motor using time-stepping finite element analysis (TS-FEA); however, this is not viable due to computational limitations. To bridge this gap, we set forth a computationally efficient method that couples magnetostatic FEA with a qd-circuit model of the cage. The proposed IM model includes the effects of saturation, winding and slot harmonics, as well as nonuniform current density distribution in the rotor bars. The model is presented in two parts. Part I focuses on the formulation of a qd cage circuit model with segmented bars. Part II is a companion paper that proposes an FEA-based parameter estimation and a solution method.
Assessment of Intraoperative Microbiological Culture in Patients with Empyema: Comparison with Preoperative Microbiological Culture

Purpose: Assessing microbiological culture results is essential in the diagnosis of empyema and appropriate antibiotic selection; however, the guidelines for the management of empyema do not mention assessing microbiological culture intraoperatively. Therefore, we tested the hypothesis that intraoperative microbiological culture may improve the management of empyema. Methods: We performed a retrospective analysis of 47 patients who underwent surgery for stage II/III empyema from January 2011 to May 2019. We compared the positivity of microbiological culture assessed preoperatively at empyema diagnosis versus intraoperatively. We further investigated the clinical characteristics and postoperative outcomes of patients whose intraoperative microbiological culture results were positive. Results: The positive rates of preoperative and intraoperative microbiological cultures were 27.7% (13/47) and 36.2% (17/47), respectively. Among 34 patients who were culture-negative preoperatively, eight patients (23.5%) were culture-positive intraoperatively. Intraoperative positive culture was significantly associated with a shorter duration of preoperative antibiotic treatment (p = 0.002). There was no significant difference between intraoperative culture-positive and -negative results regarding postoperative complications. Conclusions: Intraoperative microbiological culture may help detect bacteria in patients whose microbiological culture results were negative at empyema diagnosis. Assessing microbiological culture should be recommended intraoperatively as well as preoperatively, for the appropriate management of empyema.
Regulation of divalent metal ions to the aggregation and membrane damage of human islet amyloid polypeptide oligomers

The accumulation of human islet amyloid polypeptide (hIAPP) on the surface of pancreatic β cells is closely related to the death of the cells. Divalent metal ions play a significant role in the cytotoxicity of hIAPP. In this study, we examined the roles played by the divalent metal ions of zinc, copper and calcium in the aggregation of both hIAPP18-27 fragment and full-length hIAPP and the ability of their oligomers to damage the membrane of POPC/POPG 4 : 1 LUVs using the ThT fluorescence, TEM, AFM, CD, ANS binding fluorescence and dye leakage experiments. We prepared metal-free and metal-associated oligomers that are similar in size and aggregate slowly using the short peptide and confirmed that the ability of the peptide oligomers to damage the lipid membrane is reduced by the binding to the metal ions, which is closely linked to the reducing hydrophobic exposure of the metal-associated oligomers. The study on the full-length hIAPP showed that the observed membrane damage induced by hIAPP oligomers is either mitigated at a peptide-to-metal ratio of 1 : 0.33 or aggravated at a peptide-to-metal ratio of 1 : 1 in the presence of Zn(ii) and Cu(ii), while the surface hydrophobicity of hIAPP oligomers was reduced at both peptide-to-metal ratios. The observed results of the membrane damage were attributed to the counteraction between a decrease in the disruptive ability of metal-associated oligomer species and an increase in the quantity of oligomers promoted by the binding of the metal ions to hIAPP oligomers. The former could play a predominant role in reducing the membrane damage at a peptide-to-metal ratio of 1 : 0.33, while the latter could play a predominant role in enhancing the membrane damage at a peptide-to-metal ratio of 1 : 1. This study shows that an enhanced membrane damage could be caused by the oligomer species with a decreased instead of an increased disruptive ability, given that the abundance of the oligomer species is high enough.
Expression and clinical significance of VCAM-1, IL-6, and IL-17A in patients with allergic rhinitis.

BACKGROUND Allergic rhinitis is a chronic inflammatory disease with a high incidence, affecting the quality of life of patients. The aim of the present study was to explore the expression and clinical significance of vascular endothelial cell adhesion molecule-1 (VCAM-1), interleukin (IL)-6, and IL-17A in patients with allergic rhinitis. METHODS A total of 180 patients with allergic rhinitis, who were admitted to our hospital from March 2017 to March 2020, were enrolled as the observation group. Patients were further divided into 3 subgroups according to the degree of severity of allergic rhinitis as follows: the mild group (n=66), the moderate group (n=60), and the severe group (n=54). During the same period, 188 people without allergic rhinitis, who underwent physical examination in our hospital, were enrolled as the control group. VCAM-1, IL-6, and IL-17A levels were compared between these groups, and the correlation between the 3 levels and allergic rhinitis was analyzed. RESULTS VCAM-1, IL-6, and IL-17A levels in the observation group were significantly higher than those in the control group, and the difference was statistically significant (P<0.05). Comparison results of VCAM-1, IL-6 and IL-17A levels among the 3 groups were as follows: mild group < moderate group < severe group, and the differences between the three groups were significant (P<0.05). Pearson linear correlation showed that serum levels of VCAM-1, IL-6 and IL-17A were positively correlated with ARIA (P<0.05). Parent history of allergic rhinitis, living environment pollution, abnormally elevated VCAM-1, abnormally elevated IL-6 and abnormally elevated IL-6 were the related risk factors for inducing allergic rhinitis (P<0.05). CONCLUSIONS VCAM-1, IL-6, and IL-17A play important roles in allergic rhinitis, and the concentration will increase with disease progression. Clinical testing of VCAM-1, IL-6, IL-17A should be increased to improve the diagnostic rate for early allergic rhinitis and to prevent disease progression.
Few-Shot Conversational Dense Retrieval

Dense retrieval (DR) has the potential to resolve the query understanding challenge in conversational search by matching in the learned embedding space. However, this adaptation is challenging due to DR models' extra needs for supervision signals and the long-tail nature of conversational search. In this paper, we present a Conversational Dense Retrieval system, ConvDR, that learns contextualized embeddings for multi-turn conversational queries and retrieves documents solely using embedding dot products. In addition, we grant ConvDR few-shot ability using a teacher-student framework, where we employ an ad hoc dense retriever as the teacher, inherit its document encodings, and learn a student query encoder to mimic the teacher embeddings on oracle reformulated queries. Our experiments on TREC CAsT and OR-QuAC demonstrate ConvDR's effectiveness in both few-shot and fully-supervised settings. It outperforms previous systems that operate in the sparse word space, matches the retrieval accuracy of oracle query reformulations, and is also more efficient thanks to its simplicity. Our analyses reveal that the advantages of ConvDR come from its ability to capture informative context while ignoring the unrelated context in previous conversation rounds. This makes ConvDR more effective as conversations evolve while previous systems may get confused by the increased noise from previous turns. Our code is publicly available at https://github.com/thunlp/ConvDR.