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2,359 results for “online”
INFLUENCE OF COVID-19 PANDEMIC ON ONLINE MARKETING. (A SPECIAL REFERENCE TO JALGAON CITY.)
<p>COVID-19 pandemic has stirred every single individual around, bringing the <br>entire society to a pause in all features. It is affecting businesses, institutions, and health <br>amenities. Even marketing has not been forborne by this pandemic. This situation will have a <br>long-term influence on man-kind, though we could go back to our normal life in a few <br>months. On the other hand, due to the policy of Digital India, every part of the nation is now <br>connected with the internet, having digital gadgets to obtain online information.<br>During this pandemic situation, it is observed that people are spending more and more time <br>online. In fact, according to Forbes, during this pandemic situation internet has surged by 50-<br>70% in the world. Therefore, firms need to precisely determine their online marketing policy <br>and find unique & fine-tuned ways to communicate not only during the time but should also <br>have the long term approach. <br>The present research article critically examines and highlights the opportunities and <br>challenges of Online Marketing in and after the COVID-19 situation, as per the perception of <br>both buyers and suppliers in Jalgaon City. The researcher gathered the polls to manifest the <br>situation of Online Marketing in two parts, the general respondent consisting of the <br>consumers and specialist respondents such as the marketing personnel. A survey of 167 <br>buyers (Consumer) and 37 suppliers (marketing personnel) were conducted using the <br>convenience sampling method.</p>
THE ROLE OF GENDER IN ONLINE SHOPPING- A LITERATURE REVIEW
<p> The development of Internet has resulted in enormous business<br>prospects and opportunities and given new direction to traditional commercial<br>activities. E- Commerce emerged as the need of the hour. The business-toconsumer (B2C) is the most visible and prominent progeny of e-commerce. B2C <br>is a commercial process that starts with companies and ends with end consumers. <br>Online shopping is an emerging area in the field of E-Business and is surely<br>going to be the future of shopping in the world. The benefits of online shopping <br>are well known. The most common incentives for consumers to shop online are<br>convenience, competitive pricing, greater access to information, complementarily<br>of traditional stores and broader selections. Most of the companies are running<br>their online portals to selltheir products/services online. Though online shopping<br>has made enormous progressoutside India, its growth in the Indian market, which <br>is a large and diverse consumer market, is still not in line with the global market.<br>On-line shopping in India is significantly affected by various demographic factors <br>like age, gender, marital status, family size and income. Substantial amount of <br>research work has been carried out on all these areas. The impact of these factors<br>on online shopping behavior is fascinating to say the least. But the most <br>mysterious of them all is the impact of gender on the acceptance or rejection of <br>online shopping. Do men and women behave differently during the online <br>shopping processes or do they exhibit same kinds of behavior during this process?<br>This article will try to throw some light on the extremely valuable but often<br>neglected role of gender in the online shopping behavior of consumers.</p>
Supporting online material for Irradiation induced mineral changes of NWA10580 meteorite, Gyollai I. et al. 2024
<p>Online supporting material for the upcoming publication titled<span> Irradiation induced mineral changes of NWA10580 meteorite determined by infrared analysis by I. Gyollai et al. </span></p>
Model Output and Validation Data for Online Determination of GNSS Differential Code Biases using Rao-Blackwellized Particle Filtering
<p>This dataset contains the outputs of 10 test runs of the A-CHAIM data assimilation model used to evaluate the performance of the bias estimation procedure used by the model. The 5-minute output files for each test run are included in a separate folder.</p> <p>The IGS DCBs in SINEX format and the various global ionospheric maps used in the study are included.</p> <p>This dataset also includes all of the processed GNSS data used in the study, which were used to generate the DCBs used in each test run.<br> <br> The in-situ electron density measurements from DMSP as well as the ionosonde data from GIRO which were used to measure the performance are also included.</p>
Perception of Artificial Intelligence Among Japanese Medical Technologists and the Significance of Seminars on Emerging Technologies: An Online Survey of the Nara Association of Medical Technologists
<p><span>This study aimed to investigate Japanese medical technologists' perceptions of AI in laboratory medicine and evaluate the significance of seminars on emerging technologies. An online survey was conducted before and after a seminar titled " Artificial Intelligence and Clinical Laboratory: A Tale of the Encounter between Deep Tech and Medical Technicians" held on August 19, 2023. Responses were analyzed using Fisher's exact test, and free-text responses were analyzed using a co-occurrence network. Of the 278 pre-seminar respondents, 62.9% had positive attitudes towards AI. The AI-positive group showed significantly higher usage of AI-related web services (p<0.01) and more proactive information-seeking behavior (p<0.05) compared to the non-positive group. Post-seminar, support for "AI results must always be verified by medical technologists" significantly decreased (p<0.05). Additionally, 63.9% of the 169 participants expressed intent to actively gather information in the future. Free-text responses revealed expectations for work improvement, quality enhancement, and technological advancement, alongside concerns about AI implementation. The results suggest that while many respondents view AI positively, their expectations are predicated on maintaining traditional roles. The seminar appeared to catalyze information-seeking behavior. This study revealed Japanese medical technologists' positive perceptions of AI and concluded that seminars including explanations of emerging technologies and discussions are valuable. These findings may contribute to discussions on AI implementation in laboratory medicine and the design of continuing education programs for medical technologists.</span></p>
CEDAR, an online resource for the reporting and exploration of complexome profiling data
<p>Complexome profiling is an emerging ‘omics’ approach that systematically interrogates the composition of protein complexes (the complexome) of a sample, by combining biochemical separation of native protein complexes with mass-spectrometry based quantitation proteomics. The resulting fractionation profiles hold comprehensive information on the abundance and composition of the complexome, and have a high potential for reuse by experimental and computational researchers. However, the lack of a central resource that provides access to these data, reported with adequate descriptions and an analysis tool, has limited their reuse. Therefore, we established the ComplexomE profiling DAta Resource (CEDAR, www3.cmbi.umcn.nl/cedar/), an openly accessible database for depositing and exploring <a title="Learn more about mass spectrometry from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/mass-spectrometry">mass spectrometry</a> data from complexome profiling studies. Compatibility and reusability of the data is ensured by a standardized data and reporting format containing the “minimum information required for a complexome profiling experiment” (MIACE). The data can be accessed through a user-friendly web interface, as well as programmatically using the REST API portal. Additionally, all complexome profiles available on CEDAR can be inspected directly on the website with the profile viewer tool that allows the detection of correlated profiles and inference of potential complexes. In conclusion, CEDAR is a unique, growing and invaluable resource for the study of protein complex composition and dynamics across biological systems.</p>
Critical Gaps in Medical Research Reporting by Online News Media: Dataset
<p>Dataset for <strong>Critical Gaps in Medical Research Reporting by Online News Media</strong></p>
Figures for Developing an online data-driven approach for prognostics and health management of lithium-ion batteries
Open the record for dataset details and reuse information.
Dataset for: Online Virtual Machine Provisioning under Uncertainty: A Robust Approach to Ultimate Resource Utilization
<div> <div>In cloud resource scheduling and management, efficiency and quality are two vital yet conflicting objectives. Cloud providers, such as Huawei, prioritize quality by avoiding hotspots and aim to optimize and increase the utilization efficiency of PM resources without compromising quality. The typical online VM scheduling problem in cloud practice can be stated as follows: given a fixed number of PMs and a queue of arriving VMs, the goal is to place as many VMs as possible onto the PMs while ensuring that no hotspots occur.</div> <br> <div>The trace consists of a total of 297 instances, where each instance is represented by a JSON file named in the format X-1.json, X-2.json, and X-3.json. Here, X represents the number of PMs capable of hosting the arriving VMs, ranging from 2 to 100. For example, 50-1.json indicates that there are 50 available PMs to host the arriving VMs. For each value of X, there are three replicas denoted by the suffixes 1, 2, and 3.</div> <br> <div>There are multiple flavors of PM available in our cloud service provider. Readers can refer to our online paper, which we will provide the link for below, to learn about the specific flavor we used for academic and testing purposes. However, they are also free to use other typical flavors as per their requirements.</div> <br> <div>Each JSON file contains information about the VMs waiting to be assigned to PMs, and the fields for these VMs are as follows (each line represents a VM):</div> <br> <div> <ul> <li><strong>Created_at_point</strong>: the timestamp when the VM arrives. The list is already sorted in ascending order based on the arrival times.</li> </ul> </div> <ul> <li><strong>memory</strong>: the memory capacity of the VM defined by its flavor, measured in gigabytes (GB).</li> <li><strong>duration_point</strong>: the timestamps indicating the duration of the VM's usage. Each timestamp represents a 5-minute interval in practice.</li> <li><strong>vm_util</strong>: the real utilized capacity of the VM at each timestamp, with a similar meaning as the Hotspot Resolution trace. The length of the list is equal to the value of "duration_point".</li> </ul> </div> <div> </div>
Data archive for the peer-reviewed journal article "Major source categories of PM2.5 oxidative potential in wintertime Beijing and surroundings based on online dithiothreitol-based field measurements"
<p>This data archive accompanying the article "Major source categories of PM2.5 oxidative potential in wintertime Beijing and surroundings based on online dithiothreitol-based field measurements", which was accepted in April 2024 in the peer-reviewed journal <strong><em>Science of the Total Environment</em></strong>. This data archive contains the processed OPvDTT measurements, chemical speciation of PM2.5, and source contribution used in the manuscript.</p>
Replication package for: "The Impact of Online Competition on Local Newspapers: Evidence from the Introduction of Craigslist"
<p>Djourelova, Milena, Durante, Ruben, and Gregory J. Martin (2023). "The Impact of Online Competition on Local Newspapers: Evidence from the Introduction of Craigslist."</p><p>The replication package contains software and datasets needed to produce tables and figures in the paper, as well as data cleaning scripts and raw data.</p><p>The replication package is also available as a Git repository at <a href="https://code.stanford.edu/gjmartin/craigslist-replication-code-and-data">https://code.stanford.edu/gjmartin/craigslist-replication-code-and-data</a></p>
ELTONMPO | SITUS TARUHAN ONLINE SLOT BE 200 PERAK
<p><a href="https://heylink.me/eltonmpo/">ELTONMPO</a> Situs Taruhan game Online Terbaik dan Terpercaya dengan Sistem pembayaran paling adil , cepat dan sangat terpercaya . Menyediakan ratusan game yang dapat Anda nikmati bersama ELTONMPO yang sangat terpercaya . ELTONMPO Juga menyediakan pelayanan 24jam yang dapat Anda akses melalui Livechat dan Whatsapp yang telah kami sediakan untuk mengatasi kendala yang anda alami secara teknis maupun non-teknis.</p>
illegal gambling cases, searching from Chinese Judgement Online (裁判文书网)
Open the record for dataset details and reuse information.
A Preliminary Investigation on the Usage of Quantum Approximate Optimization Algorithms for Test Case Selection - Online Appendix
<p>QAOA-TCS - Quantum Regression Test Case Selection <br>This repository contains all the necessary resources to reproduce the results of the QAOA-TCS method. </p> <p>Dataset Files <br>The "datasets" folder contains: <br>- "sir_programs" </p> <p>SIR Programs <br>The "datasets/sir_programs" folder contains, for each SIR program considered by this project, all the files needed to gather statement coverage, execution costs, and past fault coverage information. </p> <p>For example, in the "flex" program folder: <br>- The file "fault-matrix.txt" contains rows representing flex's test cases. Each row has columns representing different versions of the program. Each cell (i,j) contains a binary value (0 or 1) indicating whether the i-th test case detects a fault in the j-th version. This configuration is called the fault matrix and provides historical fault coverage information. <br>- The folder "json_flex" contains a folder for each test case, with files like "flexi.gcov.json" to recover statement coverage and execution costs. These files detail which basic blocks were executed and how many times, enabling the calculation of total statement coverage and execution costs for each test case. </p> <p>Source Code Files </p> <p>DIVGA.m <br>The "MATLAB/DIVGA.m" file contains the pipeline for the DIVGA algorithm. For simplicity, the statement coverage, execution costs, and fault coverage data already gathered by "Notebook.ipynb" are written into text files, which DIVGA.m reads to bypass the actual datasets. </p> <p>DIVGA.m must be reconfigured for each target program. Update parameters like M, N, and gamultiobj routine settings. Ensure H_size in line 104 is less than max{N, M} + 1. Update the denominator in line 53 based on the total number of code lines in the target program. Adjust the result reporting target files as well. </p> <p>Notebook.ipynb <br>This file includes pipelines for dataset analysis, algorithm execution, and empirical comparisons. </p> <p>It has two main sections: </p> <p>1. QAOA-TCS vs SelectQA and Classical Algorithms <br> - Pipelines analyze SIR programs and compare QAOA-TCS, SelectQA, and classical algorithms. Manual configuration is needed when changing the target program, including updating file paths for Pareto fronts and configuring frontiers to build the reference. <br> - Statistical analysis requires populating the variables "algorithm_nondom_sirprogram" with the number of non-dominated solutions found during each of the 10 runs. </p> <p>Results Files <br>The "results" folder contains the outcomes of QAOA-TCS, DIV-GA, Additional Greedy, and SelectQA after experiment execution. These files enable empirical evaluations and comparisons between the methods. </p>
online Supplementary Data for INA Chapter 10
<p>online Supplementary Data for the Chapter 10 of the International Nitrogen Assessment.</p> <p>The .xlsx spreadsheet has been converted from a google sheet. The original formatting of the graphs and some formulas do not work in Excel. <br><br>The original spreadsheet can be found here: <br>https://docs.google.com/spreadsheets/d/1Yp6tqUz48Y7fA-PTsDQHPZo6qNLYZ_Wrh2Amf9_OH0U/edit?gid=422095820#gid=422095820</p>
Fortran/Python Interface in ARP-GEM1: Online Test of Neural Network Deep Convection
<p>Manuscript under review in AIES. Supporting Code and Dataset. </p> <p><strong>Abstract.</strong></p> <p>In this study, we present the integration of a neural network-based parameterization into the global atmospheric model ARP-GEM1, leveraging the Python interface of the OASIS coupler. This approach facilitates the exchange of fields between the Fortran-based ARP-GEM1 model and a Python component responsible for neural network inference. As a proof-of-concept experiment, we trained a neural network to emulate the deep convection parameterization of ARP-GEM1. Using the flexible Fortran/Python interface, we have successfully replaced ARP-GEM1's deep convection scheme with a neural network emulator. To assess the performance of the neural network deep convection scheme, we have run a 5-years ARP-GEM1 simulation where the neural network replaced ARP-GEM1's deep convection parameterization. The evaluation of averaged fields showed good agreement with output from an ARP-GEM1 simulation using the physics-based deep convection scheme. The Python component was deployed on a separate partition from the general circulation model, using GPUs to increase inference speed of the neural network.</p>
Online water quality monitoring data from full scale CS#3 DWDN for the DBP prediction model
<p>Online water quality data though the drinking water distribution network. More than 1 year of data.</p> <p>SCADA data source.</p> <p>Provide water quality of the whole system at selected locations.</p>
Online hydraulic data from full scale CS#3 DWDN for model calibration
<p>Online hydraulic data including hydraulic model for the full distribution network. More than one full year of data.</p> <p>SCADA data source.</p> <p>Provide knowledge of the water distribution network and how the water moves in the system.</p>
Chimpanzee identification and social Network construction through an online citizen science platform
<p><span><span><span><span><span><span><span><span><span><span><span>Citizen science has grown rapidly in popularity in recent years due to its potential to educate and engage the public while providing a means to address a myriad of scientific questions. However, the rise in popularity of citizen science has also been accompanied by concerns about the quality of data emerging from citizen science research projects. We assessed data quality in the online citizen scientist platform Chimp&See, which hosts camera trap videos of chimpanzees (<i>Pan troglodytes</i>) and other species across Equatorial Africa. In particular, we compared detection and identification of individual chimpanzees by citizen scientists to that of experts with years of experience studying those chimpanzees. We found that citizen scientists typically detected the same number of individual chimpanzees as experts, but assigned far fewer identifications (IDs) to those individuals. Those IDs assigned, however, were nearly always in agreement with the IDs provided by experts. We applied the data sets of citizen scientists and experts by constructing social networks from each. We found that both social networks were relatively robust and shared a similar structure, as well as having positively correlated individual network positions. Our findings demonstrate that, although citizen scientists produced a smaller data set based on fewer confirmed IDs, the data strongly reflect expert classifications and can be used for meaningful assessments of group structure and dynamics. This approach expands opportunities for social research and conservation monitoring in great apes and many other individually identifiable species. </span></span></span></span></span></span></span></span></span></span></span></p>
Zum Unterricht, online und im Hörsaal - Vorleben in Vorlesung
<p>Unterrichtsreflexion als podcast für Studierende der Lehrämter.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.