Earn Revenue Research Articles

YouTube as an information source on politics and current affairs: Supply- and demand-side perspectives

This study examines YouTube channels reporting on French news and current affairs. After identifying the relevant channels, the study identifies the top 10 viewed videos of each channel during the year 2023. Manual annotation has been conducted for content, presentation format, engagement style, and position regarding the government and mainstream media. Each channel has also been coded for its business and communication strategies. Correspondence analysis is used to extract the most emblematic news content and presentation styles, while hierarchical clustering is used to categorize the channels into clusters of different genres. From the demand-side perspective, the study also examines the network of users commenting on the top 10 videos to assess the audience overlap between the identified clusters. . Five clusters of alternative news channels are identified, differing in presentation, style, and partisanship. Network analysis showed audience overlaps, notably between satire, alternative, and re-information clusters. Most channels earn revenue through branding and cross-media activities, occasionally supporting each other. These clusters act as mediators between politics, entertainment, and re-information, serving diverse audiences. Uncertainty remains regarding future dynamics and competition with established news outlets.

Online battery scheduling for enhanced profitability and longevity in pay-for-performance frequency regulation markets

Batteries participating in frequency regulation (FR) markets earn revenue at the expense of longevity. The declining reward prices and highly stochastic automatic generation control (AGC) signals with short update intervals necessitate research into aging-aware real-time FR strategies for the battery. To address these challenges, this paper develops an online battery dispatch strategy based on Lyapunov optimization, accounting for Ah-throughput, C-rate, and temperature aging effects to enhance the service profitability of the battery. The strategy operates independently of probabilistic data from random processes, with its optimality gap inversely correlated with the control parameter. A systematic and intuitive approach is established for constructing virtual queues and determining the maximum feasible parameter. The proposed method is validated through extensive numerical simulations using actual lithium-ion battery aging data, along with year-long regulation signal and market price data from Pennsylvania-New Jersey-Maryland (PJM). Results show that the strategy can extend the battery lifespan by 83.41% and increase the net present value (NPV) by 172.67% compared to the conventional Ah-based strategy.

Hyperparameter Tuning of Long Short-Term Memory Model for Clickbait Classification in News Headlines

Abstract. The information available on the internet nowadays is diverse and moves very quickly. Information is becoming easier to obtain by the general public with the numerous online media outlets, including news portals that provide up-to-date information insights. Various news portals earn revenue from advertising using pay-per-click methods that encourage article writers to use clickbait techniques to attract visitors. However, the negative effects of clickbait include a decrease in journalism quality and the spread of hoaxes. This problem can be prevented by using text classification to classify clickbait in news titles. One method that can be used for text classification is a neural network. Artificial neural networks use algorithms that can independently adjust input coefficient weights. This makes this algorithm highly effective for modeling non-linear statistical data. The artificial neural network algorithm, especially the Long Short-Term Memory (LSTM), has been widely used in various natural language processing fields with satisfying results, including text classification. To improve the performance of the neural network model, adjustments can be made to the model's hyperparameters. Hyperparameters are parameters that cannot be obtained through data and must be defined before the training process. In this research, the Long Short-Term Memory (LSTM) model was used in clickbait classification in news titles. Sixteen neural network models were trained with different hyperparameter configurations for each model. Hyperparameter tuning was carried out using the random search algorithm. The dataset used was the CLICK-ID dataset published by William & Sari, 2020[1], with a total of 15,000 annotated data. The research results show that the developed LSTM model has a validation accuracy of 0.8030, higher than William & Sari's research, and a validation loss of 0.4876. Using this model, researchers were able to classify clickbait in news titles with fairly good accuracy. Purpose: The study was to develop and evaluate a LSTM model with hyperparameter tuning for clickbait classification on news headlines. The thesis also aims to compare the performance of simple LSTM and bidirectional LSTM for this task. Methods: This study uses CLICK-ID dataset and applies different text preprocessing techniques. The dataset later was used to build and train 16 LSTM models with different hyperparameters and evaluates them using validation accuracy and loss. This study uses random search for hyperparameter tuning. Result: The results of the study show that the best model for clickbait classification on news headlines is a bidirectional LSTM model with one layer, 64 units, 0.2 dropout rate, and 0.001 learning rate. This model achieves a validation accuracy of 0.8030 and a validation loss of 0.4876. The results also show that hyperparameter tuning using random search can improve the performance of the LSTM models by avoiding zero probabilities and finding the optimal values for the hyperparameters. Novelty: This study compares and analyzes the different preprocessing methods on text and the different configurations of the models to find the best model for clickbait classification on news headlines. The study also uses hyperparameter tuning to tune the model into the best model and finding the optimal values for the hyperparameters.

More of the shareable same

This paper shows the disproportionate influence Facebook exercises over Indian journalism and how it induces conformity and isomorphism in the journalistic field by nudging journalists to incessantly produce more of the same “shareable” content. It focuses on the efforts of 7 alternative news startups in South India to diversify news coverage, as gleaned through 11 in-depth interviews. These startups have a clear reformative agenda, criticizing and hoping to distinguish themselves from the mainstream media’s elite-controlled, partisan, sensationalist reporting that ignores issues affecting the marginalized. Key to these startups’ claims to be alternative is the dedicated, ground reporting of issues faced by the LGBTQ+ community, Dalits and Adivasis. As digital-only publications, they depend on Facebook to circulate their content, interact with the audience and earn revenue. Using the theoretical framework of platformization (Nieborg & Poell, 2018), this paper demonstrates how this dependence keeps startups locked in a perpetual loop of precarity, trying to placate the algorithm with shareable content to stay visible, hoping to eventually get enough subscribers to make it on their own. The constant churning of shareable content detracts organizational resources and leaves their content undistinguishable from the mainstream, postponing the realization of independence to a later date.

Understanding the role of financial capacity in the delivery of opioid use disorder treatment

Opioid treatment programs must have adequate financial capacity to sustain operations and deliver a high standard of care for individuals suffering from opioid use disorder. However, there is limited consistency in the health services literature about the concept and relationship of organizational financial capacity and key outcome measures (wait time and retention). In this study, we explored five common measures of financial capacity that can be applied to opioid treatment programs: (a) reserve ratio, (b) equity ratio, (c) markup, (d) revenue growth, and (e) earned revenue. We used these measures to compare financial capacity among 135 opioid treatment programs across four data collection points: 2011 (66 programs), 2013 (77 programs), 2015 (75 programs), and 2017 (69 programs). We examined the relationship between financial capacity and wait time and retention. Findings from the literature review show inconsistencies in the definition and application of concepts associated with financial capacity across business and social service delivery fields. The analysis shows significant differences in components of financial capacity across years. We observed an increase in average earned revenue and markup in 2017 compared to prior years. The interaction between minorities and markup was significantly associated with higher likelihood of waiting (IRR = 1.077, p < .05). Earned revenue (IRR = 0.225, p < .05) was related to shorter wait time in treatment. The interaction between minorities and equity ratio is also significantly associated with retention (IRR = 0.796, p < .05). Our study offers a baseline view of the role of financial capacity in opioid treatment and suggests a framework to determine its effect on client-centered outcomes.

Techno-environ-economic-energy-exergy-matrices performance analysis of evacuated annulus tube with modified parabolic concentrator assisted single slope solar desalination system

In the present study, a sophisticated novel design of solar desalination system with evacuated annulus tube collector augmented unique combination of modified compound parabolic concentrators has been analyzed and simulated with the help of the computational tool (MATLABR2015a) for Techno-Environ-Economic-Energy-Matrices observations under the specific meteorological conditions of New Delhi, India. The current approach emphasizes the utility of modified parabolic concentrator integrated evacuated tube that effectively improves the solar absorbing performance of the irradiated solar energy uniformly through its periphery, as well as enhancing the thermo siphons working loom appreciably than the conventional applications of vacuum tubes. The proposed system is being optimized to get the maximum possible basin water temperature as 99.5 °C for the larger water depth (0.16m) at the same orientation of still top cover and evacuated tubes (30°). The maximum circulation rate (thermo siphon) is achieved as 54.96 kg/h. The daily overall energy-exergy efficiencies are 50.8% and 3.8%, respectively. The daily yield (16.2 kg/day or 5.4 kg/m2 with respect to the solar energy collection area and its production cost (0.005 $/l) at a nominal selling price (0.07 $/l) found good. The energy-exergy based CO2 mitigates and environmental earned revenue are 131.97 tons, 67.44 tons, $1318.36, and $673.77, respectively. The establishment cost of the system is quite low at $214.92, and the system's productivity is found as 940.8%, which is more than 100% that depicts the system as appreciably feasible. The noticeable yield output at low production cost, environmental revenue credits, high mitigation, and low pay-off time makes the system compatibly sustainable and feasible with smaller and effective collector areas for the respective solar irradiations.

All Earned Revenue is Not Created Equal: Revenue Embeddedness as a Framework for Exploring Crowding-In/Crowding-Out Effects

Nonprofit organizations increasingly rely on earned revenue to sustain their mission-driven activities. Previous research examining the effects of earned revenue on other income streams tends to study earned revenue in the aggregate. Using panel data from 12,372 organizations from 2010 to 2015, this analysis applies a framework of revenue embeddedness to link earned revenue activities to mission and analyze the effects of earned revenue activities on donations. Earned revenue activities offering new products or services to existing donors appear to complement individual donations. These findings have theoretical and practical applications related to how nonprofits pursue earned revenue.

Scenario-based assessment for optimal planning of multi-carrier hub-energy system under dual uncertainties and various scheduling by considering CCUS technology

This paper provides a scenario-based assessment strategy for the optimal scheduling of a hub by considering uncertain parameters (electricity price and wind turbine output power) and carbon capture utilization and storage (CCUS) technology simultaneously. CCUS technology helps overcome pollution issues, on the one hand, and earn revenue for the power system, on the other hand. Three different planning horizons which include deterministic, stochastic, and robust models are investigated and compared. Energy hub (EH) aims to minimize the total cost and environmental pollution simultaneously while considering the effect of objective function priority. The problem is then modeled as real mixed integer programming (RMIP) and is solved in GAMS software by using CPLEX; the results are compared with other solvers. Epsilon constrains (EPC) method and fuzzy satisfying approach are used to select the optimal solution based on the presented model. In the end, a sensitivity analysis is performed. Results show that by utilizing CCUS technology, the cost and pollution for all the planning horizons are reduced properly. CCUS’s profits of about $44.6, $42.7 $, and $50.4 for deterministic, stochastic, and robust planning are obtained, respectively. Moreover, considering function priority has a significant impact on the final results.

Impact of Quality Certification on Used Merchandise Transaction Behavior in Developing Countries, Involving Retailers and Consumers

The trade-in of used merchandise globally is high, as it allows the consumer to satisfy their needs at a lesser price. As the category grows over time, the quality requirement and assurance are also increasing and supported with relevant quality certification. For understanding the quality certification in used merchandise, the paper explores the evolutionary process of the phenomena. The research attempts to create an evolutionary game model encompassing retailers and consumers trading used merchandise. Further, the model used for the analysis assumes that retailers have examined and repair the (E and R) capability of the used inventory. The model suggests that purchase and selling prices do not affect the stable evolutionary strategy (ESS); however, the cost of quality certification significantly impacts the model. The E and R capability of retailers impact the transaction as it reduces the price of used merchandise. The transaction cost and utility of the merchandise are motivating factors for the used market. Based on the research outcomes, government regulators should participate in the used merchandise transaction by providing a low-cost quality certification. The governmental authorities should intercede to offer a fair environment for both retailers and consumers and earn revenue as a minimum fee on the transaction.

Resource management in the federated cloud environment using Cournot and Bertrand competitions

Cloud federation helps cloud providers to scale up by renting resources from other providers when the workload increased. Moreover, cloud providers with idle or underutilized resources can sell their resources to others and earn revenue inside a cloud federation. One of the most critical problems in the federated cloud environment is the management of resources and cloud providers. The game theory seems to be an excellent way to model cloud federation. This paper introduces a new model for resource management between cloud providers in a centralized federated cloud environment, based on the well known Cournot and Bertrand games. To resolve the problem of heterogeneous resources, a physical resource unit which has specific computational features and can be shared between cloud providers is introduced in this paper. Besides, by introducing a new revenue-sharing approach between providers, this paper increased the collaboration of different providers. This model is implemented by the federated Cloudsim tool, and experiments show that the Cournot model outperforms others in terms of overall benefit and responsiveness. In other words, Cournot model can respond to an acceptable number of requests while making more profit. Besides, the reviews show that the proposed model works better than other methods in terms of time.

Energy sharing strategy based on call auction trading: Energy bank system

Under the pressure of climate change, renewable energy development has been assisted by subsidies or preferential policies such as feed-in-tariff. However, renewable energy cannot rely on policies as before. To stimulate the further development of renewable energy, this paper proposed an energy bank system (EBS) using a sharing economy model, i.e. a trading platform for multiple microgrids with renewable distributed generation (RDG) or hybrid energy storage devices. EBS is a similar idea with a virtual bank in the financial sector. Money is electrical energy, whereas storing energy is an analogy to depositing money, RDG becomes a kind of investment. To maximize the utilization efficiency of the RDG, participants of EBS can trade electricity with each other and earn revenues. In this paper, the figure of distributed energy charging (FDEC) is proposed for the energy management and dispatch, the call-auction method with the rule of maximum transaction volume is proposed for the energy trading and calculation. In the case studies, based on the practical data in Australia, the simulation results prove advanced economic advantages and high effectiveness of EBS.

The Effect of Trust and Service Quality to Customer Value Moderated by the Perceived Ease of Use (Case Study Bluebird Taxi in Indonesia)

Taxi is one of the means of land transportation growing and developing quite rapidly since the 90s has become one of the prima donas of alternative transportation in Indonesia. PT. Blue Bird Pusaka is a business entity that concentrates one of its business sectors in the transportation services industry sector, namely taxis. The rapid development of the business world today causes companies to face intense competition. Competition is not only between conventional taxi companies but also with online taxis. Blue Bird's revenue in 2015 still recorded a pretty good revenue growth performance. However, once entering 2016, Blue Bird's income continues to decline every quarter. In the first quarter of 2016, Blue Bird's revenue was valued at Rp1.27 trillion, down 11 percent from the fourth quarter of 2015 amounting to Rp1.43 trillion. In the following quarter, Blue Bird's revenue reached Rp1.19 trillion, down 6 percent from the first quarter of 2016. In the third quarter, the company's revenue dropped again to Rp1.17 trillion. In the fourth quarter, the company earned revenues of Rp1.15 trillion, down 2 percent from the previous quarter. In early 2017, the downward trend in Blue Bird's income continued. In the first quarter of 2017, the company's revenue dropped 10 percent by posting revenues of Rp1.03 trillion. This achievement has also been the lowest point since 2015. This study aims to examine the phenomenon of customer value, seen from the trust and quality of service and perceived ease of use as a moderating variable. This type of research is a conclusive design research that is the type of inference research that aims to test a particular hypothesis, either through in-depth research on a problem (descriptive) or looking for relationships between variables (correlative) between the independent variable and the dependent variable. The analytical method uses testing with Moderated Regression Analysis (MRA) with its analysis tool SPSS 22. The outcome of this study shows that trust has a positive effect on customer value and service quality has a positive effect on customer value. Perceive ease of use as moderating variable between trust and service quality to customer value both of them are negatively and significantly. This condition occurs because most respondents aged 35 years and over where the category of respondents are not too familiar with technology and the use of technology for them is not something that makes it easy to interact. Keywords : trust, service quality, perceived ease of use, customer value DOI: 10.7176/EJBM/12-18-09 Publication date: June 30th 2020

Test of an online tool to facilitate NCCN guideline-compliant access to cancer genetics care.

1542 Background: Minority populations experience inequities of access to cancer genetics. We developed and tested an online family history collection and interpretation tool, InheRET, to determine acceptability, validity and utility.Patients are mostly unable to recall accurate family history in clinic and providers have little time to collect the 3-generation pedigree. Thus, ~90% of high risk patients remain unidentified. We evaluated the impact InheRET has on facilitating National Comprehensive Cancer Network (NCCN) Guideline-compliant referrals for cancer genetic counseling. Methods: Patients from 3 clinics were consented online to participate. A user experience survey for patients and providers followed the health history questionnaire. Results: 628 patients were consented over a year, 555 (&gt;88%) completed the tool. 439 (79%) completed the post-questionnaire user experience. Review of Inheret's recommendations by a genetic counselor found 100% accuracy. Ease of Use: 84-87% of patients reported tool was easy to use. Understandability: 92-97% of patients reported tool was easy to understand. No significant differences were reported between those with high school (n=28, avg age 50.1 yrs) compared to those with advanced degrees (n=139, avg age 45.4 yrs); patients age 70+ experienced increased difficulties. Among primary care patients (n=135), 43 established patients were newly identified as meeting NCCN referral criteria. Healthcare providers found InheRET useful, did not require extra clinical time, and all wish to continue to use it. The patient provided data were more complete and encompassed more family members than with paper forms. Turn-around-times to receive the patient’s information were decreased from 4-6 weeks to ~ 72 hours. A patient scheduling backlog of 400 patients was cleared using InheRET. Previously, 40% of cancer genetics patients were lost to follow up, due to not completing their intake forms. This number was reduced to 6.5%. Conclusions: Patients find InheRET to be easy to use and understand and they complete this health history tool more frequently and in greater detail than by paper forms. InheRET provides accurate results, verified by in person interviews, in a timely fashion, saving clinical time, possibly enabling increase in earned clinical revenues (under analysis), and improving patient care overall. Importantly, the 43 primary care patients identified to be at increased risk were already established patients, who had not been previously identified as such by their healthcare providers as being at increased risk.

Contract-based utilization of plug-in electric vehicle batteries for day-ahead optimal operation of a smart micro-grid

Abstract The development of smart micro-grids makes the use of new and green technologies like plug-in electric vehicles more suitable. The plug-in electric vehicles can play a role in storing energy due to their network connection capability. The smart micro-grid can use this stored energy to reduce its operating cost. In this paper, a new mechanism based on the contractual agreements between the owners of plug-in electric vehicles and the smart micro-grid is proposed to provide the system's energy during the operating day. The proposed model enables plug-in electric vehicles parked in official parking to be integrated into the operation of smart micro-grid and earn revenue. The operation optimization is formulated as a two-stage stochastic mixed-integer linear problem and is implemented on energy management of a typical smart micro-grid as a case study. The simulation results confirm the effectiveness of the proposed design for both smart micro-grid and plug-in electric vehicles in the case of expected cost reduction. Also, the performance of the stochastic model for uncertainty handling is shown by the value of the stochastic solution.

Personal Information Classification on Aggregated Android Application’s Permissions

Android is offering millions of apps on Google Play-store by the application publishers. However, those publishers do have a parent organization and share information with them. Through the ‘Android permission system’, a user permits an app to access sensitive personal data. Large-scale personal data integration can reveal user identity, enabling new insights and earn revenue for the organizations. Similarly, aggregation of Android app permissions by the app owning parent organizations can also cause privacy leakage by revealing the user profile. This work classifies risky personal data by proposing a threat model on the large-scale app permission aggregation by the app publishers and associated owners. A Google-play application programming interface (API) assisted web app is developed that visualizes all the permissions an app owner can collectively gather through multiple apps released via several publishers. The work empirically validates the performance of the risk model with two case studies. The top two Korean app owners, seven publishers, 108 apps and 720 sets of permissions are studied. With reasonable accuracy, the study finds the contact number, biometric ID, address, social graph, human behavior, email, location and unique ID as frequently exposed data. Finally, the work concludes that the real-time tracking of aggregated permissions can limit the odds of user profiling.

The role of power-to-gas in the future energy system: Market and portfolio effects

Electricity systems based on renewables have an increasing demand for flexibility. This paper considers the potential of power-to-gas to provide flexibility and enhance system integration of renewables. Existing research on power-to-gas typically analyses the system effects of a predetermined power-to-gas unit without endogenising the investment decision. Moreover, insights related to market and portfolio effects of power-to-gas are rare. To this end this work presents a stochastic electricity market model. Market players considered include generating firms with different generation portfolios and different consumer groups. Firms earn revenues from an energy market, a capacity market and a feed-in premium for renewable generation. They maximise their profits by optimising the operation of existing assets and investing in new generation assets and power-to-gas. Firms with renewable generation benefit from investing in power-to-gas. While the technology itself is loss-making, power-to-gas particularly increases demand and hence prices in low-load hours. Therefore, renewable generation becomes more profitable, which justifies the investment. Metrics such as LCOE, which consider each technology in isolation, fail to capture this effect. The increase in the electricity price results in higher costs to consumers and so the overall transfer from consumers to wind generators increases in the presence of power-to-gas.

From the Halls of Congress to K Street: Government Experience and its Value for Lobbying

Lobbying presents an attractive postcongressional career, with some former congressional members and staffers transitioning to lucrative lobbying careers. Precisely why congressional experience is valued is a matter of ongoing debate. Building on research positing a relationship between political uncertainty and demand for lobbyists, we examine conditions under which lobbyists with past congressional experience prove most valuable. To assess lobbyist earnings, we develop a new measure, Lobbyist Value Added, that reflects the marginal contribution of each lobbyist on a contract, and show that previous measures understate the value of high‐performing lobbyists. We find that former staffers earn revenues above their peers during times of uncertainty, and former members of Congress generate higher revenue overall, which we identify by comparing revenues generated by individuals who narrowly won election to those who narrowly lost. These findings help characterize when lobbyists with different skillsets prove most valuable and the value added by government experience.

Revenue Embeddedness and Competing Institutional Logics: How Nonprofit Leaders Connect Earned Revenue to Mission and Organizational Identity

The increasing reliance on earned revenue displayed by nonprofits in the US has raised mission-related organizational identity concerns. However, the effect of a market-driven activity on mission-driven service may vary based on revenue embeddedness: the activity’s connection to the organization’s mission. This study draws on the competing logics of isomorphism and resource dependence to examine how the pursuit of earned revenue affects the organization’s perception of its mission and projection of identity. The authors examine how leaders use language to connect market to mission, presents additional dimensions of embeddedness, and offers propositions for future research.

Revenue characteristics of long-haul low cost carriers (LCCs) and differences to full-service network carriers (FSNCs)

Low cost carriers (LCC) have smaller cost advantages on the long- than on the short-haul compared to network carriers (FSNCs). Hence, revenue competitiveness is critical. To evaluate long-haul LCC performance, this paper contributes a new metric for bench-marking the revenue per equivalent flight capacity. Second, a revenue model combining traffic, fare, load factor, and seat data is developed. Third, the revenue per flight capacity is evaluated across selected city-pairs. Long-haul LCCs earn revenue per flight capacity comparable to FSNCs. Lower direct yields compensated by fewer low-yield connecting passengers and significantly more passengers per aircraft have been identified as key factors.

PENGARUH PAJAK DAERAH DAN RETRIBUSI DAERAH TERHADAP PENDAPATAN ASLI DAERAH (PAD)

Local revenue (PAD) is a local earned revenue collected by the regional regulation in accordance with the legislation. Sources of revenue are derived from local tax, local retribution, regional owned enterprises and local wealth management results, and other revenues. The more the local needs can be financed by PAD, the higher the quality of the local autonomy.&#x0D; This study aims to determine the influence of local tax and local retribution to local revenue of Bandung period of 2011-2015 either simultaneously or partially.&#x0D; The population in this study is a Realized Regional Government Budget Report of Bandung in 2011-2015. Sample selection technique used is judgment sampling and obtained 60 samples, consisting of January to December 2011-2015. The data analysis method was multiple regression analysis.&#x0D; The results show that there is simultaneous significant difference between local tax and local retribution to local revenue. Partially, local tax has significant positive effect on PAD, while the local retribution has no significant effect on PAD