Usage Consumption Research Articles

Digital Echoes of Discrimination: Investigating the Reception of Hate Speech Against Korean Chinese in Online Communities

abstract: This study aims to investigate the circulation, consumption, and learning of anonymous users’ hatred in online communities, focusing on the reception process of hate speech targeting Korean Chinese people, a group long discriminated against by native Koreans and the media. It identifies critical factors shaping attitudes toward Korean Chinese (i.e., Chaoxianzu), defining hate as long-term sentiments resulting from repeated immediate emotions triggered by specific incidents and social identities. This study adopts a grounded theory approach, employing interviews to construct a paradigm model that captures the authentic perspectives of online community users. Following the coding process, 17 categories and 38 subcategories emerged. Online audiences consolidate or maintain their attitudes through admission, resistance, exploration, and reproduction after exposure to hate expressions toward Korean Chinese. Self-intervention, personal experience, quality of verbal stimulation, and patriotism function as interventional conditions or environmental contexts throughout this process.

Life Cycle Assessment Framework for Diagnostic Imaging

The increasing focus on environmental sustainability is becoming essential in the diagnostic imaging sector, which is accredited for about 10% of the healthcare industry’s carbon footprint. A multitude of research initiatives investigated the environmental impacts of diagnostic imaging, encompassing factors like electricity consumption, carbon emissions, and waste generation from these procedures. Life Cycle Assessment (LCA) stands as a prominent method for structural assessment of environmental impacts, offering a detailed framework for examining the environmental consequences of specific processes. The aim of this study includes analyzing existing LCA approaches in literature to identify their limitations and to suggest an elaborate framework for LCA application in diagnostic imaging.Out of 17 original articles on environmental sustainability in radiology published since 2014, but only a part, 29.4% (5/17), described an LCA approach. The different characteristics of these studies provide valuable insights, enabling the proposal of a more comprehensive LCA research methodology.The reviewed articles did not present a uniform research outcome. According to the GreenHouse Gas (GHG) Protocol Corporate Standard, the optimal outcome for assessing environmental impact is the calculation of greenhouse gas emissions. For a thorough methodological approach in LCA, it is essential to cover all direct and indirect emissions associated with diagnostic imaging. All studies (100%, 5/5) considered the electricity consumption of imaging equipment. Usage of consumables was included in 80% (4/5) of the studies. Only 40% (2/5) of articles considered auxiliary equipment like computers and contrast-medium injectors, as well as heating, ventilation and air conditioning (HVAC) systems. Factors like equipment manufacture, staff travel, and waste generation, though crucial to overall greenhouse gas emissions, were each covered in only 20% (1/5) of the studies. The articles also varied in their LCA versions, with two employing the detailed Cradle-to-Grave approach, while others used partial Cradle-to-Gate and Input-Output LCA methods.The insights from this analysis could lead to a valuable framework for a new LCA methodological approach in diagnostic imaging. This novel approach is designed to overcome the limitations observed in existing research, offering a more comprehensive analysis. By enhancing the LCA framework, it will be possible identify the phases in diagnostic imaging that have the most substantial environmental impact, allowing for the development of more targeted strategies to reduce GHG emissions associated with diagnostic procedures.

The COVID-19 pandemic's impact on user consumption: A shift towards very efficient load forecasting

The COVID-19 pandemic has prompted significant shifts in energy consumption patterns, necessitating a departure from traditional data analysis methods. Accurate energy forecasting models play a crucial role in various domains of planning and energy management. It is possible to predict the demand for electricity consumption using a variety of techniques. Fundamentally, this research comprises two main components. The first phase assesses the influence of the COVID-19 pandemic on user consumption patterns through time series analysis. The subsequent segment is dedicated to long-term load forecasting, employing both statistical methods termed as Holt-Winters and Prophet algorithms and an Artificial Neural Network approach known as Long Short-Term Memory. These approaches aim to predict electricity demand amid the diverse and intricate context of the COVID-19 pandemic. It utilizes both conventional time series forecasting methods and advanced Artificial Intelligence models to enhance the accuracy of load forecasting. Since rapid societal and economic shifts are unpredictable during a pandemic, methods based on Artificial Intelligence are becoming extremely important due to its high precision. Though, it is problematic to conduct load prediction with high accuracy because of influencing factors like climatic, socioeconomic, and seasonal aspects. Finally, using hourly-driven power consumption data from Houston, Texas, USA, the Long Short-Term Memory model outperforms the Holt-Winters and Prophet models in a test of generalizability and accuracy. It is possible to verify the accuracy of experimental findings by analyzing the resulting constraints and influential factors.

The Influence of Online Social and Physical Presence on User Consumption Decisions in TikTok Livestreaming: A Scoping Review.

This scoping review aimed to clarify and redefine the concepts of social and physical presence in the context of online livestreaming environments. Physical presence involves technical elements and nonhuman-to-human interaction, factors that inevitably influence social presence, which has human-to-human interactions at its core. Considering one type of presence to the exclusion of the other may not provide sufficiently informed decisions for user consumption. However, most previous studies have only studied either physical or social presence factors; few have systematically examined both to explain their influence. This review examined how the factors under these two presence influenced users' consumption decision-making process in TikTok live-stream retail purchases by synthesizing 60 studies conducted from 2019 to 2023 using Arksey and O'Malley's 5-step framework. Evidence of the specific attributes by which presence affects users' consumption decisions was elicited and reorganized. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework for scoping review and guided by the joanna briggs institute (JBI) methodological guidelines, the results reveal that out of the 60 studies, 36 were influenced by social presence and 24 by physical presence. When livestreaming online, social presence tends to include the user and anchor perspectives, which prompts users to make consumption decisions. However, online physical presence includes products, technology, and scenes as its main dimensions, and users make consumption decisions through perceptual control. This review clarifies new media livestreaming communication and the key factors influencing users' consumption decision-making systems. It also suggests that integrating online social and physical presence in future research will yield a better understanding of livestreaming purchase decisions.

Analysis and mining of user behavior on e-commerce platforms based on decision trees and RFM models

User transaction behavior has driven the development of e-commerce. This article reveals the key role of the development of the e-commerce industry and user behavior in driving it through in-depth research on e-commerce sales data. Firstly, by grouping and summarizing user IDs through Pandas, a comprehensive user consumption situation was obtained, including consumption frequency, amount, etc., and user consumption behavior was visualized through visual means. On the basis of constructing effective features, Matplotlib was used to depict the number of users who receive coupons every week and day, providing a basis for the establishment of subsequent coupon distribution prediction models. By splitting the payment dates, an RFM model was constructed to analyze the purchase time period, frequency, consumption amount, and the proportion of each user's time behavior, forming a detailed user profile. Finally, the decision tree model is applied to predict the probability of coupon usage.

Analisis Behavioral Intention Mobile Banking dengan Model UTAUT2

The development of information technology has been very developed at this time, people have started to study and use technology, one of which is mobile banking. Mobile banking allows customers to complete financial transactions electronically without having to interact directly with bank employees. How to use mobile banking can be influenced by several supporting factors. The purpose of this research is to examine and analyze the influence of consumer usage behavior intentions on usage behavior using the UTAUT2 model. Using a purposive sampling method, this study used 145 mobile banking users and distributed online questionnaires from June to July 2023. By applying PLS-SEM as an intervention tool, this study found that performance expectancy, effort expectancy, social influence, facilitating conditions and price values ​​have no effect on behavioral intention, while hedonic motivation and habit have a positive effect. Then behavioral intention has no influence on use behavior.

Revolutionizing the cultural industry: The transformative power of data processing algorithms

This paper provides a comprehensive analysis of the impact of data processing algorithms on the cultural industry, focusing on user behavior analysis, dynamic content adaptation, content discovery, efficient distribution networks, and driving creative innovation. Through the application of sophisticated machine learning models and quantitative analysis, this study demonstrates the transformative potential of algorithms in enhancing user engagement, optimizing distribution channels, and fostering creative innovation. By analyzing vast datasets from leading cultural content platforms, social media, and collaborative creation platforms, the paper reveals significant improvements in user consumption rates, engagement metrics, global market penetration, and the development of novel content formats. The findings underscore the importance of algorithmic insights in shaping the future of the cultural industry, offering personalized experiences, streamlining distribution, and expanding creative horizons.

Accessible Autonomous Transportation and Services: Design Considerations From the Perspective of Consumers and Providers

ObjectiveTo understand the priorities and preferences of people with disabilities (PwDs) and older adults regarding accessible autonomous vehicles (AVs) to address existing transportation barriers. DesignTwo national surveys, Voice of the Consumer and Voice of the Provider, were conducted to gather feedback from accessible AV consumers and providers, respectively, in the United States. SettingThis U.S.-based study focused on PwDs and older adults who may face transportation challenges and those who provide or design AV solutions. ParticipantsThe 922 consumers and 45 providers in the surveys encompassed a diverse range of disability types, caregiver roles, and age groups (N = 967). InterventionsNot applicable. Main Outcome MeasuresThe main outcomes were consumer usage needs and provider preferences for features in accessible autonomous transportation. Patterns in usage needs and feature preferences through 2-step clustering algorithm were applied subsequent to the descriptive analysis of participant demographics and their responses. ResultsParticipants strongly preferred AV features enhancing personal transportation, especially for rural medical appointments. Most sought comprehensive AV automated features. Wheelchair users emphasized accessible entrances, particularly for lower-income brackets ($25,000-$49,000). Provider priorities closely aligned with consumer preferences, reinforcing content validity. ConclusionsThe study highlights the importance of prioritizing wheelchair accessibility in AVs and improving access to medical appointments, especially in rural and low-income communities. Implications include developing inclusive AV services for PwDs and underserved populations. The research establishes a foundation for a more equitable and accessible transportation landscape through AV technology integration.

Application of form honeypot based on CSS style in ticket booking system

Since various means of transportation and entertainment activities are restricted by objective conditions, the number of tickets is always limited, so ticket scalpers continue to be a companion product to this day. In the current Internet environment, the web application of the ticketing system faces challenges from ticket-grabbing robots controlled by ticket dealers. The current security procedures have significantly increased the workload and time consumption of effective users. The question now is whether ticket-grabbing bots can be stopped without increasing the workload of legitimate users and being as unnoticed by users as possible. In response to this problem, this article proposes a ticket booking system model integrated with a form-based honeypot, which can achieve the function of deceiving robots without being noticed by ordinary users. This model provides a feasible method for implementing network applications that are both secure and efficient, ensuring that users have a more seamless and protected experience.

Environmentally sustainable design guide for haemodialysis facilities: An Australian and New Zealand society of Nephrology initiative.

Haemodialysis facilities have a large environmental impact due to high energy, water and consumable usage by haemodialysis equipment. As climate change and natural resource scarcity escalate, all the while the number of people requiring dialysis increases, there is an urgent need for dialysis facilities that meet care needs while minimising environmental impact. To address this, the Australian and New Zealand Society of Nephrology engaged an environmental sustainability consulting practise to develop a best practise guide for the environmentally sustainable design and operation of haemodialysis facilities. Four opportunity areas were considered, namely energy, water, waste and resource recovery, and additional sustainability. A total of 28 environmental improvement initiatives were identified. The majority (n = 23) were general measures that could be applied across all healthcare settings, while five were specific to haemodialysis facilities. Recommendations were made regarding specific measures that should be undertaken and/or standards that must be met to achieve the intent of each initiative. These were stratified to enable their application to both existing dialysis facilities and new builds. The lifecycle stage of a haemodialysis facility to which each initiative applied was highlighted, as was its potential impact. This guide provides a tailored and comprehensive resource for the kidney care community to enable the integration of best practise sustainability considerations into both existing and new facilities. If broadly implemented, it has the potential to markedly improve the environmental impact of haemodialysis provision.

RFM user value tags and XGBoost algorithm for analyzing electricity customer demand data

With the increasing demand for electricity, predicting user electricity demand has become an essential task. The electricity demand characteristics of users in the electricity market are different, so it is necessary to classify and predict users. Aiming at the above problems, a classified forecasting model of electricity demand based on recent consumption, frequency, monetary (RFM), K-means, XGBoost and dynamic time warping (DTW) algorithm is proposed. The experiment showcases that among the electricity consumption of commercial users, the first type of load has the lowest proportion in autumn, at around 18.6 %; The second type of load has the highest proportion in autumn, about 81.3 %; Accurate classification has been made for the consuming quantity of electricity of commercial users. The average error in the forecasting results of the RFM-KM-XGboost model and the actual value of commercial electricity demand is about 0.07 kW; The average errors between the forecasting results of SVM model and RF model and the true values are about 0.2 kW and 0.14 kW, respectively; It indicates that the forecasting error of the RFM-KM-XGBoost model is smaller. The above results indicate that the RFM-KM-XGBoost model can extract users' electricity demand characteristics by classifying user types and load types, and make more accurate predictions of electricity demand for different types of users.

Study on the evaluation model of the contribution of power industry expansion considering the influencing factors of new energy consumption

In the application of new energy consumption system engineering, in order to evaluate the contribution of electric power industry expansion, an evaluation model of electric power industry expansion contribution considering the influencing factors of new energy consumption is constructed. In the process of power industry expansion, the growth of new energy installed capacity, power system regulation ability, power grid interconnection and electricity demand are the core factors that affect the change of power contribution to power industry expansion. Using the characteristic extraction method of power consumption behavior of users with industrial expansion, after extracting two characteristics, namely, the utilization hours of user’s industrial expansion capacity and the proportion of new energy load put into operation under the change of four major factors, the monthly industrial expansion power consumption of typical users is predicted by the monthly industrial expansion power consumption forecasting method of users considering industrial expansion capacity, and then the growth curve of user’s industrial expansion power consumption is drawn. Based on the forecast method of monthly industry expansion electricity generated by industry expansion quantity, the industry expansion quantity of typical individual users is calculated, and the industry expansion quantity is input into RBF network model trained by particle swarm optimization algorithm to complete the forecast of monthly industry expansion electricity; Finally, the contribution ratio of each influencing factor is calculated, and the evaluation of power industry expansion contribution considering the influencing factors of new energy consumption is completed. After testing, this model can be used as an available model for evaluating the contribution of electric power industry under the condition of considering the influencing factors of new energy consumption.

Sun Stats: An IoT Node MCU Integrated Web Application with Real-Time Energy Monitoring and Cost Forecasting

Rising global concerns about energy consumption and its environmental impact have fueled interest in renewable and sustainable energy sources, with solar energy emerging as a promising solution. In response to climate change challenges and the depletion of finite fossil fuel resources, there is a pressing need for innovative energy management solutions. This paper introduces "SunStats," an IoT NodeMCU integrated web application designed for real-time energy monitoring and cost (consumption) forecasting.
 SunStats combines weather data and cost forecasting to provide tailored capabilities for local residential users. Integrating smart technologies and data-driven approaches, it aims to empower consumers with valuable insights into their energy consumption patterns.
 It has a real-time energy monitoring feature, facilitated by the use of NodeMCU, allowing users to continuously track their energy consumption patterns. The user-friendly interface enables consumers to access real-time data on their electricity usage, facilitating informed decisions about energy management. Additionally, the application's integration of weather data offers contextual insights into how weather conditions influence energy usage, empowering users to adjust their consumption behavior accordingly.
 Beyond real-time energy monitoring and weather integration, SunStats incorporates cost forecasting. Utilizing historical energy usage patterns and weather data, the application predicts future energy consumption for users. This forecasting feature provides consumers with valuable information about potential fluctuations in energy consumption, enabling effective planning and informed decision-making.
 The primary goal of SunStats is to empower users to become proactive energy managers, offering transparency into real-time energy data, weather insights, and cost forecasts. This knowledge enables users to implement energy-saving strategies, optimize efficiency, and contribute to a more sustainable future.

Determination of per- and polyfluoroalkyl compounds in paper recycling grades using ultra-high-performance liquid chromatography–high-resolution mass spectrometry

Globally, per- and polyfluoroalkyl substances (PFAS)–related research on paper products has focused on food packaging with less consideration on the presence of PFAS at different stages of the paper recycling chain. This study analysed the prevalence of PFAS in paper grades used for the manufacture of recycled paperboard. The presence of PFAS was attributed to the use of PFAS-containing additives, consumer usage, exposure to packed goods as well as contamination during mingling, sorting, collection, and recovery of paper recycling material. Q Orbitrap mass spectrometry was used to analyse the paper samples after accelerated solvent extraction and solid phase extraction. The distribution and possible propagation of 22 PFAS were determined in pre-consumer, retail and post-consumer paper products. Post-consumer samples had the highest combined average concentration (ΣPFAS) at 213 ng/g, while the ΣPFAS in retail (159 ng/g) and pre-consumer samples (121 ng/g) was detected at lower concentrations. This study showed that waste collection and recycling protocols may influence PFAS propagation and that measures must be developed to minimise and possibly eliminate exposure opportunities.

Political economy of music in the digital age

This paper seeks to unravel and understand the conditions and ways in which cultural capitalism operates that drive its production, exchange, distribution and consumption in the digital age; as well as understanding the powers that hold it and those it serves, the circuits in which it is heard, the spaces in which it is prohibited, its context and historical transformations. Music as an artistic production and symbolic object has always been linked to its characteristic instruments and technologies of its time. But when the economy catches up with it and it becomes merchandise, other means and channels are put into play for its dissemination, storage and control. It seeks to demonstrate those stealthy operations with which intermediaries operate. An analysis is made regarding the logic of consumption and production of users, on power relations and asymmetries between some actors in the music industry.

A Study on the Shopping Life through Mobile Visual Search

Purpose - To examine the influence of mobile visual search as a strategic technology service on consumer perceived economic value and customer commitments, which in turn affect consumer's usage intention of mobile visual search. This study also explores the moderating effect of different levels of consumer online shopping orientation. Design/methodology/approach - One-by-one open-ended in-depth interview was first undertaken to 15 Korean consumers to figure the features of mobile visual search. Then a conceptual model was built to verify the hypotheses that indicate the impact of mobile visual search on consumer perceived economic value and customer commitment, which further influence consumer’s usage intention. Findings - The results show Convenience, Information quality, Personalization, Text-free search interface design and Visual communication of mobile visual search positively influence consumer perceived economic value and customer commitment and in turn positively affect consumer's usage intention. Moreover, the different levels of consumer online shopping orientation also found to have different effects on consumers’ perception and behavior of using mobile visual search in online fashion shopping. Research implications or Originality - The present study verified that mobile visual search is a service tool that consumers want to use in the online fashion shopping journey since it provides economic benefits.

Backdoor Adjustment via Group Adaptation for Debiased Coupon Recommendations

Accurate prediction of coupon usage is crucial for promoting user consumption through targeted coupon recommendations. However, in real-world coupon recommendations, the coupon allocation process is not solely determined by the model trained with the history interaction data but is also interfered with by marketing tactics desired to fulfill specific commercial goals.This interference creates an imbalance in the interactions, which causes the data to deviate from the user's natural preferences. We refer to this deviation as the matching bias. Such biased interaction data affects the efficacy of the model, and thus it is necessary to employ debiasing techniques to prevent any negative impact. We investigate the mitigation of matching bias in coupon recommendations from a causal-effect perspective. By treating the attributes of users and coupons associated with marketing tactics as confounders, we find the confounders open the backdoor path between user-coupon matching and the conversion, which introduces spurious correlation. To remove the bad effect, we propose a novel training paradigm named Backdoor Adjustment via Group Adaptation (BAGA) for debiased coupon recommendations, which performs intervened training and inference, i.e., separately modeling each user-coupon group pair. However, modeling all possible group pairs greatly increases the computational complexity and cost. To address the efficiency challenge, we further present a simple but effective dual-tower multi-task framework and leverage the Customized Gate Control (CGC) model architecture, which separately models each user and coupon group with a separate expert module. We instantiate BAGA on five representative models: FM, DNN, NCF, MASKNET, and DEEPFM, and conduct comprehensive offline and online experiments to demonstrate the efficacy of our proposed paradigm.

Demand response model: A cooperative-competitive multi-agent reinforcement learning approach

This study introduces a novel Demand Response (DR) Model based on Multi-agent Reinforcement Learning (MARL-DR), comprised of a pricing and incentives scheme aimed at improving the accuracy of existing demand response strategies. Furthermore, this new approach represents a flexibility solution to prevent sharp price variations caused by the high penetration of unconventional renewable energy, which are directly passed on to end-users. The model employs a cooperative-competitive MARL-DR technique based on Q-learning, with the goal of determining optimal prices and incentives that maximize benefits for both customers and electric Service Provider (SP). In this regard, the model has the capability to offer customers pricing options in both real-time and time-of-use, to actively adjust each user's consumption. Additionally, through demand characterization factors, such as the coincidence factor (CF), the of typical user behavior is improved, and the influence of individual user demand on system peak demand is more accurately detected. It is also demonstrated that the cooperative-competitive approach offers better performance compared to other approaches. Finally, a sensitivity analysis is presented at various stages of the model to verify its accuracy and efficiency in pricing and incentives formulation.

Factors Influencing Electricity Consumption in Rural Households

Over time, several relationships have been defined between electricity consumption and a region’s social and economic variables, with income as the main factor. This paper uses multiple correspondence analysis to identify the categories of dwellings and, from a graphical point of view (positioning maps), the effects of the different characteristics that influence the electricity consumption of households in rural areas of Cundinamarca, Colombia. In this analysis, the consumption of residential users responded mainly to what they can afford or acquire based on their income, consumption habits, and the characteristics of the technology. Furthermore, this study highlights the implications of these findings for policymakers and energy providers, providing valuable insights for developing targeted strategies to promote energy efficiency and sustainability in rural areas. This research contributes to a deeper understanding of the dynamics of electricity consumption and highlights the importance of tailoring energy-related interventions to the specific socio-economic context of rural communities, in this case in Cundinamarca.

Deep Learning and User Consumption Trends Classification and Analysis Based on Shopping Behavior

Driven by the wave of digitalization, the booming development of the e-commerce industry urgently requires in-depth analysis of user shopping behavior to improve service experience. In view of the limitations of traditional models in dealing with complex shopping scenarios, this study innovatively proposes a deep learning model: the VATA model (a combination of variational autoencoder, transformer, and attention mechanism). Through this model, the authors strive to classify and analyze user shopping behavior more accurately and intelligently. Variational autoencoder (VAE) can learn the potential representation of user personalized historical data, capture the implicit characteristics of shopping behavior, and improve the ability to deal with actual shopping situations. Transformer models can more comprehensively capture the dependencies between shopping behaviors and understand shopping. The overall structure of behavior plays an important role.