Specific User Research Articles

Optimizing mining projects: how business intelligence enhances data-driven decisions

The project management sector grapples with the challenge of handling a vast amount of information. Data compilation is essential to support effective decision-making processes by project managers. This paper emphasizes the significance of both project planning and execution monitoring. It concurrently explores the applicability of Business Intelligence (BI) software and its contributions to enhancing project management. The methodology employed a literature review combined with the authors' expertise in engineering projects. The findings reveal that BI offers substantial benefits. These include reducing the time required to transform large datasets into clear and concise graphical information that can be readily presented and dynamically adjusted to meet specific user needs. A study conducted in a mining company demonstrated a significant reduction in project management activities. By utilizing Power BI software, they achieved a 50% reduction in total time dedicated to project management activities. Additionally, there was a remarkable 83% reduction in the time spent on report issuance and updates. This outcome enables that project team members are aligned with consolidated information, facilitating real-time decision-making and fostering greater integration and productivity.

Evaluating the real-world usability of BCI control systems with augmented reality: a user study protocol.

Brain-computer interfaces (BCI) enable users to control devices through their brain activity. Motor imagery (MI), the neural activity resulting from an individual imagining performing a movement, is a common control paradigm. This study introduces a user-centric evaluation protocol for assessing the performance and user experience of an MI-based BCI control system utilizing augmented reality. Augmented reality is employed to enhance user interaction by displaying environment-aware actions, and guiding users on the necessary imagined movements for specific device commands. One of the major gaps in existing research is the lack of comprehensive evaluation methodologies, particularly in real-world conditions. To address this gap, our protocol combines quantitative and qualitative assessments across three phases. In the initial phase, the BCI prototype's technical robustness is validated. Subsequently, the second phase involves a performance assessment of the control system. The third phase introduces a comparative analysis between the prototype and an alternative approach, incorporating detailed user experience evaluations through questionnaires and comparisons with non-BCI control methods. Participants engage in various tasks, such as object sorting, picking and placing, and playing a board game using the BCI control system. The evaluation procedure is designed for versatility, intending applicability beyond the specific use case presented. Its adaptability enables easy customization to meet the specific user requirements of the investigated BCI control application. This user-centric evaluation protocol offers a comprehensive framework for iterative improvements to the BCI prototype, ensuring technical validation, performance assessment, and user experience evaluation in a systematic and user-focused manner.

Combining Machine Learning techniques and Genetic Algorithm for predicting run times of High Performance Computing jobs

This study proposes a novel approach combining Machine Learning (ML) techniques and Genetic Algorithms (GA) for predicting High-Performance Computing (HPC) job run times. The objective is to create a prediction method universally applicable to any HPC system, irrespective of workload characteristics, application specific parameters, user behavior, or hardware architecture. Since user-supplied run time estimates are often inaccurate, we aim to categorize job runtimes into several classes, allowing users to select appropriate classes for their jobs. A Genetic Algorithm is developed to optimally define these runtime classes, determining both the number of classes and the time intervals represented by them. Four Machine Learning algorithms (K Nearest Neighbours, Support Vector Regression, Extreme Gradient Boosting and Deep Neural Networks) are implemented for run time prediction. A unique set of features extracted from historical job data serves as input to the Machine Learning models. The generalized nature of our method is demonstrated by validating its performance on data from six clusters with distinct configurations, applications and runtime distributions. Our results illustrate the superior performance of Machine Learning models incorporating GA-defined runtime classes for all datasets. Across all six datasets, our method achieves R2 scores exceeding 0.8, and accuracy greater than 0.7.

Improving Task-Agnostic Energy Shaping Control of Powered Exoskeletons with Task/Gait Classification.

Emerging task-agnostic control methods offer a promising avenue for versatile assistance in powered exoskeletons without explicit task detection, but typically come with a performance trade-off for specific tasks and/or users. One such approach employs data-driven optimization of an energy shaping controller to provide naturalistic assistance across essential daily tasks with passivity/stability guarantees. This study introduces a novel control method that merges energy shaping with a machine learning-based classifier to deliver optimal support accommodating diverse individual tasks and users. The classifier detects transitions between multiple tasks and gait patterns in order to employ a more optimal, task-agnostic controller based on the weighted sum of multiple optimized energy-shaping controllers. To demonstrate the efficacy of this integrated control strategy, an in-silico assessment is conducted over a range of gait patterns and tasks, including incline walking, stairs ascent/descent, and stand-to-sit transitions. The proposed method surpasses benchmark approaches in 5-fold cross-validation ( ), yielding 93.17 ± 7.39% cosine similarity and 77.92 ± 19.76% variance-accounted-for across tasks and users. These findings highlight the control approach's adaptability in aligning with human joint moments across various tasks.

Accounting for a capillary pressure jump in a saturated porous medium for a more correct calculation of hydrocarbon reserves

Relevance. The correct calculation of hydrocarbon reserves in various fields (oil, gas, gas condensate) is an important state task, because it allows you to properly organize field development in the future and ensure the rational use of natural resources of the state. In particular, the values of geological and recoverable reserves are assigned to a specific subsurface user and are recorded in departmental documents. Aim. To describe the effect associated with the calculations of the thermodynamic equilibrium of the mixture of hydrocarbons of the Karachaganak oil and gas condensate field at various formation depths with the capillary pressure jump taken into account. This makes it possible to clarify the value of the potential condensate-gas factor of reservoir gas and, as a result, to give a more accurate assessment of the geological reserves of hydrocarbon raw materials. Object. Analysis of the thermodynamic equilibrium of the hydrocarbon mixture of Karachaganak oil and gas condensate field, taking into account the capillary pressure jump, which takes place in a porous medium under reservoir conditions. Methods. Numerical modeling, analytical research. Results. Based on the previously developed methodology for calculating the phase equilibrium with the capillary pressure jump the correction of the potential condensate-gas factor of the formation gas of the Karachaganak oil and gas condensate field was carried out at various formation depths. The range of values of the condensate-gas factor difference for calculations both with and without capillary pressure jump was from 7,04 g/m3, with a condensate-gas factor value equal to 393 g/m3 for formation depth of 4000 m, to 64,47 g/m3, with a condensate-gas factor value equal to 547 g/m3 for formation depth of 4600 m. Based on the updated condensate-gas factor estimate, it is possible to clarify the condensate recovery coefficient during the development of a field without maintaining reservoir pressure or with partial maintaining reservoir pressure.

Cryptocurrency’s emergence in North Cyprus: a mixed-methods exploration of attitudes and usage trends of an emerging payment method

PurposeThe purpose of this study is to investigate offline retailers’ cryptocurrency adoption through the unified theory of acceptance and use of technology (UTAUT/2), technology acceptance model (TAM) and other related variables.Design/methodology/approachA mixed-method (QUANT-Qual) was employed. The study employed a quantitative approach (partial least squares-structural equation modeling) for Study 1 and a qualitative focus group discussion for Study 2. The qualitative segment enhances the comprehension and interpretation of outcomes derived from the quantitative analysis.FindingsStudy 1 identified six crucial factors in cryptocurrency adoption: trust, personal innovation, hedonic motivation, social influence, safety and a positive attitude. Perceived ease of use, price value and satisfaction were non-significant. Study 2 elaborates on the findings, and highlights the reasons for the insignificant factors.Research limitations/implicationsThis research contributes to theoretical knowledge by empirically testing established technology acceptance theories like UTAUT/2 and TAM in the context of retail cryptocurrency adoption. It introduces previously unexplored factors, such as personal innovativeness, hedonic motivation and safety/reliability, enriching the theoretical landscape.Practical implicationsThis study provides practical insights for cryptocurrency stakeholders, including government, regulators, consumers, businesses and the financial sector, regarding retailers' intentions to adopt cryptocurrencies in North Cyprus.Social implicationsThis study can enhance understanding of factors facilitating cryptocurrency integration into mainstream retail, potentially reshaping traditional finance. Economically, adoption can boost financial inclusion, offering efficient transaction methods. This transition may disrupt existing economic models, drive payment technology innovation and change consumer behavior toward digital assets.Originality/valueThis study stands out by using a mixed-method approach to comprehensively examine cryptocurrency adoption by offline retailers, considering various factors and providing a holistic perspective, unlike previous research that often focused on specific user groups.

Efficient Clothing Fashion Prediction using Machine Learning

Fashion trend forecasting is a crucial task for both academia and industry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real fashion trends. Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from social media with extracted time series fashion element records and user information. Furthermore, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Machine Learning which takes advantage of the capability in modeling time series data. Moreover, it leverages internal and external knowledge in fashion domain that affects the time-series patterns of fashion element trends. Such incorporation of domain knowledge further enhances the deep learning model in capturing the patterns of specific fashion elements and predicting the future trends. Extensive experiments demonstrate that the proposed ML model can effectively capture the complicated patterns of objective fashion elements, therefore making preferable fashion trend forecast.

Implementation and early outcomes with Pathogen Reduced Cryoprecipitated Fibrinogen Complex.

Cryoprecipitated antihemophilic factor (cryo) has been used for fibrinogen replacement in actively bleeding patients, dysfibrinogenemia, and hypofibrinogenemia. Cryo has a shelf life of 4 to 6 hours after thawing and a long turnaround time in issuing the product, posing a major limitation of its use. Recently, the US Food and Drug Administration approved Pathogen Reduced Cryoprecipitated Fibrinogen Complex (INTERCEPT Fibrinogen Complex [IFC]) for the treatment of bleeding associated with fibrinogen deficiency, which can be stored at room temperature and has a shelf life of 5 days after thawing. We identified locations and specific end users with high cryoprecipitate utilization and waste. We partnered with our blood supplier to use IFC in these locations. We analyzed waste and turnaround time before and after implementation. Operative locations had a waste rate that exceeded nonoperative locations (16.7% vs 3%) and were targeted for IFC implementation. IFC was added to our inventory to replace all cryo orders from adult operating rooms, and waste decreased to 2.2% in these locations. Overall waste of cryoprecipitated products across all locations was reduced from 8.8% to 2.4%. The turnaround time for cryoprecipitated products was reduced by 58% from 30.4 minutes to 14.6 minutes. There has been a substantial decrease in waste with improved turnaround time after IFC implementation. This has improved blood bank logistics, improved efficiency of patient care, and reduced costly waste.

A Proposal for a Mobility-Control Data Transfer Mechanism Based on a Block Network Utilizing End-to-End Authentication Data

Advances in communications technology have had tremendous influence on network implementation to date, and as communication devices get smaller and technologies more intelligent, a great number of solutions are emerging that converge communications with a multitude of domains. In particular, as transportation modes assisting with mobility have developed, many studies are in the pipeline on the domain of mobility. The development of mobility services has prompted the spread of car sharing, which also highlights the need for a system that controls shared vehicles. The data of such control system can be used not only for the operational management of vehicles but also the management of users in terms of user driving analysis or monitoring safe driving. In addition, it can be utilized in a variety of other applications that analyze traffic flow based on vehicle driving data, determine required vehicle maintenance intervals, or schedule vehicle maintenance in advance. However, if forged/altered by a third party, control data might pose a lot of challenges to data utilization. This study proposes a mechanism that manages control data on a blockchain to enhance the integrity of control data created in the communications process. In this paper, we propose a mechanism for applying a blockchain to the communication process between clients and servers. In the proposed mechanism, platform operators generate and provide independent identification information and identification numbers for users. The identification information and identification numbers are used to prove that the generated blocks were created by specific users. Data generated by clients create a block with a hash including user identification information alongside the user identification number. Verification of the generated block is performed on the platform vendor’s server, validating that the user identification number in the block matches the server-side user identification information and the hash of the data within the block. Thus, even when the contents of the block are shared in a public blockchain environment, user identification information is only held by the server and clients, preventing third parties from impersonating specific users to create data blocks. Furthermore, the verification process of the data includes hashes received from other nearby vehicles, preventing data tampering by the block creator.

Fostering Artificial Intelligence-based supports for informal caregivers: a systematic review of the literature

Informal or unpaid caregivers, commonly known as family caregivers, are responsible for providing the 80% of long-term care in Europe, which constitutes a significant portion of health and social care services offered to elderly or disabled individuals. However, the demand for informal care among the elderly is expected to outnumber available supply by 2060. The increasing decline in the caregiver-to-patient ratio is expected to lead to a substantial expansion in the integration of intelligent assistance within general care. The aim of this systematic review was to thoroughly investigate the most recent advancements in AI-enabled technologies, as well as those encompassed within the broader category of assistive technology (AT), which are designed with the primary or secondary goal to assist informal carers. The review sought to identify the specific needs that these technologies fulfill in the caregiver’s activities related to the care of older individuals, the identification of caregivers’ needs domains that are currently neglected by the existing AI-supporting technologies and ATs, as well as shedding light on the informal caregiver groups that are primarily targeted by those currently available. Three databases (Scopus, IEEE Xplore, ACM Digital Libraries) were searched. The search yielded 1002 articles, with 24 articles that met the inclusion and exclusion criteria. Our results showed that AI-powered technologies significantly facilitate ambient assisted living (AAL) applications, wherein the integration of home sensors serves to improve remote monitoring for informal caregivers. Additionally, AI solutions contribute to improve care coordination between formal and informal caregivers, that could lead to advanced telehealth assistance. However, limited research on assistive technologies like robots and mHealth apps suggests further exploration. Future AI-based solutions and assistive technologies (ATs) may benefit from a more targeted approach to appeasing specific user groups based on their informal care type. Potential areas for future research also include the integration of novel methodological approaches to improve the screening process of conventional systematic reviews through the automation of tasks using AI-powered technologies based on active learning approach.

The performance of digital technologies for measuring tuberculosis medication adherence: a systematic review

IntroductionDigital adherence technologies (DATs), such as phone-based technologies and digital pillboxes, can provide more person-centric approaches to support tuberculosis (TB) treatment. However, there are varying estimates of their performance for...

Enhancing Human-Computer Interaction in Socially Inclusive Contexts: Flow Heuristics and AI Systems in Compliance with DIN EN ISO 9241 Standards

In the domain of generative Artificial Intelligence (AI), the deployment of user-centered Human-Computer Interaction (HCI) methodologies, compliant with the DIN EN ISO 9241 standard, furnishes a substantive methodological scaffold. These methodologies have demonstrably succeeded in accomplishing specific, contextual objectives across a spectrum of applications, affirming their efficacy and user satisfaction. The investigation leverages a systematic theoretical framework and investigator triangulation to explore the customization of flow heuristics with respect to social permeability, inclusion, and accessibility. This approach facilitates the assimilation of multi-dimensional perspectives, thereby augmenting both accessibility and the user experience within these delineated domains. Within the ambit of enhancing HCI in socially inclusive settings, current heuristic models aimed at simplifying complexity in AI systems manifest notable shortcomings, particularly in relation to social permeability, inclusion, and accessibility. Academic scrutiny not only highlights these deficiencies in the application of DIN EN ISO 9241-210 standards but also uncovers substantial lacunae vis-à-vis the intricate demands of DIN EN ISO 9241-110. These encompass facets such as task appropriateness, self-descriptiveness, expectation conformity, error tolerance, controllability, individualizability, and learning facilitation, whose integrated and considered implementation is indispensable for the efficacious tailoring of interactive systems to meet specific user requirements in inclusive settings. This study significantly bolsters the epistemological base essential for the formulation of a conceptual framework tailored for AI deployment in educational and social management contexts, in alignment with DIN EN ISO 9241 standards. The study aims to articulate a detailed practical guide that outlines the requisite mechanisms, metrics, and characteristics necessary for user-centric adaptations in AI-enhanced systems. The objective is to augment the efficacy of flow induction and elevate user satisfaction within realms characterized by social permeability, inclusion, and accessibility.

Game Theory Applications in Equilibrium Water Pricing of Multiple Regional Sources and Users

China has been facing a contradiction between its water supply and demand for a long time, and it is urgent that it builds a “water-saving society”. Previous research on water pricing mainly focused on the policy level, only considering the supply and demand sides and ignoring the participation of local governments. Due to the inconsistent approval mechanisms, different times of cost formation, and lack of a scientific pricing relationship between multiple water sources and multiple users, with the current water pricing in China, it is difficult to solve the complex problem of water resource allocation. This study aimed to promote the qualitative supply of water and the reuse of reclaimed water, achieve a high-quality and low-cost water supply, and explore the role of water-pricing mechanisms in the regulation of multi-source water supply configurations. The mechanism of regulation is constructed for the complex, multi-source, multi-user water resource system, and a three-party game model is established among local governments, water supply enterprises, and water users. A reasonable pricing system with a rational price relationship is obtained, and Pinghu City in Zhejiang Province is taken as the research area. The prices of the external water supply to residential users, non-resident users, and special users are 4.48 CNY/m3, 6.28 CNY/m3, and 7.12 CNY/m3. The external supply prices for resident users, non-resident users, and special users are 3.81 CNY/m3, 5.25 CNY/m3, and 6.05 CNY/m3. The external supply price for non-resident users is 2.62 CNY/m3 for reclaimed water. The results of the study showed that when the government’s contribution did not exceed the amount that it would have been without the inclusion of recycled water and the annual payment of water users did not exceed their ability to pay, the income of water suppliers increased by 69%. The three balanced objectives of rational water resource configuration by the local government, reasonable profit for the water supply enterprise, and reasonable payment by water users have been achieved. This study provides theoretical and methodological support for the implementation of water pricing for multi-source and multi-user water supplies in water-scarce areas.

S-LDM: Server local dynamic map for 5G-based centralized enhanced collective perception

The automotive field is undergoing significant technological advances, which includes making the next generation of autonomous vehicles smarter, greener and safer through vehicular networks, which are often referred to as Vehicle-to-Everything (V2X) communications. Together with V2X, centralized maneuver management services for autonomous vehicles are increasingly gaining importance, as, thanks to their complete view over the road, they can optimally manage even the most complex maneuvers targeting L4 driving and beyond. These services face the challenge of strictly requiring a high reliability and low latency, which are tackled with the deployment at orchestrated Multi-Access Edge Computing (MEC) platforms. In order to properly manage safety-critical maneuvers, these services need to receive a large amount of data from vehicles, even though the useful subset of data is often related to a specific context on the road (e.g., to specific road users or geographical areas). Decoding and post-processing a large amount of raw messages, which are then for the most part filtered, increases the load on safety-critical services, which should instead focus on meeting the deadlines for the actual control and management operations. On this basis, we present an innovative open-source, 5G & MEC enabled service, called Server Local Dynamic Map (S-LDM). The S-LDM is a service that collects information about vehicles and other non-connected road objects using standard-compliant messages. Its primary purpose is to create a centralized dynamic map of the road that can be shared efficiently with other services managing L4 automation, when needed. By doing so, the S-LDM enables these services to widely and precisely understand the current situation of sections of the road, offloading them from the need of quickly processing a large number of messages. After a detailed description of the service architecture, we validate it through extensive laboratory and pilot trials, involving the MEC platforms and production 5G networks of three major European network operations and two Stellantis vehicles equipped with V2X On-Board Units (OBUs). We show how it can efficiently handle high update rates and process each messages in less than few tenths of microseconds. We also provide a complete scalability analysis with details on deployment options, providing insights on where new instances should be created in practical 5G-based V2X scenarios.

Strategic Adoption: Unveiling the Impact of E-BUPOT Application in Government Agencies

<span lang="EN-US">The e-Bupot application for government agencies is an information technology solution provided by the Directorate General of Taxes (DJP). Specifically designed for government agencies, this application aims to simplify the fulfillment of tax obligations and enhance overall convenience. Despite being in use for over a year, there is a noticeable research gap focusing on the acceptance and satisfaction levels among users of the e-Bupot application. Therefore, additional research is imperative to assess taxpayer acceptance and understand its role in bolstering taxpayer compliance.The research was conducted across all government agencies in Bali province, Indonesia. It integrates the Meta-UTAUT model along with the ISS Model to analyze information technology acceptance. Adopting a quantitative approach, the sampling technique used in this research was purposive, with the survey completed by 172 respondents. Data analysis for this research utilized the PLS method with SmartPLS version 3 software.Based on the analysis results, effort expectancy, information quality, service quality, system quality, attitude, trust, and behavioral intention are identified as factors that encourage use behavior in utilizing the e-Bupot application, ultimately influencing tax compliance. Future studies are recommended to explore and detail specific aspects of social influence and facilitating conditions in different contexts or among specific user groups</span>

Automated Extraction of Structured Data from the Social Network Instagram

The paper explores the extraction of structured information from the social network Instagram through a suitable application programming interface, namely the unofficial Instagram Private API. It focuses on creating a computer program that identifies which posts a user has tagged as "Likes" and then stores this information for profiling specific user profiles. The introduction of the paper highlights the general use of social media in modern society and the importance of personal data for these platforms. It specifies the aim of the study, which is to extract information from Instagram and then analyse it for user profiling. It then describes the evolution of the social network Instagram and key features such as different types of posts. This paper further focuses on the solution and implementation by using Python programming language to minimize the load on Instagram servers and reduce the risk of detection of automated processes. It describes the process of setting up new Instagram accounts, the obstacles in obtaining login credentials, and the need to simulate human behaviour to bypass the network's defence mechanisms. It then focuses on the actual retrieval of information such as the users followed, their posts and information about which posts the user has marked as favourites. It mentions that extracting data from closed profiles is difficult and elaborates on the technical challenges associated with this task. A significant part of this paper is a discussion of Instagram's defence mechanisms that respond to automated computer programs. It describes access denial, account blocking, and identity verification prompts such as CAPTCHA tests. Finally, the conclusion summarizes the results obtained, which indicate the acquisition of approximately 90,000 records for user profiling. It discusses the shortcomings of a fully automated solution due to Instagram's account creation conditions and defence mechanisms. It mentions the need for further research and highlights key gaps and challenges in this area. Overall, the study highlights the technical and security challenges in extracting information from Instagram and emphasises the need for further research and improvements in the technical procedures for extracting data from the platform.

Hand Gesture Recognition Based on High-Density Myoelectricity in Forearm Flexors in Humans.

Electromyography-based gesture recognition has become a challenging problem in the decoding of fine hand movements. Recent research has focused on improving the accuracy of gesture recognition by increasing the complexity of network models. However, training a complex model necessitates a significant amount of data, thereby escalating both user burden and computational costs. Moreover, owing to the considerable variability of surface electromyography (sEMG) signals across different users, conventional machine learning approaches reliant on a single feature fail to meet the demand for precise gesture recognition tailored to individual users. Therefore, to solve the problems of large computational cost and poor cross-user pattern recognition performance, we propose a feature selection method that combines mutual information, principal component analysis and the Pearson correlation coefficient (MPP). This method can filter out the optimal subset of features that match a specific user while combining with an SVM classifier to accurately and efficiently recognize the user's gesture movements. To validate the effectiveness of the above method, we designed an experiment including five gesture actions. The experimental results show that compared to the classification accuracy obtained using a single feature, we achieved an improvement of about 5% with the optimally selected feature as the input to any of the classifiers. This study provides an effective guarantee for user-specific fine hand movement decoding based on sEMG signals.

Research on the Application of Artificial Intelligence Technology in the Banking Internet Finance Industry

This paper presents a collaborative filtering algorithm based on reinforcement learning theory. Then, the personalized bank financial recommendation system for users is constructed in the massive data environment. Tags mimic different types of user interest points to build a representative personalized data set. The collaborative screening of bank financial products is realized using the simulation results and users’ historical access records. The ranking calculation of related financial products is added to the general bank financial product recommendation system. This method can more accurately express the query results for a specific user. It is found that the collaborative filtering algorithm based on enhanced learning theory can improve the efficiency of collaborative screening of bank financial products. The best results can be obtained by combining the two organically. This paper proposes that the recommendation algorithm of reinforcement learning bank financial products based on user preference and collaborative filtering is feasible.

Identification of User Requirements and their Influencing Factors Based on Online Reviews and Operational Data

It is crucial for enterprises to clearly identify user needs during the process of formulating product design improvement plans. Therefore, it is essential to comprehensively and accurately identify user needs, explore the reasons behind the emergence of these needs, and incorporate user opinions into the process of product design improvement. A method is proposed to comprehensively and accurately capture user requirements and address the challenge of identifying the underlying causes of user requirements. This method utilizes online comments and operational data to identify user requirements and their influencing factors. First, text sentiment analysis techniques are employed to quantify the importance and performance values of product feature topics. Second, we construct a quadrant model to identify product features requiring improvement, and the original negative comments related to these features are traced. However, the quadrant model alone is insufficient to reflect specific product issues that users are concerned about. Therefore, a functional structure model based on product issues is designed to filter and identify factors that influence user requirements using operational data. Finally, a Bayesian network inference approach is utilized to identify the key influencing factors on user requirements, enabling analysis of the causes behind user requirements and the proposal of product design improvement strategies. The feasibility and effectiveness of the proposed method are validated through experiments conducted on heavy-duty truck data. By analyzing the original negative comments related to the power characteristics, specific user demands regarding the insufficient power of the product were identified, such as “obviously insufficient power when climbing slopes” and other issues. Based on the vehicle power system functional structure model, combined with expert knowledge and operational data, factors related to the state of parts and user behavior that may affect “insufficient vehicle power” were identified. Based on the analysis results, suggestions were made to improve the engine intake air temperature control strategy and to enhance vehicle performance by promoting correct user behavior through informational campaigns.

Applying Multi-Armed Bandit algorithms for music recommendations at Spotify

This study explores the application of multi-armed bandit algorithms in enhancing music recommendation systems, with a focus on Spotify. It delves into the Explore-Then-Commit (ETC), Upper Confidence Bound (UCB), and Thompson Sampling (TS) algorithms, evaluating their efficacy within the Spotify context. The primary objective is to determine which algorithm optimally balances exploration and exploitation to maximize user satisfaction and engagement. The research reveals that the ETC algorithm, with its rigid exploration and exploitation phases, incurs a notably higher regret value. This rigidity can lead to missed opportunities in identifying optimal choices and hinder adaptability to evolving user preferences. Conversely, the UCB and TS algorithms exhibit remarkable adaptability and a flexible balance between exploration and exploitation. This flexibility translates into more personalized and satisfactory user experiences in music recommendations. However, the selection of the most appropriate algorithm should be contingent on the size and characteristics of the specific user dataset, as well as the fine-tuning of algorithm parameters to align with user preferences and behaviors.