User Demands Research Articles

Pricing drinking water testing in northern Haiti: Financial sensitivity to operating costs, user demand, and economic conditions

Safe drinking water availability is a concern in Haiti. Public systems have limited coverage and reliability. Private wells and local water sources are often of unknown or poor quality. Public health events, such as the 2010 cholera outbreak, demonstrate vulnerability to water contamination. To address these concerns, a drinking water laboratory was established at the Campus Henri Christophe in Limonade, a branch of the State University of Haiti, to meet water testing demands from local clients such as for-profit kiosks, institutions, industries, and municipal water systems. This study assessed the financial viability of a university-based drinking water laboratory in Haiti by calculating Internal Rate of Return and Net Present Value. Sensitivity analysis was used to identify the range of conditions under which laboratory revenues would cover operating costs. To achieve an acceptable profitability level, the laboratory must perform microbiological testing for routine monitoring samples and test an average of five samples per day. Price-based incentives for new clients have relatively small impacts on profitability. Finally, international and Haitian inflation cause some variation in profitability. These economic factors will be among the key drivers of laboratory operation costs. The results underscore the main factors that must be considered to make the laboratory successful and the importance of strategic marketing for laboratory managers to encourage clients to regularly test drinking water and emphasize microbiological testing.

Research and application of low-carbon economic dispatch of urban building integrated energy system

Abstract Global warming is a serious threat common to all countries. The technological revolution and sustainable development of energy have become the international consensus. While ensuring economic benefits, promoting low carbon environmental protection of comprehensive energy system has become a top priority. Therefore, this paper designs a low-carbon economic scheduling system based on consistency theory. First, the importance of user demand response should be considered in the integrated energy system to ensure the economy of scheduling. Secondly, the improved step-step carbon trading mechanism is used in the algorithm to improve the low carbon performance of scheduling. Then, the distributed consistency algorithm is used to schedule the integrated energy system to realize the most energy output of each unit. In addition, a noise function is added to the algorithm for encryption and desensitization to prevent user privacy leakage. Finally, the effectiveness of the proposed economic scheduling method.

Analysis of 4G/LTE Network Performance in North-Central Nigeria: A Comprehensive Drive Test Approach

The proliferation of mobile devices and escalating demand for data services have resulted in a substantial increase in data traffic across Nigeria, especially in the North Central region due to heavy migration of individuals and organizations towards Abuja, the federal capital. However, despite the growing adoption of 4G/LTE networks in North Central Nigeria, users encounter persistent challenges in accessing high-speed internet and poor service network. This study investigates the performance of 4G/LTE networks in North-Central Nigeria using a comprehensive drive test methodology carried out in Abuja, Lafia and Makurdi. It covered a duration of 3hrs (12.00noon to 15.00pm) for 3days (3rd - 6th January, 2024). For each test day, drive tests covered a period of data measurements divided into 3,600 time-steps, with each time-step sized at 1sec; and scaled to 1:150 units. Key metrics including network speed, latency, uptime, coverage, and signal power were evaluated across major Mobile Network Operators. Result shows that D-NGN has the highest mean speed (9.543Mbps), packet loss (2.007%), uplink percentage (97.714%), and Network coverage (87.514%). It has the least latency (25.921) but high packet loss (2.007%). However, C-NGN shows lowest mean speed (6.638Mbps), uplink percentage (38.706%) and Signal power (-78.057dBm) but lowest mean packet loss (0.121%). A-NGN has the highest mean signal power (-61.867dBm) while B-NGN has highest latency (44.070ms) and lowest Network coverage (60.731%). This reveals that D-NGN has dominance in network performance, outperforming A-NGN, B-NGN, and C-NGN. It is recommended that MNOs in North-Central Nigeria should strategically enhance their network infrastructure to improve service delivery, particularly in suburban and rural areas where performance lags. D-NGN, the frontrunner in network speed and coverage, could further solidify its market leadership by addressing identified latency and packet loss issues especially in Makurdi. Moreover, MNOs with lower performance metrics could invest in expanding coverage and optimizing network configurations to meet user demands, thereby increasing competitiveness and customer satisfaction across the region.

Deep Learning-Driven Workload Prediction and Optimization for Load Balancing in Cloud Computing Environment

Abstract Cloud computing revolutionizes as a technology that succeeds in serving large-scale user demands. Workload prediction and scheduling tend to be factors dictating cloud performance. Forecasting the future workload in due to avoid unfair resource allocation, emerges to be a crucial inspecting feature for enhanced performance. The aforementioned issues of interest are addressed in our work by soliciting a Deep Learning driven Max-out prediction model, which efficiently forecasts the future workload by providing a balanced approach for enhanced scheduling with the Tasmanian Devil-Bald Eagle Search (TDBES) optimization algorithm. The results obtained proved that the TDBES scored efficacy in makespan with 16.75%, migration cost with 14.78%, and a migration efficiency rate of 9.36% over other existing techniques like DBOA, WACO, and MPSO, with additional error analysis of prediction performance using RMSE, MAP, and MAE, among which our contributed approach overrides traditional methods with least error.

Establishing a user demand hierarchy model driven by a mental model for complex operating systems

In complex human-computer interactions, issues such as task failures, system malfunctions, and frequent accidents caused by user errors are common. Therefore, it is necessary to study complex system interactions to enhance overall efficiency. This study focuses on the task interface of a digital twin system for computer numerical control (CNC) machine tools and examines the relationship between the user mental model and interface design elements. Key mental information is obtained through questionnaires and interviews, forming the basis for establishing a user mental model. High-frequency information words are extracted, experimental samples are designed, and importance rating surveys are conducted. Quantitative analysis methods, including factor analysis and weight calculation, are utilized to analyze the needs of target users. Consequently, a user demand hierarchy model is constructed. This approach aims to effectively reduce user errors in the human-computer interaction process within complex systems and enhance cognitive efficiency.

Evidence of the need for developing an integrated model of mathematics teachers' readiness as agents of change

This study aims to identify the need to develop an integrated model of the readiness of mathematics teachers as agents of change. Teachers' readiness is crucial for the effectiveness of the teaching process and the enhancement of pedagogical innovation. This study employs a qualitative methodology. A semi-structured interview was conducted with 14 study participants to ensure that the proposed model fits the demands of users. This includes education and teaching experts from Malaysia’s southern and central regions. The interview data were transcribed and analyzed thematically using ATLAS.ti24 software. Results indicate a need to develop an integrated model of mathematics teachers' readiness as agents of change. All study participants emphasized the significance of addressing mathematics teacher readiness. Three main themes have been identified: (1) pedagogical needs, (2) strengthening mathematics teacher competence and (3) the configuration model as a guideline and checklist. The findings emphasize the importance of addressing mathematics teachers' readiness. An integrated model based on professional knowledge and pedagogical skills was proposed to support teachers in acting as agents of change. This study provides valuable insights for mathematics teachers and stakeholders. The findings indicate directions for future research particularly in the systematic development and design of the proposed integrated model which aims to enhance teacher readiness and pedagogical innovation.

Innovations in android mobile computing: a review of best practices and emerging technologies

This review paper explores the dynamic landscape of Android mobile computing, focusing on best practices and emerging technologies driving innovation in the field. The paper begins by discussing the current state of Android development, highlighting key challenges and market trends. It then delves into essential best practices for designing intuitive interfaces, optimizing app performance, and ensuring robust security. Additionally, the paper examines the transformative impact of emerging technologies such as artificial intelligence, 5G, augmented reality, virtual reality, blockchain, and the Internet of Things on Android app development. Finally, the paper offers insights into future trends in the Android ecosystem, emphasizing the importance of continuous innovation to meet evolving user demands and maintain a competitive edge. This comprehensive review provides developers and stakeholders with valuable knowledge to navigate the complexities of Android mobile computing and leverage new growth opportunities

Scenario-Based Analysis of High-End New Energy Vehicle User Demands

With the rise in global environmental awareness and the rapid development of new energy technologies, high-end new energy vehicles (NEV) have become an important direction for the automotive industry. Consumer demand is evolving from merely seeking a means of transportation to desiring a comprehensive experience across various scenarios, emphasizing high quality, high performance, and environmental friendliness. Understanding the characteristics and trends of high-end NEV users' demands is crucial for car manufacturers in formulating market strategies, product design, and service improvement. However, current focus is often on policies and product design, lacking analysis of actual usage scenarios. To address this gap, this article employs scenario research methods to delve into the needs of high-end NEV users, selecting typical scenarios such as urban commuting, long-distance travel, and business operations. The research indicates that high-end NEV users are primarily young, highly educated, middle-to-high income individuals who exhibit rational consumption habits. Their most common usage scenarios include commuting to work, transporting family members, and shopping. In these contexts, there is a pronounced demand for vehicle safety performance, ride comfort, and intelligent features, each showing differentiated characteristics across various user groups. For instance, female users demonstrate a particularly strong preference for comfort-related configurations, whereas married users with children have the highest demand for spacious and convenient features. Based on these insights, this paper offers targeted recommendations from the perspectives of product design and marketing.

Addressing localized thermal comfort needs of the human body through advanced personal thermal management garments design and evaluation

Personal thermal management clothing has garnered significant attention in recent years for offering innovative solutions to address extreme environments. By examining the “human-clothing-environment” system, we have analyzed the contribution and developmental trajectory of personal thermal management clothing, considering the diverse human needs and application environments. Local variations in human physiology necessitate localized thermal comfort solutions provided by personal thermal management clothing. Despite advancements, many Personal thermal management garments still encounter challenges such as bulky size, heavy weight, intricate donning process, subpar performance, limited temperature control, and low durability. Furthermore, aspects like comfort, ergonomics, and aesthetics, tailored to specific usage scenarios, have often been overlooked. Enhancing the standardized design and evaluation processes for personal thermal management clothing items is crucial to better cater to individual preferences and usage demands, offering recommendations for development to align with individual requirements, enhance effectiveness, and promote energy conservation.

Two-layer optimal scheduling method for regional integrated energy system considering flexibility characteristics of CHP system

A regional integrated energy system (RIES) guarantees the fulfillment of a diversified load demand of users by coordinating all types of energy equipment and is a two-layer optimal scheduling method that takes into consideration the flexibility of the characteristics of the combined heat and power (CHP) system is proposed in this paper. First, the thermoelectric output characteristics of hydrogen fuel cell (HFC) and CHP units are analyzed to establish an energy equipment model that takes into consideration the variability of the operating conditions. Subsequently, a two-layer optimal scheduling model of the RIES is established, where the upper layer takes into consideration the operating costs, carbon emission penalties, and renewable energy consumption capacity to determine the strategy for allocating the energy flow, and the lower layer considers the energy efficiency as the optimization objective to determine the output modes of the HFC and CHP units. Finally, an adaptive closed-loop control strategy is used to avoid the continuous startup and shutdown of the CHP unit. The simulation results show that the proposed method can effectively reduce operating costs and carbon emissions, improve energy efficiency and renewable energy consumption, and promote the efficient utilization of integrated energy resources.

Proactive self‐healing techniques for cloud computing: A systematic review

SummaryEnsuring the seamless operation of cloud computing services is paramount for meeting user demands and ensuring business continuity. Fault‐tolerant self‐healing techniques play a crucial role in enhancing the reliability and availability of cloud platforms, minimizing downtime and ensuring uninterrupted service delivery. This article systematically categorizes and analyzes existing research on fault‐tolerant self‐healing techniques published between 2005 and 2024. We provide a comprehensive technical taxonomy organizing self‐healing techniques based on fault tolerance processes, encompassing considerations for both reliability and availability. Additionally, we evaluate applications of proactive self‐healing techniques, highlighting their achievements, and limitations in enhancing service continuity. Strategies to address identified weaknesses are discussed, alongside future research challenges and open issues in the domain of cloud resilience. Through this analysis, the article contributes to understanding self‐healing techniques in cloud computing, offering insights into their effectiveness in ensuring service continuity. The findings aim to guide future research efforts in developing more robust and resilient cloud infrastructures, ultimately enhancing overall service reliability and availability. By emphasizing the importance of fault tolerance and self‐healing techniques, this article lays the foundation for advancing the state‐of‐the‐art in cloud computing.

A simple load model based on hybrid mechanism and data-driven approach for district heating in building complex

Accurate prediction of heating loads in district heating systems is essential for the implementation of demand-driven heating. This work presents a novel heating load prediction model that is particularly suitable for complex multi-user buildings. The input characteristics of the model are established through the heat transfer mechanism, considering factors such as the cumulative impact of outdoor temperature and user demand (indoor temperature). The specific form of the heating load function is determined using the MLR-PSO (Multiple Linear Regression-Particle Swarm Optimization) method. Only the indoor and outdoor temperatures need to be provided for the model to calculate future heating loads. Practical engineering tests demonstrated that the model achieved normalized mean bias errors of daily loads between 4.98 % and 5.54 % across different heating seasons, with a minimum annual relative deviation of 0.75 % for annual loads. Additionally, the model helps guide the operation of heating systems. For example, during the 2021–2022 heating season, setting the target indoor temperature at 18 °C reduced weekly energy consumption by 15.3 % compared to the previous season. This approach may be employed to construct a simple load model for existing heating systems to accurately predict both short-term and long-term loads, providing valuable insights into the management and control of heating systems.

MS-GD-P: priority-based service deployment for cloud-edge-end scenarios

In cloud-edge-end scenarios, how to achieve rational resource allocation, implement effective service deployment, and ensure high service quality has become a hot research topic in academic domains. Service providers usually deploy services by considering the characteristics of different geographical regions, which helps to meet the diverse needs of users in different regions and optimize resource allocation and utilization. However, due to the widespread distribution of users and limited server resources, providing all types of services to users in every geographical region is not feasible. In addition, edge servers are prone to operational failures caused by software anomalies, hardware malfunctions, and malicious attacks, which will decrease service reliability. To address the problems above, this paper proposes a metric for service priorities based on user demands and regional characteristics for different geographical regions. Building upon this foundation, a Multi-Service Geographic region Deployment based on Priority (MS-GD-P) is proposed. This method takes user coverage and service reliability into consideration, which facilitates users’ needs for multiple services in different geographical regions. Experimental results on real datasets demonstrate that MS-GD-P outperforms baseline methods in user coverage and service reliability.

User Requirement Identification and Dynamic Analysis Based on Improved Kano Model

The widespread adoption of e-commerce has rendered online reviews an indispensable source for acquiring knowledge about product features. The Kano model serves as a classic framework for categorizing customer requirements. It was proposed by the Japanese scholar Noriaki Kano in 1984 as a tool primarily used to help businesses better understand and categorize customer needs, guiding product development and service quality improvement. However, existing research on mining online review information often relies predominantly on static analysis, lacking comprehensive categorization of user demands. To address this gap, this paper introduces an enhanced Kano model for the identification and dynamic analysis of user demands. This model classifies review data based on emotional polarity and user attention, leveraging the characteristics inherent in online reviews. Furthermore, it constructs a user demand evolution model using time series analysis and utilizes SnowNLP for emotional calculations. By conducting word frequency statistics and employing LDA models to analyze product features, this paper identifies the evolving trends in user demands. Additionally, the exponential smoothing method is used to forecast the trend in users’ emotional values towards the product. The findings demonstrate that this model effectively extracts valuable insights regarding product feature information from user reviews, thereby offering novel perspectives and methodologies for related fields. Consequently, this contributes significantly to advancing the identification and dynamic analysis of user demands.

Investigating the Effect of ChatGPT-like New Generation AI Technology on User Entrepreneurial Activities

ChatGPT, characterized by its reliance on big data, robust algorithms, and significant computational power, has become a benchmark AI application product, signifying a new breakthrough in AI technology. The emergence of applications based on ChatGPT-like next-generation AI technology has triggered a series of interconnected transformations in human society's ways of thinking, production, living, and governance. However, the academic community has yet to conduct research specifically on innovation and entrepreneurship. Against this backdrop, this study explores the effect of the novel features of ChatGPT-like next-generation AI technology on user entrepreneurs, driving factors, and the entrepreneurial process. The findings reveal the following: (1) User entrepreneurs collect extensive user data through ChatGPT-like AI technology and intelligently analyze it to achieve optimal entrepreneurial judgments and decisions. (2) User entrepreneurs utilize ChatGPT-like AI technology to understand the latent needs of users and to acquire user demand information, such as product shortcomings and appeals. (3) ChatGPT-like AI technology enhances the entrepreneurial intention of user entrepreneurs, stimulates their creative thinking, and expands and deepens their social networks, thereby strengthening their identification with entrepreneurial opportunities. (4) ChatGPT-like AI technology drives and empowers the three-stage evolution of user entrepreneurship: idea generation, prototype development, and commercialization of innovative products. This study not only provides new insights and theoretical foundations for user entrepreneurship research to better explore and leverage the application of ChatGPT-like AI technology in the entrepreneurial process but also offers significant practical implications for encouraging users to actively engage in innovation and entrepreneurship activities, supporting the achievement of sustainable digital entrepreneurship goals.

A Mobile Robot Design for Home Security Systems

Home Security Systems (HSSs) have received much attention and have been widely adopted for practical deployment. However, detection and warning accuracy still need to be improved, along with the range of the realm, making it challenging to satisfy the user demands. This study proposes a security monitoring system based on mobile robots and the Internet of Things (IoT), allowing users to monitor and control devices remotely. The mobile robot integrated sensor systems and surveillance cameras are utilized for unauthorized early intrusion detection and to give users instant warnings. The data collected by the robot were stored on the Firebase Cloud server, and a mobile application and a Telegram interface were integrated to manage and control the system. In addition, adaptive motion control was adopted to correct errors in the robot’s trajectory. The implementation results proved that this system operated effectively with a minimal response delay of 0.87–1.67 s and a high detection accuracy (96.25%) in two experimental cases, which makes it suitable for real-time applications.

Custom Shoe Sole Design and Modeling Toward 3D Printing.

This study introduces a design procedure for improving an individual’s footwear comfort with body weight index and activity requirements by customized three-dimensional (3D)-printed shoe midsole lattice structure. This method guides the selection of customized 3D-printed fabrications incorporating both physical and geometrical properties that meet user demands. The analysis of the lattice effects on minimizing the stress on plantar pressure was performed by initially creating various shoe midsole lattice structures designed. An appropriate common 3D printable material was selected along with validating its viscoelastic properties using finite element analysis. The lattice structure designs were analyzed under various loading conditions to investigate the suitability of the method in fabricating a customized 3D-printed shoe midsole based on the individual’s specifications using a single material with minimum cost, time, and material use.

Voice-Assisted News App using Natural Language Processing

In recent times, the advancements in Artificial Intelligence (AI) and natural language processing (NLP) have enabled the development of novel voice-assisted apps. This study introduces a cutting-edge Voice-Assisted News App that utilizes the capabilities of Alan M, a prominent AI platform that revolutionizes the way people access news. This research endeavor attempts to utilize Alan Al's capabilities to offer a personalized news experience and enhance clarity through voice interactions. Users of the Voice-Assisted News software can effortlessly request and select their preferred news topics, and the computer will generate the most relevant news updates. Through integration with diverse news sources and the application of sophisticated algorithms, the program generates an individualized news stream for every user, ensuring they receive the latest and most relevant information. Users can efficiently access story audio descriptions. The app's speech interface allows users to navigate across different news categories. The NLP algorithms developed by Alan AI offer precise understanding of user demands by ensuring smooth interaction and an authentic conversational experience

Health service requirements of mHealth app users: evidence from Hangzhou, China

PurposeChina’s healthcare system is being burdened by the increasing prevalence of chronic diseases. Therefore, this study investigated the health service requirements of mobile health applications (mHealth apps) users in Hangzhou, China. This study aimed to propose suggestions and theoretical references to improve mHealth apps and promote their development, thereby meeting public medical and health needs and creating an efficient medical service system.Design/methodology/approachWe constructed a model of health service demands using a literature review, network survey, and semi-structured interviews. We analyzed the demand attributes using the Kano model and Better-Worse index and obtained the priority ranking of demands.FindingsThe results revealed 25 demand elements in four dimensions: must-be (M), one-dimensional (O), attractive (A), and indifferent (I) requirements. The findings suggest that mHealth app developers can optimize health services by categorizing and managing health services, focusing on middle-aged users, enhancing the professionalism of health service providers, and improving the feedback mechanism.Originality/valueStudies on mHealth apps user demands, particularly on health service needs, remain scarce. This study employed a mixed-methods approach, integrating both qualitative and quantitative research techniques, to establish a priority ranking of user health service needs for mHealth apps. The study offers recommendations and theoretical references to optimize and improve mHealth app services.HighlightsConstruct a better health service requirements model for mHealth app users.Obtain the prioritization of demand elements in the model.Propose some management suggestions to improve mHealth apps.

The analysis of educational informatization management learning model under the internet of things and artificial intelligence

This study explores the influence of the Internet of Things (IoT) and Artificial Intelligence (AI)-enhanced learning models on student management in educational informatization management. A game-theoretic enhanced learning model is proposed to achieve this objective, incorporating resource scheduling strategies under fog computing and a student management system that integrates IoT and AI technologies. This model’s performance and the student management system are then tested. The results indicate that the fog computing-based hierarchical Q-learning (Q) model proposed in this study achieves faster convergence than a single Q model, reaching convergence after 80 training rounds, ten rounds earlier than the comparative algorithm. The model exhibits a lower average workload delay of 0.5 ms and fog node delay below 1 ms, showcasing significant advantages in terms of overall cost-effectiveness, thus minimizing service costs. The student management system has 3000 concurrent user connections, static page request times ranging from 0 to 25 s, login response time predominantly at 60 s, and a capacity to process up to 20 parallel tasks per second with zero errors. The system functionalities are fully realized, meeting usage demands effectively and achieving the highest average functional score of 9.03 for online interaction functionality. This study demonstrates the efficacy of the game-theoretic enhanced learning model in a fog computing environment and the positive impact of IoT and AI technologies on student management. The proposed student management system better caters to individual student needs, enhancing learning outcomes and experiences. The study's innovation lies in the integration of IoT technology with AI-enhanced learning models, coupled with the introduction of game-theoretic resource scheduling strategies, enabling the student management system to intelligently identify student requirements, allocate learning resources, and dynamically optimize the educational process, ultimately improving learning outcomes. This holds significant implications for enhancing education quality and promoting personalized student development.