User Need Research Articles

Integrated System Modal and Mistuning Identification for Integrally Bladed Rotors

Abstract The safety of Integrally Bladed Rotors (IBRs) is often assessed through bench-level vibration tests to measure amplification factors and back-out sector frequency deviations via mistuning identification (ID) algorithms. This process is usually composed of two separate steps. First, a system ID step is completed to identify system modal data. Then, this data is input into mistuning ID algorithms. Errors in identified modal data will then propagate to produce errors in the system's predicted mistuning. Obtaining robust mistuning estimates then requires larger quantities of accurate modal data. This effort seeks to attain accurate mistuning data by coupling the system and mistuning ID steps into a more parallel, versus serial, process that is capable of identifying many system modes. An iterative Polyreference-Least Squares Complex Frequency- domain (P-LSCF) algorithm finds modal data, and a mistuning ID algorithm obtains mistuning data at each iteration. An outlier detection method is proposed to remove spurious modes that cause erroneous mistuning results. Then, a weighted, least-squares regression approach is employed to remove the impact of sector-specific outlier data. This method reduces errors in identified mistuning parameters from the Fundamental Mistuning Model (FMM) ID algorithm. Furthermore, the approaches eliminate the need for users to determine true versus spurious system modes in each iteration of the P-LSCF algorithm, thus removing ambiguity. The developed approaches are tested on simulated and bench-top test data. Results show the efficacy of the developed approaches and their ability to account for uncertainty in mistuning parameters.

Defining algal bloom phenology in Lake Erie

Elucidating the impact of global climate change on aquatic ecosystems, particularly through phenological shifts in primary producers, is critical for understanding ecological resilience. Here, we focus on the phenological shifts in chlorophyll as a proxy for algae biomass and primary production in aquatic ecosystems, specifically in Lake Erie as well as concentrations of the toxin microcystin. By tracking temporal changes in each, we identified key phenological phases important to estimate duration, magnitude, and intensity of harmful algal blooms (HABs). Determining which influential biotic and abiotic factors such as temperature, wind speed, nutrient availability, and climate change is most important, is a long-term management need for Lake Erie, which can be explored using our methodology. Our novel statistical framework employing Bayesian generalized additive mixed models described seasonal chlorophyll and particulate microcystin concentration from Lake Erie and our simple geometric method identified the start, peak, and end of algal blooms. This research enhances our understanding of the ecological effects of nutrient pollution on aquatic ecosystems and provides a repeatable method for determining phenological events without the need for user defined cutoffs which aids in the management and mitigation of HABs, safeguarding water quality in regions dependent on lakes for drinking water.

Language of Cursing and its Effect on the Community, Family and Schools

This article deals with the language of cursing and its effects in schools and society. Being based on the observation and interviews, the study enlisted typical examples of language of cursing in the rural Nepal, and discusses their impacts on community, schools and families. It also explains the cursing words and further presents why people curse in society. The study concludes with the fact that language of cursing might have both positive as well as negative impacts and the user need to be careful while using them according to the context, participants and its effects. The teachers, administrators and all the concerned might be beneficial by understanding prevalent language of cursing.

Beyond Technical Specs: Using AHP to Prioritize User Needs in Turkish Basketball Arena Designs

Basketball arenas are crucial spaces that must meet specific standards to ensure the sustainability of the sport and the quality of play. While Post-Occupancy Evaluation (POE) assesses these standards, it often assumes that all performance indicators are equally important, potentially leading to a gap between design intent and actual user experience. This study aims to bridge this gap by using the Analytic Hierarchy Process (AHP) to prioritize evaluation criteria for Turkish professional basketball arenas designed for national and international matches, regional/local competitions, and training programs. Existing studies and reports indicate that basketball arenas in Turkey often fall short in meeting necessary physical requirements and exhibit performance deficiencies. To address this gap, this study identifies factors determining arena performance, categorized as technical, functional, and behavioral based on Preiser's framework. These criteria were further categorized according to different spaces within the arena, including general use areas, administrative spaces, and athlete-specific areas. A panel of expert sports facility design professionals provided judgments about the relative importance of these criteria using pairwise comparisons. The AHP method was then employed to calculate priority weights for each criterion. The results reveal the relative importance of different criteria for each space. For example, in general use areas, "furniture suitability/sufficiency" and "provision of ergonomic conditions for the disabled" ranked highest. In administrative spaces, "fire protection" and "accessibility" were deemed most important, while in athlete-specific areas, "visual and auditory privacy" and "security" took precedence. This study demonstrates the potential of AHP for developing a systematic and reliable framework for qualitative evaluations that measure user satisfaction. AHP-based evaluation models offer a valuable tool for architects, facility managers, and decision-makers to assess architectural design quality, prioritize design criteria, and support user-centered design processes.

GRAPHENE AS AN ANTIOXIDANT AND ANTIOZONANT IN TIRE SIDEWALL COMPOUNDS

ABSTRACT Tire sidewalls have an important function in achieving optimized tire performance. The sidewall compound must show good abrasion resistance, aging resistance, and tear strength; low hysteresis and minimal contribution to whole tire rolling resistance; and good adhesion to adjacent components in the tire. In addition, in both the original equipment market and the premium performance market, appearance is of considerable importance. The current system of waxes, antioxidants, and phenyl diamine antiozonants is very effective in meeting the need of the tire end user. However, recent concerns centered on the environmental impact of N′-phenyl-p-phenylenediamine, specifically N-(1,3-dimetylbutyl)-N′-phenyl-p-phenylenediamine, and staining and discoloration of the tire sidewall surface raise issues that should be addressed. Specialized grades of graphene can offer a mechanism by which tire appearance and environmental concerns can be overcome. Pristine graphene can improve compound resistance of ozonolysis and oxidation by replacing the current antiozonants and antioxidants used in tire compounds; improve tire sidewall appearance; and improve tire sidewall resistance to scuffing, abrasion, and tearing.

Collaborative Learning Platform for Academicians in Libya

Higher education academicians face a variety of difficulties, such as the need for improved collaboration, streamlined communication, and effective resource management. In order to tackle these concerns, this study investigates the unique challenges that academicians in higher education face. The study emphasizes the need for integrated features and user interfaces that are simple to understand and customized for a particular requirement of higher education. Moreover, by exploring the recognition and understanding of these barriers, the study not only clarifies the complex terrain of difficulties but also establishes the foundation for the creation of a platform for collaborative learning. With a focus on the problems faced by academicians, this platform will include features like a forum that is easy to use, the ability to share resources, a powerful search engine, a section devoted to frequently asked questions, and a chatbot that can engage users. Together, these elements seek to offer a thorough resolution to the problems noted, promoting enhanced dialogue, information sharing, and instructional effectiveness among academicians.

Secure Storage of Crypto Wallet Seed Phrase

The Expense Tracker Application is a user-friendly and efficient tool designed to help individuals manage their personal finances with ease. In the ever-changing landscape of daily expenses, this application offers a structured method for tracking income, categorizing expenditures, and generating insightful financial reports. Its intuitive and visually appealing interface ensures that users of all technological skill levels can navigate it effortlessly. The application features visual representations to help users understand their budgetary trends and set limits for different expense categories. It provides timely alerts and notifications when users approach or exceed these budget limits. With robust security measures in place, the application ensures the confidentiality of users' financial data, enhancing their awareness of spending habits and enabling informed financial decisions. Additionally, this system proposes a novel approach to cryptocurrency transactions. Instead of relying on complex public and private keys, it simplifies key management by binding these keys to the unique IP addresses of the nodes involved. This method minimizes the need for users to manage large cryptographic keys, offering a more secure and user-friendly alternative for cryptocurrency wallets. By addressing the limitations of traditional cryptocurrency systems, this approach enhances key management and overall security

Innovative resource allocation mechanism for optimizing 5G multi user‐massive multiple input multiple output system

Abstract5G networks are essential in all locations owing to the multitude of advantages they provide. As a result, the number of users has increased dramatically. Nevertheless, these users require a variety of resources in order to function efficiently. Deep learning techniques have been created to improve the precision and dependability of resource allocation in the context of 5G networks. This research utilizes an efficient recurrent neural network (ERNN) to handle resource allocation for 5G multiuser (MU)‐massive multiple input multiple output (MIMO). In order to optimize the objective functions, the first application of the multi‐objective differential evaluation algorithm (MODEA) is used. The neural network is provided with these updated goal functions in order to allocate resources. ERNN evaluates the level of need for each individual user. By partitioning the resource at this level, it maintains a high throughput while distributing it to each user. In addition, the fairness index of the resource distribution system based on neural networks is established. The suggested method achieves a data transfer rate of 290 bits per second (bps) and a fairness index of 0.97% when used by 50 users. The findings of the proposed method exhibit superior performance compared to other existing methods in the field of 5G massive MIMO.

Smart DIC: User-independent, accurate and precise DIC measurement with self-adaptively selected optimal calculation parameters

Existing subset-based digital image correlation (DIC) must rely on user-selected key calculation parameters (i.e., subset size and shape function) to proceed with displacement/deformation analysis. However, the lack of clear guidelines for selecting these parameters leads to varying choices among users, thus introducing artificial uncertainty in DIC measurements. Previous theoretical analyses and experimental studies revealed that optimal calculation parameters must account for both local speckle pattern quality and deformation at each required point. That means these key parameters should vary at each calculation point, posing a significant challenge for optimal parameter selection. To tackle this challenge, a novel Smart-DIC is proposed to achieve user-independent, accurate and precise displacement field measurements. This method comprehensively considers the local speckle pattern quality and local deformation at each calculation point, and gives the explicit formulas to determine optimal calculation parameters. Based on these optimal calculation parameters, Smart-DIC outputs accurate and precise displacement measurements without the need for users’ inputs. To validate its metrological performance, numerical tests were processed using data from DIC Challenge 1.0 and real images of tensile tests of a commercial AL-based alloy. The experimental results underscore the capacity of Smart-DIC to efficiently achieve accurate and precise displacement measurements across various scenarios by automatically selecting optimal subset sizes and shape functions.

K-Nearest neighbor smart contract classification with semantic feature enhancement

Abstract How to quickly and accurately retrieve relevant smart contracts from a huge amount of smart contracts has become an urgent need for users. The classification of smart contracts offers a solution by narrowing down the search space. Existing smart contract classification methods suffer from incomplete semantic feature extraction and a lack of consideration of the existence of rich semantics in existing smart contracts of the same class. To address the above problems, we propose a contrast learning and semantic feature embedding approach to enhance K-Nearest Neighbor (CL-SFE-IKNN). Our method fuses local features, global features, and account transaction features of the smart contract source code to perfect the semantics of the contract. Our method adopts KNN to retrieve multiple instances of contracts in the same class and assigns weights to the model output based on their labels. Meanwhile, we introduce contrastive learning and semantic feature embedding to enhance KNN retrieval to high-quality nearest neighbors of the same class. Experimental results show that by combining a KNN classifier with a traditional linear classifier, our model achieves the best performance compared with other baseline models.

Research on Design Methods for Interactive Spaces in Schools for Children with Intellectual Disabilities Considering User Needs

To scientifically enhance user perception in decision-making for designing interactive spaces in schools for children with intellectual disabilities, we propose an innovative design model that integrates the Kano model, Analytic Hierarchy Process (AHP), and Axiomatic Design (AD) theories based on user needs. Initially, multi-method research was used to gather the original user requirements which were then refined through data cleaning to establish the initial user needs. The Kano model was then employed to categorize these initial user needs. AHP was then used to construct a hierarchical analysis model for the interactive spaces in schools for children with intellectual disabilities, creating a judgment matrix to accurately calculate demand weight values at each level. Subsequently, AHP was used to select the most important demand items. The independence axiom of AD theory was used to achieve a “Z”-shaped mapping between the functional requirements (FRs) and design parameters (DPs) for the interactive spaces in schools for children with intellectual disabilities. This mapping was analyzed using a matrix approach to assess the design rationality and optimize solutions, thereby transforming user needs into design parameters. Finally, the design parameters were used to create interactive spaces through computer-aided design, and the resulting design plans were evaluated. Experimental results indicate that this design scheme effectively translates subjective concepts into specific design parameters through a qualitative and quantitative approach. This significantly enhances the user needs of interactive spaces in schools for children with intellectual disabilities and provides a scientific basis for the architectural design of these schools.

Novel model to authenticate role-based medical users for blockchain-based IoMT devices.

The IoT (Internet of Things) has played a promising role in e-healthcare applications during the last decade. Medical sensors record a variety of data and transmit them over the IoT network to facilitate remote patient monitoring. When a patient visits a hospital he may need to connect or disconnect medical devices from the medical healthcare system frequently. Also, multiple entities (e.g., doctors, medical staff, etc.) need access to patient data and require distinct sets of patient data. As a result of the dynamic nature of medical devices, medical users require frequent access to data, which raises complex security concerns. Granting access to a whole set of data creates privacy issues. Also, each of these medical user need to grant access rights to a specific set of medical data, which is quite a tedious task. In order to provide role-based access to medical users, this study proposes a blockchain-based framework for authenticating multiple entities based on the trust domain to reduce the administrative burden. This study is further validated by simulation on the infura blockchain using solidity and Python. The results demonstrate that role-based authorization and multi-entities authentication have been implemented and the owner of medical data can control access rights at any time and grant medical users easy access to a set of data in a healthcare system. The system has minimal latency compared to existing blockchain systems that lack multi-entity authentication and role-based authorization.

Virtual Desktop Assistant for Linux

A desktop assistant on Linux is a software application designed to assist users in performing various tasks on their Linux- based desktop system. It typically integrates with the desktop environment and offers a user-friendly interface for performing common tasks such as launching applications, managing files and folders, and accessing system settings. The assistant can also be customized with plugins or extensions to add additional functionality. The main goal of a desktop assistant on Linux is to make the user experience as smooth and efficient as possible. By centralizing common tasks and providing an accessible interface, the assistant eliminates the need for users to navigate complex system settings or memorize multiple keyboard shortcuts. This, in turn, makes it easier for users to perform everyday tasks on their Linux-based desktop system, increasing productivity and overall satisfaction with their experience. Keywords— Deep Learning, Machine learning, NLP, Voice Assistant, Linux, Artificial Intelligence, Speech Recognition, LLMs.

WITHDRAWN: A Technique for Enhancing Respiration Sensing in Interference Scenarios with Wi-Fi Signal

Wi-Fi-based non-contact respiratory monitoring technology plays a significant role in smart elderly care by eliminating the need for users to wear additional hardware devices. Although respiration sensing has shown impressive accuracy in ideal scenarios, achieving precise respiration monitoring in interfering scenarios, such as when other users are engaged in activities, remains challenging. This difficulty arises primarily from the weak nature of the respiratory signal, which is susceptible to interference from activities. To address this issue, this paper introduces the RespEnh system, a respiratory monitoring system that utilizes human location information to mitigate interference. Firstly, we enhance the quality of respiratory signal by leveraging the frequency diversity of Wi-Fi signal, effectively visualizing respiratory patterns even amidst interference. Secondly, we apply short time window shift to remove walking noise in the time domain while preserving respiratory signal characteristics. Finally, we integrate multi-antenna signals to enhance spatial domain performance. Experimental results demonstrate that our system achieves a relative anti-interference performance of 2.4m, even when breathing as far away as 6.4m. It effectively monitors person's respiration, with a detection rate of 80% within an error range of 1bpm.

Analyzing User Feedback on a Fan Community Platform 'Weverse': A Text Mining Approach

This study applies topic modeling to uncover user experience and app issues expressed in users' online reviews of a fan community platform, Weverse on Google Play Store. It allows us to identify the features which need to be improved to enhance user experience or need to be maintained and leveraged to attract more users. Therefore, we collect 88,068 first-level English online reviews of Weverse on Google Play Store with Google-Play-Scraper tool. After the initial preprocessing step, a dataset of 31,861 online reviews is analyzed using Latent Dirichlet Allocation (LDA) topic modeling with Gensim library in Python. There are 5 topics explored in this study which highlight significant issues such as network connection error, delayed notification, and incorrect translation. Besides, the result revealed the app's effectiveness in fostering not only interaction between fans and artists but also fans' mutual relationships. Consequently, the business can strengthen user engagement and loyalty by addressing the identified drawbacks and leveraging the platform for user communication.

User Preferences and Needs for Health Data Collection Using Research Electronic Data Capture: Survey Study.

Self-administered web-based questionnaires are widely used to collect health data from patients and clinical research participants. REDCap (Research Electronic Data Capture; Vanderbilt University) is a global, secure web application for building and managing electronic data capture. Unfortunately, stakeholder needs and preferences of electronic data collection via REDCap have rarely been studied. This study aims to survey REDCap researchers and administrators to assess their experience with REDCap, especially their perspectives on the advantages, challenges, and suggestions for the enhancement of REDCap as a data collection tool. We conducted a web-based survey with representatives of REDCap member organizations in the United States. The survey captured information on respondent demographics, quality of patient-reported data collected via REDCap, patient experience of data collection with REDCap, and open-ended questions focusing on the advantages, challenges, and suggestions to enhance REDCap's data collection experience. Descriptive and inferential analysis measures were used to analyze quantitative data. Thematic analysis was used to analyze open-ended responses focusing on the advantages, disadvantages, and enhancements in data collection experience. A total of 207 respondents completed the survey. Respondents strongly agreed or agreed that the data collected via REDCap are accurate (188/207, 90.8%), reliable (182/207, 87.9%), and complete (166/207, 80.2%). More than half of respondents strongly agreed or agreed that patients find REDCap easy to use (165/207, 79.7%), could successfully complete tasks without help (151/207, 72.9%), and could do so in a timely manner (163/207, 78.7%). Thematic analysis of open-ended responses yielded 8 major themes: survey development, user experience, survey distribution, survey results, training and support, technology, security, and platform features. The user experience category included more than half of the advantage codes (307/594, 51.7% of codes); meanwhile, respondents reported higher challenges in survey development (169/516, 32.8% of codes), also suggesting the highest enhancement suggestions for the category (162/439, 36.9% of codes). Respondents indicated that REDCap is a valued, low-cost, secure resource for clinical research data collection. REDCap's data collection experience was generally positive among clinical research and care staff members and patients. However, with the advancements in data collection technologies and the availability of modern, intuitive, and mobile-friendly data collection interfaces, there is a critical opportunity to enhance the REDCap experience to meet the needs of researchers and patients.

Visions of the Future: What Could Happen to User Authentication?

The most prevalent information system security feature for the user is the authentication process. Passwords have been the primary authentication method for decades due to their simplicity for both the user and the system provider. However, over recent years, the digitalization of services has increased the number of credentials each user must manage, making traditional password authentication problematic for the user. Strong candidates for easier and more secure authentication methods are emerging (e.g., FIDO alliance, Single-sign-on, biometrics). Still, a single method has yet to dominate the market due to the rapid changes in technology, costs of implementation, trust in these methods, and the vast number of users and digital services. Due to the varied reasons that affect the adoption of these methods, it is unclear what kinds of authentication methods will be the forerunners in the future. This study aims to envision the future of user authentication and security features emerging from the interaction of different factors. We present a qualitative interview study, which examines six experts from the fields of authentication, cybersecurity, and emerging technologies. A hermeneutic mode of analysis is used to form scenarios of the future based on the observations of different experts. The results reflect an understanding of how users and their interactions with security features, such as authentication, may change over the following decade and beyond and how security professionals intend to incorporate this knowledge into future security systems. The results shed light on the influence of society, developing technology, and the need for user- and future-proof security in the coming years. This study will have several implications, as it will contribute to forming a coherent picture of the different elements that shape the future and give an idea of how to prepare for what is coming. Furthermore, it will provide an understanding of how choices with technology today lead to different futures.

Decoding the Cloud Giants: A Comparison of AWS, Azure and GCP

The adoption of cloud services by companies and organizations is increasingly becoming essential for enhancing competitive performance in today's business environment. Cloud services represent a relatively new technological advancement. However, selecting the optimal cloud service to meet specific requirements remains one of the most significant challenges. Major commercial entities, including Amazon AWS, Microsoft Azure, and Google Cloud, provide a variety of cloud services through dedicated, reliable, and cost-effective web applications. These cloud services attract a diverse range of users and organizations across various sectors, such as healthcare, business, and education. This article aims to introduce cloud customers to the most prominent cloud services. Additionally, we explore the concept of cloud computing, defined as the delivery of computing resources over the Internet. Leading cloud service providers such as Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP) offer on-demand cloud solutions, including processing power, memory, and databases, alleviating the need for users to invest in, operate, and maintain physical computers, hardware, and servers. This study compares the performance and services of three major cloud platforms—Google Cloud Platform, Amazon AWS, and Microsoft Azure—as well as their architectures and types of cloud services. Cloud technology allows resources to be dynamically scaled without the need for significant changes in infrastructure, additional staffing, or new software development.

Supporting Self-Managed Medication Abortion: Care Needs and Preferences of SMMA Users in Three US States

Supporting Self-Managed Medication Abortion: Care Needs and Preferences of SMMA Users in Three US States

Movie Recommendation System using Machine Learning

There is already enough content available on the movie recommendation system. Showing the movie recommendations is essential so that the user need not waste a lot of time searching for the content which he/she might like. Thus, movie recommendation system plays a vital role to get user personalized movie recommendations. After searching a lot on the internet and referring to a lot of research papers, we got to know that the recommendations made using Content-based Filtering are using a single text to vector conversion technique and a single technique to find the similarity between the vectors. In this research work, we have used multiple text to vector conversion techniques and manipulated the results of the multiple algorithms to get the final recommendation list. You can think of it as a hybrid approach using the Content-based Filtering technique only.