User Need Research Articles

Patterns of lexical innovation in Nigerian English

The need for users of English as a second language in Nigeria to fill in the lexical gaps during communication where English has failed has resulted in lexical innovation in Nigerian English. Therefore, to keep communication effective and active, individuals or groups of people in the society create new words, or bend the already existing words in English to bear a ‘local colour’ and content to satisfy communicative needs. This study investigates lexical innovations in Nigerian English and their patterning, and provides explanations to the origin of these lexical items. Some lexical items that have the sociolinguistic features of Nigerian English obtained from market places, school environments and other public places were selected through a purposive sampling technique. Data was gathered through oral interviews, observations, note-taking, and literature related to lexical innovations in languages. The lexical items identified in Nigerian English were analysed along Bamiro’s categorisation processes from the point of view of Sapir-Whorf’s linguistic relativity. It was observed that lexical items in Nigerian English evolved as a result of transfer of meaning and culture, coinages, analogical creations and direct borrowings from indigenous languages.

Chronic pain self-management in middle-aged and older adults: A collective intelligence approach to identifying barriers and user needs in eHealth interventions.

ObjectiveseHealth refers to health services and health information delivered or enhanced through the internet and related technologies. The number of eHealth interventions for chronic pain self-management is increasing. However, little evidence has been found for the overall efficacy of these interventions for older adults. The aim of the current study was to use a Collective Intelligence approach to identify the barriers and specific user needs of middle-aged and older adults using eHealth for chronic pain self-management.MethodsA Collective Intelligence workshop was conducted with middle-aged and older adults to generate, clarify, select, and structure ideas in relation to barriers to eHealth use and specific design requirements for the purposes of chronic pain self-management. Prior to attending the workshop, participants received a trigger question requesting the identification of five barriers to eHealth use for chronic pain self-management. These barriers were categorised and presented to the group along with barrier-related scenarios and user need prompts, resulting in the generation of a set of ranked barriers and a set of user needs.ResultsA total of 78 barriers were identified, from which six categories emerged: Content, Support, Technological, Personal, Computer Literacy and Accessibility. Additional idea-writing and group reflection in response to these barriers revealed 97 user needs.ConclusionThis is the first study to use Collective Intelligence methods to investigate barriers to eHealth technology use and the specific user needs of middle-aged and older adults in the context of chronic pain self-management. The results of the current study provide a platform for the design and development of enhanced eHealth interventions for this population.

A Comparison of Various IoT Application Layer Protocol

Data communication over the internet is highly swayed by protocols. So, the protocols have a very crucial role in data communication. Similarly, for the Internet of things (IoT) enabled devices the protocols have also an important role. In the Application-layer of IoT-enabled devices, the protocols have a significant role in the transfer of data from server to IoT devices and vice versa. There are different protocols are there, some are taken from web based internet protocols like Hyper Text Transfer Protocol (HTTP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), etc. And some are exclusively developed for IoT devices like Message Queue Telemetry Transport (MQTT), Constraint Application Protocol (CoAP), etc. IoT-enabled devices need a lightweight, secure and fast protocols for the transfer of data. When IoT enabled devices are developed there are limited number of protocols developed for communication of IoT devices. Various protocols are developed to fulfil the need of users but they have some advantages as well as disadvantages. In this paper, analysis of the performance and comparision of these protocols through different scenarios are given.

Osmosis machine learning load balancing of healthcare tasks in cutting edge technologies with smart grid

Smart grid communication requires an embedded approach on IoT-based cloud, fog computing and big data. In order to provide e-health and m-health services, the allocation of tasks on resources in healthcare services is crucial. The primary need for users in the healthcare industry is the solution to the bottleneck of service level agreement (SLA) and accomplishes the quality of service (QoS) parameters. The add-on objective is to achieve effective resource utilization and satisfaction of the end-user application for effective communication and load balancing of tasks on cutting edge technologies. The machine learning approach in osmosis load balancing of tasks at the end of the fog service provider (FSP) level reduces the network utilisation time, latency, usage of energy, etc. The results proves that fog nodes are efficient than the cloud nodes, and also the experimental results proved that the proposed model is efficient than the various other existing approaches.

Movie Recommendation Algorithm Based on Ensemble Learning

With the rapid development of personalized services, major websites have launched a recommendation module in recent years. This module will recommend information you are interested in based on your viewing history and other information, thereby improving the economic benefits of the website and increasing the number of users. This paper has introduced content-based recommendation algorithm, K-Nearest Neighbor (KNN)-based collaborative filtering (CF) algorithm and singular value decomposition-based (SVD) collaborative filtering algorithm. However, the mentioned recommendation algorithms all recommend for a certain aspect, and do not realize the recommendation of specific movies input by specific users which will cause the recommended content of the website to deviate from the need of users, and affect the experience of using. Aiming at this problem, this paper combines the above algorithms and proposes three ensemble recommendation algorithms, which are the ensemble recommendation of KNN + text, the recommendation of user KNN + movie KNN, and the recommendation of user KNN + singular value decomposition. Compared with the traditional collaborative filtering algorithm based on matrix factorization, the method we proposed can realize the recommendation of specific movies input by specific users and make more personalized recommendations and can deal with the problem of cold start and sparse matrix processing issues to a certain extent.

Uncertain service selection using hesitant fuzzy sets and grey wolf optimisation

Dealing with complex user requirements and the exponential growth of cloud service providers in a dynamic environment is a challenging task. In practice, the quality of service (QoS) constitutes a crucial feature in cloud service selection (CSS). We generally assume that the QoS of cloud services is inherently dynamic, and consequently, CSS can be viewed as a complex multi-criteria decision-making (MCDM) issue. In this paper, we propose a grey wolf-based (GW) approach that uses entropy, cross-entropy and hesitant fuzzy sets to select the near-optimal cloud service compositions that best meet the user's need. According to the experiments, the proposed approach is very promising in terms of the computation time and the degree of satisfaction of global constraints.

Workflow and performance of intraoperative CT, cone-beam CT, and robotic cone-beam CT for spinal navigation in 503 consecutive patients.

A direct comparison of intraoperative CT (iCT), cone-beam CT (CBCT), and robotic cone-beam CT (rCBCT) has been necessary to identify the ideal imaging solution for each individual user's need. Herein, the authors sought to analyze workflow, handling, and performance of iCT, CBCT, and rCBCT imaging for navigated pedicle screw instrumentation across the entire spine performed within the same surgical environment by the same group of surgeons. Between 2014 and 2018, 503 consecutive patients received 2673 navigated pedicle screws using iCT (n = 1219), CBCT (n = 646), or rCBCT (n = 808) imaging during the first 24 months after the acquisition of each modality. Clinical and demographic data, workflow, handling, and screw assessment and accuracy were analyzed. Intraoperative CT showed image quality and workflow advantages for cervicothoracic cases, obese patients, and long-segment instrumentation, whereas CBCT and rCBCT offered independent handling, around-the-clock availability, and the option of performing 2D fluoroscopy. All modalities permitted reliable intraoperative screw assessment. Navigated screw revision was possible with each modality and yielded final accuracy rates > 92% in all groups (iCT 96.2% vs CBCT 92.3%, p < 0.001) without a difference in the accuracy of cervical pedicle screw placement or the rate of secondary screw revision surgeries. Continuous training and an individual setup of iCT, CBCT, and rCBCT has been shown to permit safe and precise navigated posterior instrumentation across the entire spine with reliable screw assessment and the option of immediate revision. The perceived higher image quality and larger scan area of iCT should be weighed against the around-the-clock availability of CBCT and rCBCT technology with the option of single-handed robotic image acquisition.

What Else Is New? Open Data Users Need to Know What’s Changed

What Else Is New? Open Data Users Need to Know What’s Changed

Performance Analysis of LEACH, LEACH-C, TS-LEACH, MOD-LEACH in WSN

Energy efficiency has been a major research topic in Wireless Sensor Networks (WSN). Because the sensor networks are powered by batteries, they will die after a given amount of time. As a result, extending the life of sensor equipment by optimizing data dissipation in a cost-effective manner remains a difficulty. Generally, Flat, Hierarchical and location-based routing are the three types of routing Protocol. Awareness of the network structure and Routing Protocol is essential, and it must be suitable for the user need. The survey presents Comparison of various LEACH Protocols in WSN

PROGRAMMATIC SYSTEM FOR INTERACTIVE REPRESENTATION OF SCIENTIFIC INSTITUTION RESULTS

Our time is characterized by rapidly increased information quantity. The volumes of accumulated data become so large that the manual handling of this data by experts of certain subject areas becomes almost impossible. In such circumstances, it is extremely important to develop effective tools that could provide users the information, presented in the form of large arrays of spatially and thematically distributed information resources (documents). An example of an array of spatially and thematic distributed documents is the set of information that characterizes scientific achievements of the institutions of the National Academy of Sciences of Ukraine. Such information may be useful for a wide range of users, but can bring especially great benefits to young researchers — students, engaged in scientific and educational activities. The article proposes the approach to build the information and analytical system designed to provide the access to an array of information about the achievements of scientific institutions for young researchers. The system is characterized by flexible ontology driven architecture, which allows dynamical modifying of its composition and structure for the need of a particular user or project. The article describes the architecture of the ontology driven lexicographic system, based on which the system of interactive representation of achievements is constructed. In addition, the structure and interrelations of the software components that are part of the system are described. The software system is being developed within the framework of research “Interactive Base of Knowledge of Scientific Achievements of the Institutions of National Academy of Science of Ukraine” by the researchers of National Centre “Junior Academy of Sciences of Ukraine”.

The Impact of the use of E-money on Interest Rates with the Amount of Money Circulation as an Intervening Variable: A case study of Indonesia

Users of e-money payment instruments in Indonesia continue to experience growth every year. An increase in the need for e-money users to transact, causes an increase in the need for money supply in Indonesia. So that the stability of money circulation needs to be controlled, namely through monetary policy interest rates. The purpose of this study is to examine the impact of the use of e-money on interest rate policies in Indonesia, through the money supply as an intervening variable. The study was conductedat Bank Indonesia, using a statistical test model of path analysis. The test results show that the use of e-money has a significant direct effect on interest rates. While the indirect effect tests, shows that the use of e-money through the variable money supply as an intervening variablehas no significant effect

In-Bed Posture Classification Based on Sparse Representation in Redundant Dictionaries

Non-orthogonal signal representation using redundant dictionaries gradually gained popularity over the last decades. Sparse methods find major application in signal denoising, audio declipping, time-frequency analysis, and classification, to name a few. This paper is inspired by the exceptional results of sparse representation classification originally suggested for face recognition. We compare the method to other common classifiers using simulated as well as real datasets. In the latter the proposed method is tested with real pressure data from a bed equipped with a matrix of 30 × 11 pressure sensors. Here the method outperforms standard classification methods (surpassing 91 % accuracy) without need of parameter selection or special user's skills. Furthermore it offers a means of dealing with occlusions, whose results are presented as well.

User's Virtual Social Needs and Playlist Selection Behavior: Design Implications for Online Music Platform

User's Virtual Social Needs and Playlist Selection Behavior: Design Implications for Online Music Platform

How to Specify Software Users Want and Need? Execution Time, Team Configuration and Contingency Factors for Optimizing Little Design Up-Front (Lduf)

How to Specify Software Users Want and Need? Execution Time, Team Configuration and Contingency Factors for Optimizing Little Design Up-Front (Lduf)

A Design Proposal of Instant Food and HMR Cooking Device through Analysis of Change in Food Life and User Needs

최근 한국의 세대 기준이 1인 및 맞벌이 가구로 점점 변해 가면서 시간과 비용을 아끼려는 소비자 니즈가 증가하고, 코로나19 감염 우려로 사람이 많은 곳을 꺼리게 되면서 즉석 및 가정 간편식의 구매가 급증하고 있다. 본 연구는 즉석 및 가정 간편식 제품을 편리하게 조리할 수 있는 조리기기 디자인 제안을 목표로 맞벌이 및 1인 가구 증가와 코로나19 발생에 따른 한국인의 식생활 변화를 살펴보고, 즉석 및 가정 간편식과 가정 내 조리기기에 대하여 조사 분석하였다. 또한, 즉석 및 가정 간편식의 선호도가 높은 남녀 139명을 대상으로 한 설문조사와 5명을 대상으로 한 사용자 관찰분석을 통하여 사용자 니즈를 분석하였다. 이를 바탕으로 바코드 인식 모듈, 정수기, 인덕션, 전자레인지, 전용 용기로 구성된 즉석 및 가정 간편식 조리기기 디자인을 제안하였다.Recently, as Korea s household standards gradually change to single-person and double-income households, consumer needs to save time and money are increasing, and purchases of instant and home convenience foods are increasing rapidly as concerns over COVID-19 infection increase. This study investigated and analyzed instant and home convenience foods and home cooking devices by examining the changes in Korean diet caused by the increase in double-income and single-person households and the outbreak of COVID-19. In addition, user needs were analyzed through a survey of 139 men and women with high preference for instant and home convenience meals and a user observation analysis of 5 people. Based on this, an instant and home-friendly cooking appliance design consisting of barcode recognition modules, water purifiers, induction, microwave ovens, and dedicated containers was proposed.

Pilots for Healthy and Active Ageing (PHArA-ON) Project: Definition of New Technological Solutions for Older People in Italian Pilot Sites Based on Elicited User Needs.

Background: The Pilots for Healthy and Active Ageing (PHArA-ON) project aimsto ensure reality smart and active living for Europe’s ageing population by creating a set of integrated and highly customizable interoperable open platforms with advanced services, devices, and technologies and tools. The aim of the present study was to determine the needs and preferences of older people and their caregivers for improving healthy and active aging and guiding the technological development of thePHArA-ON system. Methods: A pre-structured interview was administered to older adults, informal caregivers and professional caregivers (including social operators) taking part in the piloting sessions. Results: Interviews were carried out in Umana Persone Social Enterprise R&D Network (UP) in Tuscany, and Ospedale Casa SollievodellaSofferenza (CSS) in Apulia. A total of 22 older adults, 22 informal caregivers, 13 professional caregivers and 4 social operators were recruited. A prioritization analysis of services, according to the stakeholder’s needs, has determined two fundamental need categories: Heath Management (i.e., stimulation and monitoring), and Socialisation (i.e., promoting social inclusion). Conclusions: The main scientific contributions to this study are the following: to design and evaluate technology in the context of healthy and active ageing, to acquire relevant knowledge on user needs to develop technologies that can handle the real life situations of older people, obtain useful insights about the attitude and availability of end-users in using technologies in clinical practice, and to provide important guidelines to improve the PHArA-ON system. Specific experimentation stages were also carried out to understand which kind of technology is more acceptable, and to obtain feedback regarding the development priority related to the impact of the proposed services. Research through fruitful and continuous interaction with the different subjects involved in the development process of the system, as well as with stakeholders, enabled the implementation of a platform which could be further and easily integrated and improved.

A Study of Digital Natives' Tendency to Digital Games in the Context of the Consumer Society

With the infiltration of world variables into life, societies have begun to approach each issue from a different perspective. Consumption has started to reach quite different dimensions especially with the development of communication technologies and becoming difficult to control. This development process not only experienced in new communication technologies, while affecting the consumer society also prepares the ground for the 21st century children, who are described as digital natives, to become a part of the consumption process. The concept of play, which is an important element of childhood, has also undergone an important change and transformation process from the past to the present, and even with the effect of communication technologies, games have been transferred to digital environments. The games on these digital platforms create a need for child users and child users are encouraged to consume in this way. The aim of this study is to analyze the consumption trend of today's children, who are considered as digital natives, towards consumption through digital games with the qualitative content analysis method and to reveal the consumption effect of digital games on the child user.

Magnetic Multiplex Loop Mediated Isothermal Amplification (MM-LAMP) technique for simultaneous detection of dengue and chikungunya virus

Dengue and chikungunya viruses are arthropod borne virus spread through common vector instigating infection in human. There has been an increased recognition that more attention needs to be paid to similar sympotoms caused by both of the virus as they spread in the same region at same time. It warrants need of cost effective, user friendly and rapid multiplex diagnostic technique which could simultaneously diagnose and identify between two virus diseases in resource poor setting. A magnetic multiplex loop mediated isothermal amplification (MM-LAMP) technique was developed by coupling multiplex LAMP with magnetic particle-based naked eye visualization to overcome the shortcoming of simultaneous detection of both diseases. In recent years this technology has emerged as a particularly attractive candidate as amplification reaction process completes within 45 min. The first step involves multiplexing biotin and digoxigenin coated dengue and chikungunya primers respectively in LAMP reaction followed by precipitation of the amplified DNA with polyethylene glycol (PEG) buffer and finally clumping with streptavidin and anti-digoxigenin coated magnetic particle for virus discrimination and naked eye visualization. The DNA detection limit of MM LAMP visualization was 51.65 ng/μl which is comparable to the electrophoresis base UV light visualization. The results showed potential superiority over standard methods polymerase chain reaction (PCR). This current advancement empowers multiplex LAMP utility in resource limited setting without using any of the florescent dyes, turbidimeter, or the sophisticated quantitative PCR machine etc which restrict multiplex LAMP technique to laboratorial use only. We have proposed a novel method without such limitations. This technique has potential as a point of care technique for simultaneous detection of two diseases.

A Compact Model for Lyophilizer Equipment Capability Estimation.

This work presents a compact model for the equipment capability limit of a common configuration of pharmaceutical lyophilizers, a product chamber separated from the condenser by a duct and isolation valve, at a wide range of design parameters. The equipment capability limit is one of the most important characteristics determining the lyophilization design space for a particular product, container, and equipment combination. Experimental measurements of equipment capability are time-consuming and expensive, especially at the production scale. Numerical modeling using computational fluid dynamics may reduce the number of experiments and provide insights into the physics of the process with high resolution. The computational fluid dynamics (CFD) modeling has been used in this work to develop a compact model for lyophilizer equipment capability. This eliminates the need for end users to create a full CFD model of the equipment and process. Full CFD and compact model simulations for laboratory and pilot-scale lyophilizers have been compared with tunable diode laser absorption spectroscopy measurements of the water vapor mass flow during ice slab tests. The compact model results average deviation from the experimental data is within 10%, whereas the full CFD simulations are within 5%. The compact model is based on several key parameters which are the main characteristics of a lyophilizer affecting the equipment capability curve. These parameters are discussed, and their effect on the modeling results is shown.

Deep-Reinforcement-Learning-Based Sustainable Energy Distribution for Wireless Communication

Many countries and organizations have proposed smart city projects to address the exponential growth of the population by promoting and developing a new paradigm for maximizing electricity demand in cities. Since Internet of Things (IoT)-based systems are extensively used in smart cities where huge amounts of data are generated and distributed, it could be challenging to directly capture data from a composite environment and to offer precise control behavior in response. Proper scheduling of numerous energy devices to meet the need of users is a demand of the smart city. Deep reinforcement learning (DRL) is an emerging methodology that can yield successful control behavior for time-variant dynamic systems. This article proposes an efficient DRL-based energy scheduling approach that can effectively distribute the energy devices based on consumption and users&#x0027; demand. First, a deep neural network classifies the energy devices currently available in a framework. The DRL then efficiently schedules the devices. Edge-cloud-coordinated DRL is shown to reduce the delay and cost of smart grid energy distribution.