User Status Research Articles

The Effects of Oral Nicotine Pouch Packaging Features on Adult Tobacco Users' and Non-Users' Product Perceptions.

Oral nicotine pouches (ONPs) are novel products that are marketed as "tobacco-free" alternatives to cigarettes and smokeless tobacco (ST). This study examined the effects of ONP packaging features on adult tobacco users' and non-users' product perceptions. Adult tobacco users (cigarettes, ST, and dual cigarette/ST) and non-users (total N = 301) viewed ONP pack images in a 4 × 3 × 2 between-subject experiment testing the effects of the displayed flavor (cool mint, coffee, dark frost, and smooth), nicotine concentration (none displayed on the package, 3 mg, and 6 mg), and addiction warning label (yes or no). The outcomes were perceived substitutability of ONPs for cigarettes and ST and perceived risks. We modeled the effects of tobacco user status and the experimental factors on these outcomes. All tobacco user groups perceived ONPs to be significantly less harmful and less addictive than non-users. There were significant effects of nicotine concentration on perceived risks. Compared to packages that did not display nicotine concentration, packages displaying 6 mg nicotine concentration produced significantly lower perceived harm (β = -0.23, 95% CI -0.44, -0.02), perceived addictiveness (β = -0.28, 95% CI -0.51, -0.05), risk appraisals of harm (β = -0.50, 95% CI -0.88, -0.12) and risk appraisals of addictiveness (β = -0.53, 95% CI -0.95, -0.11). The study findings demonstrate that the nicotine concentration displayed on ONP packaging can affect adults' perceptions of ONPs. Further research on the effects of ONP packaging features emphasizing nicotine (e.g., "tobacco free" nicotine claims) on tobacco users and non-users is needed to assess their potential public health impact.

Smart Sports Outward Bound Training Assistant System Based on WSNs

The outward-bound training has been a popular manner to exercise in daily life. However, there lacks an intelligent assistant system to monitor the real-time status of users to avoid accidents during training. In order to fill this gap, this paper established an intelligent system to monitor fatigue status during outward-bound training by using surface electromyography (sEMG) signals. The system consists of three parts: a wearable device, edge node, and cloud server. First, the wearable device collects sEMG signals. Second, the edge node processes the collected sEMG signals and sends the sEMG signal features to the cloud server. Finally, the cloud server returns the results to edge node according to a stored classification model that learnt from massive historical sEMG signals. The experimental results show the effectiveness of the proposed system.

P-PBFT: An improved blockchain algorithm to support large-scale pharmaceutical traceability

To solve the problems of high latency, high system overhead, and small supported scale in the current application of pharmaceutical traceability combined with blockchain technology, an algorithm called Pharmaceutical-Practical Byzantine Fault Tolerance (P-PBFT) based on PBFT, grouping, and credit voting is proposed. The algorithm combines the characteristics of a pharmaceutical supply chain, optimizes the consistency protocol in the original algorithm, divides large-scale network nodes into different consensus sets by response speed, and performs grouping consensus. The algorithm's credit model and voting mechanism dynamically updates user status according to the behavior of nodes in consensus, evaluates the reliability of users, and also serves as a basis for electing management nodes. Experimental results show that the improved P-PBFT consensus algorithm provides smaller latency and higher throughput for pharmaceutical traceability systems, supports larger-scale traceability, effectively alleviates the dramatic increase in communication among network nodes, and reduces the influence of malicious nodes.

The Influence of Users' Status in Online User Community on Entrepreneurial Intention

This paper mainly explores the influence of users' status in online user community on entrepreneurial intention. In the online user community, users share, communicate, improve each other, and participate voluntarily. In this process, the status of users in the community has changed. Different status of users in online communities will affect user self-efficacy, thereby affecting user entrepreneurial intention. At the same time, entrepreneurial self-efficacy will also influence user entrepreneurial intention through entrepreneurial opportunity identification. We selected the CSDN community as the research object, used major social platforms to conduct a questionnaire survey on users in the community, and explored the impact of users' status in the online user community on entrepreneurial intention.

A Mobile Application for Supporting and Monitoring Elderly Population to Perform the Interventions of the FINGER Study.

Alzheimer's disease is a progressive disease that is caused by the destruction of brain neurons. It seems it affects a large group of the world's population that is estimated around 47 million and is expected to triple by 2050. Slowly but surely, the patient's condition is deteriorating, due to the increase of symptom severity, rendering him/her in need of special care. A great percentage of these cases can be attributed to some common modifiable risk factors such as hypertension, obesity, a lack of exercise, alcohol misuse, smoking, unhealthy diet, and a low level of education. The Finnish Geriatric Intervention Study (FINGER Study) proves that some interventions focused on the abovementioned risk factors of the individual's daily life can contribute to delay the occurrence of Alzheimer's disease. Concurrently, the rapid development of smart devices encourages the use of health applications that provide guiding tools and suggestions based on the user's status. The outcome of this paper is the development of a mobile application, to implement and monitor the interventions proposed by the FINGER Study. Based on the user's profile, it offers the ability to evaluate the likelihood of cognitive decline, monitor the process, and help delay the disease's occurrence.

Legal status of a subsoil user as a special participant in business activities

Legal status of a subsoil user as a special participant in business activities

Intelligent sitting posture classifier for wheelchair users.

In recent years, there has been growing interest in postural monitoring while seated, thus preventing the appearance of ulcers and musculoskeletal problems in the long term. To date, postural control has been carried out by means of subjective questionnaires that do not provide continuous and quantitative information. For this reason, it is necessary to carry out a monitoring that allows to determine not only the postural status of wheelchair users, but also to infer the evolution or anomalies associated with a specific disease. Therefore, this paper proposes an intelligent classifier based on a multilayer neural network for the classification of sitting postures of wheelchair users. The posture database was generated based on data collected by a novel monitoring device composed of force resistive sensors. A training and hyperparameter selection methodology has been used based on the idea of using a stratified K-Fold in weight groups strategy. This allows the neural network to acquire a greater capacity for generalization, thus allowing, unlike other proposed models, to achieve higher success rates not only in familiar subjects but also in subjects with physical complexions outside the standard. In this way, the system can be used to support wheelchair users and healthcare professionals, helping them to automatically monitor their posture, regardless physical complexions.

Impact of Color on Human Behavior Case – Interior Space

Colour is considered as the basic visual perception of any space. There are numerous developed theories and assumptions related to the aesthetic comfort offered by colours, and to their effect on human psyche. In this paper, it is inspected how the choice of colour changes with age and its impact on that age group of people. This investigation will focus all the factors those are predicted to be more influential in architecture, interior spaces and the psychological status of users. The research aim of this study is to increase the concern about the importance of the interaction between interior space and human behaviour responding to the colour of the interior space. Paper talks about the importance of colour in an interior space where the different user groups spend most of their time like- schools for kids, offices for adults and meditation centre, old age homes for senior citizens. This research would help others to understand the importance of colour in the interior space and how people of different age groups respond to it. Based on the analysis of the conducted surveys and interviews, conclusions are drawn about the effect colours have on human behaviour in an interior space.

Safety of Laryngeal Electromyography and Repetitive Stimulation

Laryngeal Electromyography (LEMG) is a procedure used to assess electrical signals from laryngeal muscles. It is considered to be the gold standard test for examining vocal fold paresis/paralysis. Repetitive stimulation studies, which usually are performed at the time of LEMG, can aid in assessment of neuromuscular function. Electromyography poses risks to the patient due to the use of electricity and needles. The purpose of this paper was to evaluate the safety of LEMG and discuss the possible complications. This was a retrospective review of patients from January 2015 through February 2022. Information from patient charts included age, sex, chief complaint, past medical history, family medical history, medications, social history, strobovideolaryngoscopy results, type of paresis, professional voice user status, presence of pacemaker, laryngeal electromyography and repetitive stimulation study results, and reported complications. Complications were considered to be anything that differed from the norm. Pain was included as a complication. Data were analyzed for relationships and significance. Five hundred patients were included. Eighteen of the 500 (3.6%) experienced complications. Four (2.1%) males and 14 (4.5%) females experienced complications. Nine of the 18 (50%) reported pain after the procedure, 1 (5.56%) had excessive bleeding (controlled with pressure), 5 (27.78%) reported voice changes, and 3 (16.67%) experienced difficulty with completion of the procedure. No patients with pacemakers or patients taking anticoagulants had complications, but repetitive stimulation studies were not performed on patients with pacemakers. LEMG and repetitive stimulation studies are safe and effective procedures to examine laryngeal muscles. Repetitive stimulation studies are not recommended for patients with pacemakers.

CAFE and SOUP: Toward Adaptive VDI Workload Prediction

For Virtual Desktop Infrastructure (VDI) system, effective resource management is rather important where turning off spare virtual machines would help save running cost while maintaining sufficient virtual machines is essential to secure satisfactory user experience. Current VDI resource management strategy works in a passive manner by either reactively driving available capacity based on user demands or following manually configured schedules, which may lead to unnecessary running costs or unsatisfactory user experience. In this article, we propose a first attempt toward proactive VDI resource management, where two adaptive learning approaches for VDI workload prediction are proposed by learning from multi-grained historical features. For non-persistent desktop pool, based on the aggregation session count of pool-sharing users, the CAFE approach induces a pool-level workload predictive model by utilizing coarse-to-fine historical features extracted from aggregation workload data. For persistent desktop pool, based on the session connection status of individual users within the same pool, the SOUP approach induces user-level workload predictive model by incorporating encoded multi-grained features extracted from the logon behavior of individual users into an aggregation pool-level model. Extensive experiments on datasets of real VDI customers and electricity load evidently verify the effectiveness of the proposed adaptive approaches for VDI workload prediction as well as other workload prediction tasks.

Intelligence and Usability Empowerment of Smartphone Adaptive Features

In adaptivity, the interface of the device automatically adjusts and assists the user. The adaptive user interfaces can adapt their activities by monitoring user status, the state of the system, and the current situation according to the adaptation strategy. Usually, the intensity of adaptation is measured in effectiveness, efficiency, and satisfaction to analyze the smartphone’s adaptive features. The adaptive features of light-emitting diode (LED) notifications, voice commands, face recognition, screen rotation, kid mode, drive mode, night mode, Swift Keyboard, s-health, gesture recognition, and fingerprint are selected for both iOS and Android platforms. Task completion within a specific time frame is used to measure effectiveness and efficiency, while satisfaction is calculated using the after-scenario questionnaire (ASQ). A total of 550 users are involved in the experimentation. The usability evaluation is measured for smartphone features. The effectiveness of adaptive features contains higher adaptivity in face recognition (87%) and voice command (85%). Furthermore, the satisfaction level is greater for adaptive features than non-adaptive features. This study indicates that adaptive features can only be used after a thorough examination of the user’s context. Furthermore, the usability evaluation shows that there is a dire need for adaptive smartphone features to provide ease and satisfaction to the user.

Comparison and Prediction of Data Mining Models to Determine the Classification of Family Planning Program User Status

Data mining is the process of analyzing a sample to determine the best performing algorithm. An easy way to extract information or insights from large amounts of data is by using the techniques involved in data mining. There are several methods of classification which can be used to determine the level of a certain acuity. In Indonesia, family planning program is the most common program in the government to control population growth. A decision tree, a logistic regression, a naive Bayes model, and a gradient boosted model are used in this study. To perform classification family planning program User Status in Mangunharjo sub-district, the variable used is the wife's age, age of the youngest child, stages of prosperous family, and number of children. The training data comparison testing is 70:30. This study was tested by using the AUC value and t-test. The best value for accuracy is the Decision Tree algorithm with a percentage of 94.2% and an AUC value of 0.939. From the results of this test, it can be concluded that for a comparison of all tests performed on the dataset, the Decision Tree algorithm model can be said to be better than the other three algorithm models.

CFX: Contention-Free Channel Access for IEEE 802.11ax

Orthogonal frequency-division multiple access (OFDMA) has attracted great attention as a key technology for uplink enhancement for Wi-Fi, since it can effectively reduce network congestion and channel access delay. Unfortunately, the traditional random access protocol of Wi-Fi seldom allows these benefits to be achieved, especially in dense network environments, as the access point (AP) rarely gains the channel access needed to trigger OFDMA uplink transmissions due to severe frame collisions. To address this problem, we propose a new channel access scheme called Contention-Free Channel Access for 802.11ax (CFX). In the proposed scheme, users can access the channel without contention, since they are guaranteed a transmission opportunity immediately after another user's transmission. To realize CFX on top of the existing Buffer Status Report/BSR Poll (BSR/BSRP) exchange protocol of 802.11ax, we develop an additional scheme based on shared channel access that helps the AP to obtain the buffer status of users and manage a contention-free channel access schedule. In addition, in order to appropriately utilize the savings from the reduced frame collisions, we conduct sum throughput maximization using an actor-critic proximal policy optimization (PPO)-based deep reinforcement learning approach. The results of an extensive evaluation show that CFX not only significantly improves the uplink performance of Wi-Fi in terms of throughput and channel access delay but can also dynamically adjust the parameters in response to changes in the network status.

Tobacco Whack-A-Mole: A Consumption Taxonomy of Cigar & Other Combustible Tobacco Products among a Nationally Representative Sample of Young Adults.

Little filtered cigars and cigarillos (LCCs) are consumed infrequently, co-administered with marijuana, and concurrently used with other tobacco products. Reliance on the past 30-day use estimate, a marker of tobacco user status, may underestimate the dynamic nature of intermittent LCC and other tobacco product use. We developed a framework to capture the intermittent nature of exclusive LCC use and dual/poly use with cigarettes and large cigars using broader timing of last product use categories and product use modality (e.g., with marijuana). Data come from the baseline C'RILLOS study, a U.S. nationally representative sample of young adults aged 18-34 (n = 1063) collected in October 2019. We developed a consumption taxonomy framework that accounted for respondents' modality of LCC use (i.e., use with tobacco, LCC-T, or use with marijuana as blunts, LCC-B), the exclusive use of LCCs and other tobacco products (i.e., cigarettes, and large cigars) or their co-use and the timing of last product use (i.e., ever and past 30 days, past 3 months, past 6 months, greater than 6 months). Seventy-five percent of our sample reported ever use of any combustible tobacco product, including LCCs. The most common ever use pattern was poly use of LCC-T + LCC-B + cigarettes (16%). Our consumption taxonomy framework demonstrated the fluid nature of combustible tobacco product use among LCC users. For instance, among past 30-day cigarette users, 48% reported using LCC-T, 39% reported using LCC-B, and 32% reported using large cigars in the past 3 months or more. The tobacco use field currently classifies 'tobacco users' based on the product they smoked in the past 30 days. Any tobacco product use beyond the past 30-day period is considered 'discontinued use' and not the focus of intervention or tobacco regulatory science decisions. We documented the substantial proportion of young adult LCC, cigarette, and large cigar users who either exclusively or dual/poly used these combustible products in recent (e.g., past 3 months) periods. To prevent underestimation of use, surveillance measures should assess the use modality, timing of last product use, and exclusive/multiple product use to more accurately identify the smoking status of young adult LCC users.

Trends in Smart Helmets With Multimodal Sensing for Health and Safety: Scoping Review.

As a form of the Internet of Things (IoT)-gateways, a smart helmet is one of the core devices that offers distinct functionalities. The development of smart helmets connected to IoT infrastructure helps promote connected health and safety in various fields. In this regard, we present a comprehensive analysis of smart helmet technology and its main characteristics and applications for health and safety. This paper reviews the trends in smart helmet technology and provides an overview of the current and future potential deployments of such technology, the development of smart helmets for continuous monitoring of the health status of users, and the surrounding environmental conditions. The research questions were as follows: What are the main purposes and domains of smart helmets for health and safety? How have researchers realized key features and with what types of sensors? We selected studies cited in electronic databases such as Google Scholar, Web of Science, ScienceDirect, and EBSCO on smart helmets through a keyword search from January 2010 to December 2021. In total, 1268 papers were identified (Web of Science: 87/1268, 6.86%; EBSCO: 149/1268, 11.75%; ScienceDirect: 248/1268, 19.55%; and Google Scholar: 784/1268, 61.82%), and the number of final studies included after PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) study selection was 57. We also performed a self-assessment of the reviewed articles to determine the quality of the paper. The scoring was based on five criteria: test environment, prototype quality, feasibility test, sensor calibration, and versatility. Smart helmet research has been considered in industry, sports, first responder, and health tracking scenarios for health and safety purposes. Among 57 studies, most studies with prototype development were industrial applications (18/57, 32%), and the 2 most frequent studies including simulation were industry (23/57, 40%) and sports (23/57, 40%) applications. From our assessment-scoring result, studies tended to focus on sensor calibration results (2.3 out of 3), while the lowest part was a feasibility test (1.6 out of 3). Further classification of the purpose of smart helmets yielded 4 major categories, including activity, physiological and environmental (hazard) risk sensing, as well as risk event alerting. A summary of existing smart helmet systems is presented with a review of the sensor features used in the prototyping demonstrations. Overall, we aimed to explore new possibilities by examining the latest research, sensor technologies, and application platform perspectives for smart helmets as promising wearable devices. The barriers to users, challenges in the development of smart helmets, and future opportunities for health and safety applications are also discussed. In conclusion, this paper presents the current status of smart helmet technology, main issues, and prospects for future smart helmet with the objective of making the smart helmet concept a reality.

Assessment of the Health Status of Women in Prison: A Comparison between Drug Users versus Non-users.

This study aims to analyze the history of substances use, clinical diagnoses, contacts with healthcare, and health complaints. Also, it aims to compare the health status of substance users versus non-users. Health records of 93 detained women from a prison in northern Portugal, with a mean age of 38.02 years old, were reviewed and coded using both International Classification of Primary Care-2 and International Classification of Diseases-10th. Data revealed high percentages of detainees with a history of substance use and pre-existing clinical diagnosis. During the first month of imprisonment, on average, participants had complained to a health professional 14.63 times. Substance users were younger and presented more mental health problems and health complaints. Health policies and professional practices in prison must include immediate assessment of a woman's health concerns and foster the development of efficient protocols to address those problems, especially substance use disorder treatment.

Factors Influencing Increased Use of Technology to Communicate With Others During the COVID-19 Pandemic: Cross-sectional Web-Based Survey Study.

Communication via technology is regarded as an effective way of maintaining social connection and helping individuals to cope with the psychological impact of social distancing measures during a pandemic. However, there is little information about which factors have influenced increased use of technology to communicate with others during lockdowns and whether this has changed over time. The aim of this study is to explore which psychosocial factors (eg, mental health and employment) and pandemic-related factors (eg, shielding and time) influenced an increase in communication via technology during the first lockdown in the United Kingdom. A cross-sectional, web-based survey was conducted between April and July 2020, examining thoughts, feelings, and behaviors associated with the pandemic, including communicating more using technology (eg, via messaging, phone, or video). We collected sociodemographic information, employment status, mental health service user status, and depression symptoms. We used hierarchical logistic regression to test which factors were associated with communicating more using technology during the lockdown. Participants (N=1464) were on average 41.07 (SD 14.61) years old, and mostly women (n=1141; 77.9%), White (n=1265; 86.4%), and employed (n=1030; 70.4%). Participants reported a mild level of depression (mean 9.43, SD 7.02), and were communicating more using technology (n=1164; 79.5%). The hierarchical regression indicated that people who were employed and experiencing lower levels of depression were more likely to report increased communication using technology during a lockdown period of the COVID-19 pandemic, and over time, men communicated more using technology. Increased use of technology to communicate was related to greater communication and the inability to see others due to the social distancing measures enacted during the lockdown. It was not related to a general increase in technology use during the lockdown. Although most participants reported increased use of technology to communicate during a lockdown period of the COVID-19 pandemic, this was more apparent in the employed and those experiencing low levels of depression. Moving forward, we should continue to monitor groups who may have been excluded from the benefits of support and communication using technology.

The Moderating Role of Cannabis Usage and Voting Intention on Oregon Residents' Support for Cannabis Tourism

Changes in perceptions and laws on recreational cannabis use have shifted significantly in recent years. Yet research in this field is still scarce. This study explores residents' perceptions on impact and support for cannabis tourism using cannabis user status and voting intention as moderators. Results from a quantitative survey distributed to Oregon residents (n = 700) reveal overall positive perceptions on cannabis legalization and cannabis tourism. More specifically, perceived positive impact had more influence on support for cannabis tourism among nonusers than users, while users' support appeared to be steady regardless of their perceived level of positive impact. The study findings also showed a significant relationship between voting intention and support for cannabis tourism but an insignificant interaction between voting intention and impact factors, indicating that voting intention may be treated as a separate construct affecting residents' support, rather than a moderator between impact factors and support level. Considering the continued evolvement in the legal and economic landscapes of cannabis tourism, policymakers and industry professionals should engage in continual conversations on how to plan and manage this new tourism segment for community and state development.

Challenges Faced in Provision of Information Resources and Services in County Assembly Libraries: Case of Siaya and Kericho Counties

Libraries have the key responsibility of ensuring provision of appropriate information resources and services for its users. Special libraries were established in the County Assemblies of Kenya and were expected to meet the information and research needs of all members of staff. The success of such libraries would be judged by their ability to achieve the mentioned objectives. However, this could only be possible if the librarians were fully supported by the County Assembly management and provided with the necessary conducive environment to deliver. The purpose of this study therefore, was to establish challenges faced by librarians in their endeavor to provide information resources and services. The study was conducted in Siaya and Kericho County Assembly Libraries both located in County Assembly Premises of respective towns. The pilot study was done in Kisumu County Assembly Library and purposive sampling was used for the study. The two libraries were selected because combination of staff in both would make enough target population. Exploratory research design was used. Data was collected using Questionnaires with a sample size of 162. Qualitative data was collected, analysed and summarized thematically giving a narrative conclusion. Statistical Package for Social Sciences version 27 was used to summarize, code and analyze quantitative data according to the objectives of the study. Data collected was analyzed, discussed and inferences made to come up with a report. The findings indicated that the two County Assembly libraries have qualified librarians who manage them. However, the librarians faced some challenges which hampered their performance thus inability to achieve their objectives. The findings further indicated that, there was serious lack of involvement in organization’s policy making, existence of politics within the management, inadequate budget allocations, lack of resource and services for users with special needs, failure to construct a purpose-built library and inadequate consideration of beliefs, lifestyles and status of users when providing information resources and services. The study recommended that the management ensures involvement of all libraries and other departments in policy making to ensure that their needs are catered for, organizational politics to be completely avoided to ensure smooth implementation of set objectives. Adequate budget allocation to the library is also recommended that a purpose-built library be constructed to accommodate all categories of users. Users’ beliefs, lifestyles and status should also be taken into account when providing information resources and services.

Anxiety sensitivity and cigarette use on cannabis use problems, perceived barriers for cannabis cessation, and self-efficacy for quitting among adults with cannabis use disorder

Many individuals diagnosed with cannabis use disorder (CUD) report a desire to quit using cannabis due to problems associated with use. Yet, successful abstinence is difficult for a large subset of this population. Thus, the present study sought to elucidate potential risk factors for cannabis use problems, perceived barriers for quitting, and diminished self-efficacy for remaining abstinent. Specifically, this investigation examined cigarette user status, anxiety sensitivity, and the interplay between these individual difference factors in terms of cannabis-related problems, perceived barriers for cannabis cessation, and self-efficacy for quitting cannabis use. The sample consisted of 132 adult cannabis users who met criteria for CUD and were interested in quitting (38 % female; 63.6 % Black; Mage = 37.22; SDage = 28.79; 54.6 % current tobacco users). Findings revealed a significant interaction, such that anxiety sensitivity was related to cannabis use problems and perceived barriers for cannabis cessation among current cigarette users, but not among cigarette non-users. There was no significant interaction for self-efficacy for remaining abstinent. The current findings suggest that cigarette users constitute a subgroup that may be especially vulnerable to the effects of anxiety sensitivity in terms of cannabis use problems and perceived barriers for quitting cannabis use.