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The purpose of this paper is to develop a comprehensive research model that can explain potential customers’ behavioral intentions to adopt and use smart home services.

This study proposes and validates a new theoretical model that extends the theory of planned behavior. Partial least squares analysis is employed to test the research model and corresponding hypotheses on data collected from 216 survey samples.

Mobility, security/privacy risk, and trust in the service provider are important factors affecting the adoption of smart home services.

To increase potential users’ adoption rate, service providers should focus on developing mobility-related services that enable people to access smart home services while on the move using mobile devices via control and monitoring functions.

This study is the first empirical attempt to examine user acceptance of smart home services, as most of the prior literature has concerned technical features.

This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO₂), ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), as well as particulate matter (PM) (PM₁, PM_2.5 and PM₁₀) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations.

The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out.

CO₂ (430.34 ppm), NO₂ (93.31 ppb), O₃ (19.94 ppb), SO₂ (40.87 ppb), PM₁ (29.31 µg/m3), PM_2.5 (43.56 µg/m³), PM₁₀ (50.70 µg/m³), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO₂ (18%) and low PM₁₀ (49%) contributions, NO₂ and PM₁₀ were the most common pollutants in all of the months. The mean air quality index (AQI) for NO₂ indicated that the AQI was “moderate” (51–100). In contrast to SO₂, whose AQI ranged from “moderate” to “very unhealthy,” O₃'s AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM₁, PM_2.5 and PM₁₀ made up the majority of PC1’s contribution, both PM_2.5 and PM₁₀ were deemed “hazardous.”

The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs.

Lack of literature in terms of indoor air quality (IAQ) in Akure, Ondo State. With this work, the information obtained will assist all stakeholders in policy formulation and implementation. Again, the low-cost sensor used is new to this part of the world.

At present, the research on energy consumption of human clothing mainly focuses on behavior observation method, questionnaire survey method, heart rate monitoring method and electronic motion sensor, etc. In order to solve the problem of energy consumption caused by clothing with different characteristics, an identification method of energy consumption for different types of clothing was proposed.

The model robot was designed to reproduce the motion state by simulating the human body in the working mode, and the protective energy consumption test platform was built. In order to explore the influence of different characteristics of clothing on the energy consumption of equipment system, orthogonal experiments were carried out on the model robot experimental platform, and a mathematical model for predicting the energy consumption of clothing based on Tabu search algorithm to optimize support vector machine regression (TS-SVR) optimized by tabu algorithm was proposed.

Compared with three regression prediction algorithms, the accuracy of the model was quantified by the determination coefficient and root mean square error according to the predicted value of the model and the actual value of the experiment. The results showed that the model based on TS-SVM can predict the energy consumption of human body more accurately.

Based on TS-SVR model, it can well predict the relationship between clothing with different characteristics and physical energy consumption, and can accurately evaluate the clothing grade of different characteristics.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

The use of conductive yarns or wires to design and construct fabric-based strain sensors is a research area that is gaining much attention in recent years. This is based on a profound theory that conductive yarns will have a variation in resistance if subjected to tension. What is not clear is to which types of conductive yarns are most suited to delivering the right sensitivity. The purpose of this paper is to look at strain sensors knitted with conductive composite and coated yarns which include core spun, blended, coated and commingled yarns. The conductive components are stainless steel and silver coating respectively with polyester as the nonconductive part. Using Stoll CMS 530 flat knitting machine, five samples each were knitted with the mentioned yarn categories using 1×1 rib structure. Sensitivity tests were carried out on the samples. Piezoresistive response of the samples reveals that yarns with heterogeneous external structures showed both an increase and a decrease in resistance, whereas those with homogenous structures responded linearly to stress. Stainless steel based yarns also had higher piezoresistive range compared to the silver-coated ones. However, comparing all the knitted samples, silver-coated yarn (SCY) proved to be more suitable for strain sensor as its response to tension was unidirectional with an appreciable range of change in resistance.

Conductive composite yarns, namely, core spun yarn (CSY1), core spun yarn (CSY2), silver-coated blended yarn (SCBY), staple fiber blended yarn (SFBY) and commingled yarn (CMY) were sourced based on specifications and used to knit strain sensor samples. Electro-mechanical properties were investigated by stretching on a fabric tensile machine to ascertain their suitability for a textile strain sensor.

In order to generate usable signal for a strain sensor for a conductive yarn, it must have persistent and consistent conductive links, both externally and internally. In the case of composite yarns such as SFBY, SCBY and CMY where there were no consistent alignment and inter-yarn contact, resistance change fluctuated. Among all six different types of yarns used, SCY presented the most suitable result as its response to tension was unidirectional with an appreciable range of change in resistance.

This is an original research carried out by the authors who studied the electro-mechanical properties of some composite conductive yarns that have not been studied before in textile strain sensor research. Detailed research methods, results and interpretation of the results have thus been presented.

EXTENSIVE proposals recently issued by the FAA require installation of digital flight data recorders with specific parameters and/or cockpit voice recorders (CVRs) in a broad category of fixed and rotating wing aircraft. To be so equipped are aircraft operated by air carriers or commuters and in addition, certain selected smaller types operated in general aviation. Over the last few years a number of significant events have occurred that have necessitated the reconsideration of requirements. The NTSB considers that although recent rules have eliminated foil‐type flight recorders, concern is still felt about the adequacy of certain minimum standards. There is an urgent need to update the mandatory parameter list and to define new parameters, improved accuracies, range and sampling intervals.

Nursing homes experience high nursing staff turnover. Nursing staff in nursing homes is comprised of gray and blue collar workers that include registered nurses (RNs), licensed practical nurses (LPNs), and certified nurse assistants (CNAs). The relationship between human resource management (HRM)practices, organizational culture, and nursing staff turnover is examined in underresourced (high Medicaid) nursing homes. Survey data from 348 nursing home administrators (NHAs) of USA high Medicaid (85% or higher) facilities were merged with secondary data sources for 2017–2018. The dependent variables (nursing staff turnover rates) consisted of the percentages of RNs, LPNs, and CNAs that had voluntarily quit the organization during the past year. The independent variables were: (1) HRM practices (employee-centered and high involvement practices); and (2) organizational culture: clan, market, hierarchical, and non-dominant. Organizational and market variables were controlled for. Data were modeled using Poisson log-linear regression, and propensity score weights were used to adjust for potential survey non-response bias. Results show high involvement HRM practices and having a clan culture are associated with lower RN, LPN, and CNA staffing turnover. Study findings suggest that organizational culture and HRM practices may be instrumental in reducing nursing turnover in underresourced nursing homes.

This research investigated the feasibility of using an inductively coupled antenna as the basis of applying a systems approach to smart clothing. In order to simulate real-life situations, the impact of the distortions and relative displacement of different fabric layers (with affixed antennas) on the signal quality was assessed. The paper aims to discuss these issues.

A spiral antenna was printed on different fabric substrates. Obstructive conditions of the inductively coupled fabric layers were investigated to find out how much influence these conditions had on transmission performance. Reflected signals and transmitted signals were observed, while fabric antennas were subjected to displacement (distance and dislocation) or deformation (stretching and bending). The threshold of physical obstacles was estimated based on statistical analyses.

The limits of physical conditions that enable proper wireless transmission were estimated up to ∼2 cm for both distance and dislocation, and ∼0.24 K for bending deformation. The antenna performance remained within an acceptable level of 20 percent transmission up to 10 percent fabric stretch. Based on well-established performance metrics used in clothing environment on the body, which employs 2-5 cm of ease, the results imply that the inductively coupled antennas may be suitable for use in smart clothing.

This research demonstrates that the use of inductively coupled antennas on multiple clothing layers could offer the basis of a new “wireless” system approach to smart clothing. This would not only result in performance benefits, but would also significantly improve the aesthetics of smart clothing which should result in new markets for such products.

This study aims to investigate the working way of the e-Pulse portal in Türkiye as a sample of a next-generation digital tool for health data management. Accordingly, this study focuses on explaining the structure and key services of the e-Pulse portal in the context of health data management.

This study is a technical paper that will explain how the e-Pulse portal works in Türkiye. Accordingly, the data are based on secondary sources and mostly the official website of the e-Pulse portal. As a sample case, this study investigates the e-Pulse portal from Türkiye. The data are categorized by tables, and some key factors are classified based on review results.

As a result of the review of the e-Pulse portal's sample account, it is seen that the e-Pulse portal provides comprehensive data for personal health data for both individuals and healthcare professionals. By permitting healthcare professionals, users or patients can share their personal health data on specific dates and numbers whenever they need and want. When sharing recorded personal health data, citizens or patients can get more efficient healthcare service on the time.

By giving descriptive evidence and review through the e-Pulse portal, countries with high-populated can see the key e-services and elements to manage health data through digital tools. On the other side, this study has some limitations. This study investigated the e-Pulse portal and its e-services for Türkiye and gave some findings mostly based on subjective deduction. Another digital portal can give different findings for the literature.

Based on the e-Pulse portal case, it is determined that by creating a digital portal with recorded personal up-to-date health data, healthcare services can be ensured more efficiently among high-populated countries in the long term. While population growth and pandemic possibilities such as COVID-19 increase throughout the world, serving more patients with these portals will increase efficiency and service quality, provided that patient information is well protected.

This study reveals key e-services and segments to provide personal health data management by a next-generation digital tool based on the e-Pulse portal. The main contribution of this study is expected to guide other countries when adapting next-generation technology or systems to manage health data in the future.