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Older adults consistently prefer aging in place, which requires a high level of community support and services that are currently lacking. With a rapidly aging population, the present infrastructure for healthcare will prove even more inadequate to meet seniors' physical and mental health needs. A paradigm shift away from the sole focus on delivery of interventions at an individual level to more prevention-focused, community-based approaches will become essential. Recent initiatives have been proposed to promote healthy lifestyles and preventive care to enable older adults to age in place. Prominent among these are the World Health Organization's Global Age-Friendly Communities (AFC) Network, with 287 communities in 33 countries, and AARP's Network of AFCs with 77 communities in the United States. In an AFC, older adults are actively involved, valued, and supported with necessary infrastructure and services. Specific criteria include affordable housing, safe outdoor spaces and built environments conducive to active living, inexpensive and convenient transportation options, opportunities for social participation and community leadership, and accessible health and wellness services. Active, culture-based approaches, supported and developed by local communities, and including an intergenerational component are important. This article provides a brief historical background, discusses the conceptualization of the AFC, offers a list of criteria, narrates case studies of AFCs in various stages of development, and suggests solutions to common challenges to becoming age-friendly. Academic geriatric psychiatry needs to play a major role in the evolving AFC movement to ensure that mental healthcare is considered and delivered on par with physical care.

Low-cost optical particle sensors have the potential to supplement existing particulate matter (PM) monitoring systems and to provide high spatial and temporal resolutions. However, low-cost PM sensors have often shown questionable performance under various ambient conditions. Temperature, relative humidity (RH), and particle composition have been identified as factors that directly affect the performance of low-cost PM sensors. This study investigated whether NO 2 , which creates PM 2.5 by means of chemical reactions in the atmosphere, can be used to improve the calibration performance of low-cost PM 2.5 sensors. To this end, we evaluated the PurpleAir PA-II, called PA-II, a popular air monitoring system that utilizes two low-cost PM sensors and that is frequently deployed near air quality monitoring sites of the Environmental Protection Agency (EPA). We selected a single location where 14 PA-II units have operated for more than 2 years, since July 2017. Based on the operating periods of the PA-II units, we then chose the period of January 2018 to December 2019 for study. Among the 14 units, a single unit containing more than 23 months of measurement data with a high correlation between the unit's two PMS sensors was selected for analysis. Daily and hourly PM 2.5 measurement data from the PA-II unit and a BAM 1020 instrument, respectively, were compared using the federal reference method (FRM), and a per-month analysis was conducted against the BAM-1020 using hourly PM 2.5 data. In the per-month analysis, three key features – namely temperature, relative humidity (RH), and NO 2 – were considered. The NO 2 , called collocated NO 2 , was collected from the reliable instrument collocated with the PA-II unit. The per-month analysis showed that the PA-II unit had a good correlation (coefficient of determination R2>0.819) with the BAM-1020 during the months of November, December, and January in both 2018 and 2019, but their correlation intensity was moderate during other months, such as in July and September 2018 and August, September, and October 2019. NO 2 was shown to be a key factor in increasing the value of R2 in the months when moderate correlation based on only PM 2.5 was achieved. This study calibrated a PA-II unit using multiple linear regression (MLR) and random forest (RF) methods based on the same three features used in the analysis studies, as well as their multiplicative terms. The addition of NO 2 had a much larger effect than that of RH when both PM 2.5 and temperature were considered for calibration in both models. When NO 2 , temperature, and relative humidity were considered, the MLR method achieved similar calibration performance to the RF method. In addressing the feasibility of utilizing distant NO 2 measurements for calibration in lieu of collocated data, the study highlights the effectiveness of distant NO 2 when correlated strongly with collocated measurements. This finding offers a practical solution for situations where obtaining collocated NO 2 data proves to be challenging or costly. We assessed the performance of different PA-II units to determine their efficacy. Our investigation reveals a significant enhancement in calibration performance across different PA-II units upon integrating NO 2. Importantly, this improvement remains consistent even when employing models trained with different PA-II units within the same location. Overall, this investigation emphasizes the significance of NO 2 in improving calibration for low-cost PM 2.5 sensors and presents insights into leveraging distant NO 2 measurements as a viable alternative for calibration in the absence of collocated data. [ABSTRACT FROM AUTHOR]

Low-cost optical particle sensors have the potential to supplement existing particulate matter (PM) monitoring systems to provide high spatial and temporal resolution. However, low-cost PM sensors have often shown questionable performance under various ambient conditions. Temperature, relative humidity (RH), and particle composition have been identified as factors that directly affect the performance of low-cost PM sensors. This study investigated if NO2, which creates PM2.5 by chemical reactions in the atmosphere, can be used to improve the calibration performance of low-cost PM2.5 sensors. To this end, we evaluated the PurpleAir PA-II, called PA-II, a popular air monitoring system that utilizes two low-cost PM sensors that is frequently deployed near air quality monitoring sites of the Environmental Protection Agency (EPA). We selected a single location where 14 PA-II units have operated for more than two years since July 2017. Based on the operating periods of the PA-II units, we then chose the period of Jan. 2018 to Dec. 2019 for study. Among the 14 units, a single unit containing more than 23 months of measurement data with a high correlation between the unit's two PMS sensors was selected for analysis. Daily and hourly PM2.5 measurement data from the PA-II unit and a BAM 1020 instrument, respectively, were compared using the federal reference method (FRM), and a per-month analysis was conducted against the BAM-1020 using hourly PM2.5 data. In the per-month analysis, three key features, temperature, relative humidity (RH), and NO2, were considered. The NO2, called colocated NO2, was collected from the reliable instrument colocated with the PA-II unit. The per-month analysis showed the PA-II unit had a good correlation (coefficient of determination, R 2 > 0.819) with the BAM-1020 during the months of Nov., Dec., and Jan. in both 2018 and 2019, but their correlation intensity was moderate during other months, such as July and Sep. 2018, and Aug., Sep., and Oct. 2019. NO2 was shown to be a key factor in increasing the value of R 2 in the months when moderate correlation based on only PM2.5 was achieved. This study calibrated a PA-II unit using multiple linear regression (MLR) and random forest (RF) methods based on the same three features used in the analysis studies as well as their multiplicative terms. The addition of NO2 had a much larger effect than that of RH when both PM2.5 and temperature were considered for calibration in both models. When NO2, temperature, and relative humidity were considered, the MLR method achieved similar calibration performance to the RF method. Since it is practically infeasible to colocate a reliable NO2 instrument colocation with high accuracy at low-cost PM sensors, we investigated the effectiveness of using NO2 data (which we call distant NO2), collected from monitoring sites deployed at locations far from the considered low-cost PM sensor for calibration performance enhancement. It was shown that the use of distant NO2 enhances the calibration performance compared to calibration without NO2 when it is highly correlated with colocated NO2. Overall, PA-II units have good agreement with PM2.5 monitoring systems of high quality. Moreover, the calibration performance can be improved by using machine learning algorithms and by considering temperature, RH, and especially NO2. [ABSTRACT FROM AUTHOR]

This paper proposes a new packet rate scheduling scheme for a non-real time data services over the uplink of a burst switching based direct sequence code division multiple access (DS/CDMA) system to support integrated voice/data services. We consider the most general form of optimization problem formulation to determine the optimal number of transmission time groups along with their data rates, which minimizes the average packet transmission delay. An ordered packet length based groupwise transmission (OLGT) scheme is proposed as a simple heuristic solution to this problem, and we present some analytical results for performance comparison with other possible schemes. Index Terms--Direct sequence CDMA, optimal scheduling, order statistics, packet scheduling algorithm, radio resource management.

A model-based control methodology was developed for automated regulation of mean arterial pressure and cardiac output in critical care subjects using inotropic and vasoactive drugs. The control algorithm used a multiple-model adaptive approach in a model predictive control framework to account for variability and explicitly handle drug rate constraints. The controller was experimentally evaluated on canines that were pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output. The controller performed better as compared to experiments on manual regulation of the hemo-dynamic variables. After the model bank was determined, mean arterial pressure was held within [+ or -] 5 mm Hg 88.9% of the time with a standard deviation of 3.9 mm Hg. The cardiac output was held within [+ or -] 1 l/min 96.1% of the time with a standard deviation of 0.5 l/min. The manual runs maintain mean arterial pressure only 82.3% of the time with a standard deviation of 5 mm Hg, and cardiac output 92.2% of the time with a standard deviation of 0.6 l/min. Index Terms--Biomedical control systems, canine experiments drug infusion, model predictive control, multiple models.

In situations where the round-trip delay between the mobile and the base stations is smaller than the correlation time of the channel, power-control schemes using feedback from the base station can effectively compensate for the fast fading due to multipath. In this paper, we study several closed-loop power-control (CLPC) algorithms by analysis and detailed simulation. We introduce a new loglinear model for analyzing the received power correlation statistics of a CLPC scheme. The model provides analytical expressions for the temporal correlation of the power-controlled channel parameterized by the update rate, loop delay, and vehicle speed. The received power correlation statistics quantify the ability of closed-loop power control to compensate for the time-varying channel. To study more complex update strategies, detailed simulations that estimate the channel bit-error performance are carried out. Simulation results are combined with coding bounds to obtain quasi-analytic estimates of the reverse link capacity in a direct-sequence code-division multiple-access (DS-CDMA) cellular system. The quasi-analytic approach quantifies the performance improvements due to effective power control in both single-cell and multicell DS-CDMA systems operating over both frequency-nonselective and frequency-selective fading channels. The effect of nonstationary base stations on the system performance is also presented. Index Terms - Cellular systems, closed-loop power control, DS-CDMA.

In the development of encoding algorithms for image, video, and other mixed media transmissions, it is important to note that the channel "seen" by the applications is the physical channel as modified by the error-correcting mechanisms used at the physical level. Therefore, the statistics of the residual error process is relevant to the design of encoding algorithms. In this paper, we study the second- and third-order statistics of the residual error process when block transmissions are performed over a bursty channel. The effect of interleaving is explicitly studied. The conditions under which a Markovian model for the block errors is adequate are identified. Derivations of the parameters of the block error process are then presented in terms of the parameters of the bit/symbol error process. At higher data speeds an effective interleaving strategy is found to require a very large buffer. Index Terms - Bursty errors, correlated channel, higher order statistics, Gilbert model, interleaving, Markov approximation, residual block errors.

In this paper, we study the correlation properties of the fading mobile radio channel. Based on these studies, we model the channel as a one-step Markov process whose transition probabilities are a function of the channel characteristics. Then we present the throughput performance of the Go-Back-N and selective-repeat automatic repeat request (ARQ) protocols with timer control, using the Markov model for both forward and feedback channels. This approximation is found to be very good, as confirmed by simulation results. Index Terms - ARQ, correlated errors, fading channel, Markov model, time diversity, unreliable feedback.