Defining and Validating Criteria to Identify Populations Who May Benefit From Home-Based Primary Care.

BACKGROUND: Home-based primary care (HBPC) is an important care delivery model for high-need older adults. Currently, target patient populations vary across HBPC programs, hindering expansion and large-scale evaluation. OBJECTIVES: Develop and validate criteria that identify appropriate HBPC target populations. RESEARCH DESIGN: A modified Delphi process was used to achieve expert consensus on criteria for identifying HBPC target populations. All criteria were defined and validated using linked data from Medicare claims and the National Health and Aging Trends Study (NHATS) (cohort n=21,727). Construct validation involved assessing demographics and health outcomes/expenditures for selected criteria. SUBJECTS: Delphi panelists (n=29) represented diverse professional perspectives. Criteria were validated on community-dwelling Medicare beneficiaries (age ≥70) enrolled in NHATS. MEASURES: Criteria were selected via Delphi questionnaires. For construct validation, sociodemographic characteristics of Medicare beneficiaries were self-reported in NHATS, and annual health care expenditures and mortality were obtained via linked Medicare claims. RESULTS: Panelists proposed an algorithm of criteria for HBPC target populations that included indicators for serious illness, functional impairment, and social isolation. The algorithms Delphi-selected criteria applied to 16.8% of Medicare beneficiaries. These HBPC target populations had higher annual health care costs [Med (IQR): $10,851 (3316, 31,556) vs.$2830 (913, 9574)] and higher 12-month mortality [15% (95% CI: 14, 17) vs. 5% (95% CI: 4, 5)] compared with the total validation cohort. CONCLUSIONS: We developed and validated an algorithm to define target populations for HBPC, which suggests a need for increased HBPC availability. By enabling objective identification of unmet demands for HBPC access or resources, this algorithm can foster robust evaluation and equitable expansion of HBPC

An Integrated Framework for Infectious Disease Control Using Mathematical Modeling and Deep Learning.

Infectious diseases are a major global public health concern. Precise modeling and prediction methods are essential to develop effective strategies for disease control. However, data imbalance and the presence of noise and intensity inhomogeneity make disease detection more challenging. Goal: In this article, a novel infectious disease pattern prediction system is proposed by integrating deterministic and stochastic model benefits with the benefits of the deep learning model. Results: The combined benefits yield improvement in the performance of solution prediction. Moreover, the objective is also to investigate the influence of time delay on infection rates and rates associated with vaccination. Conclusions: In this proposed framework, at first, the global stability at disease free equilibrium is effectively analysed using Routh-Haurwitz criteria and Lyapunov method, and the endemic equilibrium is analysed using non-linear Volterra integral equations in the infectious disease model. Unlike the existing model, emphasis is given to suggesting a model that is capable of investigating stability while considering the effect of vaccination and migration rate. Next, the influence of vaccination on the rate of infection is effectively predicted using an efficient deep learning model by employing the long-term dependencies in sequential data. Thus making the prediction more accurate

Insights from the numerical analysis of axially loaded piles in liquefiable soils

Axially loaded piles in liquefiable soils can undergo severe settlement due to an earthquake event. During shaking, the settlement is caused by the decreased shaft and tip capacity from excess pore pressures (ue) generated around the pile. Post shaking, soil settlement from the reconsolidation of liquefied soil surrounding the pile results in the development of additional load (known as drag load), causing downdrag settlement of the pile. Estimating the axial load distribution and pile settlement is essential for designing and evaluating the performance of axially loaded piles in liquefiable soils. In practice, a simplified neutral plane solution method is used, where the liquefied soils are modeled as a consolidating layer without considering the effect of ue generation/dissipation. A TzQzLiq analysis models the load and settlement response of axially loaded piles in liquefiable soils by accounting for the effect of excess pore pressure (ue) generation/dissipation on the shaft and tip capacity. This paper presents the deficiencies of the simplified neutral plane method in predicting the drag load as well as the downdrag settlement by comparing it with the TzQzLiq analysis validated with hypergravity model tests. The results show that the drag load and the downdrag settlement predicted by the neutral plane method might be over- or under-estimated depending on the pile load, the rate of ue dissipation, and the soil settlement. For the cases studied, it was found that most of the pile settlement occurs during shaking due to the decrease in the pile's tip resistance from the development of ue in the soil surrounding it. While large drag loads develop during reconsolidation, the resulting downdrag settlement is small. While the neutral plane method generally predicted a downdrag settlement comparable to that of the TzQzLiq analysis, it overpredicted drag load and could not predict co-seismic settlement. Finally, the study advocates for the development and use of a displacement-based procedure (accounting for all the mechanisms occurring during and after an earthquake event) such as based on TzQzLiq analysis in accurately evaluating the performance of the pile (i.e., the pile settlement and the maximum load), thus providing an overall safe, efficient, and optimized design

Modulating the behavior of ethyl cellulose-based oleogels: The impact food-grade amphiphilic small molecules on structural, mechanical, and rheological properties

This work evaluates the ability of various lipid-based amphiphilic small molecules (ASMs) to modulate the mechanical and rheological properties of oleogels principally structured by ethyl cellulose (EC). Six ASMs varying in the chemical structure of their polar headgroups were used to produce EC-ASM oleogels. Stearic acid (StAc), monoacylglycerol (MAG), sodium stearoyl lactylate (SSL), and citric acid esters of monoglycerides (CITREM) all provided a dramatic enhancement in gel strength, while lactic acid (LACTEM) and acetic acid (ACETEM) esters produced only a marginal increase. Those additives which crystallized above 20 °C displayed pronounced changes in their network organization and crystal morphology in the presence of EC. Differences in the solid/liquid phase change behavior were also observed in select samples using differential scanning calorimetry. Both the small and large amplitude oscillatory shear responses were dependent on the ASM which was dependent on the chemistry of the headgroup, crystal network organization, and ability to plasticize the polymer network. The extent of thixotropic recovery was largely dependent on the polarity of functional groups in the ASMs, but was also influenced by the formation of a secondary crystal network. In general, ASMs which formed larger, system-spanning crystal networks (MAG, StAc) produced more brittle gels, while the highly hydrophilic, charged headgroup of SSL promoted a homogeneous distribution of small crystals, resulting in a tougher material. In the absence of a crystal network, stronger polar species in the ASM headgroup produced higher gel strength and increased elasticity. Thus, both ASM chemical structure and crystallization properties strongly contribute to the functionality of the resulting combined oleogelator systems

Benefit of Varying Navigation Strategies in Robot Teams

Inspired by recent human studies, this paper investigates the benefits of employing varying navigation strategies in robot teams. We explore how mixed navigation strategies impact task completion time, environment exploration, and overall system effectiveness in multi-robot systems. Experiments were conducted in a simulated rectangular environment using Clearpath PR2 robots and evaluated different navigation strategies observed in humans: 1) Route (RT) knowledge where agents follow a predefined path, 2) Survey (SW) knowledge where agents take the shortest path while avoiding obstacles, 3) Mixed strategies with varying proportions, such as 40% RT and 60% SW (0.4RT 0.6SW) and 60% RT and 40% SW (0.6RT 0.4SW), and 4) An additional strategy where agents switch from RT to SW 10% of the time (0.9RT 0.1SW). While SW strategy is the most time-efficient, RT strategy covers more of the environment. Mixed strategies offer a balanced trade-off. These findings highlight the advantages of variability in navigation strategies, suggesting benefits in both biological and robotic populations. Additionally, we have observed that human participants in a similar study would start on a route, and then 10% of the time switch to survey. Therefore, we investigate a 90% Route 10% Survey (0.9RT 0.1SW) strategy for individual team members. While a pure Survey strategy is the most efficient regarding time taken and a pure Route strategy covers more of the environment, a mixture of strategies appears to be a beneficial tradeoff between time taken to complete a mission and area coverage. These results highlight the advantages of population variability, suggesting potential benefits in both biological and robotic populations

Efficient separation of carbon dioxide and methane in high-pressure and wet gas mixtures using Zr-MOF-808

The capture and separation of carbon dioxide (CO2) has been the focus of a plethora of research in order to mitigate its emissions and contribute to global development. Given that CO2 is commonly found in natural gas streams, there have been efforts to seek more efficient materials to separate gaseous mixtures such as CO2/CH4. However, there are only a few reports regarding adsorption processes within pressurized systems. In the offshore scenario, natural gas streams still exhibit high moisture content, necessitating a greater understanding of processes in moist systems. In this article, a metal-organic framework synthesis based on zirconium (MOF-808) was carried out through a conventional solvothermal method and autoclave for the adsorption of CO2 and CH4 under different temperatures (45–65 °C) and pressures up to 100 bar. Furthermore, the adsorption of humid CO2 was evaluated using thermal analyses. The MOF-808 synthesized in autoclave showed a high surface area (1502 m2/g), a high capacity for CO2 adsorption at 50 bar and 45 °C and had a low selectivity to capture CH4 molecules. It also exhibited a fine stability after five cycles of CO2 adsorption and desorption at 50 bar and 45 °C − as confirmed by structural post-adsorption analyses while maintaining its adsorption capacity and crystallinity. Furthermore, it can be observed that the adsorption capacity increased in a humid environment, and that the adsorbent remained stable after adsorption cycles in the presence of moisture. Finally, it was possible to confirm the occurrence of physisorption processes through nuclear magnetic resonance (NMR) analyses, thus validating the choice of mild temperatures for regeneration and contributing to the reduction of energy consumption in processing plants

Clinical Evidence of a Photoreceptor Origin in Diabetic Retinal Disease.

CLINICAL RELEVANCE: Although diabetes is associated with a classic microvascular disease of the retina, it is also increasingly being recognized as a cause of retinal neuropathy. Preclinical evidence suggests that retinal neuropathy in diabetes manifests in part as photoreceptor dysfunction, preceding the development of vascular features in experimental models. It remains unknown whether such findings are relevant to patients with diabetes. METHODS: Here, we review 4 lines of clinical evidence suggesting that diabetes-associated photoreceptor pathology is linked to the development of retinal microvascular disease. RESULTS: First, a major population-based investigation of susceptibility loci for diabetic retinopathy (DR) implicated a photoreceptor protein product as a protective factor. Next, electroretinography and other studies of visual function collectively show that rod and/or cone-derived abnormalities occur decades before the development of vascular features of DR. Third, protection from DR seemingly develops in patients with coincident retinitis pigmentosa, as suggested by several case series. Finally, based on anatomic features, we propose that the beneficial effect of macular laser in DR occurs via ablation of diseased photoreceptors. CONCLUSIONS: The evidence we present is limited due to the small patient populations used in the studies we cite and due to the lack of methodologies that allow causative relationships to be inferred. Collectively, however, these clinical observations suggest that photoreceptors are involved in early diabetic retinal disease and may in fact give rise to the classic features of DR. FINANCIAL DISCLOSURES: Proprietary or commercial disclosures may be found in the Footnotes and Disclosures at the end of this article

Assessing Overheating Risk and Energy Impacts in California's Residential Buildings

Extreme heat causes more weather-related deaths in the United States than any other natural hazard, and these events are projected to increase in frequency, intensity, and duration. As a result, it is critical to ensure safe thermal conditions in homes while minimizing excessive cooling energy use. In California, where the median age of homes is 45 years and nearly 40% lack mechanical cooling, this deficiency undermines one of the most essential goals of housing: to shelter people from outdoor weather of a warming planet. We aim to quantify the overheating risk in the housing sector to support the development of public policies related to maximum safe indoor thermal limits and building energy use. Using the ResStock modeling framework, we created over 52,000 building models to represent California's residential housing stock and assessed overheating risks by simulating indoor temperatures and analyzing the energy impacts of adding cooling systems.Our findings reveal significant regional disparities. Southern counties face the highest overheating risk, while coastal areas are less vulnerable due to oceanic temperatures. Inland counties, such as the Sierra Nevada region, are also less affected due to higher altitudes. Approximately 1.6 million homes will require retrofitting with cooling systems, which could increase peak electricity demand by 2%. Our sensitivity analysis showed that increasing the threshold temperature reduces the number of homes subject to overheating risk. These results significantly impact the electricity grid, emphasizing the need to consider passive cooling options like shading and cool roofs or energy-efficient cooling options like fans and evaporative coolers