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Home palliative care clinicians provide end-of-life care for patients from diverse social and economic backgrounds. They include patients who live alone—a single-person household.1 Auon et al. found that 7–12% of patients under palliative care lived alone for more than a year.3 Demographic trends increasingly highlight this group to be a growing population with pressing concerns (e.g. the number of single-person households in Singapore has almost doubled between 2012 and 2022).2 With the greying population, the trend of patients living alone is expected to grow.

As communication technology advances, high-gain antennas are being investigated for a variety of long-distance applications. One such high gain antenna is reflectarray, it has combined advantageous characteristics of a reflector and phased array antennas. However, to design the reflectarray antenna its phase range, bandwidth, gain, cross polarization and the side lobe levels must be in optimal range to meet the requirements for distant applications. In this article, we have reviewed various research works of reflectarray antenna and highlighted the techniques to develop an efficient high gain reflectarry antenna. Recently, for high gain applications, as per end user requirement, there is a need to vary the frequency and radiation characteristics in a controlled manner. This can be achieved by using reconfigurable reflectarray antennas. In this article, reconfigurable mechanisms and active elements used for reconfiguration are reviewed in detail. In the end of this survey, some of the works on reflectarray antenna which were published by the research society are compared, summarizing the important parameters such as operating frequency, phase range, gain bandwidth, peak gain, cross polarization levels, and side lobes.

BackgroundAutofluorescence-based imaging has the potential to non-destructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research.MethodsAutofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier. From the analysis of the autofluorescence images, two novel stress-based image phenotypes were computed to determine the temporal variation in stressed tissue under progressive drought across different genotypes, i.e., the average percentage stress and the moving average percentage stress.ResultsThe study demonstrated that both the computed phenotypes consistently discriminated against stressed versus non-stressed tissue, with oilseed type (R500) being less prone to drought stress relative to the other two Brassica rapa genotypes (CC and VT).ConclusionAutofluorescence signals from the 365/400 nm excitation/emission combination were able to segregate genotypic variation during a progressive drought treatment under a controlled greenhouse environment, allowing for the exploration of other meaningful phenotypes using autofluorescence image sequences with significance in the context of plant science.

The present study is carried out using CFX, a commercial computational fluid dynamics (CFD) software, to estimate the thermal characteristics of a heat pipe. A heat pipe consists of three concentric circular tubes with three different split sections: Evaporator, adiabatic, and condenser sections. A detailed parametric study takes water and methanol as working fluids – Additionally, a study investigates the heat pipe with condenser section subjected to ambient air. The complete analysis is performed for an existing heat pipe with given design characteristics such as orientation, diameter and length, wick structure, and the stated circumstances and intake flow to the evaporator. The results are reported and compared in graphical form. It is observed that the water improves the heat pipe thermal features in terms of temperature than that of methanol. The maximum surface temperature is recorded in the evaporator zone at 132 oC, while the lowest temperature was measured at the conclusion of the condenser portion at 87.6 oC. The condenser portion's extreme surface temperature is 79.93 oC, whereas the evaporator portion's extreme surface temperature is 127 oC. When attributes are compared at atmospheric pressure, water outperforms methanol in terms of merit. This shows that compared to methanol, water has greater heat transfer properties. [ABSTRACT FROM AUTHOR]

The present study was carried out during summer season at the centre of All India Coordinated Research Project on pigs in Tirupati (India) on 24 Large White Yorkshire crossbred weaner pigs of 8 weeks of age and 9-10 kg of body weight, which were randomly divided into four groups (I, II, III and IV) of six animals each (3 males and 3 females). The Group-I was taken as control, Group-II was provided with fan for cooling, Group-III was provided with water splashing over the body, and Group-IV was provided with wet gunny bags around the pen. The study revealed that the housing management interventions had significant effect on all aspects studied. Among the four experimental groups, the Group III (water splashing) had shown the lowest values of temperature-humidity index (78.85±0.13), physiological parameters such as rectal temperature (101.7±0.03 oF), respiration rate (38.17±0.86 breaths/min) and pulse rate (70.36±0.19 beats/min), serum biochemical parameters such as SGOT (55.34±1.38 IU/L) and SGPT (32.67±0.78 IU/L), and the highest values of growth parameters such as final body weight (34.76±0.11 kg) and overall Average Daily Gain (0.28±0.00 kg/day). From the results, it was concluded that splashing water over the animal body proved to be a more effective cooling practice for providing better microenvironment and maintaining better growth performance, as well as normal physiological and biochemical functions in the body, among the housing management practices examined. [ABSTRACT FROM AUTHOR]

This article explores the experiences of terminally ill individuals who live alone in palliative care. The study found that there were significant differences among these individuals based on race, marital status, means test categories, death/discharge location, and length of stay in home palliative care. The patients demonstrated a strong desire to maintain independence and often rejected clinical advice to be admitted into care facilities. Clinicians faced difficulties engaging with these patients but were eventually able to build rapport and provide more frequent care. The study highlights the need for tailored care that addresses the unique needs of patients who live alone, including their health, psychological, emotional, and cultural needs. Further research is needed to better understand the psychosocial and cultural complexities of this patient group. [Extracted from the article]

Background: Autofluorescence-based imaging has the potential to nondestructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research. Methods: Autofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier. From the analysis of the autofluorescence images, two novel stressbased image phenotypes were computed to determine the temporal variation in stressed tissue under progressive drought across different genotypes, i.e., the average percentage stress and the moving average percentage stress. Results: The study demonstrated that both the computed phenotypes consistently discriminated against stressed versus non-stressed tissue, with oilseed type (R500) being less prone to drought stress relative to the other two Brassica rapa genotypes (CC and VT). Conclusion: Autofluorescence signals from the 365/400 nm excitation/emission combination were able to segregate genotypic variation during a progressive drought treatment under a controlled greenhouse environment, allowing for the exploration of other meaningful phenotypes using autofluorescence image sequences with significance in the context of plant science. [ABSTRACT FROM AUTHOR]