Question

The following applications such as a home burglar system, combined household heating and cooling system, and hospital recovery room are essential to our daily lives. Please Answer the following three questions.

What are the physical quantities that need to be measured for each application and why they are important?

Discuss any limitations and trade-offs that are involved in each application.

Lastly, Identify the sensor category and sensor technology that could be used. Be sure to explain your reasoning.

Answer 1

In recent years, the effects of the physical environment on the healing process and well-being have proved to be increasingly relevant for patients and their families (PF) as well as for healthcare staff. The discussions focus on traditional and institutionally designed healthcare facilities (HCF) relative to the actual well-being of patients as an indicator of their health and recovery. This review investigates and structures the scientific research on an evidence-based healthcare design for PF and staff outcomes. Evidence-based design has become the theoretical concept for what are called healing environments. The results show the effects on PF and staff from the perspective of various aspects and dimensions of the physical environmental factors of HFC. A total of 798 papers were identified that fitted the inclusion criteria for this study. Of these, 65 articles were selected for review: fewer than 50% of these papers were classified with a high level of evidence, and 86% were included in the group of PF outcomes. This study demonstrates that evidence of staff outcomes is scarce and insufficiently substantiated. With the development of a more customer-oriented management approach to HCF, the implications of this review are relevant to the design and construction of HCF. Some design features to consider in future design and construction of HCF are single-patient rooms, identical rooms, and lighting. For future research, the main challenge will be to explore and specify staff needs and to integrate those needs into the built environment of HCF.
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Global Environmental Change

Volume 39, July 2016, Pages 15-25

Water saving potentials and possible trade-offs for future food and energy supply

Author links open overlay panelKerstinDamerauaOscar P.R.van Vlieta
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https://doi.org/10.1016/j.gloenvcha.2016.03.014Get rights and content
Under a Creative Commons license
open access

Highlights

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An overall increase in water demand for both food and energy supply is not inevitable.

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Changes in food preferencescould lead to a reduction of water resource use despite rising population numbers.

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A trend towards more renewables and electric transport has the potential to limit an increasing water demand in the energy sector.

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A growth of global first-generation biofuel use to 7% until 2050 would result in a water demand for the energy sector higher than for current food supply.

Abstract

The sufficient supply of food and energy requires large amounts of fresh water. Mainly required for irrigation, but also processing and cooling purposes, water is one of the essential resources in both sectors. Rising global population numbers and economic development could likely cause an increase in natural resource demand over the coming decades, while at the same time climate change might lead to lower overall water availability. The result could be an increased competition for water resources mainly in water-stressed regions of the world in the future. In this study we explore a set of possible changes in consumption patterns in the agricultural and energy sector that could be primarily motivated by other goals than water conservation measures—for example personal health and climate change mitigation targets, and estimate the indirect effect such trends would have on global water requirements until 2050. Looking at five world regions, we investigated three possible changes regarding future food preferences, and two possible changes in future resource preferences for electricity and transport fuels. We find that while an increase in food supply as a result of higher protein demand would lead to an increase in water demand as well, this trend could be counteracted by other potential dietary shifts such as a reduction in grains and sugars. In the energy sector we find that an increasing water demand can be limited through specific resource and technology choices, while a significant growth of first-generation biofuels would lead to a drastic rise in water demand, potentially exceeding the water requirements for food supply. Looking at the two sectors together, we conclude that an overall increase in water demand for both food and energy is not inevitable and that changes in food and energy preferences could indeed lead to an alleviation of water resource use despite rising population numbers.A sensor is a device that detects and responds to some type of input from the physical environment. The specific input could be light, heat, motion, moisture, pressure, or any one of a great number of other environmental phenomena.Sensors are used to measure physical quantities such as temperature, light, pressure, sound, and humidity. They send signals to the processor. For example: A security alarm system may have an infraredsensor which sends a signal when the beam is broken.Logical reasoning is a powerful mechanism to derive new and implicit knowledge
from semantically annotated sensor data, and to answer complex user queries.
The work in Semantic Sensor Web demonstrates some examples (e.g. “Poten-
tially Icy”) of using rule-based reasoning to deduce new ontological assertions
from known instances [5]. In one of Sheth et al’s example, sensor data is first
annotated with temporal data extracted from video with respect to a Time do-
main ontology. Then they present how videos can be retrieved by using semantic
temporal concepts such as “within”, “contains”, and “overlaps”. Here we pro-
vide an example as an extension to these examples and show how the semantic
annotation with the linked data contribute to answering sensor data queries. In
particular, we annotate the observation and measurement data by linking it to
geographic data published by DBpedia.