1. 1. The writers present the general theory of evaluation that is being developed by their group.
2. 2. The evaluation of a human environment is a complex mental process.
3. 3. In an effort to express numerically the quality of an environment, one tends to oversimplify the complex aspects of it and the entailing problems in relation to its inhabitants.
4. 4. In this paper, some examples are taken in the evaluation of thermal environments, wherein much has been said and done in setting up numerical scales to express human comfort, and yet neither clear-cut explanations nor convincing logic seem to exist to terminate the argument over the widely scattered and sometimes seemingly contradicting experimental data.
5. 5. The writers suggest that many of the reasons for this confusion may be traced back to the oversimplified notion of evaluation.
6. 6. It is shown that there are various possibilities when looking at the scales of evaluation.
7. 7.|The nominal scale, least studied of all the four traditional scales, may be given a prominent place in evaluating a thermal environment. The pseudo-interval order scale is another example.
Author Keywords: evaluation; scales; thermal environment; classification; pseudo-interval order 相似文献
The transition toward a circular economy (CE) is key in decarbonizing the built environment. Despite this, knowledge of—and engagement with—CE philosophies remains limited within the construction industry. Discussion with practitioners reveals this to be contributed to by a lack of clarity regarding CE principles, with numerous organizations recommending implementation of differing and sometimes conflicting principles. In addition, a systematic assessment of how building designs consider CE is made difficult by the multiple design areas required to be considered and the large amount of design data required to do so. The absence of a systematic CE assessment causes a lack of comparability across designs, preventing benchmarking of CE practices in building design at present. This paper details the development of Regenerate, a CE engagement tool for the assessment of new and existing buildings, established in an effort to overcome the aforementioned barriers to the adoption of CE within the construction sector. A CE design workflow for the built environment is proposed, comprising four overarching circularity principles (Design for Adaptability; Design for Deconstructability; Circular Material Selection; Resource Efficiency) and contributing design actions. In addition to engaging stakeholders by enabling the assessment of building designs, the tool retrieves key data for further research. Information on completed design actions as well as recycling and waste metrics is collected to facilitate future CE benchmarking. “Bill of materials” data (i.e., material quantities) is also compiled, with this being key in material stock modeling research and embodied carbon benchmarking. 相似文献
Theory and analyses of fisheries data sets indicate that harvesting can alter population structure and destabilise non-linear processes, which increases population fluctuations. We conducted a factorial experiment on the population dynamics of Daphnia magna in relation to size-selective harvesting and stochasticity of food supply. Harvesting and stochasticity treatments both increased population fluctuations. Timeseries analysis indicated that fluctuations in control populations were non-linear, and non-linearity increased substantially in response to harvesting. Both harvesting and stochasticity induced population juvenescence, but harvesting did so via the depletion of adults, whereas stochasticity increased the abundance of juveniles. A fitted fisheries model indicated that harvesting shifted populations towards higher reproductive rates and larger-magnitude damped oscillations that amplify demographic noise. These findings provide experimental evidence that harvesting increases the non-linearity of population fluctuations and that both harvesting and stochasticity increase population variability and juvenescence. 相似文献
Since the 1970's the management of aquatic habitats has changed from piecemeal monitoring to the ecosystem approach; this was initiated in the North American Great Lakes, comprising social, economic, and environmental aspects. The information included in this paper is based on the presentation made at the Seminar On Ecosystem Approach To Water Management held in Oslo, Norway during 1991. Recently, the multidisciplinary, holistic, and integrated concept of ecosystem health has emerged, and is being advanced for the implementation of an ecosystem approach to environmental management, which has resulted in the formation of an international society (Aquatic Ecosystem Health & Management Society) and the publication of a primary journal (Journal of Aquatic Ecosystem Health). The information has been updated to incorporate new developments and recent progress about the Society and the journal since the Oslo Seminar. 相似文献
Organisms modify their development and function in response to the environment. At the same time, the environment is modified by the activities of the organism. Despite the ubiquity of such dynamical interactions in nature, it remains challenging to develop models that accurately represent them, and that can be fitted using data. These features are desirable when modeling phenomena such as phenotypic plasticity, to generate quantitative predictions of how the system will respond to environmental signals of different magnitude or at different times, for example, during ontogeny. Here, we explain a modeling framework that represents the organism and environment as a single coupled dynamical system in terms of inputs and outputs. Inputs are external signals, and outputs are measurements of the system in time. The framework uses time-series data of inputs and outputs to fit a nonlinear black-box model that allows to predict how the system will respond to novel input signals. The framework has three key properties: it captures the dynamical nature of the organism–environment system, it can be fitted with data, and it can be applied without detailed knowledge of the system. We study phenotypic plasticity using in silico experiments and demonstrate that the framework predicts the response to novel environmental signals. The framework allows us to model plasticity as a dynamical property that changes in time during ontogeny, reflecting the well-known fact that organisms are more or less plastic at different developmental stages. 相似文献