The C23A system, an exmaple of quantitative control of plant growth associated with a data base

The architecture of the C23A (Chambers de Culture Automatique en Atmosphere Artificielles) system for the controlled study of plant physiology is described. A modular plant growth chambers and associated instruments (I.R. CO2 analyser, Mass spectrometer and Chemical analyser); network of frontal processors controlling this apparatus; a central computer for the periodic control and the multiplex work of processors; and a network of terminal computers able to ask the data base for data processing and modeling are discussed. Examples of present results are given. A growth curve analysis study of CO2 and O2 gas exchanges of shoots and roots, and daily evolution of algal photosynthesis and of the pools of dissolved CO2 in sea water are discussed

Understanding the adaptive capacity of Australian small-to-medium enterprises to climate change and variability

Abstract Small-to-medium enterprises (SMEs) comprise 96 per cent of all private businesses in Australia. The SME sector is the economy’s largest employer and the largest contributor to GDP. Moreover, SMEs play a significant role within socio-economic systems: they provide employment, goods and services and tax revenue for communities. Climate change may result in adverse business outcomes including business interruptions, increased investment and insurance costs, and declines in financial indicators such as measures of value, return and growth. After natural disasters, SMEs face greater short-term losses than larger enterprises, and may have lower adaptive capacity for various reasons. This study examines the underlying factors and processes shaping adaptive capacity of Australian SMEs’ to climate change and associated sea level rise. Specifically, the research asks the following questions: 1) How have SMEs considered and integrated adaptation into business planning? 2) What are the key underlying processes that constrain and influence the adaptive capacities of SMEs? and 3) What types of support are required to promote SME business continuity under a changing climate? The study adopts theories from Political Ecology and draws on literature on vulnerability and hazards to understand the processes that mediate the adaptive capacity of SMEs. The empirical research involved an online survey targeting SMEs, attending business engagement events hosted by chambers of commerce, 30 semi-structured interviews with secondary stakeholders, five case studies involving SMEs and secondary stakeholders, and finally a stakeholder workshop which brought together participants from both groups. The central conclusion of this study is that underlying contextual processes are critical to enhancing the adaptive capacity of SMEs. These processes include: the social relationships between SMEs and support organisations; the relationships within support organisations themselves; the agency of SMEs to direct resources toward building resilience into business continuity; SMEs’ perceptions of climate risks; and power struggles between support organisations. Unfavourable combinations of these processes have the potential to limit the adaptive choices that SMEs can adopt in order to overcome climate change and other related stresses on business continuity. These processes generate vulnerability and often occur at scales external to the SMEs;including relationships between different tiers of government as well as between various support organisations working with SMEs. These contextual processes have been largely overlooked in formal programmes that aim to build business resilience. The programmes have tended to be reactive and have tended to focus on business recovery during and after disasters rather than on altering the vulnerability context of SMEs through anticipatory prevention and preparedness or adaptation planning. This study suggests that the success of efforts to build the adaptive capacity of SMEs to future climate and related stresses will depend on how they address these underlying processes to facilitate the ability of SMEs to exercise their agency in pursuing adaptive choices that they value

Evaluation of System Performance for Microalga Cultivation in Photobioreactor with IOTs (Internet of Things)

Photobioreactors are a closed system concept of microalgae cultivation which is mostly done to control the development of intensive cultivation. The use of the internet to control microalgae has been carried out so that cyber physic interaction occurs by using the Internet of Things (IOTs) where this concept is an evolution of the concept of internet use that aims to expand the benefits of internet connectivity that is connected continuously with the ability to control remotely (remote control), data sharing (data sharing), continuous monitoring (real time monitoring) and up to date (up to date). This research aims to design a microalgae cultivation system as a source of food and energy for the future with a photobioreactor integrated with IOTs, so that it can be monitored continuously, controlled and used as a model for the development of greater microalgae cultivation technology. Development of automation in the cultivation of microalgae needs to be done to improve productivity and maintain quality so that the cultivation of microalgae can lead to industrialization, so that the development of microalgae as raw material for various needs can be optimized. Cultivation in a closed system photobioreactor, will produce microalgae that are not contaminated by external contaminants, growth analysis can be done based on the parameters that affect it, including the cultivation room temperature, lighting level (luminance), and the color of water in the process of photosynthesis microalgae, and also control of water circulation by using air lift (aerator). All processes carried out in this cultivation are done semi-automatically, because there is still a process of human interaction in setting parameters and controls in the process of harvesting microalgae. In this study microalgae was evaluated by using 4 cultivation tubes using 2 treatments giving fertilizer with different doses, where 2 tubes had the same dose, while 2 other tubes with different dosages. One tube with the same dose is used as a control. Visualization of controlled parameters includes, temperature parameters, light intensity, water color changes. The observed parameters will be displayed in a graphical user interface (GUI) in real time using the internet.  The liitation of this studi is how the system for microalga cultivation in a fotobioreactor can monitored by sensor and visualization in a remote monitoring such as computer connected to internet and also any ather devices.  The target of this research is to obtined time series data that can be analized  and monitored

Effect of Temperature on the Dynamic Response of Adhesively Mounted Accelerometers

This paper focuses on the effect of temperature on the frequency response function (FRF) of three different structural adhesives; namely a two component methylmethacrylate (HBM X60), a modified silane (Terostat 939) and a cyanoacrylate (Loctite 454). The structural adhesives are commonly used in vibration analysis to mount accelerometers on structures or machines. The stiffness of the adhesive can influence the response function on large frequency band, affecting the proportional excitation between the structure and the accelerometer. In the “system structure + adhesive + accelerometer”, the adhesive may acts like a filter between the source and the sink of vibrations. A variation of the dynamic response of the filter could lead to an erroneous analysis. The authors already investigated the relation between the frequency response function and operating conditions of the test. This paper expands the research by considering the temperature effect in order to depict a complete picture of the adhesive behavior on dynamic response of an accelerometer. A design of experiments (DOE) approach was used to test two bonded aluminum bases at different levels of temperature and frequency of the external sinusoidal excitation, supplied by an electromagnetic shaker. The results clearly demonstrate that the adhesive is not able to change the system response, therefore the signal transmission is good in the entire range of temperature regardless the adhesive chosen

Solar thermal heating and cooling. A bibliography with abstracts

This bibliographic series cites and abstracts the literature and technical papers on the heating and cooling of buildings with solar thermal energy. Over 650 citations are arranged in the following categories: space heating and cooling systems; space heating and cooling models; building energy conservation; architectural considerations, thermal load computations; thermal load measurements, domestic hot water, solar and atmospheric radiation, swimming pools; and economics

Mitigating Electromagnetic Noise When Using Low-Cost Devices in Industry 4.0

Transitioning toward Industry 4.0 requires major investment in devices and mechanisms enabling interconnectivity between people, machines, and processes. In this article, we present a low-cost system based on the Raspberry Pi platform to measure the overall equipment effectiveness (OEE) in real time, and we propose two filtering mechanisms for electromagnetic interferences (EMIs) to measure OEE accurately. The first EMI filtering mechanism is the database filter (DBF), which has been designed to record sealing signals accurately. The DBF works on the database by filtering erroneous signals that have been inserted in it. The second mechanism is the smart coded filter (SCF), which is used to filter erroneous signals associated with machine availability measurements. We have validated our proposal in several production lines in a food industry. The results show that our system works properly, and that it considerably reduces implementation costs compared with proprietary systems offering similar functions. After implementing the proposed system in actual industrial settings, the results show a mean error (ME) of -0.43% and a root mean square error (RMSE) of 4.85 in the sealing signals, and an error of 0% in the availability signal, thus enabling an accurate estimate of OEE

Mitigating Electromagnetic Noise When Using Low-Cost Devices in Industry 4.0

[EN] Transitioning toward Industry 4.0 requires major investment in devices and mechanisms enabling interconnectivity between people, machines, and processes. In this article, we present a low-cost system based on the Raspberry Pi platform to measure the overall equipment effectiveness (OEE) in real time, and we propose two filtering mechanisms for electromagnetic interferences (EMIs) to measure OEE accurately. The first EMI filtering mechanism is the database filter (DBF), which has been designed to record sealing signals accurately. The DBF works on the database by filtering erroneous signals that have been inserted in it. The second mechanism is the smart coded filter (SCF), which is used to filter erroneous signals associated with machine availability measurements. We have validated our proposal in several production lines in a food industry. The results show that our system works properly, and that it considerably reduces implementation costs compared with proprietary systems offering similar functions. After implementing the proposed system in actual industrial settings, the results show a mean error (ME) of -0.43% and a root mean square error (RMSE) of 4.85 in the sealing signals, and an error of 0% in the availability signal, thus enabling an accurate estimate of OEE.This work was supported in part by the Government of Aragon and the European Social Fund "Construyendo Europa desde Aragon" under Grant T40_20D Research Group, and in part by the "Ministerio de Ciencia, Innovacion y Universidades, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018," Spain, under Grant RTI2018-096384-B-I00.Herrero, ÁC.; Sangüesa, JA.; Martínez, FJ.; Garrido, P.; Tavares De Araujo Cesariny Calafate, CM. (2021). Mitigating Electromagnetic Noise When Using Low-Cost Devices in Industry 4.0. IEEE Access. 9:63267-63282. https://doi.org/10.1109/ACCESS.2021.3074588S6326763282