Phycochemical and Biological Activities From Different Extracts of Padina antillarum (Kützing) Piccone

Seaweeds are non-vascular, photosynthetic that inhabit the coastal regions commonly within rocky intertidal or submerged reef-like habitats and have been one of the richest and most promising sources of bioactive primary and secondary metabolites with antimicrobial properties. They selectively absorb elements like Na, K, Ca, Mg, I, and Br from the seawater and accumulate them in their thalli. Padina antillarum (Kützing) Piccone is a member of Phaeophycota and has remarkable phycochemistry as well as bioactivity. The phycochemical tests of the different extracts showed the presence of alkaloids, terpenoids, saponins, tannins, steroids, and phenols. The relative percentage of Oxirane, tetradecyl (C16H32O), and Cyclononasiloxane (C18H54O9Si9) are higher while Tetrasiloxane (C16H50O7Si8) is lowest in Gas Chromatography – Mass Spectrometry analysis. FRAP, %inhibition, the total antioxidant value of P. antillarum was higher in methanolic extract. Hexane, chloroform extracts showed no zone of inhibition against Bacillus subtilis, Escherichia coli, Klebsiella pneumonia, Staphylococcus aureus, and Staphylococcus epidermidis. The methanolic extract of P. antillarum exhibits a maximum zone of inhibition against S. epidermidis (18.66 ± 0.09). Antifungal activity of the P. antillarum in hexane extract exhibited no zone of inhibition against Aspergillus niger and Penicillium notatum while the chloroform extract yields maximum zone (37 ± 0.012, 21.66 ± 0.03). Diabetes mellitus is one of the most familiar chronic diseases associated with carbohydrate metabolism. It is also an indication of co-morbidities such as obesity, hypertension, and hyperlipidemia which are metabolic complications of both clinical and experimental diabetes. The treatment of P. antillarum methanol extract in mice reduced the body weight loss, low level of triglycerides, and elevated HDL cholesterol level as compared to diabetic mice

Analysis And Performance Mapping Of “Component To System” For A Parabolic Trough Collector Applied To Process Heating Applications

The slogan “Heat is half” is of importance to keep in mind that nearly 50 % of the final energy use is in the form of heat. The global efforts for future decarbonised heating systems are based on hydrogen and electrification of heating etc. Solar thermal technology is a key component of greener industrial heating solutions. Solar thermal technologies for process heating application has decade long history of implementation and are gaining significant interest from all around the world. The performance prediction of solar thermal technologies on the system level is more complicated compared to photovoltaic, due to the effect of performance on system boundary conditions such as variation in meteorological parameters, load demand, temperature levels, thermal storage type. The central focus of this paper is on the use of a parabolic trough collector (PTC) for process heating applications in the medium temperature range. The aim of this paper is to map the performance of PTC collector into an industrial system, and to analyse the decrease in collector thermal output from component level to system level. The simulations are implemented in TRNSYS and MATLAB. The results are visualized using QGIS tool to generate the heat map for performance parameters for a range of solar fractions

Analysis And Performance Mapping Of “Component To System” For A Parabolic Trough Collector Applied To Process Heating Applications

The slogan “Heat is half” is of importance to keep in mind that nearly 50 % of the final energy use is in the form of heat. The global efforts for future decarbonised heating systems are based on hydrogen and electrification of heating etc. Solar thermal technology is a key component of greener industrial heating solutions. Solar thermal technologies for process heating application has decade long history of implementation and are gaining significant interest from all around the world. The performance prediction of solar thermal technologies on the system level is more complicated compared to photovoltaic, due to the effect of performance on system boundary conditions such as variation in meteorological parameters, load demand, temperature levels, thermal storage type. The central focus of this paper is on the use of a parabolic trough collector (PTC) for process heating applications in the medium temperature range. The aim of this paper is to map the performance of PTC collector into an industrial system, and to analyse the decrease in collector thermal output from component level to system level. The simulations are implemented in TRNSYS and MATLAB. The results are visualized using QGIS tool to generate the heat map for performance parameters for a range of solar fractions

ChatGPT for Fast Learning of Positive Energy District (PED): A Trial Testing and Comparison with Expert Discussion Results

Positive energy districts (PEDs) are urban areas which seek to take an integral approach to climate neutrality by including technological, spatial, regulatory, financial, legal, social, and economic perspectives. It is still a new concept and approach for many stakeholders. ChatGPT, a generative pre-trained transformer, is an advanced artificial intelligence (AI) chatbot based on a complex network structure and trained by the company OpenAI. It has the potential for the fast learning of PED. This paper reports a trial test in which ChatGPT is used to provide written formulations of PEDs within three frameworks: challenge, impact, and communication and dissemination. The results are compared with the formulations derived from over 80 PED experts who took part in a two-day workshop discussing many aspects of PED research and development. The proposed methodology involves querying ChatGPT with specific questions and recording its responses. Subsequently, expert opinions on the same questions are provided to ChatGPT, aiming to elicit a comparison between the two sources of information. This approach enables an evaluation of ChatGPT’s answers in relation to the insights shared by domain experts. By juxtaposing the outputs, a comprehensive assessment can be made regarding the reliability, accuracy, and alignment of ChatGPT’s responses with expert viewpoints. It is found that ChatGPT can be a useful tool for the rapid formulation of basic information about PEDs that could be used for its wider dissemination amongst the general public. The model is also noted as having a number of limitations, such as providing pre-set single answers, a sensitivity to the phrasing of questions, a tendency to repeat non-important (or general) information, and an inability to assess inputs negatively or provide diverse answers to context-based questions. Its answers were not always based on up-to-date information. Other limitations and some of the ethical–social issues related to the use of ChatGPT are also discussed. This study not only validated the possibility of using ChatGPT to rapid study PEDs but also trained ChatGPT by feeding back the experts’ discussion into the tool. It is recommended that ChatGPT can be involved in real-time PED meetings or workshops so that it can be trained both iteratively and dynamically

Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts

The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments

architecture-building-systems/CityEnergyAnalyst: CityEnergyAnalyst v.3.34.2

Enhanced zone helper to store street address [v3.34.2] 19 September 2023, in Zurich & Singapore zone_helper now saves the street addresses and postal codes of the buildings fetched from OpenStreetMap. The enhanced zone_helper also stores house names and residential types (for Singapore HDB Buildings only). The CEA Team # What's Changed Release 3.34.1 by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3380 Update sg_energy_optimization.yml by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3381 correcting typos by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3382 Saving additional info zone helper by @shizhongming in https://github.com/architecture-building-systems/CityEnergyAnalyst/pull/3388 Full Changelog: https://github.com/architecture-building-systems/CityEnergyAnalyst/compare/v.3.34.1...v3.34.