Efficient optimisation of building design using a genetic algorithm

Several conventions on climate change, such as the Kyoto Protocol, the European Union Protocol, and the UK White Paper, have been issued to control and reduce greenhouse gas emissions. Research has been conducted to find friendly environment sources of energy such as renewable energy, and to reduce greenhouses gas emissions by reducing the use of conventional energy. Analysis of the energy consumption from a perspective of end-use indicates that buildings are one of the main energy consumers. Optimization of building design has the potential to save 22% to 32% of building energy consumption [Caldas and Norford, 2001; EU, 2002; and Wetter and Wright, 2003]. There are several optimization algorithms that have been developed to solve engineering problems. However, in this research a probabilistic optimization algorithm (a binary encoded Genetic Algorithms, GA), has been implemented to optimize building design with the aim of finding nearoptimum design solutions with the minimum number of new function calls. The main aim of this research is to identify a GA structure and control parameters that is effective in solving whole building optimization problems, including large scale constrained problems having many design variables. The research is restricted to the single objective, minimising building energy. The performance of the GA was evaluated for two building optimization problems, both based on an example five zone air-conditioned building located in Chicago, USA. The first example is for an unconstrained minimization of building energy use, the optimization of the building construction design. The second problem extends this to include the HVAC system control variables and as a result, includes constraints on the occupant thermal comfort. In each experiment, the performance of the GA was examined for different population sizes, crossover probability, and the mutation rate. The maximum number of new function calls (and building simulations) was restricted in each experiment set (this being the GA stopping criterion). The number of new function calls was selected to allow the optimization problem to be solved in a practical time. For the unconstrained problem, 12 GA control parameter sets were evaluated (with a total of 60,000 building simulations). Whereas for the constrained problem, eight sets of parameters were evaluated. Again the experiments were requiring a further 60,000 trial simulations. The results showed that GA performance was insensitive to most GA control parameter values, such as crossover probability and mutation rate. However, the control parameter that had the most significant effect was the population size. The small population sizes (5 individuals) gave better results on the unconstrained problem, whereas the mid-size population size (15 individuals) showed better result with the constrained problem. It can be concluded from this research that a binary encoded GA with small population sizes can be used to solve unconstrained building optimization problems with 500 or less building simulation calls. However, large scale constrained building optimization problems require in the order of 2000-3000 simulations.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Efficient Genetic Algorithm sets for optimizing constrained building design problem

The main aim of this paper is to find the appropriate set of Genetic Algorithm (GA), control parameters that attain the optimum, or near optimum solutions, in a reasonable computational time for constrained building optimization problem. Eight different combinations of control parameters of binary coded GA were tested in a hypothetical building problem by changing 80 variables. The results showed that GA performance was insensitive to some GA control parameter values such as crossover probability and mutation rate. However, population size was the most influential control parameter on the GA performance. In particular, the population sizes (15 individuals) require less computational time to reach the optimum solution. In particular, a binary encoded GA with relatively small population sizes can be used to solve constrained building optimization problems within 750 building simulation calls

Alpha-amylase and alpha-glucosidase enzyme inhibition and antioxidant potential of 3-oxolupenal and katononic acid isolated from Nuxia oppositifolia

Nuxia oppositifolia is traditionally used in diabetes treatment in many Arabian countries; however, scientific evidence is lacking. Hence, the present study explored the antidiabetic and antioxidant activities of the plant extracts and their purified compounds. The methanolic crude extract of N. oppositifolia was partitioned using a two-solvent system. The n-hexane fraction was purified by silica gel column chromatography to yield several compounds including katononic acid and 3-oxolupenal. Antidiabetic activities were assessed by α-amylase and α-glucosidase enzyme inhibition. Antioxidant capacities were examined by 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) scavenging assays. Further, the interaction between enzymes (α-amylase and α-glucosidase) and ligands (3-oxolupenal and katononic acid) was followed by fluorescence quenching and molecular docking studies. 3-oxolupenal and katononic acid showed IC50 values of 46.2 µg/mL (101.6 µM) and 52.4 µg/mL (119.3 µM), respectively against the amylase inhibition. 3-oxolupenal (62.3 µg/mL or 141.9 µM) exhibited more potent inhibition against α-glucosidases compared to katononic acid (88.6 µg/mL or 194.8 µM). In terms of antioxidant activity, the relatively polar crude extract and n-butanol fraction showed the greatest DPPH and ABTS scavenging activity. However, the antioxidant activities of the purified compounds were in the low to moderate range. Molecular docking studies confirmed that 3-oxolupenal and katononic acid interacted strongly with the active site residues of both α-amylase and α-glucosidase. Fluorescence quenching results also suggest that 3-oxolupenal and katononic acid have a good affinity towards both α-amylase and α-glucosidase enzymes. This study provides preliminary data for the plant’s use in the treatment of type 2 diabetes mellitus

Encapsulated deep eutectic solvent for esterification of free fatty acid

A novel encapsulated deep eutectic solvent (DES) was introduced for biodiesel production via a two-step process. The DES was encapsulated in medical capsules and were used to reduce the free fatty acid (FFA) content of acidic crude palm oil (ACPO) to the minimum acceptable level (< 1%). The DES was synthesized from methyltriphenylphosphonium bromide (MTPB) and p-toluenesulfonic acid (PTSA). The effects pertaining to different operating conditions such as capsule dosage, reaction time, molar ratio, and reaction temperature were optimized. The FFA content of ACPO was reduced from existing 9.61% to less than 1% under optimum operating conditions. This indicated that encapsulated MTPB-DES performed high catalytic activity in FFA esterification reaction and showed considerable activity even after four consecutive recycling runs. The produced biodiesel after acid esterification and alkaline transesterification met the EN14214 international biodiesel standard specifications. To our best knowledge, this is the first study to introduce an acidic catalyst in capsule form. This method presents a new route for the safe storage of new materials to be used for biofuel production. Conductor-like screening model for real solvents (COSMO-RS) representation of the DES using σ-profile and σ-potential graphs indicated that MTPB and PTSA is a compatible combination due to the balanced presence and affinity towards hydrogen bond donor and hydrogen bond acceptor in each constituent

A rapid and simple single-step method for the purification of Toxoplasma gondii tachyzoites and bradyzoites

This study describes a simple method for the large-scale isolation of pure Toxoplasma gondii tachyzoites and bradyzoites. T. gondii tachyzoites were obtained from infected human foreskin fibroblasts (HFFs) and peritoneal exudates of mice, while tissue cysts containing bradyzoites were collected from chronically infected mice. Harvested cells and brain tissues were incubated in Hanks balanced salt solution (HBSS), containing 0.25% trypsin and 0.5% taurodeoxycholic acid (TDC) for 5 min. Subsequent washes in phosphate buffered saline (PBS) were conducted, and the cell viability of the preparations was good, as determined by flow cytometry and ability to reinfect HFF cells and propagate in mice. The purification procedure allowed for a rapid preparation of pure T. gondii tachyzoites and bradyzoites in sufficient quantity that can be used for downstream procedures. The advantage of the new method is that it is convenient and inexpensive.The National Natural Science Foundation of China (No. 31502071), Youth Innovative Talents Project of Guangdong province Education Department (No. 2017KQNCX212), Guangdong province (2017GDK07), Start-up Research Grant Program provided by Foshan University, Foshan city, Guangdong province for distinguished researchers, Guangdong Science and Technology Plan Project (Grant No: 1244060045607389XC), and School of Life Science and Engineering fund (Grant No: KLPREAD201801-02).http://www.wileyonlinelibrary.com/journal/vms3am2021Paraclinical Science

Synthesis, characterization and antibacterial activity studies of new 2‑pyrral‑L‑amino acid Schif base palladium (II) complexes.

Three new 2-pyrral amino acid Schif base palladium (II) complexes were synthesized, characterized and their activity against six bacterial species was investigated. The ligands: Potassium 2-pyrrolidine-L-methioninate (L1), Potassium 2-pyrrolidine-L-histidinate (L2) and Potassium 2-pyrrolidine-L-tryptophanate (L3) were synthesized and reacted with dichloro(1,5- cyclooctadiene)palladium(II) to form new palladium (II) complexes C1, C2 and C3, respectively. 1 NMR, FTIR, UV–Vis,elemental analysis and conductivity measurements were used to characterize the products. The antibacterial activities of the compounds were evaluated against Gram-positive Staphylococcus aureus (S. aureus, ATCC 25923), methicillin-resistant Staphylococcus aureus (MRSA, ATCC 33591), Staphylococcus epidermidis (S. epidermidis, ATCC 12228) and Streptococcus pyogenes (S. pyogenes, ATCC 19615) and, gram-negative Pseudomonas aeruginosa (P. aeruginosa, ATCC 27853) and Klebsiella pneumoniae (K. pneumoniae, ATCC 13883) using the agar well difusion assay and microtitre plate serial dilution method. The palladium complexes were active against the selected bacteria with the imidazole ring containing complex C2 and indole heterocyclic ring containing complex C3 showing the highest activity

Quantitative assessment of energy conservation due to public awareness campaigns using neural networks

This case study aims to quantitatively assess the impact of an energy conservation campaign that was launched under the name "Trsheed" in Kuwait in the summer of 2007. Most electric energy (EE) consumption in the summer in the country is used in air conditioning and past trends indicate a strong correlation between ambient weather conditions and energy demand. The size and attitude of the population is an important factor in this regard; Kuwait has an expatriate population that is larger than the indigent population, and whose size is closely linked to economic activities that are largely dependent on oil revenues and varies with fluctuations of oil prices. Three neural network architectures (NNs) were evaluated in terms of their ability to estimate future EE demand based on previous trends. Backpropagation neural networks were found to be most suitable for this purpose in comparison to General Regression and Polynomial NNs. The inputs to the NNs investigated included hourly weather condition indicators; specifically the dry-bulb temperature and relative humidity. The output of the NNs was the hourly energy demand. An analysis based on actual weather data from 2004 to 2007 was performed to gauge the impact of the energy conservation campaign in the summer of 2007. Results of a second NN analysis show that round-the-clock mean weather conditions may be used to predict total future energy demand over a period of time (daily, weekly or monthly), but future peak loads should be estimated separately using mean weather conditions during peak hours only. Savings in national energy demand, as a result of future conservation campaigns, are estimated to be more than 5% and 4% in total and peak demands, respectively.Neural networks Electric power demand Energy conservation

Study of the Binding of Cuminaldehyde with Bovine Serum Albumin by Spectroscopic and Molecular Modeling Methods

Here, we investigated the interaction of cuminaldehyde with a model carrier protein, bovine serum albumin (BSA). The formation of the BSA–cuminaldehyde complex was confirmed through ultraviolet–visible (UV–Vis) spectroscopy and further proven by detailed intrinsic fluorescence spectroscopic measurements. As observed, cuminaldehyde quenched the intrinsic tryptophanyl fluorescence of BSA. The fluorescence data, before the analyses, were corrected for the inner filter effect (IFE) because of the significant absorption of cuminaldehyde at the excitation wavelength that was employed in the measurements. The typical Stern–Volmer plots were slightly nonlinear; they exhibited negative deviation toward the x-axis, a typical phenomenon that is observed with proteins possessing more than one tryptophan residue. Thus, the modified Stern–Volmer equation was employed to analyze the data. The analyzed data revealed that the interaction of cuminaldehyde with BSA proceeded via a static quenching mechanism and that there was a fair 1 : 1 binding between them. The interaction was strengthened by hydrophobic forces and hydrogen bonding. A lowered concentration of cuminaldehyde did not affect the secondary structure of BSA, although an increased one partially exposed the protein by decreasing its α-helical contents. The molecular dockings and simulations of BSA and cuminaldehyde further confirmed the formation of the stable BSA–cuminaldehyde complex. The in silico results also revealed that the contributions of the hydrophobic interaction and hydrogen bonding were the driving forces that imparted the stability