Integrated Dynamic Photovoltaic Facade for Enhanced Building Comfort and Energy Efficiency.

This simulation study explores the potential of a novel façade design with integrated control system comprising a dynamic photovoltaic (PV) facade integrated with dimming lighting control to enhance the work environment in office buildings and achieve energy-efficient solutions. Parametric modeling using the Grasshopper plug-in for Rhino software 7, coupled with energy simulation through the Honeybee environmental plug-in for the EnergyPlus program, are used in the methodology. The integrated control strategy was simulated to study in a single office space, utilizing the Daysim engine to assess indoor daylight quality and focusing on Daylight Factor (DF) and Daylight Glare Probability (DGP). Additionally, two artificial lighting control systems were examined for potential integration with the dynamic PV facade to minimize lighting load. The study employs the Galapagos evolutionary solver function embedded within Grasshopper to identify optimum solutions. The dynamic PV façade achieves substantial reductions in overall energy consumption, cutting it by 73% in June, 54% in July, 54.5% in August, and 52.55% in September. The results demonstrate substantial reductions in total energy consumption, with notable savings in heating and cooling due to the dynamic facade's ability to balance and control solar radiation during working hours. Moreover, the dynamic PV facade contributes to electricity generation, demonstrating its potential to improve visual comfort, decrease energy consumption, and generate electric energy through rotational adjustments and varying transparency levels.

Diversity and Distribution of ß-Lactamase Genes Circulating in Indian Isolates of Multidrug-Resistant Klebsiella pneumoniae.

Klebsiella pneumoniae (Kp) has gained prominence in the last two decades due to its global spread as a multidrug-resistant (MDR) pathogen. Further, carbapenem-resistant Kp are emerging at an alarming rate. The objective of this study was (1) to evaluate the prevalence of ß-lactamases, especially carbapenemases, in Kp isolates from India, and (2) determine the most prevalent sequence type (ST) and plasmids, and their association with ß-lactamases. Clinical samples of K. pneumoniae (n = 65) were collected from various pathology labs, and drug susceptibility and minimum inhibitory concentrations (MIC) were detected. Whole genome sequencing (WGS) was performed for n = 22 resistant isolates, including multidrug-resistant (MDR) (n = 4), extensively drug-resistant (XDR) (n = 15), and pandrug-resistant (PDR) (n = 3) categories, and genomic analysis was performed using various bioinformatics tools. Additional Indian MDRKp genomes (n = 187) were retrieved using the Pathosystems Resource Integration Center (PATRIC) database. Detection of ß-lactamase genes, location (on chromosome or plasmid), plasmid replicons, and ST of genomes was carried out using CARD, mlplasmids, PlasmidFinder, and PubMLST, respectively. All data were analyzed and summarized using the iTOL tool. ST231 was highest, followed by ST147, ST2096, and ST14, among Indian isolates. blaampH was detected as the most prevalent gene, followed by blaCTX-M-15 and blaTEM-1. Among carbapenemase genes, blaOXA-232 was prevalent and associated with ST231, ST2096, and ST14, which was followed by blaNDM-5, which was observed to be prevalent in ST147, ST395, and ST437. ST231 genomes were most commonly found to carry Col440I and ColKP3 plasmids. ST16 carried mainly ColKP3, and Col(BS512) was abundantly present in ST147 genomes. One Kp isolate with a novel MLST profile was identified, which carried blaCTX-M-15, blaOXA-1, and blaTEM-1. ST16 and ST14 are mostly dual-producers of carbapenem and ESBL genes and could be emerging high-risk clones in India.

Inexact inventory-theory-based optimization of oily waste management system in shoreline spill response.

The oily waste generated from the cleanup operations during shoreline spill response can result in challenging environmental and socioeconomic problems. In this study, an inexact inventory-theory-based optimization model (ITOM) for oily waste management during shoreline spill response was developed to support the spill management team. The most appropriate facilities and optimal waste allocation scheme under uncertainty can be selected to achieve minimum total system cost. To satisfy the demand of oily waste treatment, these oily waste management facilities can be selectively opened depending on the situation. In the combination with the economic order quantity model of inventory theory, the developed model can provide the optimal solutions of batch size and order cycle for treatment facilities to minimize the inventory cost. A case study was used to demonstrate the application of ITOM. The obtained solutions include the facilities selection and waste allocation for waste collection and destocking stages under different risk levels. These solutions can provide a good guideline with managers to analyze the trade-offs between system cost and constraint-violation risks. The developed model has high application potential as a job-aid tool to manage the oily waste generated from oiled shoreline cleanup operations.