A cost effective approach to design of energy efficient residential buildings

The objective of this study is to identify cost-optimal efficiency packages at several levels of building energy savings. A two-story residential building located in Jordan is selected as a case study. DesignBuilder software is used to predict the annual energy usage of a two-story residence in Irbid, Jordan. Real-time experimental data from a single isolated controlled room was used to verify the proposed model. In addition to energy analysis, the economic, environmental, and social benefits of the proposed design have been investigated. The sequential search optimization approach is used to estimate the minimum cost of the building while considering various design scenarios. In addition, the impact of various energy conservation techniques on residential buildings is assessed, and the payback period for each program is calculated. Ultimately, the optimal combination of design to achieve energy efficiency measures has been identified in several climate regions. The simulations results predict that the annual electricity consumption can be reduced up to 50% if the proper combinations of energy conservation measures are selected at the lowest cost. The payback period is 9.3 years. Finally, energy efficiency measures can lead to a total of 9470 jobs/year job opportunities.The study provide practical framework to link between energy performance criteria and economic goals of building. Linking the energy performance requirements to economic targets provides guidelines for homeowners, contractors, and policymakers for making a suitable decision regarding the retrofitting of existing residential buildings. The study focuses on developing new methodologies that support minimizing costs during a building's lifecycle while maximizing environmental benefits which can not be identified by a series of parametric analyses using individual energy-efficient measures.     

Disassembly Sequence Planning: A Survey

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled. They contain reusable resource that can be recycled and used to generate desired economic benefits. Therefore, performing their efficient disassembly is highly important in green manufacturing and sustainable economic development. Their typical examples are electronic appliances and electromechanical/mechanical products. This paper presents a survey on the state of the art of disassembly sequence planning. It can help new researchers or decision makers to search for the right solution for optimal disassembly planning. It reviews the disassembly theory and methods that are applied for the processing, repair, and maintenance of obsolete/discarded products. This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling. This survey should stimulate readers to be engaged in the research, development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.      

Machine-learning modeling of hourly potential thermal refuge area: A case study from the Sainte-Marguerite River (Quebec,Canada)

To understand the temporal and spatial variability of thermal refuges, this study focused on modeling potential thermal refuge area (PTRA) at a sub-daily time-step in two tributary confluences of the Sainte-Marguerite River (Canada) during the summers of 2020 and 2021. Aquatic ectotherm species, such as Atlantic salmon (Salmo salar), seek these refuges to avoid heat stress during high summer river temperatures. To investigate the temporal variability of these PTRA, we employed inverse weighted distance interpolation to delineate the hourly area available at both confluences. We then analyzed the impact of the atypical low flow conditions of summer 2021 on the diel cycle of PTRA extremes using the coefficient of variation and the generalized additive model (GAM). Finally, we used four supervised machine-learning regression models and three to five hydrometeorological predictors to estimate hourly PTRA availability: multivariate adaptive splines regression (MARS), GAM, support vector machine regression (SVM), and random forest regression (RF). The results showed that tree-based and kernel-based regression models, RF and SVM, outperformed GAM and MARS. RF had the highest accuracy at both sites, with a relative root mean square error and Nash–Sutcliffe efficiency coefficient (Nash) of 13% and 93%, respectively. Our study discovered that under warm conditions in August 2021, small perennial tributary inflows in combination with low mainstem discharge could create high and constant PTRA at confluences, potentially providing vital thermal refuges for cold-water taxa. These refuges may be especially important at the local level, within a specific stretch or section of the river. Given the decreasing availability of thermal refuges for salmonids, it is crucial to monitor stream temperatures at small spatial and temporal scales using data-driven techniques in order to understand stream temperature heterogeneity at tributary confluences.     

Factorial design–machine learning approach for predicting incident durations

This research proposes a hybrid approach for predicting incident duration that integrates the salient features of both factorial design of experiments (DOE) and machine learning (ML). This study compares DOE with another widely used technique, forward sequential feature selection (FSFS). Moreover, to confirm the effectiveness and robustness of the proposed approach, multiple ML techniques are employed, including linear regression, decision trees, support vector machines, ensemble trees, Gaussian process regression, and artificial neural networks. The study results are validated using data from the Houston TranStar incidents archive with over 90,000 records. The accuracy of the developed predictive models is compared based on multiple techniques (i.e., no feature selection–ML, FSFS–ML, and DOE–ML). The results revealed that the significant factors affecting incident duration identified by both DOE and FSFS include the type of vehicles involved, type of lanes affected, number of vehicles involved, number of emergency responses dispatched, incident severity level, and day of the week. The comparative results of the different feature selection and modeling approaches revealed that the hybrid DOE–ML approach outperformed the other tested analysis approaches. The best-performing model under the DOE–ML approach was the SVM with cubic kernel model. It reduced the modeling time by 83.8% while increasing the prediction error by merely 0.02%, which is not significant. Therefore, the prediction accuracy could be slightly downgraded in return for a substantial reduction in the number of variables utilized, resulting in substantial savings in the modeling time and required dataset.     

Investigating the pilot-scale performance of a hydroponic nutrient solution as potential draw solution for fertilizer drawn forward osmosis and hydroponic agriculture of lettuce

Clean Technologies and Environmental Policy - The objective of this research is to test the feasibility of a large-scale application of fertilizer drawn forward osmosis using a concentrated...     

The effect of Nigella sativa seed extract concentration on crystal structure,band gap and antibacterial activity of ZnS-NPs prepared by green route

The present article investigates the effect of Nigella sativa seed extract concentration on crystal structure, band gap, and antibacterial activity of ZnS-NPs prepared by the green route. The study used a facile and eco-friendly method to obtain zinc sulfide nanoparticles (ZnS-NPs) using Nigella sativa seed (NSS) extract as a capping agent. XRD, SEM, FTIR, and UV–vis spectroscopy were employed to study the properties. The samples were also evaluated for antibacterial activity, and the obtained result shows pure crystalline ZnS-NPs with a cubic structure as being affirmed via powder XRD. FTIR spectra assert the existence of NSS extract as a capping agent. The SEM images of the obtained nanoparticles revealed that the prepared powders were composed of a mixture of fine and large grains of the product particles. The optical band gap computed from Tauc’s plot is found to decrease from 3.37 to 3.11 eV (Red shift). The antibacterial activity of ZnS-NPs was assessed on selected Gram-positive and Gram-negative bacteria strains (S. aureus and E. coli, respectively). The inhibition zones of ZnS-NPs were 26 and 19 mm for S. aureus and E. coli, respectively. The improved optical and antibacterial properties of ZnS-NPs make them suitable for optoelectronic applications and antibiotic development.

     

Research on steady characteristics of human-robot system for preventing elderly falls during walking

Due to the ever-increasing demand of caregivers and the high cost of nursing the elderly, researchers have been developing the elderly assistant robots (EARs) for assisting the elderly. To improve the safety of the elderly during walking, the steady characteristics of the EAR are discussed for preventing elderly falls during walking in this paper. Initially, the walking elderly was modeled as an inverted pendulum, and the steadiness region of the human and the general elderly fall conditions were obtained. The dynamics of the human-robot system were derived for preventing the general elderly falls. Also, the steadiness of the human and the robot were analyzed, respectively. Finally, experiments were conducted to verify the effectiveness of the models. The results demonstrated that the system met the requirements of steadiness, and hence, the designed robot could prevent elderly fall during walking. Thus, this study provides a theoretical basis for the effective control and the practical application of the EARs.

     

Effect of Oxidizer on PANI for Producing BaTiO3@PANI Perovskite Composites and Their Electrical and Electrochemical Properties

Journal of Inorganic and Organometallic Polymers and Materials - Polyaniline (PANI) has received significant attention in basic and applied studies because it has electrical and electrochemical...