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Abstract This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass‐based biofuel production in Michigan. Using three life cycle assessment (LCA) databases—US lifecycle inventory (USLCI) database, GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real‐world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.

Abstract Energy crops for biofuel production, especially switchgrass (Panicum virgatum), are of interest from a climate change perspective. Here, we use outputs from a crop growth model and life cycle assessment (LCA) to examine the global warming intensity (GWI; g CO2 MJ−1) and greenhouse gas (GHG) mitigation potential (Mg CO2 year−1) of biofuel systems based on a spatially explicit analysis of switchgrass grown on marginal land (abandoned former cropland) in Michigan, USA. We find that marginal lands in Michigan can annually produce over 0.57 hm3 of liquid biofuel derived from nitrogen‐fertilized switchgrass, mitigating 1.2–1.5 Tg of CO2 year−1. About 96% of these biofuels can meet the Renewable Fuel Standard (60% reduction in lifecycle GHG emissions compared with conventional gasoline; GWI ≤37.2 g CO2 MJ−1). Furthermore, 73%–75% of these biofuels are carbon‐negative (GWI less than zero) due to enhanced soil organic carbon (SOC) sequestration. However, simulations indicate that SOC levels would fail to increase and even decrease on the 11% of lands where SOC stocks >>200 Mg C ha−1, leading to carbon intensities greater than gasoline. Results highlight the strong climate mitigation potential of switchgrass grown on marginal lands as well as the needs to avoid carbon rich soils such as histosols and wetlands and to ensure that productivity will be sufficient to provide net mitigation.

Synthetic biology brings engineering disciplines to create novel biological systems for biomedical and technological applications. The substantial growth of the synthetic biology field in the past decade is poised to transform biotechnology and medicine. To streamline design processes and facilitate debugging of complex synthetic circuits, cell-free synthetic biology approaches has reached broad research communities both in academia and industry. By recapitulating gene expression systems in vitro, cell-free expression systems offer flexibility to explore beyond the confines of living cells and allow networking of synthetic and natural systems. Here, we review the capabilities of the current cell-free platforms, focusing on nucleic acid-based molecular programs and circuit construction. We survey the recent developments including cell-free transcription-translation platforms, DNA nanostructures and circuits, and novel classes of riboregulators. The links to mathematical models and the prospects of cell-free synthetic biology platforms will also be discussed.

As the number of single-person households continues to increase, contents related to exercise at home are increasing. Therefore, this paper proposes a framework for efficient home fitness. The home fitness framework proposed in this paper is based on a method of acquiring expert motion information and providing it to users. To this end, content creation and provision are implemented by using Kinect and Unreal game engine. In addition, it is possible to provide customized home fitness in consideration of the user's athletic ability. The proposed system first captures the expert's motion and stores it. We propose a method that can efficiently store stop motion and dynamic motion storage. In the case of each user, since there is a difference in exercise ability for each individual, an adverse effect may occur if an excessively accurate motion is requested. Therefore, to solve these problems, we present the parts that can be considered for each joint. For the exercise motion provided by the expert, a method was provided that allows the user to adjust the degree of matching for each joint in consideration of user's own exercise ability. That is, joint parts that do not require exact matching could be completely excluded from matching. For parts that would be subject to large changes, a range of errors is specified. As the training progresses, the error range is reduced, and the excluded parts are presented to be matched gradually. Such adjustments are made based on expert feedback. In this way, it was possible to improve the exercise effect gradually. This paper proposes an effective method for personalized home fitness according to the user's athletic ability. This will apply to various fields besides home fitness.

According to statistics, the number of hearing-impaired persons among the disabled in Korea accounts for 27% of all persons with disabilities. However, there is insufficient support for the deaf and hard of hearing's protective devices and life aids compared to the large number. In particular, the hearing impaired misses much information obtained through sound and causes inconvenience in daily life. Therefore, in this paper, we propose a method to relieve the discomfort in the daily life of the hearing impaired. It analyzes sounds that can occur frequently and must be recognized in daily life and guide them to the hearing impaired through applications and vibration bracelets. Sound analysis was learned by using deep learning by converting sounds that often occur in daily life into the Mel-Spectrogram. The sound that actually occurs is recorded through the application, and then it is identified based on the learning result. According to the identification result, predefined alarms and vibrations are provided differently so that the hearing impaired can easily recognize it. As a result of the recognition of the four major sounds occurring in real life in the experiment, the performance showed an average of 85% and an average of 80% of the classification rate for mixed sounds. It was confirmed that the proposed method can be applied to real-life through experiments. Through the proposed method, the quality of life can be improved by allowing the hearing impaired to recognize and respond to sounds that are essential in daily life.

Numerical simulation of fluids is important in modeling a variety of physical phenomena, such as weather, climate, aerodynamics, and plasma physics. The Navier–Stokes equations are commonly used to describe fluids, but solving them at a large scale can be computationally expensive, particularly when it comes to resolving small spatiotemporal features. This trade-off between accuracy and tractability can be challenging. In this paper, we propose a novel artificial intelligence-based method for improving fluid flow approximations in computational fluid dynamics (CFD) using deep learning (DL). Our method, called CFDformer, is a surrogate model that can handle both local and global features of CFD input data. It is also able to adjust boundary conditions and incorporate additional flow conditions, such as velocity and pressure. Importantly, CFDformer performs well under different velocities and pressures outside of the flows it was trained on. Through comprehensive experiments and comparisons, we demonstrate that CFDformer outperforms other baseline DL models, including U-shaped convolutional neural network (U-Net) and TransUNet models.

Digital door locks are widely used not only in general homes such as houses and apartments, but also in spaces where external intrusion must be prevented based on high security and convenience. Recently, smart door locks with additional technologies such as fingerprint recognition and Bluetooth communication have also been developed, and the door lock market is on the rise. Digital door locks are more convenient to use compared to the existing key-type door locks. However, there are often cases of exploiting security vulnerabilities such as exploiting and invading the user's trace remaining on the door lock. This paper proposes a door lock with a structure that can complement the shape of the current door lock exposed to the outside and minimize the user's fingerprint trace. In addition, a method of reinforcing security is applied using fingerprint recognition through image processing and a random pattern number arrangement. An experiment was conducted to confirm whether the door lock of this type was actually usable, and the recognition of partially damaged fingerprints was also confirmed. It was shown that the door lock structure proposed in this paper can maximize security by combining fingerprint recognition technology and random pattern numbering while minimizing external exposure.

This study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass‐based biofuel production in Michigan. Using three life cycle assessment (LCA) databases—US lifecycle inventory (USLCI) database, GREET, and Ecoinvent—each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real‐world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production. [ABSTRACT FROM AUTHOR]

Our world is quickly moving towards the fourth industrial revolution including mobile, big data, AI, IoT, cloud computing, VR, etc. Recently, South Korea has been emphasizing convergence education to university. Thus, university has begun doing convergence education on their own by linking major subjects and liberal arts courses or linking different departments. In this paper, we analyzed learners’ satisfaction for operating convergence education effectively to increase education satisfaction and developed convergence curriculum and convergence skills required by society. for this study, a satisfaction survey is conducted for students majoring in engineering colleges. And the students’ experiences are collected through interviews and questionnaires for suggesting improved the convergence curriculum operation. We also did interviews and asked students about the meaning convergence education had for them, the impressions they had after taking the classes, and any opinions for further improvements. As a result of the analysis of student's satisfaction and satisfaction of convergence curriculum, it was analyzed as “approximately satisfied” with 3.6. Additionally, the correlation between student satisfaction and convergence curriculum satisfaction was analyzed, and the correlation coefficient showed a significant correlation with 0.732. In other words, it can be seen that students with high-student satisfaction are also highly satisfied with the convergence curriculum. Based on the result of the research and the student’s opinions, we would like to suggest that there should be subject development that is connected to careers or job searching for senior students, and additional research of practical educational methods are also needed.

Abstract Previous studies have suggested an increased risk of ischemic heart disease related to air pollution. This study aimed to explore both the short-term and long-term effects of air pollutants on the risk of ischemic heart disease after adjusting for meteorological factors. The Korean National Health Insurance Service-Health Screening Cohort from 2002 to 2013 was used. Overall, 2155 participants with ischemic heart disease and 8620 control participants were analyzed. The meteorological data and air pollution data, including SO2 (ppm), NO2 (ppm), O3 (ppm), CO (ppm), and particulate matter (PM)10 (μg/m3), were analyzed using conditional logistic regression. Subgroup analyses were performed according to age, sex, income, and region of residence. One-month exposure to SO2 was related to 1.36-fold higher odds for ischemic heart disease (95% confidence interval [95% CI] 1.06–1.75). One-year exposure to SO2, O3, and PM10 was associated with 1.58- (95% CI 1.01–2.47), 1.53- (95% CI 1.27–1.84), and 1.14 (95% CI 1.02–1.26)-fold higher odds for ischemic heart disease. In subgroup analyses, the ≥ 60-year-old group, men, individuals with low income, and urban groups demonstrated higher odds associated with 1-month exposure to SO2. Short-term exposure to SO2 and long-term exposure to SO2, O3, and PM10 were related to ischemic heart disease.

Understanding Pakistan's greenhouse gas (GHG) emission status is critical for identifying the national targets for GHG mitigation with respect to the nationally determined contributions (NDCs). This study focuses on the development of Pakistan's GHG (CO2, CH4, and N2O) inventories for agriculture, forestry and other land use (AFOLU) and waste sectors using 2006 IPCC Guidelines. This study should be seen as a direct continuation of the preceding one (Part I [ Available online at: https://doi.org/10.1016/j.accre.2020.05.002]) which discusses energy and industrial processes and product use in compliance with the 2006 and 1996 IPCC Guidelines. It also provides sector-specific comparative time series (1994–2017) analysis of GHG inventories, identification of key categories, and national GHG emissions trend for Pakistan. The results indicate an average relative difference (on average for time series 1994–2017) of 19% and 6% in total GHG emissions (CO2-eq) from AFOLU and waste sector respectively between 2006 and 1996 IPCC Guidelines. The absolute difference over the entire time series for AFOLU and waste sector was in the range of 3–67 Mt CO2-eq and 1–7 Mt CO2-eq respectively. Findings further reveal that the quantity of national GHG emissions by 2006 IPCC Guidelines is 10% lower on average for complete time series compared to 1996 IPCC Guidelines. The average relative difference for total national emissions of CO2, CH4 and N2O is −1%, 9%, and 48% respectively. Key category analysis based on 2006 IPCC Guidelines estimates identified three categories, each contributing ≥10% to the level assessment in the latest year 2017 and accounting for approximately half of the national GHG emissions. In order to further improve the reliability of GHG inventories, Pakistan needs to move from 1996 to 2006 IPCC Guidelines under a higher Tier approach particularly for identified key categories.

In order to further improve the accuracy and reliability and reduce uncertainties in the national GHG inventories for Pakistan, this study call for using 2006 IPCC Guidelines, to help to identify the national targets for GHG mitigation with respect to the nationally determined contributions (NDCs). GHG (CO2, CH4, and N2O) inventories for Pakistan have been developed by conducting a detailed sectoral assessment of IPCC source sectors, energy, industrial processes and product use (IPPU), agriculture, forestry and other land use (AFOLU), and the waste sector. Further, sector wise comparative analysis of GHG inventories (1994–2017) based on the 2006 and 1996 IPCC Guidelines have also been presented. Results indicated an average relative difference of 4% in total GHG emissions (CO2 equivalent) from energy sector between 2006 and 1996 IPCC Guidelines. With 3.6% average annual growth rate based on 2006 IPCC Guidelines, CO2 from energy sector remained the most abundant GHG emitted, followed by CH4 and N2O. While the average absolute difference in emissions of CH4 and N2O from the energy sector is notable, the total estimated GHG emissions by 2006 IPCC Guidelines duplicate those by 1996 IPCC Guidelines. In the mineral industry with 2006 IPCC Guidelines, an average annual growth rate of 6.7% is observed, contributing 64% of total IPPU sector CO2 emissions. Nevertheless, the relative difference between the two Guidelines in overall IPPU sector emissions remained negligible. There might be a need for switching to 2006 IPCC Guidelines to consider more parameters such as additional source sectors and new default emission factors that fit into national circumstances.

A crucial environmental public health risk is air pollution caused by organic contaminants, bio-aerosols, and fine particulate matter (PM2.5–10). A multi-purpose photocatalyst filter with photocatalytic nitrogen-doped titanium dioxide (PNT) incorporated into non-woven polymer fibers (NWPF) with wide applications is focused on in detail in this work. Loading of PNT on NWPF is carried out by the simple dip coating method. Para-xylene is selected as the target pollutant for the removal of organic contaminants. Higher removal efficiency of p-xylene (94.2%) is achieved under fluorescent daylight. The filtration efficiency of PNT is also evaluated to capture PM2.5–10, which is increased with higher loading content of PNT. At a high air flow rate, the filtration efficiency of the photocatalyst PNT/NWPF is 97.33%, whereas the efficiency of the pristine NWPF is 91.1%. Moreover, the PNT/NWPF filter exhibits excellent antibacterial activity (99.9%) under visible light irradiation, but the pristine NWPF filter has negligible destruction effects on pathogens. These results clearly indicate that a PNT-coated NWPF filter would be an outstanding multi-purpose material for indoor air purification systems.

Abstract The current or “conventional” paradigm for producing process energy in a biorefinery processing cellulosic biomass is on‐site energy recovery through combustion of residual solids and biogas generated by the process. Excess electricity is then exported, resulting in large greenhouse gas (GHG) credits. However, this approach will cause lifecycle GHG emissions of biofuels to increase as more renewable energy sources (wind, solar, etc.) participate in grid‐electricity generation, and the GHG credits from displacing fossil fuel decrease. To overcome this drawback, a decentralized (depot‐based) biorefinery can be integrated with a coal‐fired power plant near a large urban area. In an integrated, decentralized, depot‐based biorefinery (IDB), the residual solids are co‐fired with coal either in the adjacent power plant or in coal‐fired boilers elsewhere to displace coal. An IDB system does not rely on indirect GHG credits through grid‐electricity displacement. In an IDB system, biogas from the wastewater treatment facility is also upgraded to biomethane and used as a transportation biofuel. The GHG savings per unit of cropland in the IDB systems (2.7–2.9 MgCO2/ha) are 1.5–1.6 fold greater than those in a conventional centralized system (1.7–1.8 MgCO2/ha). Importantly, the biofuel selling price in the IDBs is lower by 28–30 cents per gasoline‐equivalent liter than in the conventional centralized system. Furthermore, the total capital investment per annual biofuel volume in the IDB is much lower (by ~80%) than that in the conventional centralized system. Therefore, utilization of biomethane and residual solids in the IDB systems leads to much lower biofuel selling prices and significantly greater GHG savings per unit of cropland participating in the biorefinery system compared to the conventional centralized biorefineries.