Machine learning approach to predict the biofuel production via biomass gasification and natural gas integrating to develop a low-carbon and environmental-friendly design: Thermodynamic-conceptual assessment.

A hybrid energy cycle (HEC) based on biomass gasification can be suggested as an efficient, modern and low-carbon energy power plant. In the current article, a thermodynamic-conceptual design of a HEC based on biomass and solar energies has been developed in order to generate electric power, heat and hydrogen energy. The planned HEC consists of six main units: two electric energy production units, a heat recovery unit (HRU), a hydrogen energy generation cycle based on water electrolysis, a thermal power generation unit (based on LFR field), and a biofuel production unit (based on biomass gasification process). Conceptual analysis is based on the development of energy, exergy and exergoeconomic assessments. Besides that, the reduction rate of pollutant emission through the planned HEC compared to conventional power plants is presented. In the planned HEC, when hydrogen energy is not needed, excess hydrogen is feed into the combustion chamber to improve system performance and reduce the need for natural gas. Accordingly, the rate of polluting gases emitted from the cycle can be mitigated due to the reduction of fossil fuels consumption. Further, based on the machine learning technique (MLT), the level of biofuel produced from the mentioned process is estimated. In this regard, two algorithms (i.e., Support vector machine and Gaussian process regression) have been employed to develop the prediction model. The findings indicated that the considered HEC can produce about 10.2 MW of electricity, 153 kW of thermal power, and 71.8 kmol/h of hydrogen energy. In both training and testing sets, the Support vector machine model exhibits better behavior compared the two Gaussian process regression model. Based on machine learning technique, with increasing gasification pressure, the level of biofuel obtained from the process does not increase significantly.

Techno-economic analysis and optimization of near-zero energy and emission neighborhoods using biomass waste.

The present study aims to simulate and design a near-Zero Energy neighborhood in one of the most significant industrial cities for reducing greenhouse gas emissions. For this building, biomass wastes are used for energy production, and also energy storage is provided using a battery pack system. Additionally, the Fanger model is used to assess the passengers' thermal comfort, and information on hot water usage is given. The transient performance of the aforementioned building is tested for one year using TRNSYS software, which was employed for this simulation. Wind turbines are considered electricity generators for this building, and any extra energy generated is stored in a battery pack for usage when the wind speed is insufficient and electricity is needed. Hot water is created using a biomass waste system and is kept in a hot water tank after being burned using a burner. A humidifier is utilized to ventilate the building, and a heat pump provides both the building's heating and cooling needs. The produced hot water is used to supply the residents' hot water. In addition, The Fanger model is considered and used for the assessment of occupants' thermal comfort. Matlab software is a powerful software used for this task. According to the findings, a wind turbine with a 6 kW generation capacity may supply the building's power needs while also charging the batteries beyond their initial capacity, and the building will have zero energy. Additionally, biomass fuel is used to give the building the required water which should be hot. On average, 200 g of biomass and biofuel are used per hour to maintain this temperature.

Spatio-Temporal Evolution and Influencing Factors of Ecological Well-Being Performance from the Perspective of Strong Sustainability: A Case Study of the Three Gorges Reservoir Area, China.

The concept of strong sustainability suggests that natural capital is irreplaceable and emphasizes that human natural consumption cannot exceed the carrying capacity of the ecological environment. In the realistic context of tightening resource constraints and ecological degradation, how to explore the optimal economic and well-being output under certain ecological constraints has become an important topic in sustainability research. Ecological well-being performance (EWP) can comprehensively reflect the efficiency of natural resources and ecological inputs into the well-being level and has become an important tool for sustainable development research. Based on strong sustainability, this paper constructs an EWP evaluation index system. It decomposes EWP into two stages: ecological economy and economic well-being, which opens the "black box" of the ecological well-being conversion process. PCA-DEA, the network super-efficiency Epsilon-based measure (Network SEBM) model, and Geodetector are used to dynamically measure the EWP in the Three Gorges Reservoir Area (TGRA) from 2010 to 2020 and analyze its spatial and temporal characteristics and influencing factors. The results show that (1) temporally, the EWP in the TGRA shows an increasing trend from 2010 to 2020, but the overall level is low. Spatially, it shows a high pattern in the east and low in the west, and spatial differences are gradually decreasing; (2) ecological economic efficiency is significantly lower than economic well-being efficiency, and ecological economic efficiency is the main reason limiting the improvement in EWP in the TGRA. The ecological well-being situation of the TGRA is not optimistic; (3) there is an overall problem of excessive ecological input and insufficient per-capita GDP and well-being output in the TGRA, and decisions should be made according to local conditions; (4) the level of economic development has an EWP level that plays a dominant role and also has a greater relationship with the policy system, socioeconomic conditions, and natural environment.

Assessment of graphene-based polymers for sustainable wastewater treatment: Development of a soft computing approach.

Since graphene possesses distinct electrical and material properties that could improve material performance, there is currently a growing demand for graphene-based electronics and applications. Numerous potential applications for graphene include lightweight and high-strength polymeric composite materials. Due to its structural qualities, which include low thickness and compact 2D dimensions, it has also been recognized as a promising nanomaterial for water-barrier applications. For barrier polymer applications, it is usually applied using two main strategies. The first is the application of graphene, graphene oxide (GO), and reduced graphene oxide (rGO) to polymeric substrates through transfer or coating. In the second method, fully exfoliated GO or rGO is integrated into the material. This study provides an overview of the most recent findings from research on the use of graphene in the context of water-barrier applications. The advantages and current limits of graphene-based composites are compared with those of other nanomaterials utilized for barrier purposes in order to emphasize difficult challenges for future study and prospective applications.

Does global positioning system-based navigation dependency make your sense of direction poor? A psychological assessment and eye-tracking study.

Background: Global positioning system (GPS)-based navigation apps are very useful in our lives. However, whether and how the usage of these apps affects spatial cognition and the sense of direction is still unclear. Methods: A total of 108 individuals were recruited and completed the GPS dependence, internet gaming behavior, and impulsivity test using scales. The eye-tracking-based general mental rotation (MR) task and target finding (TF; require individuals to find a target specified in a 3D street map in a rotated version of top 2D view map) task were used to assess their spatial cognition and the sense of direction. The correlation was used to relate GPS navigation usage, spatial cognition ability, and impulsivity. Subgroup analyses stratifying by gaming hours of individuals (< 2 h or ≥ 2 h) or maps (countryside or city) in TF task were performed. The moderating and mediating effect analyses were conducted to verify these relationships. Results: The GPS dependency score was nominal positively correlated with fixations in the TF task in the entire cohort (r = 0.202, unadjusted p = 0.036); it was significant in city (r = 0.254, p = 0.008) and gaming time of < 2 h (r = 0.459, p = 0.001) subgroups. The high-score (upper 30%) group of GPS dependency had more fixations on the original target building in the training area and indicative building in the test area than the low-score (lower 30%) group. GPS dependency was not associated with the correct rate and reaction time in the TF task or any of the indicators in the MR task (p > 0.05). The GPS dependency mediated the indirect effect of impulsivity on the fixations on TF. The internet gaming time moderated the association between GPS dependency and fixations on TF. Conclusion: The dependency on GPS-based navigation apps was associated with impaired spatial cognition but may not significantly affect the sense of direction.

Assessment of preparation time and 1-year Invisalign aligner attachment survival using flowable and packable composites.

OBJECTIVES: To compare preparation time and 1-year Invisalign aligner attachment survival between a flowable composite (FC) and a packable composite (PC). MATERIALS AND METHODS: Fifty-five participants (13 men and 42 women, mean age ± SD: 24.2 ± 5.9 years) were included in the study. Ipsilateral quadrants (ie, maxillary and mandibular right, or vice versa) of attachments were randomly assigned to the FC group (Filtek Z350XT Flowable Restorative) and the PC group (Filtek Z350XT Universal Restorative) by tossing a coin. The primary outcome was preparation time. The secondary outcome was time to the first damage of an attachment. Preparation times were compared using the paired t-test, and the survival data were analyzed by the Cox proportional hazards model with a shared frailty term, with α = .05. RESULTS: The preparation times were significantly shorter with the FC (6.22 ± 0.22 seconds per attachment) than with the PC (32.83 ± 2.16 seconds per attachment; P < .001). The attachment damage rates were 14.79% for the FC and 9.70% for the PC. According to the Cox models, attachment damage was not significantly affected by the attachment material, sex, arch, tooth location, attachment type, presence of overbite, or occurrence of tooth extraction. CONCLUSIONS: The use of a FC may save time as compared with the use of a PC. With regard to attachment survival, there was no significant difference between the two composites. None of the covariates of attachment materials (sex, arch, tooth location, attachment type, presence of overbite, oir occurrence of tooth extraction) affected attachment damage.

Patient Mix Optimization in Admission Planning under Multitype Patients and Priority Constraints.

Hospital beds are one of the most critical medical resources. Large hospitals in China have caused bed utilization rates to exceed 100% due to long-term extra beds. To alleviate the contradiction between the supply of high-quality medical resources and the demand for hospitalization, in this paper, we address the decision of choosing a case mix for a respiratory medicine department. We aim to generate an optimal admission plan of elective patients with the stochastic length of stay and different resource consumption. We assume that we can classify elective patients according to their registration information before admission. We formulated a general integer programming model considering heterogeneous patients and introducing patient priority constraints. The mathematical model is used to generate a scientific and reasonable admission planning, determining the best admission mix for multitype patients in a period. Compared with model II that does not consider priority constraints, model I proposed in this paper is better in terms of admissions and revenue. The proposed model I can adjust the priority parameters to meet the optimal output under different goals and scenarios. The daily admission planning for each type of patient obtained by model I can be used to assist the patient admission management in large general hospitals.

Using machine learning approaches to predict high-cost chronic obstructive pulmonary disease patients in China.

The accurate identification and prediction of high-cost Chronic obstructive pulmonary disease (COPD) patients is important for addressing the economic burden of COPD. The objectives of this study were to use machine learning approaches to identify and predict potential high-cost patients and explore the key variables of the forecasting model, by comparing differences in the predictive performance of different variable sets. Machine learning approaches were used to estimate the medical costs of COPD patients using the Medical Insurance Data of a large city in western China. The prediction models used were logistic regression, random forest (RF), and extreme gradient boosting (XGBoost). All three models had good predictive performance. The XGBoost model outperformed the others. The areas under the ROC curve for Logistic Regression, RF and XGBoost were 0.787, 0.792 and 0.801. The precision and accuracy metrics indicated that the methods achieved correct and reliable results. The results of this study can be used by healthcare data analysts, policy makers, insurers, and healthcare planners to improve the delivery of health services.

A Data-Driven Hybrid Three-Stage Framework for Hospital Bed Allocation: A Case Study in a Large Tertiary Hospital in China.

Beds are key, scarce medical resources in hospitals. The bed occupancy rate (BOR) amongst different departments within large tertiary hospitals is very imbalanced, a situation which has led to problems between the supply of and the demand for bed resources. This study aims to balance the utilization of existing beds in a large tertiary hospital in China. We developed a data-driven hybrid three-stage framework incorporating data analysis, simulation, and mixed integer programming to minimize the gaps in BOR among different departments. The first stage is to calculate the length of stay (LOS) and BOR of each department and identify the departments that need to be allocated beds. In the second stage, we used a fitted arrival distribution and median LOS as the input to a generic simulation model. In the third stage, we built a mixed integer programming model using the results obtained in the first two stages to generate the optimal bed allocation strategy for different departments. The value of the objective function, Z, represents the severity of the imbalance in BOR. Our case study demonstrated the effectiveness of the proposed data-driven hybrid three-stage framework. The results show that Z decreases from 0.7344 to 0.0409 after re-allocation, which means that the internal imbalance has eased. Our framework provides hospital bed policy makers with a feasible solution for bed allocation.