The concept of life skills is related to the way of life that emphasises the mutual exchange of knowledge, attitudes, and interpersonal skills in education. Its objective is to develop diverse skills among students and prepare them to face life’s challenges with determination. The World Health Organization has defined life skills as “the positive behaviours and tendencies that enable a person to adapt in day-to-day life.” Life skills are the abilities that enable a person to adapt and exhibit positive behaviour, allowing them to deal effectively with the problems and challenges of daily life. Life is a unique gift. Therefore, by equipping life with various skills, happiness, peace, and prosperity are created. In this research, with the objectives of the study in mind, an analytical examination of life skills among secondary-level students has been conducted. This research study examines the effects of living conditions, gender, and social class on students’ life skills and presents the findings. Future researchers can build upon this, and other factors affecting the research can also be explored.
Abstract Complete blood count (CBC) report features are routinely used to screen a wide array of hematological disorders. However, the complexity of disease overlap increases the probability of neglecting the underlying patterns between these features, and the heterogeneity associated with the subjective assessment of CBC reports often lead to random clinical testing. Such disease prediction analyses can be enhanced by the incorporation of machine learning (ML) algorithms for efficient handling of CBC features. Hybrid synthetic data are generated based on the statistical distribution of features to overcome the constraint of small sample size (N = 287). To the extent of our knowledge, our study is the first to employ hybrid synthetic data for modeling hematological parameters. Six ML models i.e., decision tree, random forest, support vector machine, logistic regression, gradient boosting machine, and multilayer perceptron are tested for disease prediction. This research presents ML-based models for the screening of two common blood disorders – anemia and leukemia, using CBC report features. A ‘fingerprint’ of 14 out of 21 features based on both statistical and clinical relevance is selected for model development. Exceptional performance has been observed by the random forest algorithm with 98% accuracy and 97, 98, 99, and 2% macro-averages of precision, recall, specificity, and miss-rate respectively for all classes. However, external validation of the model reveal poor generalizability on a different demographic dataset, as the model obtained an accuracy of 74%. The proposed methodology may serve as an efficient support system for the screening of anemia and leukemia. However, extensive optimization with regards to its generalizability are warranted.
Membrane distillation (MD) is a commonly used method for water treatment, but the issue of temperature polarization often leads to low vapor flux, which severely restrict the development and application. In order to improve MD performance, a novel method to create a porous layer on hydrophobic polytetrafluoroethylene (PTFE) membrane surface by Al2O3 dilute nanofluidic colloidal suspension is proposed in this study. Successful deposition was confirmed using different characterization techniques (SEM, EDS, CA). The effects of nanoparticle size and nanofluid concentration on DCMD performance at different feeding temperatures have been studied experimentally. Results demonstrate that the vapor flux enhancement varied non-linear with the increased nanofluid concentration, and the maximum enhancement of 32.46 % in vapor flux was achieved at feed temperature of 60 ℃. A robust deposition layer created by Al2O3 nanoparticles was confirmed with distillation experiment of 9 h. A fouling layer on the membrane surface was formed by flowing the nanofluid at higher concentration, which resulted in a deterioration of MD performance. Furthermore, the deposited membrane is able to maintain the high permeability when handling a 3.5 wt% NaCl solution. The work provides a simple method to enhance membrane distillation flux and a new perspective for nanofluidic suspensions in MD process.
Faculty of Economics and Management, Department Strategic Leadership and Global Management, Technical University of Berlin, Berlin, Germany Department Environmental and Reliability Engineering, Fraunhofer-Institute for Reliability and Microintegration, Berlin, Germany Faculty of Electrical Engineering and Computer Science, Transdisciplinary Sustainability Science in Electronics, Technical University of Berlin, Berlin, Germany
Abstract It is well known that global warming increases the atmospheric water vapor content, which results in substantial changes in the hydrological cycle. Using five observational data sets, the results show that an increasing trend of near‐surface water vapor pressure (AVP) over land and ocean was significant from 1975 to 1998, while such an increasing trend in AVP subsequently weakened from 1999 to 2019. This phenomenon is associated with decreased oceanic evaporation and land surface evapotranspiration in response to recent climate variations. One consequence of such a phenomenon is a large increase in near‐surface vapor pressure deficit (VPD), which in turn increases atmospheric demand for water vapor and thus aridity and drought over land. This result emphasizes the importance of water vapor change under global warming.
Abstract It is a great challenge to discover novel chemical reactions suitable for biological analysis in a living system. The development of novel protein thiol blocking agents is a crucial need for exploring protein thiol functions in protein refolding, signal transduction, and redox regulation. We are always keen on seeking novel chemical reactions applied to endogenous biological macromolecules or protein thiol sensing, blocking, and labeling. In the present work, we have successfully developed a novel agent to block protein thiol by enhanced electron‐withdrawing inductive effects. This sensing and blocking process was detailedly monitored by UV‐vis, fluorescent spectra, and SDS‐Page gel separation. The spectral studies demonstrated that the agent could react ultrafastly with thiol within seconds at μM level. Furthermore, fluorescent imaging in cells and in vivo was further used for the validation of its ability to sensing and blocking thiol, providing evidence of downregulated protein thiols in Parkinson's disease. The enhanced electron‐withdrawing inductive effect strategy in this work may provide a general guideline for designing protein thiol agent.
1 University of Florida, Institute of Food and Agricultural Sciences, Indian River Research and Education Center, Fort Pierce, Florida, USA 2 College of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou, China 3 Ministry of Education Key Laboratory of Environment Remediation and Ecosystem Health, College of Environmental and Resources Science, Zhejiang University at Zijingang, Hangzhou, China 4 USDA-ARS-Beltsville Agricultural Research Center, Beltsville, MD, USA 5 Science and Technology Branch, Agriculture and Agri-Food Canada, Ottawa, Canada
UNLABELLED The predicted mean vote (PMV) model of thermal comfort, created by Fanger in the late 1960s, is used worldwide to assess thermal comfort. Fanger based his model on college-aged students for use in invariant environmental conditions in air-conditioned buildings in moderate thermal climate zones. Environmental engineering practice calls for a predictive method that is applicable to all types of people in any kind of building in every climate zone. In this publication, existing support and criticism, as well as modifications to the PMV model are discussed in light of the requirements by environmental engineering practice in the 21st century in order to move from a predicted mean vote to comfort for all. Improved prediction of thermal comfort can be achieved through improving the validity of the PMV model, better specification of the model's input parameters, and accounting for outdoor thermal conditions and special groups. The application range of the PMV model can be enlarged, for instance, by using the model to assess the effects of the thermal environment on productivity and behavior, and interactions with other indoor environmental parameters, and the use of information and communication technologies. Even with such modifications to thermal comfort evaluation, thermal comfort for all can only be achieved when occupants have effective control over their own thermal environment. PRACTICAL IMPLICATIONS The paper treats the assessment of thermal comfort using the PMV model of Fanger, and deals with the strengths and limitations of this model. Readers are made familiar to some opportunities for use in the 21st-century information society.
Ali Azizpor, Ahmad Rajabi, Fariborz Yosefvand
et al.
In the current study, a new hybrid of the genetic algorithm (GA) and adaptive Neuro-fuzzy inference system (ANFIS) was introduced to model the discharge coefficient (DC) of triangular weirs. The genetic algorithm was implemented for increasing the efficiency of ANFIS by adjusting membership functions as well as minimizing error values. To evaluate the proficiency of the proposed hybrid method, the Monte Carlo simulations (MCS) and the k-fold validation method (k=5) was applied. The results of developed hybrid model indicate that the weir vortex angle, flow Froude number, the ratio of the weir length to its height, the ratio of the channel width to the weir length and ratio of the flow head to the weir height are the most effective parameters in the DC estimation. The quantitative examination of the proposed hybrid method indicates that the Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) are as 0.016 and 1.647 (respectively) for the superior model. Besides, the Froude number is found as the most effective variable in DC modeling through the quantitative analysis. A comparison of the developed hybrid ANFIS-GA with the individual ANFIS model in the DC estimation indicates the hybrid model outperformed than the individual one.
Zhenhua Chen, Andre L Carrel, Christina Gore
et al.
Battery electric vehicles (BEVs) have received increasing attention in recent years as BEV technical capabilities have rapidly developed. While many studies have attempted to investigate the societal impacts of BEV adoption, there is still a limited understanding of the extent to which widespread adoption of BEVs may affect both environmental and economic variables simultaneously. This study intends to address this research gap by conducting a comprehensive impact assessment of BEV adoption. Using demand estimates derived from a discrete choice experiment, the impact of various scenarios is evaluated using a computable general equilibrium model. Three drivers of BEV total cost of ownership are considered, namely, subsidy levels, cash incentives by manufacturers, and fuel costs. Furthermore, in light of current trends, improvements in BEV battery manufacturing productivity are considered. This research shows that changes in fuel price and incentives by manufacturers have relatively low impacts on GDP growth, but that the effect of subsidies on GDP and on BEV adoption is considerable, due to a stimulus effect on both household expenditures and on vehicle-manufacturing-related sectors. Productivity shocks moderately impact GDP but only affect BEV adoption in competitive markets. Conversely, the environmental impact is more nuanced. Although BEV adoption leads to decreases in tailpipe emissions, increased manufacturing activity as a result of productivity increases or subsidies can lead to growth in non-tailpipe emissions that cancels out some or all of the tailpipe emissions savings. This demonstrates that in order to achieve desired emissions reductions, policies to promote BEV adoption with subsidies should be accompanied by green manufacturing and green power generation initiatives.
Rebekah L. Martin, Owen R. Strom, Amy Pruden
et al.
Flint, MI experienced two outbreaks of Legionnaires’ Disease (LD) during the summers of 2014 and 2015, coinciding with use of Flint River as a drinking water source without corrosion control. Using simulated distribution systems (SDSs) followed by stagnant simulated premise (i.e., building) plumbing reactors (SPPRs) containing cross-linked polyethylene (PEX) or copper pipe, we reproduced trends in water chemistry and <i>Legionella</i> proliferation observed in the field when Flint River versus Detroit water were used before, during, and after the outbreak. Specifically, due to high chlorine demand in the SDSs, SPPRs with treated Flint River water were chlorine deficient and had elevated <i>L. pneumophila</i> numbers in the PEX condition. SPPRs with Detroit water, which had lower chlorine demand and higher residual chlorine, lost all culturable <i>L. pneumophila</i> within two months. <i>L. pneumophila</i> also diminished more rapidly with time in Flint River SPPRs with copper pipe, presumably due to the bacteriostatic properties of elevated copper concentrations caused by lack of corrosion control and stagnation. This study confirms hypothesized mechanisms by which the switch in water chemistry, pipe materials, and different flow patterns in Flint premise plumbing may have contributed to observed LD outbreak patterns.