An evolutionary adaptive neuro-fuzzy inference system for estimating field penetration index of tunnel boring machine in rock mass

Field penetration index (FPI) is one of the representative key parameters to examine the tunnel boring machine (TBM) performance. Lack of accurate FPI prediction can be responsible for numerous disastrous incidents associated with rock mechanics and engineering. This study aims to predict TBM performance (i.e. FPI) by an efficient and improved adaptive neuro-fuzzy inference system (ANFIS) model. This was done using an evolutionary algorithm, i.e. artificial bee colony (ABC) algorithm mixed with the ANFIS model. The role of ABC algorithm in this system is to find the optimum membership functions (MFs) of ANFIS model to achieve a higher degree of accuracy. The procedure and modeling were conducted on a tunnelling database comprising of more than 150 data samples where brittleness index (BI), fracture spacing, α angle between the plane of weakness and the TBM driven direction, and field single cutter load were assigned as model inputs to approximate FPI values. According to the results obtained by performance indices, the proposed ANFIS_ABC model was able to receive the highest accuracy level in predicting FPI values compared with ANFIS model. In terms of coefficient of determination (R2), the values of 0.951 and 0.901 were obtained for training and testing stages of the proposed ANFIS_ABC model, respectively, which confirm its power and capability in solving TBM performance problem. The proposed model can be used in the other areas of rock mechanics and underground space technologies with similar conditions. © 2021 Institute of Rock and Soil Mechanics, Chinese Academy of Science

Catalytic Decomposition of 2% Methanol in Methane over Metallic Catalyst by Fixed-Bed Catalytic Reactor

The structure and performance of promoted Ni/Al2O3 with Cu via thermocatalytic decomposition (TCD) of CH4 mixture (2% CH3OH) were studied. Mesoporous Cat-1 and Cat-2 were synthesized by the impregnation method. The corresponding peaks of nickel oxide and copper oxide in the XRD showed the presence of nickel and copper oxides as a mixed alloy in the calcined catalyst. Temperature program reduction (TPR) showed that Cu enhanced the reducibility of the catalyst as the peak of nickel oxide shifted toward a lower temperature due to the interaction strength of the metal particles and support. The impregnation of 10% Cu on Cat-1 drastically improved the catalytic performance and exhibited 68% CH4 conversion, and endured its activity for 6 h compared with Cat-1, which deactivated after 4 h. The investigation of the spent carbon showed that various forms of carbon were obtained as a by-product of TCD, including graphene fiber (GF), carbon nanofiber (CNF), and multi-wall carbon nanofibers (MWCNFs) on the active sites of Cat-2 and Cat-1, following various kinds of growth mechanisms. The presence of the D and G bands in the Raman spectroscopy confirmed the mixture of amorphous and crystalline morphology of the deposited carbon

Testing for Multiple Bubbles in Inflation for Pakistan

Detection of bubbles in financial markets is an issue of great importance as these split an enduring impact to every sector of the economy leading to substantial losses. In Pakistan, price level has seen abrupt changes which hurts the economic efficiency whereas inflation rates are usually high. So, it is important to detect bubbles present in inflation to have a check whether the hike in inflation is demand driven or there is a prevailing price exuberant behavior that results in sudden boom in inflation. There are very limited studies on detecting bubbles in inflation series for Pakistan. So, the present study takes a lead and address this very important issue by making use of recently developed state of art GSADF approach proposed by Phillips et al. (2015). The empirical analysis is based on five different series which cover inflation rates such as consumer price index (CPI) for the general, the food and the non-food items, the sensitive price index (SPI) and the wholesale price index (WPI). This approach is best suited for testing multiple bubbles as opposed to earlier methods that are designed to test for the presence of only a single bubble in any time series. The empirical findings based on monthly time series data from Jan 2006 to Jan 2019 confirm the existence of multiple bubbles in WPI and CPI non-food. However, for rest of three series, only single bubble has been witnessed. The analysis from the date stamping of bubbles reveal that all bubbles arise during the global financial crisis of 2008 which triggered oil prices resulting in domestic currency depreciation. Some important policy implications are discussed as well

Barriers in Social Distancing during Covid19 pandemic - Is a message for forced lockdown

The world currently faces the predicament of the fast-spreading COVID-19 which as of 21st April 2020 affects 210 countries over the globe. As the disease started spreading its shadow at an alarmingly rapid rate, new information about the novel coronavirus was extracted and it has been reported to be mainly transmitted directly from person-to-person, droplet spread by cough or sneezing or by fomites. Till an effective vaccine becomes available the most potent preventive measure that can be taken is for people to maintain distance and avoid gatherings. Importance of social distancing has been discussed on many forums and disseminated among the public but the problem arises when the practical implementation does not encompass the entirety of the theoretical concepts. Understanding the barriers that stand between applying social distance in community is imperative if authorities and public health sectors expect a substantial change in incident cases. It's imperative that measures should be taken to stop the spread of misinformation, and guide the masses regarding the importance of social distancing. Since the virus spreads by droplet transmission, so without these proper social distancing measures, the burden will increase and it will not be possible to put a stop to this pandemic

A Smart and Robust Automatic Inspection of Printed Labels Using an Image Hashing Technique

This work is focused on the development of a smart and automatic inspection system for printed labels. This is a challenging problem to solve since the collected labels are typically subjected to a variety of geometric and non-geometric distortions. Even though these distortions do not affect the content of a label, they have a substantial impact on the pixel value of the label image. Second, the faulty area may be extremely small as compared to the overall size of the labelling system. A further necessity is the ability to locate and isolate faults. To overcome this issue, a robust image hashing approach for the detection of erroneous labels has been developed. Image hashing techniques are generally used in image authentication, social event detection and image copy detection. Most of the image hashing methods are computationally extensive and also misjudge the images processed through the geometric transformation. In this paper, we present a novel idea to detect the faults in labels by incorporating image hashing along with the traditional computer vision algorithms to reduce the processing time. It is possible to apply Speeded Up Robust Features (SURF) to acquire alignment parameters so that the scheme is resistant to geometric and other distortions. The statistical mean is employed to generate the hash value. Even though this feature is quite simple, it has been found to be extremely effective in terms of computing complexity and the precision with which faults are detected, as proven by the experimental findings. Experimental results show that the proposed technique achieved an accuracy of 90.12%

Optimal Compressive Strength of RHA Ultra-High-Performance Lightweight Concrete (UHPLC) and Its Environmental Performance Using Life Cycle Assessment

Frequent laboratory needs during the production of concrete for infrastructure development purposes are a factor of serious concern for sustainable development. In order to overcome this trend, an intelligent forecast of the concrete properties based on multiple data points collected from various concrete mixes produced and cured under different conditions is adopted. It is equally important to consider the impact of the concrete components in this attempt to take care of the environmental risks involved in this production. In this work, 192 mixes of an ultra-high-performance lightweight concrete (UHPLC) were collected from literature representing different mixes cured under different periods and laboratory conditions. These mix proportions constitute measured variables, which are curing age (A), cement content (C), fine aggregate (FAg), plasticizer (PL), and rice husk ash (RHA). The studied concrete property was the unconfined compressive strength (Fc). This exercise was necessary to reduce multiple dependence on laboratory examinations by proposing concrete strength equations. First, the life cycle assessment evaluation was conducted on the rice husk ash-based UHPLC, and the results from the 192 mixes show that the C-783 mix (87 kg/m3 RHA) has the highest score on the environmental performance evaluation, while C-300 (75 kg/m3 RHA) with life cycle indices of 289.85 kg CO2eq. Global warming potential (GWP), 0.66 kg SO2eq. Terrestrial acidification and 5.77 m3 water consumption was selected to be the optimal choice due to its good profile in the LCA and the Fc associated with the mix. Second, intelligent predictions were conducted by using six algorithms (ANN-BP), (ANN-GRG), (ANN-GA), (GP), (EPR), and (GMDH-Combi). The results show that (ANN-BP) with performance indices of R; 0.989, R2; 0.979, mean square error (MSE); 2252.55, root mean squared error (RMSE); 42.46 MPa and mean absolute percentage error (MAPE); 4.95% outclassed the other five techniques and is selected as the decisive model. However, it also compared well and outclassed previous models, which had used gene expression programming (GEP) and random forest regression (RFR) and achieved R2of 0.96 and 0.91, respectively. Doi: 10.28991/CEJ-2022-08-11-03 Full Text: PD

Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Global, regional, and national mortality due to unintentional carbon monoxide poisoning, 2000–2021: results from the Global Burden of Disease Study 2021

Background Unintentional carbon monoxide poisoning is a largely preventable cause of death that has received insufficient attention. We aimed to conduct a comprehensive global analysis of the demographic, temporal, and geographical patterns of fatal unintentional carbon monoxide poisoning from 2000 to 2021. Methods As part of the latest Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), unintentional carbon monoxide poisoning mortality was quantified using the GBD cause of death ensemble modelling strategy. Vital registration data and covariates with an epidemiological link to unintentional carbon monoxide poisoning informed the estimates of death counts and mortality rates for all locations, sexes, ages, and years included in the GBD. Years of life lost (YLLs) were estimated by multiplying deaths by remaining standard life expectancy at age of death. Population attributable fractions (PAFs) for unintentional carbon monoxide poisoning deaths due to occupational injuries and high alcohol use were estimated. Findings In 2021, the global mortality rate due to unintentional carbon monoxide poisoning was 0·366 per 100 000 (95% uncertainty interval 0·276–0·415), with 28 900 deaths (21 700–32 800) and 1·18 million YLLs (0·886–1·35) across all ages. Nearly 70% of deaths occurred in males (20 100 [15 800–24 000]), and the 50–54-year age group had the largest number of deaths (2210 [1660–2590]). The highest mortality rate was in those aged 85 years or older with 1·96 deaths (1·38–2·32) per 100 000. Eastern Europe had the highest age-standardised mortality rate at 2·12 deaths (1·98–2·30) per 100 000. Globally, there was a 53·5% (46·2–63·7) decrease in the age-standardised mortality rate from 2000 to 2021, although this decline was not uniform across regions. The overall PAFs for occupational injuries and high alcohol use were 13·6% (11·9–16·0) and 3·5% (1·4–6·2), respectively. Interpretation Improvements in unintentional carbon monoxide poisoning mortality rates have been inconsistent across regions and over time since 2000. Given that unintentional carbon monoxide poisoning is almost entirely preventable, policy-level interventions that lower the risk of carbon monoxide poisoning events should be prioritised, such as those that increase access to improved heating and cooking devices, reduce carbon monoxide emissions from generators, and mandate use of carbon monoxide alarms.publishedVersio