Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting

One of the most significant environmental issues of blasting operations is ground vibration, which can cause damage to the surrounding residents and structures. Hence, it is a major concern to predict and subsequently control the ground vibration due to blasting. This paper presents two artificial intelligence techniques, namely, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network for the prediction of ground vibration in quarry blasting site. For this purpose, blasting parameters as well as ground vibrations of 109 blasting operations were measured in ISB granite quarry, Johor, Malaysia. Moreover, an empirical equation was also proposed based on the measured data. Several AI-based models were trained and tested using the measured data to determine the optimum models. Each model involved two inputs (maximum charge per delay and distance from the blast-face) and one output (ground vibration). To control capacity performances of the predictive models, the values of root mean squared error (RMSE), value account for (VAF), and coefficient of determination (R2) were computed for each model. It was found that the ANFIS model can provide better performance capacity in predicting ground vibration in comparison with other predictive techniques. The values of 0.973, 0.987 and 97.345 for R2, RMSE and VAF, respectively, reveal that the ANFIS model is capable to predict ground vibration with high degree of accuracy. © 2015, Springer-Verlag Berlin Heidelberg

TORQUE AND DRAG MEASUREMENT: A COMPARISON BETWEEN CONVENTIONAL, SLIM HOLE AND CASING DRILLING METHODS IN A DEVIATED WELL

Different locations and field conditions require various drilling methods to produce oil and gas. Three major operational methods are identified to be slim holes, conventional and casing drilling. Despite many affecting parameters, torque and drag are two highlighted factors for the determination of the best drilling method. Torque and drag play a significant role in the feasibility of drilling and its final costs. In this study, the influence of the torque and drag are investigated for the determination of optimum drilling type in consideration of the same S-shape well trajectory for all cases. Consequently, slim hole drilling showed minimum torque and drag value which meant that this method can be a good choice. Furthermore, casing drilling displayed maximum torque and drag in this study resulting in high sensitivity to the high degree of deviation. On the other hand, due to the constant weight of casing while drilling, this method showed a smooth increasing trend in terms of drag force while slim hole and conventional drilling showed significant changes in different parts of well trajectory. The number of build and drop sections significantly affect the amount of torque loss, while up to 50% to 70% of torque loss was observed in the drop or build section

Affecting Factors on the Quality of Resident Education in Emergency Department; a Cross-Sectional Study

مقدمه: با توجه به تفاوت در زیرساخت های سیستم بهداشت و درمان کشورمان با کشور های پیشرفته، نیاز است که آموزش دستیاری طب اورژانس به گونه ای جهت دهی گردد که ضمن پاسخ دهی به نیازهای درمانی جامعه، اهداف آموزشی مشخص شده برای دستیاران این رشته را نیز با راهنمایی و کمک اعضای هیئت علمی پوشش دهد. شاید اولین قدم در این راه بررسی وضعیت موجود و نظرسنجی از دستیاران و اعضای هیئت علمی فعال در رشته تخصصی طب اورژانس باشد. بنابراین، مطالعه حاضر با هدف بررسی عوامل موثر بر کیفیت آموزش دستیاری در بخش اورژانس طراحی و اجرا گردید. روش کار: ابتدا گروهی متشکل از ۵ عضو هیئت علمی باتجربه نظرات خود را در مورد عوامل مؤثر بر آموزش دستیاری در قالب مصاحبه بیان کردند که منتج به طراحی پرسشنامه ای با 27 سرفصل گردید که در نهایت و بعد از بررسی روایی و پایایی، پرسشنامه ای شامل ۲۳ آیتم تهیه شد. این آیتم ها در سه گروه عوامل فردی، عوامل محیطی و عوامل مربوط به بیماران طبقه بندی شدند. نمونه گیری به روش سرشماری انجام شد و کلیه دستیاران و اعضای هیئت علمی گروه طب اورژانس دانشگاه علوم پزشکی تهران که در محدوده زمانی اجرای این مطالعه در این رشته تخصصی فعالیت داشتند قابلیت ورود به مطالعه را داشتند. نظر سنجی کمی با استفاده از مقیاس لیکرت 5 امتیازی انجام شد. سپس داده ها مورد تحلیل آماری قرار گرفت تا میزان توافق در هر مورد در گروه دستیاران و اعضای هیئت علمی بررسی شود. یافته ها: در مجموع ۵۷ دستیار با میانگین سنی 12/6 ± ۷۵/۳۲ سال و ۲۳ عضو هیئت علمی با میانگین سنی 54/5 ± ۶۵/۳۹ سال در مطالعه شرکت کرده و پرسشنامه ها را تکمیل نمودند. میانگین امتیاز سه دسته از عوامل مورد بررسی شامل عوامل فردی، محیطی و مربوط به بیمار از نظر کلیه شرکت کنندگان به ترتیب برابر با 12/0 ± 17/1، 15/0 ± 09/1 و 22/0 ± 52/1 بود. میانگین این امتیازات به تفکیک سه دسته از عوامل مورد بررسی بین دستیاران و اعضای هیئت علمی رشته طب اورژانس شرکت کننده در مطالعه تفاوت معنی داری نداشت (05/0 < p). از نظر اعضا هیئت علمی کم کردن تعداد شیفت ها باعث بهبود کیفیت آموزش دستیاری نمی شود. ولی ایشان معتقد بودند که شیفت های ۱۲ ساعته، راندهای بالینی در بخش اورژانس و آموزش دستیاران به یکدیگر در بهبود کیفیت آموزش موثر است که اختلاف نظر دستیاران با اعضای هیئت علمی در این موارد معنی دار بود (05/0 > p). نتیجه گیری: اعضای هیئت علمی و دستیاران طب اورژانس نظر یکسانی درباره ساعات کاری و تعداد شیفت های بالینی و تاثیر  آن بر آموزش دستیاری ندارند. اعضای هیئت علمی طب اورژانس معتقد بودند شیفت های ۱۲ ساعته در مقایسه با شیفت های ۸ ساعته امکان آموزش بیشتری را فراهم می کنند و کاهش تعداد شیفت های بالینی کیفیت آموزش را خواهد کاست.Introduction: Considering the differences between the infrastructures of healthcare systems in Iran and advanced countries, there is a need for directing the education of emergency medicine residents in a way that not only meets the treatment needs of the society, but can also cover the determined educational goals for the residents of this specialty with the guidance and help of the faculty members. The first steps might be evaluating the present status and surveying the residents and faculty members who are active in emergency medicine specialty. Therefore, the present study was designed and performed with the aim of evaluating the factors affecting the quality of resident education in emergency department (ED). Methods: Initially, a group that consisted of 5 experienced faculty members expressed their opinions on the factors affecting the quality of resident education in an interview, which resulted in the design of a questionnaire with 27 topics that led to preparation of a 23-item questionnaire after validity and reliability evaluation. These items were classified in 3 groups of personal factors, environmental factors, and patient-related factors. Consecutive sampling was done and all the residents and faculty members of emergency medicine in Tehran University of Medical Sciences who were active in this specialty during the study period were eligible to participate in the study. A quantitative survey was done using 5-point Likert scale. Then the data were statistically analyzed to evaluate the agreement rate of the residents and faculty members in each item. Results: In total, 57 residents with the mean age of 32.75 ± 6.12 years and 23 faculty members with the mean age of 39.65 ± 5.54 years participated in the study and filled out the questionnaires. Mean scores of the 3 categories of evaluated factors, namely personal, environmental, and patient-related factors from the viewpoint of all participants were 1.17 ± 0.12, 1.09 ± 0.15, and 1.52 ± 0.22, respectively. The mean scores calculated for the 3 studied categories were not significantly different between the residents and faculty members of emergency medicine who participated in the study (p > 0.05). In the opinion of faculty members, decreasing the number of shifts does not lead to improvement in the quality of resident’s training. However, they believed that 12-hour shifts, clinical rounds in ED and the residents teaching to each other are effective in improvement of the quality of their education and the opinion of residents and faculty members were significantly different in these cases (p < 0.05). Conclusion: Faculty members and residents of emergency medicine do not share the same opinion on working hours, and the number of clinical shifts and their effect on resident training. The faculty members believed that 12-hour shifts provide more opportunities for education compared to 8-hour shifts and reducing the number of clinical shifts would decrease the quality of education.

Effect of SVM Kernel Functions on Bearing Capacity Assessment of Deep Foundations

Pile foundations are vastly utilized in construction projects where their capacities (pile bearing capacity, PBC) should be determined in different stages of construction. A highly reliable and accurate prediction model can lead to many advantages, such as reducing the construction cost, shortening the construction timeline, and providing safety construction. Hence, the aim of this study is the developments of statistical and artificial intelligence (AI) models for predicting bearing capacities of 141 piles. At the preliminary of the study, features or inputs of this study to predict PBC were selected trough simple regression analysis. Then, this study presents different kernels of support vector machine (SVM) technique, i.e., the dot, the radial basis function (RBF), the polynomial, the neural, and the ANOVA to predict the PBC. The aforementioned models were evaluated by several performance indices and their results were compared using a simple ranking system. The results showed that the SVM-RBF model is able to achieve the highest coefficient of determination, R2 values which are 0.967 and 0.993 for training and testing stages, respectively. It is important to mention that a multiple regression model was also employed to predict PBC values. The other SVM kernels were provided a high degree of accuracy for estimating PBC, however, the SVM-RBF model is recommended to be used as a powerful, highly reliable, and simple solution for PBC prediction

Unemployment in Socially Disadvantaged Communities in Tennessee, US, During the COVID-19

Urban studies related to previous pandemics and impacts on cities focused on vulnerable categories including poor and marginalized groups. We continue this tradition and analyze unemployment outcomes in a context of a multi-dimensional social disadvantage that is unfolding during the ongoing public health crisis. For this, we first propose an approach to identify communities by social disadvantage status captured by several key metrics. Second, we apply this methodology in the study of the effect of social disadvantage on unemployment during the COVID-19 and measure the COVID-19-related economic impact using the most recent data on unemployment. The study focuses upon vulnerable communities in in the southeastern US (Tennessee) with a concentration of high social vulnerability and rural communities. While all communities initially experienced the impact that was both sudden and severe, communities that had lower social disadvantage pre-COVID were much more likely to start resuming economic activities earlier than communities that were already vulnerable pre-COVID due to high social disadvantage with further implications upon community well-being. The impact of social disadvantage grew stronger post-COVID compared with the pre-pandemic period. In addition, we investigate worker characteristics associated with adverse labor market outcomes during the later stage of the current economic recession. We show that some socio-demographic groups have a systematically higher likelihood of being unemployed. Compared with the earlier stages, racial membership, poverty and loss of employment go hand in hand, while ethnic membership (Hispanics) and younger male workers are not associated with higher unemployment. Overall, the study contributes to a growing contemporaneous research on the consequences of the COVID-19 recession. Motivated by the lack of the research on the spatial aspect of the COVID-19-caused economic recession and its economic impacts upon the vulnerable communities during the later stages, we further contribute to the research gap

Pattern-based calibration of cellular automata by genetic algorithm and Shannon relative entropy

While cellular automata (CA) are considered an effective algorithm to model urban growth, their precise calibration can be challenging. The Shannon relative index (SRI) is an indicator of urban sprawl accounting for dispersion or concentration of built-up/non-built-up areas. This study uses SRIs directly in the calibration of CA as patterns, applying a genetic algorithm (GA). Moreover, the kappa coefficient is used in the calibration process. CA was calibrated using data for 2001 and 2006 and validated using 2011 data to model urban growth in Shelby County, TN. Results indicate that the kappa coefficient achieves the highest value using the proposed method (89.48%) compared with a GA without patterns (86.15%, which underestimates 32.22 km2) or logistic regression (85.83%, which underestimates 36.76 km2). A more precise calibration of urban growth using the proposed method helps city planners to provide more realistic models for the future of the region

The Neighborhood Impact of Industrial Blight: A Path Analysis

Historically, industry shaped the space-economy of the American city, a major source of employment opportunity for residents that selected housing nearby or within a convenient or affordable commuting distance. However, the contemporary American city is structurally characterized by abandoned, blighted, vacant industrial properties due to urban expansion, deindustrialization and the suburbanization of both jobs and population. The urban studies literature rarely documents the neighborhood impact of industrial blight akin to studies of residential blight. We determine the proximity-effect of industrial blight on the neighborhood thought of not as an isolated and closed entity, but as a connected and open entity within the city and the region. Unlike studies confined to the property value impact, we determine Pearson correlations of industrial blight and vacancy expansively with the socio-economic and physical characteristics of neighborhoods. We use path analysis to determine direct, indirect, and total neighborhood impact of industrial blight and vacancy. The census block group and parcel-level geographic information system (GIS) provide our principal sources of data. The block group geography contains the neighborhood as a fundamental spatial unit. We determine how the neighborhood impact varies with distance from the blighted, vacant industrial property

Mapping the morphology of sprawl and blight: A note on entropy

The urban expansion from the city center to the suburb and beyond is indicated by Shannon entropy, a robust and versatile measure of sprawl. However, the metropolitan regionwide entropy masks the morphology of land cover and land use consequential to urban expansion within the city-region. To surmount the limitation, we focus on the block-group, which is a US census defined socio-spatial unit that identifies the metropolitan region\u27s development pattern structurally, forming tracts that comprise neighborhoods. The concentration and dispersion of land use and land cover by block-group reveals a North American metropolitan region\u27s commonly known but rarely measured spatial structure of its urban and suburban sprawl. We use parcel data from county assessor of property (GIS) and land cover pixel data from the National Land Cover Data (NLCD) to compute block-group land-use and land-cover entropy. The change in block group entropy over a decade indicates whether the city- region\u27s land use and land cover transition to a concentrated or dispersed pattern. Furthermore, we test a hypothesis that blight correlates with sprawl. Blight and sprawl are among the key factors that plague the metropolitan region. We determine the correlations with household income as well as (block group) distance from the city center. It turns out, blight is among the universally held distance-decay phenomena. The share of the block group\u27s blighted properties decays (nonlinearly) with distance from the city center. Highlights for public administration, management and planning: •The metropolitan region\u27s outward growth is highlighted by mapping the changing morphology of the block group within the city-region. •The block group entropy is computed with land use (parcel) and land cover (pixel) data. •The block group entropy change indicates the pattern of the land use and land cover transition with concentration or dispersion. •We test the hypothesis that blight correlates with sprawl with statistical models. •The block group\u27s blighted properties decrease (nonlinearly) with distance from the city center