The Agglomeration Mechanism of Network Emerging E-commerce Industry Based on Social Science

Network emerging e-commerce refers to the development of wireless broadband technology, smart terminal technology, near-field network, etc. as the driving force. It is the emerging e-commerce represented by the continuous development of modern e-commerce and the integration of commerce. This paper proposes to use Michael Porter’s cluster theory method, income increasing algorithm, and spatial Gini coefficient method to sort out and analyze the research results of industrial agglomeration problems, further study the relationship of e-commerce industry agglomeration mechanism, and build agglomeration simulation model , the construction of the centripetal force model of the industrial agglomeration area, through the analysis of the production factors of the e-commerce industry, and then study the influence of each factor on the development of the e-commerce industry. Finally, this paper selects and uses 16 standard mechanical data sets to investigate and analyze the agglomeration mechanism of the e-commerce industry, which verifies the accuracy and overall applicability of the method.

On the correlation and sensitivity of so far statistical randomness tests based on runs

Abstract—Random numbers play a very important role in cryptography. More precisely, almost cryptographic primitives are ensured their security based on random values such as random key, nonces, salts... Therefore, the assessment of randomness according to statistical tests is really essential for measuring the security of cryptographic algorithms. In this paper, we focus on so far randomness tests based on runs in the literature. First, we have proved in detail that the expected number of gaps (or blocks) of length in a random sequence of length is . Secondly, we have evaluated correlation of some tests based on runs so far using Pearson coefficient method [5, 6] and Fail-Fail ratio one [7, 8]. Surprisingly, the Pearson coefficient method do not show any strong linear correlation of these runs-based tests but the Fail-Fail ratio do. Then, we have considered the sensitivity of these runs tests with some basic transformations. Finally, we have proposed some new runs tests based on the sensitivity results and applied evaluations to some random sources. Tóm tắt—Số ngẫu nhiên đóng một vai trò quan trọng trong mật mã. Cụ thể, độ an toàn của hầu hết các nguyên thủy mật mã đều được đảm bảo dựa trên các giá trị ngẫu nhiên như khóa, nonce, salt… Do đó, việc đánh giá tính ngẫu nhiên dựa trên các kiểm tra thống kê là thực sự cần thiết để đo độ an toàn cho các thuật toán mật mã. Trong bài báo này, chúng tôi tập trung vào các kiểm tra ngẫu nhiên dựa vào run trong các tài liệu. Đầu tiên, chúng tôi chứng minh chi tiết rằng kỳ vọng số các gap (khối) độ dài trong một chuỗi ngẫu nhiên độ dài là . Sau đó, chúng tôi đánh giá mối tương quan của một số kiểm tra dựa vào run bằng phương pháp hệ số Pearson [5, 6] và tỷ số Fail-Fail [7, 8]. Đáng ngạc nhiên là phương pháp hệ số Pearson không cho thấy bất kỳ mối tương quan tuyến tính mạnh nào của các kiểm tra dựa vào run, trong khi đó tỷ số Fail-Fail lại chỉ ra. Tiếp theo, chúng tôi xem xét độ nhạy của các kiểm tra run này với một số phép biến đổi cơ bản. Cuối cùng, chúng tôi đề xuất một số kiểm tra run mới dựa trên các kết quả độ nhạy và đánh giá áp dụng chúng cho một số nguồn ngẫu nhiên.

PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

Characterization of Thickness Loss in a Storage Tank Plate with Piezoelectric Wafer Active Sensors

In terms of the structural health inspection of storage tanks by ultrasonic guided wave technology, many scholars are currently focusing on the tanks’ floor and walls, while little research has been conducted on storage tank roofs. However, the roof of a storage tank is prone to corrosion because of its complex structure and unique working environment. For this purpose, this paper proposes a reflection/transmission signal amplitude ratio (RTAR) coefficient method for corrosion depth assessment. We studied the relationship between the RTAR coefficient, the corrosion depth, and the guided wave frequency to establish a depth assessment model. More importantly, unlike the traditional reflection coefficient method, the characteristics of guided wave signals, including the propagation and attenuation, are introduced in this model for accurate assessment. To eliminate the interference of residual vibration and improve the detection accuracy of defects, we built a corrosion detection system by using piezoelectric sensors and carried out field tests to verify the performance of the proposed method. We demonstrate that corrosion defects with a minimum depth of 0.2 mm can be quantitatively evaluated.

Subhealth Risk Perception Scale: Development and Validation of a New Measure

Background. To develop an individual’s physical subhealth risk perception scale and evaluate its reliability and validity, so as to provide a measurement tool for individual physical health risk. Methods. A questionnaire on the perception risk of physical subhealth was developed. Using a random sampling method, 785 people in the Anhui provincial physical examination centre were selected as the research participants. Of the questionnaires returned, 770 were valid, giving an effective rate of 98%. Firstly, the Pearson correlation coefficient method was used to study the correlation of 35 items in the initial scale, and then, polychoric factor structure analysis was carried out by using the Pratt D matrix to optimize the item structure. The Cronbach’α coefficient method was used to test the internal consistency reliability, and a structural equation model was used to explore the construct validity of the scale. The discriminant validity of the scale was obtained by factor analysis. A general linear model was used to analyse the relationship between the clinical manifestations of physical subhealth and the level of risk perception, and the convergent validity of the scale was evaluated. Results. All the data of 35 items were significantly correlated at the 0.01 level. The correlation coefficients between a1 and a2, a3 and a4, b1 and b2, b2 and b3, c4 and c5, c5 and c6, c6 and c7, c8 and c9, d1 and d2, d2 and d3, e5 and e6, g1 and g2, g2 and g3, and g2 and g4 were greater than 0.6. The items with correlation coefficients greater than 0.6 were reduced by a Pratt D matrix. The resulting physical subhealth risk perception scale covers five factors with a total of 18 items. The Cronbach’α coefficient of the scale was 0.889, and the Cronbach’α coefficients of the five factors F1-F5 were 0.780, 0.825, 0.801, 0.736, and 0.704, respectively. Structural equation model analysis showed that χ 2 / df = 3.43 , p < 0.001 , RMSEA = 0.08 , GFI = 0.88 , NFI = 0.84 , AGFI = 0.84 , and CFI = 0.88 . Factor analysis showed that factors F1–F5 had significant correlations ( p < 0.01 ), and the correlation coefficients were less than the corresponding square root value of AVE. Based on the subhealth clinical manifestations of the participants, the general linear model was used to explore the convergent validity of the scale, and the results indicated that the scale passed the convergent validity test. Conclusions. We propose a physical subhealth risk perception scale amounting to 18 items, which includes five dimensions: health knowledge (2 items), risk perception (5 items), trust selection (4 items), information channel (4 items), and social groups (3 items). The reliability and validity of the physical subhealth risk perception scale are acceptable. Applying the scale into practice has potential to improve the overall public health level.

Comparison between exact and approximate methods for geometrically nonlinear analysis prescribed in design standards for steel and reinforced concrete structures

abstract: Current practices in structural engineering demand ever-increasing knowledge and expertise concerning stability of structures from professionals in this field. This paper implements standardized procedures for geometrically nonlinear analysis of steel and reinforced concrete structures, with the objective of comparing methodologies with one another and with a geometrically exact finite element analysis performed with Ansys 14.0. The following methods are presented in this research: Load Amplification Method, from NBR 8800:2008; the γ z coefficient method, from NBR 6118:2014; the P-Delta iterative method and the α c r coefficient method, prescribed in EN 1993-1-1:2005. A bibliographic review focused on standardized approximate methods and models for consideration of material and geometric nonlinearities is presented. Numerical examples are included, from which information is gathered to ensure a valid comparison between methodologies. In summary, the presented methods show a good correlation of results when applied within their respective recommended applicability limits, of which, Eurocode 3 seems to present the major applicability range. The treated approximate methods show to be more suitable for regular framed structures subjected to regular load distributions.

Preventive maintenance planning considering machines’ reliability using group technology

PurposeThe purpose of this paper is to develop an efficient and effective preventive maintenance (PM) plan that considers machines’ maintenance needs in addition to their reliability factor.Design/methodology/approachSimilarity coefficient method in group technology (GT) philosophy is used. Machines’ reliability factor is considered to develop virtual machine cells based on their need for maintenance according to the type of failures they encounter.FindingsUsing similarity coefficient method in GT philosophy for PM planning results in grouping machines based on their common failures and maintenance needs. Using machines' reliability factor makes the plan more efficient since machines will be maintained at the same time intervals and when their maintenance is due. This helps to schedule a standard and efficient maintenance process where maintenance material, tools and labor are scheduled accordingly.Practical implicationsThe proposed procedure will assist maintenance managers in developing an efficient and effective PM plans. These maintenance plans provide better inventory management for the maintenance materials and tools needed using the developed virtual machine cells.Originality/valueThis paper presents a new procedure to implement PM using the similarity coefficient method in GT. A new similarity coefficient equation that considers machines reliability is developed. Also a clustering algorithm that calculates the similarity between machine groups and form virtual machine cells is developed. A numerical example adopted from the literature is solved to demonstrate the proposed heuristic method.

Regional Differential Decomposition and Formation Mechanism of Dynamic Carbon Emission Efficiency of China’s Logistics Industry

Among China’s five major industries, the logistics industry is the only one in which carbon emission intensity is continuing to increase, so it is of great importance in developing a low-carbon economy for China. Thus, some scholars have learned about carbon emission efficiency (CEE) in logistic industry recently; however, few of them have considered the inner structure, regional differentiation, or dynamic items of CEE. To fill this gap, we first calculate the dynamic carbon emission efficiency of China’s logistics industry (CEELI) (2001–2017) using the three-stage DEA-Malmquist model, and then using the Dagum Gini coefficient method, the Kernel Density Estimation (KDE), and the panel vector auto-regression (PVAR) model to analyze regional differential decomposition and their formation mechanism. The results indicate that the dynamic CEELI is ‘inefficient’ overall; it shows a decreasing trend, and the decline of dynamic efficiency mainly comes from technical backwardness rather than efficiency decline. Moreover, the domestic differences are gradually narrowing; the Gini inequality between regions and the density of trans-variation between regions are the main reasons for the gap between different regions and different periods.