Deep Convolutional Neural Network Approach for COVID-19 Detection

Coronavirus disease 2019 (Covid-19) is a life-threatening infectious disease caused by a newly discovered strain of the coronaviruses. As by the end of 2020, Covid-19 is still not fully understood, but like other similar viruses, the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected persons. The accurate detection of Covid-19 cases poses some questions to scientists and physicians. The two main kinds of tests available for Covid-19 are viral tests, which tells you whether you are currently infected and antibody test, which tells if you had been infected previously. Routine Covid-19 test can take up to 2 days to complete; in reducing chances of false negative results, serial testing is used. Medical image processing by means of using Chest X-ray images and Computed Tomography (CT) can help radiologists detect the virus. This imaging approach can detect certain characteristic changes in the lung associated with Covid-19. In this paper, a deep learning model or technique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray images. The entire model proposed is categorized into three stages: dataset, data pre-processing and final stage being training and classification.     

The advantages of using impact resonance test in dynamic modulus master curve construction through the abbreviated test protocol

Asphalt concrete behavior is heavily dependent on temperature and loading rate. Hence, the material is typically tested at a range of temperatures and loading frequencies to capture its properties. Results are then used to develop a master curve exhibiting material behavior at the full spectrum of loading frequencies. An abbreviated testing protocol, under AASHTO PP 61-13, proposes a practical approach for development of this master curve. In this practice, the low temperature asymptote of the master curve is dominated by the limiting maximum modulus estimated through the Hirsch model. In this study, the dynamic modulus (DM) testing coupled with impact resonance (IR) test was used to evaluate the effect of this limiting maximum modulus on construction of the asphalt concrete master curve. Three different asphalt mixtures prepared with the same gradation and binder content, but different grades of stiffness were tested. The DM testing was performed at multiple temperatures and loading frequencies. The IR tests were conducted on the same specimens at the same temperatures. Two sigmoid functions (MEPDG and Richards models), and three shift factors (Arrhenius, Williams–Landel–Ferry, and polynomial) were utilized in the analysis. Richards sigmoid function coupled with polynomial shift factor provided the best fitting accuracy to the measured data. It was observed that the limiting maximum modulus obtained from experimental data was underpredicted by that obtained from the Hirsch model. The results indicated potential benefits of the IR test as a complementary testing tool to the abbreviated DM testing protocol to reliably characterize asphalt concrete.     

Decentralised decision-making with information sharing vs. centralised decision-making in supply chains

In recent years, implementing coordination mechanisms in decentralised supply chains to reduce the well-known negative effects of decentralisation, such as the ‘bullwhip effect’, has become a considerable challenge. Furthermore, with the dramatic developments in information and communication technologies, real-time information sharing has become increasingly easier to implement. In this work, we study a mono-product divergent supply chain composed of a supplier, a warehouse, retailers and customers in the context of decentralised and centralised decisions. The main objective of this study is to compare a decentralised supply chain combined with different scenarios of simultaneous upstream and downstream information sharing vs. a centralised supply chain. A mathematical model is developed to compare the logistics costs in the two decision contexts. The experimental results clearly show that the simultaneous sharing of customer demand and supplier-warehouse lead time information in a decentralised supply chain yields nearly equivalent logistics costs as the centralised supply chain context. However, the main beneficiary of the sharing is the warehouse, which receives approximately two-thirds of the benefit. Thus, incentives and revenue sharing contracts should be implemented to motivate and balance the benefits between supply chain partners.     

Exploring creative thinking in graphically mediated synchronous dialogues

This paper reports on an aspect of the EC funded Argunaut project which researched and developed awareness tools for moderators of online dialogues. In this study we report on an investigation into the nature of creative thinking in online dialogues and whether or not this creative thinking can be coded for and recognized automatically such that moderators can be alerted when creative thinking is occurring or when it has not occurred after a period of time. We outline a dialogic theory of creativity, as the emergence of new perspectives from the interplay of voices, and the testing of this theory using a range of methods including a coding scheme which combined coding for creative thinking with more established codes for critical thinking, artificial intelligence pattern-matching techniques to see if our codes could be read automatically from maps and ‘key event recall’ interviews to explore the experience of participants. Our findings are that: (1) the emergence of new perspectives in a graphical dialogue map can be recognized by our coding scheme supported by a machine pattern-matching algorithm in a way that can be used to provide awareness indicators for moderators; (2) that the trigger events leading to the emergence of new perspectives in the online dialogues studied were most commonly disagreements and (3) the spatial representation of messages in a graphically mediated synchronous dialogue environment such as Digalo may offer more affordance for creativity than the much more common scrolling text chat environments. All these findings support the usefulness of our new account of creativity in online dialogues based on dialogic theory and demonstrate that this account can be operationalised through machine coding in a way that can be turned into alerts for moderators.     

Scatter search technique for exam timetabling

At universities where students enjoy flexibility in selecting courses, the Registrar’s office aims to generate an appropriate exam timetable for numerous courses and large number of students. An appropriate, real-world exam timetable should show fairness towards all students, respecting the following constraints: (a) eliminating or minimizing the number of simultaneous exams; (b) minimizing the number of consecutive exams; (c) minimizing the number of students with two or three exams per day (d) eliminating the possibility of more than three exams per day (e) exams should fit in rooms with predefined capacity; and (f) the number of exam periods is limited. These constraints are conflicting, which makes exam timetabling intractable. Hence, solving this problem in realistic time requires the use of heuristic approaches. In this work, we develop an evolutionary heuristic technique based on the scatter search approach for finding good suboptimal solutions for exam timetabling. This approach is based on maintaining and evolving a population of solutions. We evaluate our suggested technique on real-world university data and compare our results with the registrar’s manual timetable in addition to the timetables of other heuristic optimization algorithms. The experimental results show that our adapted scatter search technique generates better timetables than those produced by the registrar, manually, and by other meta-heuristics.     

Problems of spatial linkage of a geo-referenced Demographic and Health Survey (DHS) dataset to a population census: A case study of Egypt

GPS coordinates are increasingly available as spatial references on population surveys in the developing world, where high-resolution address and street mapping are absent. This potentially offers opportunities to enhance national census data by spatial linkage with survey sources. The paper explores the use of GPS-referenced Demographic and Health Survey (DHS) data in combination with census data in Egypt and identifies errors in coordinate referencing. The study develops a practical approach to the measurement of spatial uncertainty in this situation and assessment of its impact on data linkage. The analysis specifically addresses the analytical implications at three different spatial scales and is internationally relevant to the handling of GPS-referenced DHS data in GIS.     

An Experimental and Theoretical Comparison of Model Selection Methods

Machine Learning - We investigate the problem of model selection in the setting of supervised learning of boolean functions from independent random examples. More precisely, we compare methods for...     

A generalization of the Chebyshev type inequalities for Sugeno integrals

In this paper, we give a generalization of the Chebyshev type inequalities for Sugeno integral with respect to non-additive measures. The main results of this paper generalize most of the inequalities for Sugeno integral obtained by many researchers. Also, some conclusions are drawn and some problems for further investigations are given.     

Styrene N-vinylpyrrolidone metal-nanocomposites as antibacterial coatings against Sulfate Reducing Bacteria

Copolymer of styrene, and vinylpyrrolidone was prepared by various techniques. Different nanometals and nanometal oxides were added into the copolymer as antimicrobial agents against Sulfate Reducing Bacteria (SRB). The nanocomposite chemical structure was confirmed by using FTIR, ¹H NMR spectroscopy and thermogravimetric analysis (TGA). The biocidal action of these nanocomposites against the SRB was detected using sulfide determination method in Postgate medium B. The data indicated that the nanocomposites had an inhibitory effect on the growth of SRB and reduced the bacterial corrosion rate of mild steel coupons. The prepared nanocomposites have high inhibition efficiency when applied as coatings and show less efficiency when applied as solids or solution into SRB medium. The copolymer and its nanocomposites effectively reduced the total corrosion rate as determined by total weight loss method.     

A Reconfigurable TDMP Decoder for Raptor Codes

A Raptor code is a concatenation of a fixed rate precode and a Luby-Transform (LT) code that can be used as a rateless error-correcting code over communication channels. By definition, Raptor codes are characterized by irregularity features such as dynamic rate, check-degree variability, and joint coding, which make the design of hardware-efficient decoders a challenging task. In this paper, serial turbo decoding of architecture-aware Raptor codes is mapped into sequential row processing of a regular matrix by using a combination of code enhancements and architectural optimizations. The proposed mapping approach is based on three basic steps: (1) applying systematic permutations on the source matrix of the Raptor code, (2) confining LT random encoding to pseudo-random permutation of messages and periodic selection of row-splitting scenarios, and (3) developing a reconfigurable parallel check-node processor that attains a constant throughput while processing LT- and LDPC-nodes of varying degrees and count. The decoder scheduling is, thus, made simple and uniform across both LDPC and LT decoding. A serial decoder implementing the proposed approach was synthesized in 65 nm, 1.2 V CMOS technology. Hardware simulations show that the decoder, decoding a rate-0.4 code instance, achieves a throughput of 36 Mb/s at SNR of 1.5 dB, dissipates an average power of 27 mW and occupies an area of 0.55 mm².